Engineering › Building and Construction

Traffic Prediction and Management Techniques

Works Count: 61805

Wikipedia

Description

This cluster of papers focuses on the application of deep learning, neural networks, and spatio-temporal data analysis for traffic flow prediction and forecasting in urban environments. The research covers topics such as short-term forecasting, graph convolutional networks, time series analysis, and the integration of intelligent transportation systems.

Keywords

Deep Learning; Traffic Flow; Short-Term Forecasting; Spatio-Temporal Data; Neural Networks; Urban Traffic; Graph Convolutional Networks; Time Series Analysis; Intelligent Transportation Systems; Probabilistic Forecasting

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

2010-12-04

M G Karlaftis , Eleni I. Vlahogianni

Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

2003-10-20

Billy M. Williams , L A Hoel

This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on … This article presents the theoretical basis for modeling univariate traffic condition data streams as seasonal autoregressive integrated moving average processes. This foundation rests on the Wold decomposition theorem and on the assertion that a one-week lagged first seasonal difference applied to discrete interval traffic condition data will yield a weakly stationary transformation. Moreover, empirical results using actual intelligent transportation system data are presented and found to be consistent with the theoretical hypothesis. Conclusions are given on the implications of these assertions and findings relative to ongoing intelligent transportation systems research, deployment, and operations.

Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

2013-03-19

Marco Lippi , Matteo Bertini , Paolo Frasconi

The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have … The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods.

Predicting Taxi–Passenger Demand Using Streaming Data

2013-06-14

Luís Moreira-Matias , João Gama , Michel Ferreira +2 more

Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering … Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.

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Real-time road traffic prediction with spatio-temporal correlations

2010-11-19

Wanli Min , Laura Wynter

Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

2015-04-07

Xiaolei Ma , Zhimin Tao , Yinhai Wang +2 more

A real-time traffic signal control system: architecture, algorithms, and analysis

2001-12-01

Pitu B. Mirchandani , Larry Head

Accuracy measures: theoretical and practical concerns

1993-12-01

Spyros Makridakis

Traffic Flow Prediction With Big Data: A Deep Learning Approach

2014-01-01

Yisheng Lv , Yanjie Duan , Wenwen Kang +2 more

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly … Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

Short-term traffic forecasting: Where we are and where we’re going

2014-02-05

Eleni I. Vlahogianni , Matthew G. Karlaftis , John Golias

Travel-Time Prediction With Support Vector Regression

2004-12-01

Chih-Hsien Wu , Jan-Ming Ho , Dong-Hyun Lee

Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector … Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.

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Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

2008-07-28

Manoel Castro-Neto , Young‐Seon Jeong , Myong K. Jeong +1 more

Current map-matching algorithms for transport applications: State-of-the art and future research directions

2007-06-28

Mohammed Quddus , Washington Y. Ochieng , Robert B. Noland

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Dynamic prediction of traffic volume through Kalman filtering theory

1984-02-01

Iwao Okutani , Yorgos J. Stephanedes

A gradient boosting method to improve travel time prediction

2015-04-05

Yanru Zhang , Ali Haghani

Data-Driven Intelligent Transportation Systems: A Survey

2011-07-26

Junping Zhang , Fei‐Yue Wang , Kunfeng Wang +3 more

For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to … For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D 2 ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D 2 ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D 2 ITS, discussing the functionality of its key components and some deployment issues associated with D 2 ITS Future research directions for the development of D 2 ITS is also presented.

Short-term traffic flow prediction using seasonal ARIMA model with limited input data

2015-06-12

S. Vasantha Kumar , Lelitha Vanajakshi

Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models … Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data. A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data. The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4–10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.

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A Bayesian Network Approach to Traffic Flow Forecasting

2006-03-01

Shiliang Sun , Chao Zhang , Ganglin Yu

A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a … A new approach based on Bayesian networks for traffic flow forecasting is proposed. In this paper, traffic flows among adjacent road links in a transportation network are modeled as a Bayesian network. The joint probability distribution between the cause nodes (data utilized for forecasting) and the effect node (data to be forecasted) in a constructed Bayesian network is described as a Gaussian mixture model (GMM) whose parameters are estimated via the competitive expectation maximization (CEM) algorithm. Finally, traffic flow forecasting is performed under the criterion of minimum mean square error (mmse). The approach departs from many existing traffic flow forecasting models in that it explicitly includes information from adjacent road links to analyze the trends of the current link statistically. Furthermore, it also encompasses the issue of traffic flow forecasting when incomplete data exist. Comprehensive experiments on urban vehicular traffic flow data of Beijing and comparisons with several other methods show that the Bayesian network is a very promising and effective approach for traffic flow modeling and forecasting, both for complete data and incomplete data.

Comparison of parametric and nonparametric models for traffic flow forecasting

2002-08-01

Brian L. Smith , Billy M. Williams , Rudy Oswald

Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning

2014-04-10

Wenhao Huang , Guojie Song , Haikun Hong +1 more

Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered … Traffic flow prediction is a fundamental problem in transportation modeling and management. Many existing approaches fail to provide favorable results due to being: 1) shallow in architecture; 2) hand engineered in features; and 3) separate in learning. In this paper we propose a deep architecture that consists of two parts, i.e., a deep belief network (DBN) at the bottom and a multitask regression layer at the top. A DBN is employed here for unsupervised feature learning. It can learn effective features for traffic flow prediction in an unsupervised fashion, which has been examined and found to be effective for many areas such as image and audio classification. To the best of our knowledge, this is the first paper that applies the deep learning approach to transportation research. To incorporate multitask learning (MTL) in our deep architecture, a multitask regression layer is used above the DBN for supervised prediction. We further investigate homogeneous MTL and heterogeneous MTL for traffic flow prediction. To take full advantage of weight sharing in our deep architecture, we propose a grouping method based on the weights in the top layer to make MTL more effective. Experiments on transportation data sets show good performance of our deep architecture. Abundant experiments show that our approach achieved close to 5% improvements over the state of the art. It is also presented that MTL can improve the generalization performance of shared tasks. These positive results demonstrate that deep learning and MTL are promising in transportation research.

Combining kohonen maps with arima time series models to forecast traffic flow

1996-10-01

Mascha C. van der Voort , Mark Dougherty , Susan Watson

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The Dynamogenic Factors in Pacemaking and Competition

1898-07-01

Norman Triplett

A copy of the official bicycle records made up to the close of the season of 1897 was obtained from the Racing Board of the League of American Wheelmen, and … A copy of the official bicycle records made up to the close of the season of 1897 was obtained from the Racing Board of the League of American Wheelmen, and from these records certain facts are given, which, with the help of the chart showing the times made for certain distances by professionals in the three kinds of races principally dealt with, will make clearer the discussion following. The lower curve of the chart represents the record for the distances given in the unpaced efforts against time. The middle curve the paced race against time, and the upper curve the best time made in competition races with pacemaker.

Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

2017-02-12

Junbo Zhang , Yu Zheng , Dekang Qi

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, such as inter-region traffic, … Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, such as inter-region traffic, events, and weather. We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the inflow and outflow of crowds in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. Experiments on two types of crowd flows in Beijing and New York City (NYC) demonstrate that the proposed ST-ResNet outperforms six well-known methods.

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LSTM network: a deep learning approach for short‐term traffic forecast

2017-01-13

Zheng Zhao , Weihai Chen , Xingming Wu +2 more

Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful … Short-term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short-term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short-term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

2017-04-10

Xiaolei Ma , Zhuang Dai , Zhengbing He +3 more

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to … This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

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Deep learning for short-term traffic flow prediction

2017-03-17

Nicholas G. Polson , Vadim Sokolov

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Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

2017-10-24

Jintao Ke , Hongyu Zheng , Hai Yang +1 more

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Some Theoretical Aspects of Road Traffic Research

1952-01-01

J G Wardrop

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

2018-04-26

Huaxiu Yao , Fei Wu , Jintao Ke +6 more

Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel … Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from large-scale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-of-the-art methods.

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A hybrid deep learning based traffic flow prediction method and its understanding

2018-03-20

Yuankai Wu , Huachun Tan , Lingqiao Qin +2 more

Big Data Analytics in Intelligent Transportation Systems: A Survey

2018-04-23

Li Zhu , F. Richard Yu , Yige Wang +2 more

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount … Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction

2019-08-22

Ling Zhao , Yujiao Song , Chao Zhang +5 more

Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic … Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN.

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Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

2019-07-17

Shengnan Guo , Youfang Lin , Ning Feng +2 more

Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities … Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of transportation. However, it is very challenging since the traffic flows usually show high nonlinearities and complex patterns. Most existing traffic flow prediction methods, lacking abilities of modeling the dynamic spatial-temporal correlations of traffic data, thus cannot yield satisfactory prediction results. In this paper, we propose a novel attention based spatial-temporal graph convolutional network (ASTGCN) model to solve traffic flow forecasting problem. ASTGCN mainly consists of three independent components to respectively model three temporal properties of traffic flows, i.e., recent, daily-periodic and weekly-periodic dependencies. More specifically, each component contains two major parts: 1) the spatial-temporal attention mechanism to effectively capture the dynamic spatialtemporal correlations in traffic data; 2) the spatial-temporal convolution which simultaneously employs graph convolutions to capture the spatial patterns and common standard convolutions to describe the temporal features. The output of the three components are weighted fused to generate the final prediction results. Experiments on two real-world datasets from the Caltrans Performance Measurement System (PeMS) demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines.

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Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

2019-07-17

Geng Xu , Yaguang Li , Leye Wang +4 more

Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task … Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regions. Existing approaches mainly focus on modeling the Euclidean correlations among spatially adjacent regions while we observe that non-Euclidean pair-wise correlations among possibly distant regions are also critical for accurate forecasting. In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting. We first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi-graph convolution. To utilize the global contextual information in modeling the temporal correlation, we further propose contextual gated recurrent neural network which augments recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. We evaluate the proposed model on two real-world large scale ride-hailing demand datasets and observe consistent improvement of more than 10% over stateof-the-art baselines.

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Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

2019-07-17

Huaxiu Yao , Xianfeng Tang , Hua Wei +2 more

Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate … Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For example, an accurate taxi demand prediction can assist taxi companies in pre-allocating taxis. The key challenge of traffic prediction lies in how to model the complex spatial dependencies and temporal dynamics. Although both factors have been considered in modeling, existing works make strong assumptions about spatial dependence and temporal dynamics, i.e., spatial dependence is stationary in time, and temporal dynamics is strictly periodical. However, in practice the spatial dependence could be dynamic (i.e., changing from time to time), and the temporal dynamics could have some perturbation from one period to another period. In this paper, we make two important observations: (1) the spatial dependencies between locations are dynamic; and (2) the temporal dependency follows daily and weekly pattern but it is not strictly periodic for its dynamic temporal shifting. To address these two issues, we propose a novel Spatial-Temporal Dynamic Network (STDN), in which a flow gating mechanism is introduced to learn the dynamic similarity between locations, and a periodically shifted attention mechanism is designed to handle long-term periodic temporal shifting. To the best of our knowledge, this is the first work that tackle both issues in a unified framework. Our experimental results on real-world traffic datasets verify the effectiveness of the proposed method.

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Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

2019-11-28

Zhiyong Cui , Kristian Henrickson , Ruimin Ke +1 more

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn … Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model's loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

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Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis

2019-12-20

Amir Bahador Parsa , Ali Movahedi , Homa Taghipour +2 more

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Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting

2020-04-03

Chao Song , Youfang Lin , Shengnan Guo +1 more

Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this … Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.

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GMAN: A Graph Multi-Attention Network for Traffic Prediction

2020-04-03

Chuanpan Zheng , Xiaoliang Fan , Cheng Wang +1 more

Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal … Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.

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Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

2020-08-20

Zonghan Wu , Shirui Pan , Guodong Long +3 more

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time … Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.

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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

2018-07-01

Bing Yu , Haoteng Yin , Zhanxing Zhu

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long … Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

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Graph neural network for traffic forecasting: A survey

2022-06-23

Weiwei Jiang , Jiayun Luo

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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems

2022-02-10

Iqbal H. Sarker

Abstract Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence … Abstract Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today’s needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on “AI-based Modeling” with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

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ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES

1979-01-01

Mohammed S. Ahmed , A R Cook

This paper investigated the application of analysis techniques develoepd by Box and Jenkins to freeway traffic volume and occupancy time series. A total of 166 data sets from three surveillance … This paper investigated the application of analysis techniques develoepd by Box and Jenkins to freeway traffic volume and occupancy time series. A total of 166 data sets from three surveillance systems in Los Angeles, Minneapolis, and Detroit were used in the development of a predictor model to provide short-term forecasts of traffic data. All of the data sets were best represented by an autoregressive integrated moving-average (ARIMA) (0,1,3) model. The moving-average parameters of the model, however, vary from location to location and over time. The ARIMA models were found to be more accurate in representing freeway time-series data, in terms of mean absolute error and mean square error, than moving-average, double-exponential smoothing, and Trigg and Leach adaptive models. Suggestions and implications for the operational use of the ARIMA model in making forecasts one time interval in advance are made. /Author/

Prediction of Hazardous Weather Phenomena Using Artificial Intelligence

2025-06-24

Ladislav Choma , Мартін Келемен , Matej Antoško +2 more | Acta Avionica Journal

The prediction of hazardous weather phenomena is a critical component in ensuring the safety and efficiency of air transport operations. This paper focuses on evaluating the potential of artificial intelligence … The prediction of hazardous weather phenomena is a critical component in ensuring the safety and efficiency of air transport operations. This paper focuses on evaluating the potential of artificial intelligence (AI) in forecasting fog—one of the most significant weather conditions affecting airport visibility. The methodological framework combines empirical methods (observation, measurement, experimentation) with theoretical approaches (analysis, synthesis, modeling). Emphasis is placed on the application of machine learning and deep learning techniques for processing meteorological data collected from Sliac military airport. The study compares conventional numerical weather prediction models (e.g., WRF) with AI-based approaches such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) neural networks, and ensemble models. Results indicate that AI models achieve higher accuracy in short-term fog prediction while reducing computational requirements. Experiments demonstrated success rates of up to 90% using ensemble techniques. The findings confirm that AI represents a promising tool for developing modern predictive meteorological systems in aviation. Challenges identified include limited data availability, the need for high-quality datasets, and the complexity of model interpretation. Future work should include expanding the data scope to multiple airports and incorporating satellite and radar data. The proposed approach offers a strong foundation for the advancement of intelligent, automated decision-support systems in both civil and military aviation meteorology.

Non-Specific Obstacle Detection in the Railway Track Area Based on Deep Learning

2025-06-24

Wanting Lou , Kun Miao , Paul Schonfeld +1 more | Engineering Research Express

Abstract Train operation safety is vital for railway systems, with obstacles intruding into railway tracks posing significant risks. However, existing research often focuses on specific obstacles, limiting the ability to … Abstract Train operation safety is vital for railway systems, with obstacles intruding into railway tracks posing significant risks. However, existing research often focuses on specific obstacles, limiting the ability to detect a wide range of potential hazards like landslides or uncommon animals. Furthermore, many methods do not define the intrusion area, leading to excessive computational burden. Therefore, this paper detects non-specific obstacle (excluding humans and trains) that may affect the train operation safety and utilizes a segmentation network to delineate the area of obstacle intrusion. We propose two methods based on deep learning, namely a stepwise detection method (SDM) and an integrated detection method (IDM). The SDM uses a segmentation network to define track areas and reduce detection frequency, achieving 94% [email protected] for detection and 87% mIoU for track segmentation. In contrast, the IDM employs parallel multi-task networks for more accurate detection in complex areas, achieving 94% [email protected] and 89% mIoU, but at a higher computational cost. Through a comparative analysis, this research shows that the SDM is suitable for simpler track areas with lower computational requirements, while the IDM is optimal for complex and crucial track areas requiring high detection accuracy and real-time performance. This study provides new strategies for the application of railway obstacle detection method to the actual railway environment.

Machine Learning Traffic Flow Prediction Models for Smart and Sustainable Traffic Management

2025-06-24

Rusul Abduljabbar , Hussein Dia , Sohani Liyanage | Infrastructures

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this … Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning.

Design And Development Of A Big Data Architecture For Traffic Control Systems: A Comprehensive Review

2025-06-24

Ashish Sharma Naveen Sankhla | European Economic Letters (EEL)

The exponential growth of urban populations and vehicular traffic has created unprecedented challenges for traditional traffic management systems. This paper presents a comprehensive review of big data architectures designed for … The exponential growth of urban populations and vehicular traffic has created unprecedented challenges for traditional traffic management systems. This paper presents a comprehensive review of big data architectures designed for modern traffic control systems, examining their components, implementation strategies, and performance implications. The research analyzes how emerging technologies including Internet of Things (IoT) sensors, machine learning algorithms, and distributed computing platforms are revolutionizing traffic management through real-time data processing and intelligent decision-making capabilities. The study explores architectural frameworks that handle the volume, velocity, variety, and veracity characteristics of traffic data while ensuring scalability, reliability, and cost-effectiveness. Key findings indicate that hybrid architectures combining centralized and distributed elements, leveraging technologies such as Apache Kafka, Hadoop, and cloud computing platforms, provide optimal solutions for real-time traffic control applications. The paper concludes with recommendations for future developments in intelligent transportation systems and identifies critical challenges in implementing large-scale big data traffic management solutions.

Estimating temporal truck traffic patterns on rural highway systems: A spatially-aware machine learning approach

2025-06-24

Sina Abolhoseini , Jonathan D. Regehr , Babak Mehran | Journal of Transport Geography

Road CrashAnalysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies

2025-06-24

Lars I. Skaug , Mehrdad Nojoumian , Nolan Dang +1 more | Applied Sciences

Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the … Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform road safety policies. This systematic review synthesizes the state of the art in road crash data analysis methodologies, focusing on the application of statistical and machine learning techniques to extract insights from crash databases. We systematically searched for peer-reviewed studies on quantitative crash data analysis methods and synthesized findings by using narrative synthesis due to methodological diversity. Our review included studies spanning traditional statistical approaches, Bayesian methods, and machine learning techniques, as well as emerging AI applications. We review traditional and emerging crash data sources, discuss the evolution of analysis methodologies, and highlight key methodological issues specific to crash data, such as unobserved heterogeneity, endogeneity, and spatial–temporal correlations. Key findings demonstrate the superiority of random-parameter models over fixed-parameter approaches in handling unobserved heterogeneity, the effectiveness of Bayesian hierarchical models for spatial–temporal analysis, and promising results from machine learning approaches for real-time crash prediction. This survey also explores emerging research frontiers, including the use of big data analytics, deep learning, and real-time crash prediction, and their potential to revolutionize road safety management. Limitations include methodological heterogeneity across studies and geographic bias toward high-income countries. By providing a taxonomy of crash data analysis methodologies and discussing their strengths, limitations, and practical implications, this paper serves as a comprehensive reference for researchers and practitioners seeking to leverage crash data to advance road safety.

Study of Intelligent Traffic Management System for North India

2025-06-23

Saurabh Nagar | Journal of Informatics Education and Research

In recent years, the urbanization in North Indian cities has been accelerated at such a rate and existing road infrastructure has been suffocated to such an extent, the traffic congestion … In recent years, the urbanization in North Indian cities has been accelerated at such a rate and existing road infrastructure has been suffocated to such an extent, the traffic congestion has been made too thick, the travel time is too long, the vehicular emissions and the amount of fuel consumption are too high. The traditional traffic control mechanisms are now obsolete in cities like Delhi, Chandigarh, Lucknow, Jaipur which have experienced exponential growth in the population and entries of the vehicles. However, these legacy systems are built on fixed time signal control, as well as manual surveillance, they are not flexible nor scalable enough to cope with the demand of the modern dynamic urban mobility problems. This research presents that there is the feasibility and the effectiveness of implementing an Intelligent Traffic Management System (ITMS) designed specifically for these urban centers. The study takes a systematic review on the current traffic patterns and infrastructure limitations leveraging machine learning algorithms, real time traffic data and Internet of Things (IoT) technologies. Data driven diagnostics find key congestion points, and a modelled integration of adaptive traffic signals, ANPR and smart surveillance is used to evaluate operational enhancement. Advanced traffic engineering tools are used to simulate traffic flow efficiency, commuter safety, and environmental sustainability potential improvements. The findings point to the need to accelerate towards intelligent, data-centric solutions for urban mobility of North India.

Gated attention unit and mask attention network for traffic flow forecasting

2025-06-23

Shuyuan Leng | Neural Computing and Applications

AI-driven road traffic management: a comprehensive review of outlier detection techniques and challenges

2025-06-22

Garima Rathi , Shailesh D. Kamble , Nonita Sharma | Knowledge and Information Systems

Artificial Intelligence on the Road or Road to Artificial Intelligence?

2025-06-22

Miroslav Petrović , Dušan Mladenović , Mladen Nedeljkov | Put i saobraćaj

Artificial intelligence (AI) is last years the hottest buzzword, started from the world of information technologies and spreading across various areas of everyday activities where it can be applied. However, … Artificial intelligence (AI) is last years the hottest buzzword, started from the world of information technologies and spreading across various areas of everyday activities where it can be applied. However, reality is that AI is with us for the longer time than we initially think of. In this paper we explain foundations and definitions of AI, followed by comparation of AI applied in transport, called Intelligent Transportation Systems (ITS). Main areas of state of the art AI-based technologies in road transport and traffic applications, already in use or ready to be used, are presented. At the same time, major obstacles for implementation of AI technologies which are on the horizon in those areas have been discussed.

Highway Rest Area Truck Parking Occupancy Prediction Using Machine Learning: A Case Study from Poland

2025-06-22

Artur Budzyński , M. Cieśla | Infrastructures

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the … Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure.

Real World Uses of OCR for Modernization of Traffic system

2025-06-22

| IJIREEICE

Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning

2025-06-22

Chenglin Ma , Meiyan Zhou , Wenchao Kang +2 more | ISPRS International Journal of Geo-Information

As a crucial component of the Belt and Road Initiative (BRI), China Railway Express (CR Express) plays a pivotal role in enhancing regional connectivity and economic integration. However, the systematic … As a crucial component of the Belt and Road Initiative (BRI), China Railway Express (CR Express) plays a pivotal role in enhancing regional connectivity and economic integration. However, the systematic evaluation of CR Express node cities remains understudied, hindering the optimization of logistics networks and sustainable development goals. This study pioneers a data-driven approach by integrating multi-source geospatial data and advanced machine learning algorithms to develop a comprehensive evaluation framework spanning five critical dimensions: economic vitality, ecological sustainability, logistics capacity, network connectivity, and policy support. By comparing the evaluation performance of six machine learning models, an optimal decision-making model is identified, and the evaluation indicators are rigorously screened to provide robust decision-support for the establishment of CR Express assembly centers. The Random Forest model outperformed comparative algorithms with 99.5% prediction accuracy (8.33% higher than conventional classification models), particularly in handling multi-dimensional interactions between urban development factors. Feature importance analysis identified 11 decisive indicators from node city evaluation empirical indicators, where CR Express trade volume (weight = 0.1269), logistics hub classification (weight = 0.1091), and operational frequency (weight = 0.0980) emerged as the top three predictors. Spatial predictions highlight five strategic cities (Changsha, Wuhan, Shenyang, Jinan, Hefei) as prime candidates for CR Express assembly centers, providing actionable insights for national logistics planning under the BRI framework.

Deep Learning Based Novel Vehicle Detection and Counting System Using Intelligent Transportation Systems

2025-06-21

S Bhavaneeswaran | International Journal for Research in Applied Science and Engineering Technology

It proposes deep learning-based vehicle detection and counting system designed to enhance real-time traffic monitoring and urban planning. The system employs advanced object detection models, including YOLO (You Only Look … It proposes deep learning-based vehicle detection and counting system designed to enhance real-time traffic monitoring and urban planning. The system employs advanced object detection models, including YOLO (You Only Look Once) and Faster R-CNN, to identify and track vehicles in various environmental conditions, such as different lighting, weather, and traffic densities. Unlike traditional sensor-based methods, which are prone to inefficiencies and high maintenance costs, this approach offers a scalable and cost-effective solution with high accuracy. The core functionality of the system revolves around the integration of robust tracking mechanisms, which enable precise vehicle counting through line-crossing or region-based techniques. By tracking vehicles across multiple frames, the system ensures accurate counts, even in the presence of occlusions or overlapping vehicles.This deep learning-based system is designed to integrate seamlessly into intelligent transportation systems, smart cities, and urban planning efforts, providing real-time data for decision-making. It offers significant improvements in traffic management, addressing the limitations of traditional vehicle detection methods. With its ability to handle complex scenarios and provide real time analytics, this system plays a crucial role in optimizing traffic flow, enhancing safety, and contributing to more efficient urban transportation systems

Leveraging Machine Learning for Optimal Pilgrim Crowd Management

2025-06-20

RAMI ALZAHRANI , Nahlah Algethami | Electronics

The Hajj pilgrimage involves high crowd density within limited time and space, making traditional crowd control methods insufficient for real-time alerts or predictive safety measures. This research proposes a machine … The Hajj pilgrimage involves high crowd density within limited time and space, making traditional crowd control methods insufficient for real-time alerts or predictive safety measures. This research proposes a machine learning-based system to enhance crowd management by detecting abnormal behavior and forecasting future conditions. The study utilizes the Hajjv2 dataset, which consists of annotated video frames capturing various crowd behaviors across multiple Hajj locations. After data preprocessing and feature extraction, including crowd density, speed, direction, and object area, two models are employed: the Isolation Forest algorithm for anomaly detection and a Long Short-Term Memory (LSTM) neural network for forecasting crowd behavior. The system integrates the results of both models to issue real-time alerts based on predefined thresholds. Evaluation results indicate that the Isolation Forest model achieved an average accuracy of 91% across all test sets, effectively identifying abnormal movement patterns. The LSTM model produced reliable predictions of average crowd speed with a low Mean Squared Error (MSE) of 0.000439. Together, these models form a robust alert mechanism that enables early identification of risks. In summary, this study presents an intelligent, scalable solution for enhancing crowd safety during the Hajj. It illustrates the practical value of machine learning in enabling proactive and informed crowd management strategies.

A Multi-Scale Traffic Flow Prediction Model Based on LSTM

2025-06-20

J. J. Cui | Applied and Computational Engineering

With the accelerating pace of urbanization, traffic flow prediction faces challenges in adapting to dynamic environments. Existing studies exhibit significant limitations in integrating external factors and extending to multi-scale prediction … With the accelerating pace of urbanization, traffic flow prediction faces challenges in adapting to dynamic environments. Existing studies exhibit significant limitations in integrating external factors and extending to multi-scale prediction scenarios. Targeting the domain of traffic flow forecasting, this paper focuses on optimizing the Long Short-Term Memory (LSTM) network model, with particular attention to the impact of weather conditions on prediction accuracy and the performance differences between hourly and daily multi-scale forecasting. While mainstream approaches have improved LSTM performance through algorithmic optimization (e.g., Particle Swarm Optimization, Bayesian Optimization), a systematic solution to the integration of external factors and adaptation to different time granularities is still lacking. This study proposes an LSTM architecture that incorporates temperature as an embedded parameter, constructing a multi-factor input model and designing a dual-scale prediction framework at both the hourly (24-hour window) and daily levels (7/30/90-day windows). Using traffic flow and meteorological data from Interstate 94 in Minnesota, USA (20122018), the research explores the trade-off between external factors and time scales in LSTM modeling. The results provide a refined optimization path for traffic flow forecasting under complex scenarios.

Research on Tax Risk Identification and Assessment System Assisted by Software and Deep Learning

2025-06-20

Xingguang He | International Journal of High Speed Electronics and Systems

Tax risk management is crucial for businesses to ensure compliance and minimize financial risks. The challenges arise from the inaccuracy of historical tax data in predicting all potential risk scenarios. … Tax risk management is crucial for businesses to ensure compliance and minimize financial risks. The challenges arise from the inaccuracy of historical tax data in predicting all potential risk scenarios. The objective of the research is to improve accuracy and efficiency in detecting tax-related risks by leveraging advanced deep learning (DL) and software integration in real-world scenarios. Data for the research are gathered from various sources, including historical tax records, transaction data, and compliance reports. Data preparation includes cleaning the raw data by handling missing values, correcting inconsistencies, and normalizing the data to normalize ranges. Linear discriminant analysis is used for feature extraction, reducing dimensionality while preserving discriminative data. The research offers a novel DL approach named frilled lizard optimizer-driven intelligent gated-long short-term memory (FLO-IG-LSTM) to enhance the identification and assessment of tax risks, ensuring more reliable and automatic predictions for tax professionals and businesses. The system is implemented in Python, and the results establish the representation’s capability to identify and assess tax risks effectively, outperforming existing methods in efficiency and accuracy. The FLO-IG-LSTM model achieved an accuracy of [Formula: see text], an execution time of 2.3[Formula: see text]ms per record, a latency of 1.8[Formula: see text]ms, an F1-score of [Formula: see text], a precision of [Formula: see text], a recall of [Formula: see text], and an error attribution accuracy of [Formula: see text]. The confusion matrix revealed 1450 true positives, 1320 true negatives, 50 false positives, and 45 false negatives, highlighting a well-performing classification model with minimal errors. It offers a capable method for automating tax risk management, though additional improvements are needed to improve its scalability and flexibility across various tax systems.

AI-Secured Visitor Management System

2025-06-20

Jaya Bharath Reddy , Raghavendra Sagar , Lakshmi Lahari +2 more | Apple Academic Press eBooks

Spatiotemporal interactive dynamic Synchronous graph convolution network for traffic flow forecasting

2025-06-19

Jianhong Gan , Yifei Chen , JiaXing Wang +1 more | Journal of Intelligent Transportation Systems

Intelligent Real-Time Management Based on Wireless Sensor Network for Road Traffic Regulation in Downtown Bujumbura

2025-06-19

Fiston Niyonkuru , Ilundu Wail Walumbuka , Jérémie Ndikumagenge +1 more | Scholars Journal of Engineering and Technology

Globally, traffic management is a daily concern during peak periods. The presence of this state of affairs observed daily has a negative impact on many areas such as economy, ecology, … Globally, traffic management is a daily concern during peak periods. The presence of this state of affairs observed daily has a negative impact on many areas such as economy, ecology, air and noise pollution, and contributes to the multiplication of certain diseases such as headaches, acute respiratory infections, lung cancer and travel-related stress. The use of technology and real-time analysis facilitates road traffic management. The constant is that traffic jams are often the result of poorly designed priorities, even though the number of vehicles and population density is constantly increasing. Moreover, the quality and quantity of urban infrastructure is not sufficient to help meet this challenge. Ultimately, it would be desirable for decision-makers to adopt a strategy based on the use of advanced technologies or to build new infrastructure including three bridges in the northern neighborhoods and two bridges in the southern neighborhoods of the city of Bujumbura to deal with traffic congestion.

Assessing the risk of traffic accidents in lisbon using a gradient boosting algorithm with a hybrid classification/regression approach

2025-06-19

Nuno Alpalhão , Pedro Sarmento , Bruno Jardim +1 more | Transportation Research Interdisciplinary Perspectives

Comparative Analysis of Attention Mechanisms in Densely Connected Network for Network Traffic Prediction

2025-06-19

Myeongjun Oh , Sung Jong Oh , Jongkyung Im +5 more | Signals

Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. However, it is known that such convolution layers … Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. However, it is known that such convolution layers can only barely capture long-term spatial and temporal dependencies. To solve this problem, we propose Attention-DenseNet (ADNet), which effectively incorporates an attention module into STDenseNet to learn representations for long-term spatio-temporal patterns. Specifically, we explored the optimal positions and the types of attention modules in combination with STDenseNet. Our key findings are as follows: i) attention modules are very effective when positioned between the last dense module and the final feature fusion module, meaning that the attention module plays a key role in aggregating low-level local features with long-term dependency. Hence, the final feature fusion module can easily exploit both global and local information; ii) the best attention module is different depending on the spatio-temporal characteristics of the dataset. To verify the effectiveness of the proposed ADNet, we performed experiments on the Telecom Italia dataset, a well-known benchmark dataset for network traffic prediction. The experimental results show that, compared to STDenseNet, our ADNet improved RMSE performance by 3.72%, 2.84%, and 5.87% in call service (Call), short message service (SMS), and Internet access (Internet) sub-datasets, respectively.

Micro-Driving Behavior Analysis of Drivers in Congested and Conflict Environments Using Graph Theory

2025-06-19

Rongjun Cheng , Xudong An , Weiqi Fan +1 more | Systems

Many traffic conflicts on the roads are caused by a small proportion of drivers. Currently, there are few studies exploring the time-varying patterns of driving behavior among these drivers. This … Many traffic conflicts on the roads are caused by a small proportion of drivers. Currently, there are few studies exploring the time-varying patterns of driving behavior among these drivers. This paper proposes a generic time-series analytical framework and uses it to analyze the driving behavior patterns of many high-risk drivers, which provides a theoretical and targeted basis for vehicle warning systems. Specifically, the natural trajectory time-series data in the rear-end conflict process from congested highway sections were first obtained. Secondly, K-medoid clustering was utilized to obtain the quantitative driving behavior sequence from the trajectory. Thirdly, the driving behavior sequence was transformed into a graph structure by the co-occurrence matrix. Graph theory and Markov theory were used to analyze the obtained graph to achieve the goal of analyzing the time-varying patterns of driving behavior. The analysis found that the driving behavior transition graph network of high-risk drivers on congested highway sections does not exhibit the small-world property and this suggests that during the conflict process, the driver is unable to quickly transition between states. Additionally, vehicles consistently evolve into a rear-end conflict state along a fixed driving behavior transition route, which indicates that the causes of conflicts in congested road sections are similar. Finally, the state change of the conflict process follows the Markov property, proving that the state during the conflict process can be predicted and controlled.

Traffic state estimation and prediction based on Bayesian approach in urban road networks using AVI and floating vehicle data

2025-06-18

Jianhua Song , Bruce Hellinga , Gang Ren +4 more | Transportmetrica A Transport Science

Multi-Agent Deep Reinforcement Learning for Efficient Vehicular Mobility via Federated Fog Computing

2025-06-18

Ashok Pratap Arora , Neetesh Kumar , Pramod Kumar Singh | ACM Transactions on Cyber-Physical Systems

Optimizing vehicle waiting time is essential for an intelligent intersection management system as it directly influences traffic flow, public safety, and overall network performance. Conventional emergency vehicle management methods rely … Optimizing vehicle waiting time is essential for an intelligent intersection management system as it directly influences traffic flow, public safety, and overall network performance. Conventional emergency vehicle management methods rely on static rules that are unable to adapt to dynamic traffic conditions. This paper presents MADQL-FFC, a multi-agent deep Q-learning framework combined with federated fog computing for real-time traffic signal control at the intersection that prioritizes emergency vehicles. In this framework, deep Q-network agents are deployed at individual intersections to make localized decisions, while a federated fog computing architecture is used in information sharing among neighboring agents for coordinated decision making. This helps in decentralized, low-latency coordination across the traffic network without the need for centralized control. The proposed approach is evaluated using realistic traffic simulations based on OpenStreetMap data of Gwalior City, India, implemented in the Simulation of Urban Mobility (SUMO) simulator. Comparative results against state-of-the-art methods demonstrate that MADQL-FFC achieves reductions in waiting time for all vehicle categories. Overall, the proposed model achieves a performance improvement of 5% to 32%, 12% to 44%, 4% to 46%, and 13% to 38% in average waiting time, average vehicle speed, average queue length, and average throughput respectively, when compared to state of the art methods.

Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey

2025-06-18

Yizhe Wang , Ruifa Luo , Xiaoguang Yang | Applied Sciences

Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new … Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new opportunities to address these issues. The vast amount of “dormant” ETC data contains rich traffic information that urgently needs to be deeply mined and effectively utilized. This paper reviews the research status, key technologies, and development trends of urban traffic state sensing and analysis technologies based on ETC data. In terms of technological development, ETC systems have evolved from simple toll collection tools to comprehensive traffic management platforms, featuring unique advantages such as accurate vehicle identification, extensive spatiotemporal coverage, and stable data quality. ETC data-based traffic sensing technologies encompass traffic state representation at microscopic, mesoscopic, and macroscopic levels, enabling comprehensive sensing from individual vehicle behavior to overall network operations. The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy. In practical applications, ETC data has demonstrated enormous potential in real-time monitoring and signal control optimization, traffic prediction and artificial intelligence technologies, environmental impact assessment, and other fields. Meanwhile, ETC data-based urban traffic management is transitioning from passive responses to proactive prediction, from single functions to comprehensive services, and from isolated systems to integrated platforms. Looking toward the future, the deep integration of emerging technologies, such as vehicle–road networking, edge computing, and artificial intelligence, with ETC systems will further promote the intelligent, refined, and precise development of urban traffic management.

Quality assurance for network-level traffic signal performance measures derived from connected-vehicle data

2025-06-18

Xiaofeng Li , Peipei Xu , Yao‐Jan Wu +2 more | Journal of Intelligent Transportation Systems

Telling fortunes? Evaluation of traffic forecasting models using traffic and context features

2025-06-18

Marius Hadry , André Bauer , Robert Leppich +2 more | Applied Intelligence

Abstract The need for efficient and reliable logistics solutions has increased significantly in the last decade. Traffic forecasts are a promising source of information that can be used to improve … Abstract The need for efficient and reliable logistics solutions has increased significantly in the last decade. Traffic forecasts are a promising source of information that can be used to improve the planning of delivery schedules. However, most existing traffic forecasting approaches only support a forecasting horizon of up to an hour, which is insufficient for per-day-based schedule planning. In this paper, we focus on short-term traffic forecasting for up to four hours. We first propose a data collection process integrating traffic speed, incidents, weather, and holiday information. We have used this process to collect real-world traffic data for 115 days. We then define and evaluate twelve models for vehicle traffic forecasting, including well-known time series forecasting approaches and state-of-the-art deep learning models. Our results show that the best model in our comparison improved the accuracy by approximately 30% compared to a naive forecaster that repeats the last known value. The evaluation also shows that LSTM-based approaches are competitive to state-of-the-art models. Overall, the proposed deep-learning-based models perform best while requiring a smaller input timeframe than statistical models.

Traffic estimation in unobserved network locations using data-driven macroscopic models

2025-06-18

Pablo Guarda , Sean Qian | Transportmetrica A Transport Science

Next-Generation Transit Signal Priority with Advanced Arrival Time Prediction and Custom Traffic Signal Control Logic

2025-06-18

Burak Cesme , Jorge A. Barrios , Peter Koonce +4 more | Transportation Research Record Journal of the Transportation Research Board

Transit signal priority (TSP) is a signal timing strategy to give priority to transit by adjusting the signal operation with the goal of reducing transit delay and improving reliability. While … Transit signal priority (TSP) is a signal timing strategy to give priority to transit by adjusting the signal operation with the goal of reducing transit delay and improving reliability. While TSP can be a powerful tool, TSP deployments in the U.S. have often resulted in marginal improvements. The primary reasons for limited TSP effectiveness are short detection horizons for TSP requests (e.g., 10 s), near-side bus stops (i.e., located before crossing an intersection) that influence arrival times at the downstream traffic signal, and restrictive signal timing strategies (e.g., lock-out policies that inhibit TSP for a specified amount of time, coordinated control that offers little flexibility for TSP). This paper documents the impacts of a “next-generation” TSP system that couples with custom signal control logic for TSP through a field deployment in Portland, Oregon, U.S., using emerging data sources. The system uses cloud-based, predictive logic for estimating time of arrival, with predictions of bus arrivals available up to 2 min ahead of each intersection and updated continuously every 1 s. The custom signal control logic includes advanced TSP strategies that can take advantage of early prediction. Using data from high-resolution automatic vehicle location, analysis results show the custom signal controller logic with advanced prediction resulted in an average bus delay reduction of 29 s per intersection at major intersections (a reduction of 69% compared with baseline). Analyses using automated traffic signal performance measures and vehicle probe data showed these bus delay improvements were achieved with marginal impacts on motorists and without additional delay to pedestrians and bicycles.

A repulsive firefly algorithm-inspired dynamic weight particle swarm-optimized deep neural network to forecast traffic conditions

2025-06-17

Kian Lun Soon , Lam Tatt Soon | Engineering Optimization

Low-high frequency network for spatial–temporal traffic flow forecasting

2025-06-17

Feng Qi , Bo Li , Xiaohan Liu +2 more | Engineering Applications of Artificial Intelligence

Multi-step citywide traffic flow forecasting based on multiscale spatio-temporal transformer

2025-06-17

Shaowu Zhang , Taiyong Li , Yingqi Chen +2 more | Engineering Applications of Artificial Intelligence

Automatic Extraction of Road Interchange Networks from Crowdsourced Trajectory Data: A Forward and Reverse Tracking Approach

2025-06-17

Fengwei Jiao , Longgang Xiang , Yuanyuan Deng | ISPRS International Journal of Geo-Information

The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize … The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges.

Predicting occupant response curves in vehicle crashes via Attention-enhanced multimodal temporal Network

2025-06-17

Wenjie Wang , Xiaoyi Tai , Chang Zhou +2 more | Accident Analysis & Prevention

Characterization of Urban Traffic Flow Patterns Around General Santos City Public Market

2025-06-17

Christian Francis Nayre , Charlie Taclendo , Randy P. Asturias - +1 more | International Journal For Multidisciplinary Research

This study characterizes the traffic flow dynamics of the road network surrounding the General Santos City Public Market, focusing on four key segments: Santiago Boulevard, Acharon Boulevard, Barreras Street, and … This study characterizes the traffic flow dynamics of the road network surrounding the General Santos City Public Market, focusing on four key segments: Santiago Boulevard, Acharon Boulevard, Barreras Street, and Magsaysay Avenue. Using continuous video data collection over one week and field-based observations, traffic characteristics such as Hourly Variation, Peak Hour Volume (PHV), Daily Traffic (DT), Average Hourly Traffic (AHT), and Vehicle Composition (VC) were analyzed. Findings reveal that Acharon Boulevard recorded the highest daily traffic volume (55,402 PCU), followed by Santiago Boulevard. Peak congestion occurred between 7:00–11:00 a.m. and 3:00–6:00 p.m. The inner lanes adjacent to the market experience higher traffic volumes than the outer ones. Tricycles constituted the majority of traffic across all segments, averaging 58 percent of total vehicle composition. These results offer essential insights for traffic management strategies and future infrastructure improvements in commercial urban corridors.

Incorporating Street-View Imagery into Multi-Scale Spatial Analysis of Ride-Hailing Demand Based on Multi-Source Data

2025-06-16

Jingjue Bao , Ye Li | Applied Sciences

The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors … The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities.

Revisiting the Non-recurrent Traffic Incident Identification Problem for Real-Time Applications Using Theory-Aware Unsupervised Learning

2025-06-16

Katerina Ν. Papadatou , Panagiotis Fafoutellis , Eleni I. Vlahogianni | Data Science for Transportation

Short-term traffic flow prediction based on improved Chimp algorithm optimized BiGRU

2025-06-16

Tao Feng , Liang Chen , Qiaoru Li | International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

High resolution bus lane performance evaluation from real time update data

2025-06-16

Tingsen Xian , John D. Nelson , Emily Moylan | Transportation Research Interdisciplinary Perspectives

Comparative Analysis of Traffic Speed Data on Two-Lane Rural Road Segments: Radar Technology, Google Maps API, and License Plate Recognition

2025-06-16

Andrea Kociánová , Adam Kníž , Peter Pisca | Civil and Environmental Engineering

Abstract This paper presents a comparative analysis of traffic speed data on two-lane rural road segments using three different methods: radar technology measuring spot speeds, Google Maps API, and License … Abstract This paper presents a comparative analysis of traffic speed data on two-lane rural road segments using three different methods: radar technology measuring spot speeds, Google Maps API, and License Plate Recognition (LPR) providing space-mean speed estimates. The study employs a two-stage validation approach with LPR as the reference method, followed by direct comparison between Google Maps and radar speed data. Analysis of five road segments with varying geometry, traffic volumes, mobile signal quality, and radar placement reveals that no single method is universally superior. The findings show that Google Maps API offers a scalable, cost-effective solution with reasonable performance (MAE 5.5-8.8 km/h) under optimal conditions, but becomes unreliable in areas with weak mobile signal coverage and tends to smooth speed variations. When properly positioned, radar measurements provide high-resolution speed data with sensitivity to traffic changes (MAE 3.7-9.8 km/h against LPR), but their point-based nature creates significant dependency on sensor placement. Both methods exhibit reduced accuracy during low-speed conditions, as indicated by elevated MAPE values. Mobile network signal quality emerges as critical for Google Maps reliability, while road segment geometry and sensor positioning are paramount for radar reliability.

Adaptive traffic signal control for road networks based on dueling double deep q-network

2025-06-16

Xin Chu , Xianghui Cao | International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

Variable speed limit control method for highways based on an improved SAC algorithm

2025-06-16

Xing Xie , Junqing Shi , Junhui Ruan +1 more | International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)

Energy-optimization-based agent motion model: a hybrid approach integrating data-driven methods and trajectory prediction for intersection traffic simulation

2025-06-16

Ziqi Zhang | International Conference on Frontiers of Traffic and Transportation Engineering (FTTE 2022)