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Objectives: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptrons (MLP). This approach leverages advanced feature analysis capabilities, offering a more direct and interpretable method than traditional models. Methods: High-resolution optical cameras recorded detailed pedestrian and vehicle movements at study sites, with data processed to extract trajectories and convert them into real-world coordinates via precise georeferencing. Trained observers categorized interactions into safe passage, critical event, and conflict based on movement patterns, speeds, and accelerations. Fleiss Kappa statistic measured inter-rater agreement to ensure evaluator consistency. This study introduces a novel deep-learning model combining Transformer-based time series data capabilities with the classification strengths of a Multilayer Perceptron (MLP). Unlike traditional models, this approach focuses on feature analysis for greater interpretability. The model, trained on dynamic input variables from trajectory data, employs attention mechanisms to evaluate the significance of each input variable, offering deeper insights into factors influencing interaction severity. Results: The model demonstrated high performance across different severity categories: safe interactions achieved a precision of 0.78, recall of 0.91, and F1-score of 0.84. In more severe categories like critical events and conflicts, precision and recall were even higher. Overall accuracy stood at 0.87, with both macro and weighted averages for precision, recall, and F1-score also at 0.87. The variable importance analysis, using attention scores from the proposed transformer model, identified 'Vehicle Speed' as the most significant input variable positively influencing severity. Conversely, 'Approaching Angle' and 'Vehicle Distance from Conflict Point' negatively impacted severity. Other significant factors included 'Type of Vehicle', 'Pedestrian Speed', and 'Pedestrian Yaw Rate', highlighting the complex interplay of behavioral and environmental factors in pedestrian-vehicle interactions. Conclusions: This study introduces a deep-learning model that effectively predicts the severity of pedestrian-vehicle interactions at crosswalks, utilizing a Transformer-MLP hybrid architecture with high precision and recall across severity categories. Key factors influencing severity were identified, paving the way for further enhancements in real-time analysis and broader safety assessments in urban settings. [ABSTRACT FROM AUTHOR]

Abstract Accurate demand forecasting has become increasingly necessary in the burgeoning field of free‐floating micro‐mobility systems. However, for model training, the service area must be divided into specific areal units, which often involves grid‐based methods. Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. Therefore, a novel base areal unit generation algorithm is proposed that employs a clustering approach to enhance the prediction accuracy in free‐floating micro‐mobility system demand. The method identifies suitable base areal units by merging smaller ones while considering the similarities in temporal usage patterns and distances between different areas, mitigating the impact of MAUP during model learning. The approach was evaluated using shared e‐scooter data from two cities, Kansas City and Minneapolis, and it was compared to the traditional grid method. The findings indicate that the proposed framework generally improves prediction performance within the newly defined areal units.

Abstract Any technological innovation will be accompanied by new challenges and risks, and the connected and automated vehicles (CAVs) are no exception. Among them, the argument that cooperating platoons may fall victim to cyberattacks through wireless communication has emerged as a significant issue. Therefore, this paper designs a communication topology anomaly response system (CTARS) to ensure platoon safety, which consists of a trigger module and a control module. The primary objective of the trigger module is to distinguish abnormal vehicle behavior based on time to collision (TTC) indicators, and the control module combines the model predictive control (MPC) and bidirectional long short‐term memory (Bi‐LSTM) to achieve accurate trajectory prediction of and optimal control, working in tandem with the trigger module. Subsequently, the real dataset HISTORIC is used to calibrate the multiple vehicle intelligent driver model (IDM) and train the trajectory prediction model. Furthermore, comparative simulations are conducted, encompassing various forms of cyberattacks, in order to examine the evolution characteristics of CAVs platoons (CAVP) and evaluate the performance of CTARS. The results demonstrate the remarkable effectiveness of CTARS in safeguarding the security of CAVP during cyberattacks, thus confirming its exceptional performance.

Any technological innovation will be accompanied by new challenges and risks, and the connected and automated vehicles (CAVs) are no exception. Among them, the argument that cooperating platoons may fall victim to cyberattacks through wireless communication has emerged as a significant issue. Therefore, this paper designs a communication topology anomaly response system (CTARS) to ensure platoon safety, which consists of a trigger module and a control module. The primary objective of the trigger module is to distinguish abnormal vehicle behavior based on time to collision (TTC) indicators, and the control module combines the model predictive control (MPC) and bidirectional long short‐term memory (Bi‐LSTM) to achieve accurate trajectory prediction of and optimal control, working in tandem with the trigger module. Subsequently, the real dataset HISTORIC is used to calibrate the multiple vehicle intelligent driver model (IDM) and train the trajectory prediction model. Furthermore, comparative simulations are conducted, encompassing various forms of cyberattacks, in order to examine the evolution characteristics of CAVs platoons (CAVP) and evaluate the performance of CTARS. The results demonstrate the remarkable effectiveness of CTARS in safeguarding the security of CAVP during cyberattacks, thus confirming its exceptional performance. [ABSTRACT FROM AUTHOR]

Accurate demand forecasting has become increasingly necessary in the burgeoning field of free‐floating micro‐mobility systems. However, for model training, the service area must be divided into specific areal units, which often involves grid‐based methods. Although these methods are feasible and provide a uniform area division, they are highly susceptible to the Modifiable Areal Unit Problem (MAUP), which is a critical issue in spatial data analysis. Although MAUP can adversely affect predictive model learning, studies addressing this issue are scarce. Therefore, a novel base areal unit generation algorithm is proposed that employs a clustering approach to enhance the prediction accuracy in free‐floating micro‐mobility system demand. The method identifies suitable base areal units by merging smaller ones while considering the similarities in temporal usage patterns and distances between different areas, mitigating the impact of MAUP during model learning. The approach was evaluated using shared e‐scooter data from two cities, Kansas City and Minneapolis, and it was compared to the traditional grid method. The findings indicate that the proposed framework generally improves prediction performance within the newly defined areal units. [ABSTRACT FROM AUTHOR]

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Abstract In the era of data‐driven transportation development, traffic forecasting is crucial. Established studies either ignore the inherent spatial structure of the traffic network or ignore the global spatial correlation and may not capture the spatial relationships adequately. In this work, a Dynamic Spatial‐Temporal Network (DSTN) based on Joint Latent Space Representation (JLSR) is proposed for traffic forecasting. Specifically, in the spatial dimension, a JLSR network is developed by integrating graph convolution and spatial attention operations to model complex spatial dependencies. Since it can adaptively fuse the representation information of local topological space and global dynamic space, a more comprehensive spatial dependency can be captured. In the temporal dimension, a Stacked Bidirectional Unidirectional Gated Recurrent Unit (SBUGRU) network is developed, which captures long‐term temporal dependencies through both forward and backward computations and superimposed recurrent layers. On these bases, DSTN is developed in an encoder‐decoder framework and periodicity is flexibly modeled by embedding branches. The performance of DSTN is validated on two types of real‐world traffic flow datasets, and it improves over baselines.

Abstract Quantifying the complexity of traffic scenarios not only provides an essential foundation for constructing the scenarios used in autonomous vehicle training and testing, but also enhances the robustness of the resulting driving decisions and planning operations. However, currently available quantification methods suffer from inaccuracies and coarse‐granularity in complexity measurements due to issues such as insufficient specificity or indirect quantification. The present work addresses these challenges by proposing a comprehensive entropy‐based model for quantifying traffic scenario complexity across multiple dimensions based on a consideration of the essential components of the traffic environment, including traffic participants, static elements, and dynamic elements. In addition, the limitations of the classical information entropy models applied for assessing traffic scenarios are addressed by calculating magnitude entropy. The proposed entropy‐based model is analyzed in detail according to its application to simulated traffic scenarios. Moreover, the model is applied to real world data within a naturalistic driving dataset. Finally, the effectiveness of the proposed quantification model is illustrated by comparing the complexity results obtained for three typical traffic scenarios with those obtained using an existing multi‐factor complexity quantification method.

In the era of data‐driven transportation development, traffic forecasting is crucial. Established studies either ignore the inherent spatial structure of the traffic network or ignore the global spatial correlation and may not capture the spatial relationships adequately. In this work, a Dynamic Spatial‐Temporal Network (DSTN) based on Joint Latent Space Representation (JLSR) is proposed for traffic forecasting. Specifically, in the spatial dimension, a JLSR network is developed by integrating graph convolution and spatial attention operations to model complex spatial dependencies. Since it can adaptively fuse the representation information of local topological space and global dynamic space, a more comprehensive spatial dependency can be captured. In the temporal dimension, a Stacked Bidirectional Unidirectional Gated Recurrent Unit (SBUGRU) network is developed, which captures long‐term temporal dependencies through both forward and backward computations and superimposed recurrent layers. On these bases, DSTN is developed in an encoder‐decoder framework and periodicity is flexibly modeled by embedding branches. The performance of DSTN is validated on two types of real‐world traffic flow datasets, and it improves over baselines. [ABSTRACT FROM AUTHOR]

Traffic modeling is crucial for designing and evaluating active queue management (AQM) methods used in network routers to control congestion. The Bernoulli process (BP), commonly used to model the traffic, falls short in capturing the burstiness of Internet traffic. Besides, the Markov Modulated Bernoulli Process (MMBP) with multiple states and varying probabilities allows the determination of each state's load independently but does not set specific overall traffic loads. This limitation hinders the establishment of a baseline for evaluating AQM methods. To address these issues, this paper introduces an enhanced traffic modeling approach using the stationary distribution of the Markov Modulated Bernoulli Process (MMBP-SD). This new model calculates the stationary distribution to match the required traffic load while varying its burstiness, enabling a fair comparison with the Bernoulli process of a predefined traffic load and facilitating the assessment of AQM behaviors. The proposed approach was tested under various traffic loads and evaluated using the burstiness factor (BF) and the maximum burstiness duration (MBD). The results showed that the MMBP-SD improved the BF by 6.2% and the MBD by 118% compared to the BP. Evaluating Random Early Detection (RED) was conducted using MMBP-SD and based on delay, loss, and packet dropping. This evaluation revealed that the RED performance in terms of packet loss degrades when using a 4-state MMBP-SD (e.g., packet loss increased by 28.5%) as RED maintains the same dropping rate as in the single-state model, highlighting a limitation of the RED method. [ABSTRACT FROM AUTHOR]

Quantifying the complexity of traffic scenarios not only provides an essential foundation for constructing the scenarios used in autonomous vehicle training and testing, but also enhances the robustness of the resulting driving decisions and planning operations. However, currently available quantification methods suffer from inaccuracies and coarse‐granularity in complexity measurements due to issues such as insufficient specificity or indirect quantification. The present work addresses these challenges by proposing a comprehensive entropy‐based model for quantifying traffic scenario complexity across multiple dimensions based on a consideration of the essential components of the traffic environment, including traffic participants, static elements, and dynamic elements. In addition, the limitations of the classical information entropy models applied for assessing traffic scenarios are addressed by calculating magnitude entropy. The proposed entropy‐based model is analyzed in detail according to its application to simulated traffic scenarios. Moreover, the model is applied to real world data within a naturalistic driving dataset. Finally, the effectiveness of the proposed quantification model is illustrated by comparing the complexity results obtained for three typical traffic scenarios with those obtained using an existing multi‐factor complexity quantification method. [ABSTRACT FROM AUTHOR]

Abstract For the transport networks only equipped with sparse or isolated detectors, short‐term traffic flow forecasting faces the following problems: (1) there are only temporal information and no spatial information; (2) the noises in the traffic flow significantly affect the forecasting performance. In this paper, a lag‐related noise shrinkage stacked long short‐term memory (LSTM) network is proposed for the traffic flow forecasting task only related to temporal information. To extract effective temporal features, the optimal time lags are selected in the traffic flow and converted into lag‐related multi‐dimensional data. Then, a discrete wavelet threshold denoising shrinkage algorithm is designed to filter the noises to construct a more reliable training set. A multi‐level stacked LSTM network is employed to learn the features of the training set to map the past traffic flow to the future flow. Four benchmark datasets are to evaluate the forecasting performance by extensive experiments. The comparison with the state‐of‐the‐art models demonstrates an average improvement of 7.28% in MAPE and 6.02% in RMSE. In addition, the proposed method has been applied in the Guilin Travel Network Bus Intelligent Dispatching System. It improves the utilization of the vehicles and reduces operating costs.

Origin-Destination (OD) traffic flow estimations from traffic sensor data play an important role for transportation planning and management. This paper proposes a novel method to compare OD traffic estimated matrices (using data from traffic sensors). The proposed method uses the estimated OD traffic flow values together with COVID-19 incidence data in order to build a sequence of temporal graphs that are fed into a machine learning (ML) model. The ML model uses the input information to estimate/predict one-week ahead COVID-19 incidence data. A tailored Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) model is designed adapted to the input information. The paper evaluates the proposed method with 3 different OD estimation alternatives and compares the accuracy achieved by different configurations of the ML model with a traffic agnostic baseline model. Data from 44 provinces in Spain during 2021 providing daily COVID-19 incidence data and 635 geo-located traffic sensors providing monthly traffic counts are used to evaluate the results. The 3 traffic-aware OD estimation methods were able to outperform the baseline model, achieving model gains up to 136%. The major application of the results of this paper is a novel mechanism to validate prior OD traffic matrices.

The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we design a systematic risk index and propose an index describing the degree of data scatter based on the principle of Euclidean distance quantization. By comparing several datasets, including NGSIM, highD, INTERACTION, CitySim, and our self-collected Highway dataset, we find that the proposed metrics can effectively quantify the risk level of the dataset while helping to gain insight into the diversity and balance differences of the dataset. [ABSTRACT FROM AUTHOR]

For the transport networks only equipped with sparse or isolated detectors, short‐term traffic flow forecasting faces the following problems: (1) there are only temporal information and no spatial information; (2) the noises in the traffic flow significantly affect the forecasting performance. In this paper, a lag‐related noise shrinkage stacked long short‐term memory (LSTM) network is proposed for the traffic flow forecasting task only related to temporal information. To extract effective temporal features, the optimal time lags are selected in the traffic flow and converted into lag‐related multi‐dimensional data. Then, a discrete wavelet threshold denoising shrinkage algorithm is designed to filter the noises to construct a more reliable training set. A multi‐level stacked LSTM network is employed to learn the features of the training set to map the past traffic flow to the future flow. Four benchmark datasets are to evaluate the forecasting performance by extensive experiments. The comparison with the state‐of‐the‐art models demonstrates an average improvement of 7.28% in MAPE and 6.02% in RMSE. In addition, the proposed method has been applied in the Guilin Travel Network Bus Intelligent Dispatching System. It improves the utilization of the vehicles and reduces operating costs. [ABSTRACT FROM AUTHOR]

Abstract Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation performance. Fortunately, it is found that traffic data admits the nonlocal self‐similarity (NSS) prior. This paper incorporates the global and nonlocal low‐rank priors of traffic data and proposes a tensor completion model for spatiotemporal traffic data imputation. To be specific, the proposed method uses tensor ring (TR) decomposition with an enhanced representation capability to characterize the global low‐TR‐rank prior of traffic data, e.g. the correlation of sensor and time modes of the tensor (i.e. traffic data). An implicit plug‐and‐play (PnP)‐based regularization is further utilized to exploit the NSS prior, which depicts the nonlocal similar traffic data patterns. Furthermore, the proximal alternating minimization algorithm under the PnP framework is derived to solve this model. The experiment results on various datasets and missing scenarios show the superiority of the proposed model.

Abstract Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data.

Abstract Traffic forecasting, as a fundamental and challenging problem of intelligent transportation systems (ITS), has always been the focus of researchers. Nevertheless, accurate traffic forecasting still exists some problems due to the complex spatial‐temporal dependencies and irregularities of traffic flows. Most of the existing methods typically use the spatial adjacency matrix and complicated mechanism to model spatial‐temporal relationships separately, while ignoring the latent spatial‐temporal correlations. In this paper, a novel architecture is proposed named spatial‐temporal correlation graph convolutional networks (STCGCN) for traffic prediction. First, an informative fused graph structure is constructed to better learn the complex spatial‐temporal correlations, which breaks the limitation that the general spatial adjacency matrix cannot reflect temporal correlations. Moreover, spatial‐temporal correlation graph convolution and gated temporal convolution are performed in parallel and they are integrated into a unified layer, which enables capturing both local and global spatial‐temporal dependencies simultaneously. By stacking multiple layers, STCGCN can learn more long‐range spatial‐temporal dependencies. Experimental results on five public traffic datasets demonstrate the effectiveness and robustness of the proposed STCGCN in urban traffic forecasting.

Several interdisciplinary studies have investigated the impact of Connected and Autonomous Vehicles (CAVs) on the performance of traffic networks, which expect positive effects. Nevertheless, there will be a transitional period during which both Human-Driven Vehicles (HDVs) and CAVs shall operate simultaneously. Adequate modeling of the interactions between CAVs and HDVs is vital to understand the mixed traffic dynamics. We propose a second-order macroscopic model by reconstructing the backward propagation speed of perturbation based on the dynamic headway distance between vehicles in mixed traffic. The proposed model is validated using microscopic simulations, and it replicates the given traffic scenarios subjected to assorted Penetration Rate (PR) of CAVs. The proposed model is employed to investigate the dynamics of mixed traffic. The results demonstrate that the average traffic velocity and the Level of Service (LOS) significantly improve with the increase in the PR of CAVs. Additionally, the performance of the proposed model is compared with the well-known Jiang-Qing-Zhu (JQZ) model, and it outperforms the JQZ model. The proposed model can be employed in traffic forecasting and real-time traffic control.

This paper presents a dataset about a toll highway consisting of a toll lane, a carpool lane, and three regular lanes. The dataset contains traffic information, like the number of vehicles on each lane type and the average speed on each lane, at intervals of 6 minutes. The dataset also provides information about the individual drivers/vehicles on the highway, like their departure and arrival times and the lane used. The dataset contains a total of 90 scenarios that cover varying the driver population size, the toll price, and the overall percentage of vehicles eligible to use the carpool lane. The simulations utilize a fuzzy logic engine to emulate the process of lane selection by drivers. In order to test and demonstrate the usefulness of the dataset, machine learning-based models were built to predict whether a driver would arrive late or not at his/her final destination based on his/her lane choice and the current road conditions. Four different classification algorithms were used to compare the performance. These models achieved accuracy above 95 % , with precision and recall metrics above 90 % . [ABSTRACT FROM AUTHOR]

In this article, we introduce a comprehensive system theory based on the min-plus algebra of 2 × 2 matrices of functions. This novel approach enables the algebraic construction of traffic networks and the analytical derivation of performance bounds for such networks. We use the term "traffic networks" or "congestion networks" to refer to networks where high densities of transported particles lead to flow drops, as commonly observed in road networks. Initially, we present a model for a segment or section of a link within the network and demonstrate that the dynamics can be expressed linearly within the min-plus algebra. Subsequently, we formulate the linear system using the min-plus algebra of 2 × 2 matrices of functions. By deriving the impulse response of the system, we establish its interpretation as a service guarantee, considering the traffic system as a server. Furthermore, we define a concatenation operator that allows for the combination of two segment systems, demonstrating that multiple segments can be algebraically linked to form a larger network. We also introduce a feedback operator within this system theory, enabling the modeling of closed systems. Lastly, we extend this theoretical framework to encompass two-dimensional systems, where nodes within the network are also taken into account in addition to the links. We present a model for a controlled node and provide insights into other potential two-dimensional models, along with directions for further extensions and research. [ABSTRACT FROM AUTHOR]

Intermittent bus lanes (IBLs) can alleviate the contradiction between bus priority and the urgent demand of general vehicles for road resources. However, existing IBL strategies seldom pay attention to the setting method of the dynamic bus lanes at intersections, which leads to the still serious delay of buses at intersections in the traffic congestion environment. To tackle this issue, this research explores a novel method of setting the intermittent bus approach (IBA) of intersections for lane sharing and bus priority at intersections. In particular, a time slice division strategy with an intersection signal coordination model is developed to fully and reasonably allocate the idle time of bus lanes at intersections. Besides, considering the lane-changing demands of general vehicles at intersections, the parameters of the IBA lane system are modeled and optimized. For testing and verifying the feasibility of the proposed method, comparative experiments are conducted through microscopic traffic simulation. Results show that the proposed IBA setting method can effectively solve the problem of bus priority failure at intersections. It can maintain the continuity of vehicle running on intersection sections, which better exerts the operational benefits of dynamic bus lanes. [ABSTRACT FROM AUTHOR]

Efficient traffic management is essential in modern urban areas. The development of intelligent traffic flow prediction systems can help to reduce travel times and maximize road capacity utilization. However, accurately modeling complex spatiotemporal dependencies can be a difficult task, especially when real-time data collection is not possible. This study aims to tackle this challenge by proposing a solution that incorporates extensive feature engineering to combine historical traffic patterns with covariates such as weather data and public holidays. The proposed approach is assessed using a new real-world data set of traffic patterns collected in Ljubljana, Slovenia. The constructed models are evaluated for their accuracy and hyperparameter sensitivity, providing insights into their performance. By providing practical solutions for real-world scenarios, the proposed approach offers an effective means to improve traffic flow prediction without relying on real-time data. [ABSTRACT FROM AUTHOR]

Spatiotemporal traffic data usually suffers from missing entries in the data acquisition and transmission process. Existing imputation methods only consider the global/local structure of spatiotemporal traffic data, resulting in insufficient estimation performance. Fortunately, it is found that traffic data admits the nonlocal self‐similarity (NSS) prior. This paper incorporates the global and nonlocal low‐rank priors of traffic data and proposes a tensor completion model for spatiotemporal traffic data imputation. To be specific, the proposed method uses tensor ring (TR) decomposition with an enhanced representation capability to characterize the global low‐TR‐rank prior of traffic data, e.g. the correlation of sensor and time modes of the tensor (i.e. traffic data). An implicit plug‐and‐play (PnP)‐based regularization is further utilized to exploit the NSS prior, which depicts the nonlocal similar traffic data patterns. Furthermore, the proximal alternating minimization algorithm under the PnP framework is derived to solve this model. The experiment results on various datasets and missing scenarios show the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Data breaches are a critical issue and have become one of the top widespread risks in today’s data-driven digital environment. Hence, Secure data outsourcing is essential in today’s digital communications. The current interconnected network lack many core features, including privacy and security, which make it vulnerable to data leakages when transmitting sensitive information during critical circumstances (e.g., natural and human disasters). In this paper, we propose a network model that can be employed as a future private network paradigm to promptly and securely transmit confidential data, hence minimizing the chances of breaches. Our schemes provide the following contributions: (1) design a private novel network architecture to disseminate multilevel confidential packets using two routers; (2) Develop several heuristics to reduce an NP-hard problem to find an optimal solution for the studied problem; (3) Employee a developed scheduler for selecting the best algorithm that schedule packets securely and timely through two routers such that critical data packets associated to the same confidential level are prohibited from being transmitted at the same time; (4) conduct an analysis study to compare the developed heuristics and to prove the practicality of the proposed solution. The experimental results show the performance of the proposed heuristics. The results showed that the best heuristic is IFP2 in 84.4% of cases. [ABSTRACT FROM AUTHOR]

Traffic forecasting, as a fundamental and challenging problem of intelligent transportation systems (ITS), has always been the focus of researchers. Nevertheless, accurate traffic forecasting still exists some problems due to the complex spatial‐temporal dependencies and irregularities of traffic flows. Most of the existing methods typically use the spatial adjacency matrix and complicated mechanism to model spatial‐temporal relationships separately, while ignoring the latent spatial‐temporal correlations. In this paper, a novel architecture is proposed named spatial‐temporal correlation graph convolutional networks (STCGCN) for traffic prediction. First, an informative fused graph structure is constructed to better learn the complex spatial‐temporal correlations, which breaks the limitation that the general spatial adjacency matrix cannot reflect temporal correlations. Moreover, spatial‐temporal correlation graph convolution and gated temporal convolution are performed in parallel and they are integrated into a unified layer, which enables capturing both local and global spatial‐temporal dependencies simultaneously. By stacking multiple layers, STCGCN can learn more long‐range spatial‐temporal dependencies. Experimental results on five public traffic datasets demonstrate the effectiveness and robustness of the proposed STCGCN in urban traffic forecasting. [ABSTRACT FROM AUTHOR]

The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we design a systematic risk index and propose an index describing the degree of data scatter based on the principle of Euclidean distance quantization. By comparing several datasets, including NGSIM, highD, INTERACTION, CitySim, and our self-collected Highway dataset, we find that the proposed metrics can effectively quantify the risk level of the dataset while helping to gain insight into the diversity and balance differences of the dataset.

Models of street networks underlie research in urban travel behavior, accessibility, design patterns, and morphology. These models are commonly defined as planar, meaning they can be represented in two dimensions without any underpasses or overpasses. However, real-world urban street networks exist in three-dimensional space and frequently feature grade separation such as bridges and tunnels: planar simplifications can be useful but they also impact the results of real-world street network analysis. This study measures the nonplanarity of drivable and walkable street networks in the centers of 50 cities worldwide, then examines the variation of nonplanarity across a single city. It develops two new indicators - the Spatial Planarity Ratio and the Edge Length Ratio - to measure planarity and describe infrastructure and urbanization. While some street networks are approximately planar, we empirically quantify how planar models can inconsistently but drastically misrepresent intersection density, street lengths, routing, and connectivity.

We address the problem of shockwave formation in uncoordinated highway traffic. The problem is caused by the combination of heavy traffic and small traffic perturbations or unexpected drivers actions. We propose a novel distributed communication protocol that helps mitigate upstream shockwave formation even with extremely low system penetration rates. Based on traffic information ahead, the Cooperative Advanced Driver Assistance System (CADAS) recommends non-intuitive velocity reductions in order to redistribute traffic more uniformly and eliminate traffic peaks. Simulation results show that CADAS significantly increases the average velocity and therewith reduces the overall travel time and avoids unnecessary slowdowns. As a next step, for realism, we propose to apply CADAS to real traffic traces. Also, we extend the shockwave model from single to multiple lanes (to reduce accidents caused by lane switching).

OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

This paper addresses issues related to modeling energy consumption and emissions using microscopic traffic simulations. This paper develops a method in which a traffic model is used to calculate the energy needed to travel through selected types of intersections. This paper focuses on energy consumption and derived values of calculated energy, which can be, for example, carbon dioxide emissions. The authors present a review of the scientific literature on the study of factors affecting energy consumption and emissions and methods to estimate them in traffic. The authors implemented an energy consumption model into a microsimulation software module to estimate results as a function of varying traffic volumes at selected types of intersections and for selected traffic organization scenarios. The results of the study show the lowest energy consumption and the lowest emissions when road solutions are selected that contribute to reducing vehicle travel times on the urban street network at higher average vehicle speeds. In addition, the positive impact of the share of electric vehicles in the traffic flow on the reduction of energy consumption and emissivity was estimated. [ABSTRACT FROM AUTHOR]

The constant evolution of transportation systems and traffic in developing countries is nowadays confronted with a problem of road safety and therefore of a high accident rate, especially in the context of inter-urban road transport. In this work, we propose a communication architecture for an Intelligent Transport System (ITS) to provide surveillance in an inter-urban transport network in the context of developing countries. We introduce two types of control points: Relay Control Points (RCP) and Treatment Control Points (TrCP). We also designed two multi-objective models for the deployment of these points. In order to ensure good coverage, to minimize the cost of installation and to put more emphasis in the areas having a high number of accidents. To ensure optimal deployment of these points, we have used the Non-Dominated Sorted Genetic Algorithm II (NSGA-II) that will allow to realize the Pareto front of non-dominated solutions. The results of the simulations show the effectiveness of our proposal.

This paper proposes a nonlinear discrete event state-space model for intersecting metro lines considering the effect of transfer stations. Disturbances such as technical problems in the rolling stock and signaling systems can cause deviations in the predefined train departure times. Any delay in the metro traffic system will increase over time and propagate to other trains, leading to instability which will reduce the efficiency of the system. Transfer stations in metro networks are designed to transfer passengers between trains on different intersecting metro lines. Therefore, traffic modelling of the metro transportation system requires consideration of the effect of such transfer stations. After introducing a discrete event nonlinear model for intersecting metro lines with one or two transfer stations, the accuracy and effectiveness of the introduced model to describe the dynamic behavior of metro traffic system has been evaluated and verified using the results of simulations based on real data from two intersecting lines on the Tehran metro network.

This paper presents an analytical model of a multiservice queueing system that services elastic and adaptive BPP traffic. The model presented in the paper was developed as an extension of earlier works published by the authors. The model is based on several concepts specific to modelling multi-service systems. In order to derive the model, the idea of equivalent bandwidth and the discretisation process were used first. Subsequently, the MIM-BPP method was generalised in relation to a system with traffic that undergoes the compression mechanism. Since the proposed model is an approximate one, the results of the calculation are compared with the simulation results. The comparison of the results confirms the accuracy of the proposed model.

The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.