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Vehicle trajectory prediction can provide important support for intelligent transportation systems in areas such as autonomous driving, traffic control, and traffic flow optimization. Predicting vehicle trajectories is an extremely challenging task that not only depends on the vehicle's historical trajectory but also on the dynamic and complex social-temporal relationships of the surrounding traffic network. The trajectory of the target vehicle is influenced by surrounding vehicles. However, existing methods have shortcomings in considering both time dependency and interactive dependency between vehicles or insufficient consideration of the impact of surrounding vehicles. To address this issue, we propose a hybrid deep learning model based on a temporal convolutional network (TCN) that considers local and global interactions between vehicles. Specifically, we use a social convolutional pooling layer to capture local interaction features between vehicles and a multihead self-attention layer to capture global interaction features between vehicles. Finally, we combine these two features using an encoder-decoder structure to predict vehicle trajectories. Through experiments on the Next-Generation Simulation (NGSIM) public dataset and ablation experiments, we validate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

The intelligent transportation system (ITS) has been proven capable of effectively addressing traffic congestion issues. For vehicles to perform effectively and improve mobility under the intelligent driving environment, real-time prediction of traffic speed is undoubtedly essential. Considering the complex spatiotemporal dependency inherent in traffic data, conventional prediction models encounter many limitations. To improve the prediction performance and investigate the temporal features, this study focuses on emerging deep neural networks (DNNs) using the Caltrans Performance Measurement System (PeMS) data. This research also establishes an intelligent driving environment in the simulation and compares the traditional car-following model with deep learning methods in terms of multiple performance metrics. The results indicate that both supervised learning and unsupervised learning are superior to the simulation-based model on the freeway, and the two deep learning networks are almost identical to one another. Besides, the result reveals that all models have their latent features for different time dimensions under the low traffic loads, transition states, and heavy traffic loads. This is critical in the application of prediction technologies in ITS. The findings can assist transportation researchers and traffic engineers in both traffic operation and management, such as bottleneck identification, platooning control, and route planning. [ABSTRACT FROM AUTHOR]

Traffic prediction is the key for Intelligent Transport Systems (ITS) to achieve traffic control and traffic guidance, and the key challenge is that traffic flow has complex spatial-temporal dependence and nonlinear dynamics. Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. AGCN-T consists of three modules. In the spatial dependency extraction module, according to the similarity of historical traffic flow sequences of different loop detectors, an adjacency matrix for the road network is constructed based on a sequence similarity calculation method, Predictive Power Score (PPS), to express latent spatial dependency; and then GCN is used on the adjacency matrix to capture the global spatial correlation and Transformer is used to capture dynamic spatial dependency from the most recently flow sequences. And then, the dynamic spatial dependency is merged with the global spatial correlation to obtain the overall spatial dependency pattern. In the temporal dependency extraction module, the temporal dependency pattern of each traffic flow sequence is learned by the temporal Transformer. The prediction module integrates both patterns to form spatial-temporal dependency patterns and performs multistep traffic flow prediction. Four sets of experiments are performed on three actual traffic datasets to show that AGCN-T can effectively capture the dynamic spatial-temporal dependency of the traffic network, and its prediction performance and efficiency are better than existing baselines. AGCN-T can effectively capture the dynamics in traffic flow. In addition to traffic flow prediction, it can also be applied to other spatial-temporal prediction tasks, such as passenger demand prediction and crowd flow prediction. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and the development of the automobile industry, the contradiction between the traffic capacity of existing urban roads and the growing traffic demand has become increasingly acute. Traffic congestion is becoming increasingly prominent. The purpose of this article is to consider the characteristics of traffic risk factors and to study the method of establishing a flexible traffic system. It can relieve traffic congestion and provide a smooth and orderly traffic environment using intelligent transportation systems to control and direct traffic flow. Based on the large urban road network, this research uses the theory of coordinated control and learning mechanism, fuzzy control, dynamic reprogramming, and other theories to study phase sequence, balance peripheral load, and overall traffic flow. It also uses on-board sensors to optimize the collection and processing of network information, decompose traffic guidance work, select the optimal route, and autonomously guide the intelligent transportation system. Under the flexible demand scheme, the average load of the trunk road is reduced by a larger degree, which is 4% lower than that of the fixed demand scheme. At the same time, the average load of the branch has increased more, which is 4% higher than that of the fixed demand scheme. It can be seen that under the elastic demand scheme, the distribution of traffic flow in the road network is more balanced and the optimization effect of relieving traffic pressure on trunk roads and improving the utilization rate of branch roads is more significant. [ABSTRACT FROM AUTHOR]

Traffic volume data are the important part of research and application of intelligent transportation systems (ITS). However, data loss often happens due to various factors in the real world, which may cause large deviations in prediction or bad accuracy of optimizations. Imputation is a valid way to handle missing values. A multilayer perceptron-multivariate imputation of chain equation (MLP-MICE) regression imputation method optimized by the limit-memory-BFGS algorithm is proposed, considering the temporal and spatial characteristics of traffic volume. Also, 32 groups of simulated imputation experiments based on the detected traffic volume of road sections in the Guangdong freeway system are conducted, which take the scenarios of continuous missing and jumped missing into account. The results of the experiments show that the MLP-MICE can optimize the imputation performance in the missing value of traffic volume with the MAPE of imputation results from 6.38% to 30%. Meanwhile, the proposed model has higher imputation accuracy for the traffic volume data with a lower degree of mutation. Lastly, the performance of the proposed model of imputation in short-term traffic volume prediction is discussed using the support vector machine. The results of it show that the MAPE of prediction under the proposed model is much lower than all-zero imputation. Therefore, the proposed model in this study is positive on improving the accuracy of traffic volume prediction and intelligent traffic control and management. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme. [ABSTRACT FROM AUTHOR]

The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management. [ABSTRACT FROM AUTHOR]

A local dynamic path planning method is proposed to compensate for the lack of consideration of the movement state of surrounding vehicles, the poor comfort, and the low traffic efficiency when the existing vehicle changes lanes automatically. Firstly, the cubic polynomial is predefined, and the optimal track path is solved. According to the real-time information of environment perception, the model is continuously modified by acquiring real-time information in the course of path planning, and the regional safety of the vehicle is realized. The Carsim and simulink simulation results and actual vehicle verification show that, compared with the traditional nondynamic research method, this method can effectively solve the problem that the vehicle speed variation and the sudden intrusions of the vehicle leading to the compulsory operation of the vehicle during the course of lane-changing. The safety is also improved. In order to ensure the vehicle comfort and stability, the lane-changing time is shortened by 20%, and the efficiency of lane-changing is improved obviously. [ABSTRACT FROM AUTHOR]

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement. [ABSTRACT FROM AUTHOR]

This study aims to develop an optimal signal control algorithm for signalized intersections using individual vehicle's trajectory data under the vehicle-to-infrastructure (V2I) communication environment. The optimal signal control algorithm developed in this study consists of three modules, namely, a phase group length computation module, a split distribution module, and a phase sequence assignment module. A set of analyses using a microscopic simulation model, VISSIM, was conducted for evaluating the effectiveness of the V2I-based optimal signal control algorithm proposed in this study. The analysis results show that the performance of the V2I-based optimal signal control algorithm is superior to the actuated as well as the fixed signal control methods in an isolated intersection and a 2X3 signalized intersection network. In addition, this study investigated the minimum market penetration rate of V2I equipped vehicles for which the V2I-based optimal signal control algorithm is applicable. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) is an emerging intelligent transportation system (ITS) measure to improve operational and safety performance of motorway systems. Rule-based algorithms have been widely used in VSL applications because of their comprehensibility and ease of application. However, most of the algorithms proposed in the literature under this category are rather rough for the speed control. Pre-specified rules show some difficulties in appropriately activating/deactivating control actions in real time because of non-stationary and nonlinear nature of the traffic system. This paper proposes a fuzzy logic-based VSL control algorithm as an alternative to the existing VSL control algorithms. The proposed algorithm uses fuzzy sets instead of crisp sets to allow the separation of attribute domains into several overlapping intervals. The discretization using fuzzy sets can help to overcome the sensitivity problem caused by crisp discretization used in the existing VSL algorithms. The proposed algorithm is assessed for a test bed in Auckland using AIMSUN micro-simulator and verified against a well-known VSL algorithm. The simulation results show that the proposed algorithm outperforms the existing one to improve the efficiency performance of the motorway system with the critical bottleneck capacity increased by 6.42% and total travel time reduced by 12.39% when compared to a no-control scenario. [ABSTRACT FROM AUTHOR]

The use of traffic sensors to acquire real-time traffic information for intelligent transportation systems is becoming increasingly common. It is a challenge to determine where these sensors should be located to maximize the benefit of their use. This paper aims to illuminate the interaction of the sensor location problem (SLP) and its influencing factors, and to reveal the influencing mechanisms between those factors and the optimal sensor numbers. Firstly, we sum up the factors that influence the SLP for freeway corridors in detail and present the mathematical formulation of each factor. Then, given the parameters, which are derived from those influencing factors, the maximum integration value model (MIVM) and simplified MIVM are proposed for addressing the SLP. Finally, a real world case study, in which the simplified MIVM is used, is presented to illustrate how these factors influence the optimal sensor numbers and the maximum integration value, and also leads to the typical influencing patterns of those factors for freeway corridors. The results of the case study also demonstrate the effectiveness of the model and problem solving scheme. What is more, the suggestions for using the findings hereof in practical applications are put forward. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems have been promoted as a means to improve both the efficiency and safety of the road network. The effectiveness of advanced technologies in improving road safety has been an area of research which has thus far yielded mixed results. In order to ensure that advanced technologies deliver on their intended outcomes, more research has to be devoted to understanding road users' perceptions and reactions to these systems. This study examines drivers' perceptions of the use of dynamic message signs and their self-reported reactions to the messages displayed. In general, drivers support the use of highway electronic boards for traffic incident reports and weather information which have an impact on traffic delays and level of service. They also think that it is a good idea to display road safety messages and to remind drivers to drive safely and be courteous on the roads. Moreover, most drivers reported that they do read and think about the messages displayed and react positively to some of the road safety messages. [ABSTRACT FROM AUTHOR]

The use of Intelligent Transportation Systems (ITS) in construction work zones to disseminate traffic information has increased significantly in recent years, mainly with the use of Variable Message Signs (VMS). VMS are used based on the assumption that informed drivers will make better travel decisions, thereby reducing congestion. However, the extent of change in driver behavior is difficult to predict prior to ITS deployment. This difficulty leads to the larger problem of justifying investment in ITS. This article proposes an ITS deployment decision support tool using micro-simulation. The approach determines the required diversion effectiveness of a work zone ITS deployment using VMS. The methodology was tested using the Glenmore Trail/Elbow Drive/5th Street interchange project (GE5) in Calgary, Canada. The results indicate that the proposed approach will assist agencies in justifying ITS investment by exhibiting the potential resultant societal benefits. [ABSTRACT FROM AUTHOR]

This paper summarizes a standardized verification process for network traffic simulation models. After the general introduction of philosophy of verification, we explain detailed processes of the verification and its application to several well-known simulation models. 'Verification' here means several examination tests of simulation models using virtual data on a simple network so as to confirm their fundamental functions. In the course of model development, the developers have to examine whether the model performance is consistent with the specifications that they intend and also with the well-authorized traffic engineering theory. Because of several constraints in putting the model specifications into the computer programming such as discretizing of time and space and simplifying vehicle behaviors to some degree, the intended model specifications may not be fully achieved in a computer. Therefore, we strongly recommend the verification before applying the models to a real network. [ABSTRACT FROM AUTHOR]

Area traffic control is an important element in Intelligent Transportation System (ITS). This paper extends the lane-based optimization method to a traffic equilibrium network, which improves the operational performance of signal-controlled network. We formulate a decomposition approach to simultaneously optimize the lane markings and signal settings for a signal-controlled network that comprises two levels of optimization. At the junction level, the lane markings, control sequence, and other aspects of the signal settings are optimized for individual junctions, whereas at the network level, the group-based signal settings are optimized to take into account the re-routing characteristics of travelers and signal coordination effects that are based on a TRANSYT traffic model, which is a well-known procedure for evaluating the performance of signal-controlled networks. We use a numerical example to demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

In-vehicle information has an important social role to play in improving the efficiency and safety of travel by all modes. In this review, three generations of system are identified. The first generation consists of simple in-vehicle units relying heavily on external data. The second generation has more sophisticated in-vehicle units with colour TFT screens and DVD players for maps and entertainment. The third generation again makes use of external data, using the mobile phone network to download map sections and other data as and when required, thereby obviating the need for beacons and map CDs. For locationing, GPS (and/or Galileo, the European version of GPS) remains the favoured technology. Portable devices offering multi-modal information could improve inter-modal transport efficiency. [ABSTRACT FROM AUTHOR]

Recent advances in traffic control methods have led to flexible control strategies for use in an adaptive traffic control system (ATCS). ATCS aims at controlling the imminent traffic, which is yet to arrive and hence not known perfectly. Therefore, volume prediction is an essential part. Associated with the prediction are two aspects: resolution and accuracy. Recent studies indicate a tradeoff between prediction resolution and accuracy: finer resolutions, larger errors. It is imperative to study the relationship and tradeoff between the control strategy, prediction resolution, and its associated error, which are crucial to the development of ATCS. This study investigates this relationship through an extensive simulation of scenarios in Hong Kong with a recently developed dynamic traffic control model, DISCO. Based on the Hong Kong scenarios conducted with DISCO, the major findings include: (i) the importance of resolution outweighs that of error; (ii) dynamic timing plans generally outperform time-invariant timing plans; (iii) up to a certain extent, overestimated predictions lead to better results than underestimated predictions. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [ABSTRACT FROM AUTHOR]

The author reflects on the significance of intelligent transport system (ITS). He notes that the introduction of ITS and the concept of reliability analysis has generated a need for better understanding of road network reliability under recurrent and nonrecurrent congestion cases. The author also believes that the ITS also triggers traffic engineers to research on transport network design and analysis.

No abstract is available for this article. [ABSTRACT FROM AUTHOR]