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Traffic conflict is frequently utilized as a stand-in for crashes for analyzing traffic safety from a broader perspective for varying roadways and traffic conditions. In Indian heterogeneous traffic conditions, vehicles with various static and dynamic properties interact simultaneously in longitudinal and lateral directions, forming traffic conflicts. To this end, the present study develops crossing conflict-based safety performance functions (C-SPFs) for eight urban un-signalized T-intersections. The video-graphic survey approach was used to gather the necessary traffic data with different intersection and traffic flow characteristics. After that, from the recorded video, traffic conflicts were identified using the Post encroachment time (PET) for the selected eight study intersections. Based on the PET values, crossing conflicts were initially divided into critical conflicts (CC) and non-critical conflicts (NCC). Then, using the Poisson-Tweedie regression technique, crossing conflicts were modeled as a function of traffic flow and intersection-related parameters. The findings showed that the most important factors defining the number of CC and NCC are intersection geometry (with or without Central Island), time of day, traffic volume, and composition (offending and conflicting approach). Based on the study's findings, city planners and traffic engineers estimate the number of CC and NCC; as a result, they may project the necessary laws, rules, and regulations to enhance traffic safety operations. [ABSTRACT FROM AUTHOR]

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In China, the total energy consumption and greenhouse gas (GHG) emissions will reach considerable levels based on the current speed of urban rail transit system development. Based on the life-cycle assessment theory, this research constructs an urban rail transit system GHG emission assessment method, calculates emission outputs based on resource inputs from actual investigated data and makes a quantitative analysis of GHG emissions. The results show that in recent years, the GHG emission of urban rail transit construction and operation in China is between 2000 × 104 and 4200 × 104 tonnes of carbon dioxide equivalent (tCO2e) per year. The proportions of the construction and operational phases in this emission are 57 and 43%, respectively. In the construction phase, the GHG emission intensity per unit mileage of shield tunnels and per unit area of stations is about 1.3 × 104 tCO2e/km and 3.71 × 104 tCO2e/ha, respectively. In the operational phase, the GHG emission intensity per unit trip is 0.084 kg carbon dioxide equivalent/passenger-km. The entire life-cycle GHG emission per kilometre of urban rail transit systems is 11.69 × 104 tCO2e (with a service life of 50 years) in China. The construction phase and operation phase generated about 18.73 and 81.27% of this emission, respectively. The preliminary conclusions of this study may help shed light on the emission reduction potential of urban rail transit systems and the emission reduction targets in China. [ABSTRACT FROM AUTHOR]

Traffic congestion has several adverse effects on urban traffic networks. Increased travel times of vehicles, with the addition of excessive greenhouse emissions, can be listed as harmful effects. To address these issues, transportation engineers aim to reduce private car usage, reduce travel times through different control strategies, and mitigate harmful effects on urban networks. In this study, we introduce an innovative approach to optimizing traffic signal control settings. This methodology takes into account both pedestrian delays and vehicular emissions. Non-dominated sorting genetic algorithm-II and Multi-objective Artificial Bee Colony algorithms are adopted to solve the multi-objective optimization problem. The vehicular emissions are modeled through the MOVES3 emission model and integrated into the utilized microsimulation environment. Initially, the proposed framework is tested on a hypothetical test network, followed by a real-world case study. Results indicate a significant improvement in pedestrian delays and lower emissions. [ABSTRACT FROM AUTHOR]

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi —complementary WATs in the 5G-CLARITY project—in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms. [ABSTRACT FROM AUTHOR]

In this study, the concept of symmetry is employed to implement an intelligent fuzzy traffic signal control system for complex intersections. This approach suggests that the implementation of reduced fuzzy rules through the reduction method, without compromising the performance of the original fuzzy rule base, constitutes a symmetrical approach. In recent decades, urban and city traffic congestion has become a significant issue because of the time lost as a result of heavy traffic, which negatively affects economic productivity and efficiency and leads to energy loss, and also because of the heavy environmental pollution effect. In addition, traffic congestion prevents an immediate response by the ambulance, police, and fire brigades to urgent events. To mitigate these problems, a three-stage intelligent and flexible fuzzy traffic control system for complex intersections, using a novel hybrid reduction approach was proposed. The three-stage fuzzy traffic control system performs four primary functions. The first stage prioritizes emergency car(s) and identifies the degree of urgency of the traffic conditions in the red-light phase. The second stage guarantees a fair distribution of green-light durations even for periods of extremely unbalanced traffic with long vehicle queues in certain directions and, especially, when heavy traffic is loaded for an extended period in one direction and the short vehicle queues in the conflicting directions require passing in a reasonable time. The third stage adjusts the green-light time to the traffic conditions, to the appearance of one or more emergency car(s), and to the overall waiting times of the other vehicles by using a fuzzy inference engine. The original complete fuzzy rule base set up by listing all possible input combinations was reduced using a novel hybrid reduction algorithm for fuzzy rule bases, which resulted in a significant reduction of the original base, namely, by 72.1%. The proposed novel approach, including the model and the hybrid reduction algorithm, were implemented and simulated using Python 3.9 and SUMO (version 1.14.1). Subsequently, the obtained fuzzy rule system was compared in terms of running time and efficiency with a traffic control system using the original fuzzy rules. The results showed that the reduced fuzzy rule base had better results in terms of the average waiting time, calculated fuel consumption, and CO2 emission. Furthermore, the fuzzy traffic control system with reduced fuzzy rules performed better as it required less execution time and thus lower computational costs. Summarizing the above results, it may be stated that this new approach to intersection traffic light control is a practical solution for managing complex traffic conditions at lower computational costs. [ABSTRACT FROM AUTHOR]

When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is performed. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic forecasting benchmarks on two large-scale real-world traffic datasets. [ABSTRACT FROM AUTHOR]

• Comprehensive investigation into the current and emerging solutions, policies, and guidance employed by various agencies to mitigate wrong-way driving (WWD) activities in the United States. • Survey traffic and safety engineers from all 50 state transportation agencies and 59 tollway authorities across the nation. • The insights gathered from the survey and interviews with different agencies are invaluable in shaping safe system approaches to mitigate WWD issues. • Lay the groundwork for developing national guidelines to reduce WWD crashes and incidents on divided highways. Introduction: This paper presents a comprehensive investigation into the current and emerging solutions, policies, and guidance employed by various agencies to mitigate wrong-way driving (WWD) activities in the United States. The study utilized a two-pronged approach, involving an online survey and follow-up phone interviews with respondents from state transportation agencies, tollway authorities, and law enforcement. Methods: The initial step involved conducting an online survey to gather general insights about the existing strategies and practices used to combat WWD. The survey questionnaire, consisting of 12 questions, covered topics such as mitigation strategies/policies, guidance for selecting countermeasures, and topics/needs for national handbook. The survey was emailed to traffic and safety engineers from all 50 state transportation agencies and 59 tollway authorities across the nation. As the second step, follow-up phone interviews were conducted with respondents identified from the online survey. The interviews delved deeper into specific aspects such as crash/incident data collection methods, identification of crash-prone locations, countermeasure selection and implementation, experience with Intelligent Transportation Systems (ITS) applications, and future initiatives. Results: The findings from the survey and interviews indicated an increasing awareness and adoption of best practices to combat WWD. Various states have implemented new policies and advanced technologies to deter WWD incidents. The insights gathered from the survey and interviews with different agencies are invaluable in shaping safe system approaches and guidelines for the national handbook on WWD solutions. Practical applications: Overall, this study sheds light on the efforts and progress made by state transportation agencies, tollway authorities, and law enforcement in addressing the critical issue of WWD. By gathering valuable lessons and practices from the various agencies, this research lays the groundwork for developing national guidelines to reduce WWD crashes and incidents on divided highways. [ABSTRACT FROM AUTHOR]

The global population rise has increased vehicles on roads, complicating traffic management. Inefficient traffic control systems cause significant economic losses owing to commuter time wastage, high energy consumption, and greenhouse gas emissions. Traffic signal control systems (TSCSs) are vital in traffic management, impacting traffic flow significantly; therefore, studies are exploring new optimization approaches that adapt to changing traffic conditions. However, they concentrate on either new technology infusion or on control algorithm optimization, and do not holistically address the architectural configuration of the system. In this study, we presented a unique case study by applying an existing systematic framework to the TSCS system architecture design and selection process. This application demonstrates that TSCS enhancement is a multifaceted process that requires a comprehensive assessment of not only technical aspects, such as the control algorithm, but also factors including system architecture, security, and data integrity. Because of the increasing reliance of TSCSs on data exchange between their various subsystems, this case study also adopted a cybersecurity perspective of the system and introduced cyber resiliency as a crucial metric for evaluating TSCS architecture performance. Furthermore, through the applied framework, an optimal TSCS architectural configuration with executable options was identified by generating multiple TSCS architectural configurations using decision option patterns and identifying those on the Pareto frontier to understand the architectural decision‐making process. Traffic engineers and transportation planners can use this case study application as a guide to optimize TSCSs employed in existing transportation networks and design more efficient transportation networks for future urban development. [ABSTRACT FROM AUTHOR]

The openness and interconnectedness of industrial control systems (ICSs) is increasing, leading to a heightened risk of network-based attacks. Although research on industrial intrusion detection is ongoing, current methods often overlook the unique characteristics of industrial control flows. This study introduced an industrial network intrusion detection algorithm based on the improved gray wolf optimizer (IGWO) gated recurrent unit (GRU) model. Starting with the temporal aspects of industrial control network traffic, a simple GRU was chosen as the network model. By integrating the gray wolf optimizer (GWO) with autonomous learning methods, the algorithm could address the slow convergence caused by large volumes of industrial control network traffic. In response to the slow convergence of the GWO and its low optimization accuracy, this study developed the improved gray wolf optimizer (IGWO). By simulating an intrusion detection system (IDS) using datasets from the Natural Gas Pipeline Control System and Secure Water Treatment (SWaT) datasets, the experimental results demonstrated that the IGWO-GRU algorithm exhibited considerable advantages in terms of accuracy, false alarm rate, and false report rate, thereby enhancing the security capabilities of ICSs. [ABSTRACT FROM AUTHOR]

Macroscopic fundamental diagram (MFD)-based perimeter control has a potential to improve traffic throughput and relieve the overall congestion. In practice, not only the MFD but also the traffic demand suffers from a variety of inherent uncertainties. Hence, this paper aims to contrive a robust control technique to address the perimeter traffic flow control issue in the oversaturated region. To this end, an integral sliding mode control is proposed to drive the traffic state to the desirable condition. The developed strategy deals with practical issues such as finite time stability and range of uncertainty in both MFD and traffic demand. Theoretical analysis verifies that the developed integral sliding mode control approach can guarantee the finite time convergence of the traffic state to the desired one. The performance of the developed scheme is attested by considering various traffic scenarios with uncertain MFDs and traffic demand, in which a comparative performance study with the proportional and integral control method is conducted. It is indicated that the developed scheme can alleviate the congestion in the urban network and improve the throughput of the urban network. Practical Applications: The research presented in this article proposes a novel perimeter control technique designed to enhance traffic flow management in densely populated urban road networks, particularly during peak congestion times. By leveraging the macroscopic fundamental diagram (MFD) and integral sliding mode control theory, the method significantly boosts traffic throughput and alleviates the impact of congestion. This advancement can be seamlessly integrated into current urban traffic management systems, leading to more efficient and convenient urban commutes. Additionally, it addresses the rise in vehicle emissions due to heavy traffic, aiding in the reduction of pollution and supporting sustainable development initiatives. The technique also curtails vehicle fuel consumption and cuts down on costs associated with congestion-related delays, thereby fostering resource optimization and economic growth within urban environments. Moreover, the method is designed to be resilient, taking into account the variability of traffic demand and ensuring the robustness of the traffic control strategy. Overall, the article demonstrates the practical application of advanced control technologies to address real-world traffic challenges, providing a scalable and robust solution adaptable to realistic situations with the uncertainties of urban traffic demand. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly. [ABSTRACT FROM AUTHOR]

Macroscopic fundamental diagram (MFD)–based perimeter traffic flow control has been attempted to develop coordinated ramp metering (CRM) strategies for multibottleneck freeway networks operating around capacity. However, the effectiveness of these strategies may be limited by the minimum admissible on-ramp flow (e.g., due to queue saturation or minimum green time), potentially resulting in local congestion. This paper addresses these challenges from two aspects. First, a coordinated ramp control approach was developed to handle situations where the desired flow of an on-ramp falls below the minimum admissible flow. Second, variable speed limits (VSLs) were integrated into the coordinated RM to enlarge the available control space. The proposed approach employs a three-layered control structure. The first layer determines the total on-ramp flow entering the freeway network, using an extended MFD-based feedback perimeter regulator. This regulator utilizes dynamic network accumulation set points to enhance the regulator's effectiveness in the presence of heterogeneity. The second layer distributes the total on-ramp flow among the on-ramps, which includes determining the on-ramp flows without considering flow constraints and coordinating successive on-ramps to address potential constraint violations. The third layer determines VSL rates based on the control information of RM in the second layer and the coordinated control for successive VSLs. The proposed approach was evaluated using SUMO, a microscopic simulator, on a realistic freeway network (stretch), and compared with feedback RM, feedback VSLs, and their integration. Simulation results highlight that the proposed integrated controller (1) more effectively addresses congestion in the freeway, owing to its enhanced robustness and network-wide control benefits, (2) enlarges the control space to alleviate freeway congestion, and (3) performs better in terms of the density heterogeneity of the freeway. [ABSTRACT FROM AUTHOR]

Traffic control through video/image processing is a trending research topic within Intelligent Transportation Systems. The number of moving vehicles, the density of vehicles at the junctions, traffic anomalies, and traffic flow directly influence real-time traffic congestion. The primary goal of this work is to design an architecture that is simple and fast enough to use in real-time heterogeneous traffic scenes. The proposed methodology uses user-defined zones for determining vehicle occupancy and count based on traffic surveillance video. The angular integral projection function accompanies the background subtraction method for better localization. Traffic anomaly detection is implemented by incorporating intelligent sensor technology. Also, the skipping of frames at fixed intervals has increased the processing speed with minimal data loss. The compatibility with any Internet Protocol camera further distinguishes this approach from other state-of-the-art methods. The proposed algorithm has been tested on a publicly available traffic surveillance video dataset and real-time surveillance feeds. Experimental results from the real-time intersection scenes show an overall accuracy of 97.14% and an average processing speed of 92.91%. The accuracy of the detection module in the custom-made dataset is 98.73%. [ABSTRACT FROM AUTHOR]

Real-time traffic data is fundamental for active traffic monitoring and control. Traditionally, traffic data are collected using location-based sensors and spatial sensors. However, both sensors have well-known limitations due to installation, operations, maintenance costs, and environmental factors. This study develops a methodology to use Wi-Fi sensors for traffic state characterization on urban roads to overcome these limitations. We examine the received signal strength indicator (RSSI) patterns and identify three distinct RSSI signature patterns. These patterns are used to develop methodologies to estimate (a) Whether the position of the end of the queue is upstream or downstream of the detector, (b) Whether the traffic conditions in the vicinity of the detector are uniformly uncongested or uniformly congested, and (c) The maximum queue length and the time is taken for the queue to grow to the maximum extent. The estimates from the methodology are validated with empirical data that showed good concurrence with field conditions, and the methods proposed in this article have the potential to estimate the traffic conditions using sparse data from Wi-Fi sensors. [ABSTRACT FROM AUTHOR]

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections. [ABSTRACT FROM AUTHOR]

Road traffic accident is a leading cause of death and various life deformities worldwide. This burden is even higher among motorcycle riders in lower-to-middle-income countries. Despite the various interventions made to address the menace, the fatalities continue to be on the ascendency. One major area that has received little attention is the attitude and behaviour of motorcycle riders. The present study aimed to examine the contribution of traffic Locus of Control (LoC) and health belief on road safety attitude and behaviour. 317 motorcycle riders participated in the study. The participants completed a questionnaire comprising various sections such as motorcycle riding behaviour, road safety attitude, risk perception, the intention to use helmets, and traffic LoC. The results showed a significant positive correlation between road safety attitude and behaviour (r (295) =.33, p <.001). Drifting towards internal LoC was associated with more positive behaviour on the roads (r (295) = -.23, p <.001). Intention to use helmet, health motivation, perceived susceptibility, perceived benefits, and perceived barriers were the factors in the health belief model that were associated with road safety attitude (r (295) =.404, p <.001). Finally, the multiple linear regression model showed that road safety attitude and traffic LoC made significant contributions to road user behaviour [F(3, 293) = 13.73, p <.001]. These findings have important implications towards shaping responsible behaviour among motorcycle riders. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to enhancing indoor navigation in crowded multi-terminal airports using visible light communication (VLC) technology. By leveraging existing luminaires as transmission points, encoded messages are conveyed through modulated light signals to provide location-specific guidance. The objectives are to facilitate navigation, optimize routes, and improve system performance through Edge/Fog integration. The methodology includes the use of tetrachromatic LED-equipped luminaires with On–Off Keying (OOK) modulation and a mesh cellular hybrid structure. Detailed airport modeling and user analysis (pedestrians and luggage/passenger carriers) equipped with PINPIN optical sensors are conducted. A VLC-specific communication protocol with coding and decoding techniques ensures reliable data transmission, while wayfinding algorithms offer real-time guidance. The results show effective data transmission and localization, enabling self-localization, travel direction inference, and route optimization. Agent-based simulations demonstrate improved traffic control, with analyses of user halting and average speed. This approach provides reliable indoor navigation independent of GPS signals, enhancing accessibility and convenience for airport users. The integration of VLC with Edge/Fog architecture ensures efficient movement through complex airport layouts. [ABSTRACT FROM AUTHOR]

The centralized coordination of Connected and Automated Vehicles (CAVs) at unsignalized intersections aims to enhance traffic efficiency, driving safety, and passenger comfort. Autonomous Intersection Management (AIM) systems introduce a novel approach for centralized coordination. However, existing rule-based and optimization methods often face the challenges of poor generalization and low computational efficiency when dealing with complex traffic environments and highly dynamic traffic conditions. Additionally, current Reinforcement Learning (RL)-based methods encounter difficulties around policy inference and safety. To address these issues, this study proposes Constraint-Guided Behavior Transformer for Safe Reinforcement Learning (CoBT-SRL), which uses transformers as the policy network to achieve efficient decision-making for vehicle driving behaviors. This method leverages the ability of transformers to capture long-range dependencies and improve data sample efficiency by using historical states, actions, and reward and cost returns to predict future actions. Furthermore, to enhance policy exploration performance, a sequence-level entropy regularizer is introduced to encourage policy exploration while ensuring the safety of policy updates. Simulation results indicate that CoBT-SRL exhibits stable training progress and converges effectively. CoBT-SRL outperforms other RL methods and vehicle intersection coordination schemes (VICS) based on optimal control in terms of traffic efficiency, driving safety, and passenger comfort. [ABSTRACT FROM AUTHOR]

This study employs a hotspot areas-based data envelopment analysis (DEA) to investigate the coupling efficiency between bike-sharing demand and land use. Hotspot areas are used as decision-making units and re-sampling data are imposed spatial constraints. The elbow method is used to select the crucial indicators after exploring the correlation between coupling efficiency evaluation indexes using the grey correlation model. Since this coupling efficiency evaluation belongs to a 'multi-input and single-output' system, DEA was introduced to build a quantitative model for bike-sharing demand and land-use coupling efficiency. This paper also identifies the factors that restrict bike-sharing demand generation. More importantly, different spatial scenarios are deployed to demonstrate heterogeneous characteristics of DEA efficiency in different locations. The results of a case study in Beijing, China show that DEA efficiency generally exhibits a trend from low to high from the inside to the outside of the urban centre. The land-use factors with relatively high redundancy rates are financial insurance facilities, hotels and living facilities. [ABSTRACT FROM AUTHOR]

How to accurately predict short-term traffic travel time (TTT) is an important problem in intelligent transportation systems. Traffic data usually exhibit high non-linearities and complex patterns, and predicting TTT is a challenge. Most previous studies have used the topological adjacency of road networks to explore spatial correlations. However, a real road network contains higher-order connectivity patterns that have different statistical significance. The topology adjacency cannot reflect these higher-order connectivity patterns. To obtain topological adjacency and higher-order connection pattern information, a novel deep-learning framework (multi-motif graph convolutional recurrent neural networks) for TTT prediction is proposed. The accuracy of TTT prediction was improved with this model. There are two blocks in each unit of the model: (a) spatial blocks, which capture spatial pattern information by the multi-motif graph convolution network and motif graph embedding, and (b) temporal blocks, which capture temporal pattern information by the combination of a long short-term memory network and a fully connected layer. To prove the effectiveness and accuracy of the prediction model, experiments were conducted on real-world TTT data sets. [ABSTRACT FROM AUTHOR]

The current arterial signal coordination models only target through traffic or critical traffic, resulting in fairness issues for traffic in other directions. Moreover, these models fail to achieve system optimality between intersections along the arterial. To address these problems, this paper establishes an arterial signal coordination optimization model based on all-direction pairs of adjacent intersections. The model takes the minimum weighted green wave band center offset of all-direction pairs at the intersections as the objective function. The optimization variables are the cycle length, offset, and phase sequence. The phase sequence structure adopts the ring-and-barrier structure, and the phase sequence optimization includes not only the main road but also the minor road intersecting with it. The model is an integer linear programming model, and it optimizes all-direction pairs of the upstream and downstream intersections. By analyzing the spatiotemporal diagram of the intersections, constraints are established for the phase sequence of the main road and intersecting roads. Through optimization of the offset and phase sequence, the coordination of flow direction pairs between upstream and downstream intersections can be optimized to improve the overall traffic fairness of the arterial. [ABSTRACT FROM AUTHOR]

This study introduces an innovative scheme for classifying uncrewed aerial vehicle (UAV)-derived vehicle trajectory behaviors by employing machine learning (ML) techniques to transform original trajectories into various sequences: space–time, speed–time, and azimuth–time. These transformed sequences were subjected to normalization for uniform data analysis, facilitating the classification of trajectories into six distinct categories through the application of three ML classifiers: random forest, time series forest (TSF), and canonical time series characteristics. Testing was performed across three different intersections to reveal an accuracy exceeding 90%, underlining the superior performance of integrating azimuth–time and speed–time sequences over conventional space–time sequences for analyzing trajectory behaviors. This research highlights the TSF classifier's robustness when incorporating speed data, demonstrating its efficiency in feature extraction and reliability in intricate trajectory pattern handling. This study's results indicate that integrating direction and speed information significantly enhances predictive accuracy and model robustness. This comprehensive approach, which leverages UAV-derived trajectories and advanced ML techniques, represents a significant step forward in understanding vehicle trajectory behaviors, aligning with the goals of enhancing traffic control and management strategies for better urban mobility. [ABSTRACT FROM AUTHOR]

In urban system management and public safety, accurate traffic flow forecasting is pivotal for real-world applications such as traffic control, resource-sharing scheduling platforms, and intelligent transportation systems. The challenge lies in effectively capturing complex spatio-temporal correlations. To address this, we introduce ST-DSTN, a novel spatial-temporal fusion attention-based deep neural network that significantly advances the current state-of-the-art. ST-DSTN utilizes three distinct temporal branches to capture temporal dependencies and a convolutional neural network to encapsulate spatial dependencies. Our innovative contribution includes a spatio-temporal fusion attention module, effectively modeling the dynamic spatio-temporal dependencies of traffic data, thus improving upon existing methods. Additionally, we propose a hierarchical fusion method that permeates the entire model, capturing complex correlations at various levels through multi-level fusion. Our comprehensive experiments on the BikeNYC dataset demonstrate ST-DSTN's superior performance, supported by numerical results that substantiate its improvement over current methods, surpassing the DeepSTN+ model by 4.31%. [ABSTRACT FROM AUTHOR]

Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable potential for increasing the efficiency of traffic networks. Emerging concepts of autonomous driving and machine learning can be incorporated intelligently for improving traffic operations and control. In the coming years, traffic composition can vary in terms of autonomous vehicle (AV) penetration rate, which can lead to a heterogeneous traffic environment. Traffic control methods for such complex networks need to be designed effectively for accommodating the positive effects of AV implementation without compromising the safety and level of service in the presence of regular vehicles (RVs). An intelligent-based optimization framework for traffic signal control under heterogeneous AV-based traffic is proposed in this paper. This framework utilizes state-of-the-art machine-learning approaches to represent and design different components of the optimization process of cycle length. Further, SHapley Additive exPlanations (SHAP) is used to enhance model interpretability. The proposed optimization framework improves performance under congested traffic conditions compared with that of the conventional optimization methods. Compared to pure RV-based traffic, the penetration rates of 25%, 50%, and 100% can decrease optimized cycle lengths by 26%, 39%, and 53%, respectively, which can result in delay reductions of approximately 18%, 31%, and 56%, respectively. The proposed framework when applied in an adaptive-based manner can help in the generalization of controlling existing signalized intersections with the gradual penetration of AVs without the extra infrastructure or specific operational and connectivity capabilities of the involved vehicles. [ABSTRACT FROM AUTHOR]

AbstractIndividual trajectory data play a pivotal role in various application fields, such as urban planning, traffic control, and epidemic simulation. Despite the diverse means for data collection in current times, the real-world trajectory data in practical application remains severely limited due to concerns over personal privacy. In this study, we designed a Spatiotemporal-knowledge enhanced multi-TAsk GEnerative adversarial network (GAN), named STAGE, to generate synthetic trajectories that statistically resemble the real data without recycling personal information. In STAGE, we designed a multi-task generator with three stages of spatio-temporal generation tasks, i.e. activity-sequence generation task, township-level trajectory generation task, and neighborhood-level trajectory generation task, with the last one as the main task while the other two as auxiliary tasks. Meanwhile, we designed a spatial consistency loss in the adversarial training process to assess the spatial consistency of generated trajectories at different spatial scales. Experiment results show that compared to the baselines, trajectories generated by our method have closer data distributions to the real ones. We argued that the designs of spatiotemporal-knowledge enhanced generation tasks and training loss benefit the spatiotemporal generation processes, which help reproduce the temporal patterns of human daily activities and spatial distribution of human movements. [ABSTRACT FROM AUTHOR]

Ensuring driving safety on agglomerate fog sections of mountain highways has been a persistent challenge for the Chinese transportation industry. To enhance driving safety in agglomerate fog environments by identifying key issues related to driving safety in such conditions. A summary of the influencing factors and research progress related to driving safety in agglomerate foggy environments was provided, both domestically and internationally. Firstly, the visual characteristics of drivers was examined and the causes of optical illusions caused by agglomerate fog was discussed. The “elephant trunk” curve was further analyzed that occured prior to the appearance of agglomerate fog. The relationship between human-vehicle roadway and traffic safety in an agglomerate fog environment was then clarified. Lastly, a summary of the current control measures implemented to ensure driving safety on group fog roadways was provided. The results show that the abrupt and significant reduction in visibility caused by agglomerate fog can lead to severe visual disruptions for drivers, resulting in illusions of distance and speed. In order to improve driving safety in agglomerate foggy environments, drivers are alerted through monitoring and warning systems. Additionally, a variety of inducement facilities are being developed to enhance visual entrainment. Future research is needed to study the visual demands of driving under the influence of complex factors from the perspective of reducing the impact of visual illusions. [ABSTRACT FROM AUTHOR]

Compared to traditional traffic signal control methods, the method driven by Deep Reinforcement Learning (DRL) has shown better performance. But the problem of low sample utilization in reinforcement learning also arises. To deal with the problem, this paper presents a novel Twin Delayed Deep Deterministic Policy Gradient with Dual Buffer (TD3_DB) for traffic signal control. In the proposed framework, two experience buffers are used to store important samples and normal samples, separately, and the proportion of the two buffers is adjusted adaptively. In addition, lane pressure, describing the dynamic feature of lane traffic flow, is used for the state design of the TD3 agent, which enhances the perception of the agent toward intersections. Comprehensive experiments on different traffic flow modes has shown, the dual experience replay scheme can improve the sample utilization, and the proposed TD3_DB performs better than other methods such as original TD3, Proximal Policy Optimization (PPO), etc., effectively reducing vehicle queue length and waiting time. [ABSTRACT FROM AUTHOR]

Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections. [ABSTRACT FROM AUTHOR]

The economic impact of inefficient traffic control systems is significant owing to prolonged commute durations, and increased energy consumption. Traffic signal control systems (TSCSs) significantly influence traffic flows at intersections. Therefore, adaptive TSCSs (ATSCSs) that can adjust to traffic conditions in real‐time have been proposed as more efficient alternatives. However, the expensive implementation of these systems highlights the need for judicious investments in appropriate technologies and infrastructure. Therefore, a comprehensive technology roadmap should be built that guides the future development of traffic control and the infusion of technologies to address traffic needs. Additionally, as ATSCSs are developed and managed by local governments, the perspective of a government‐led technology roadmap is required to guide the roadmap development and implementation. Although studies have explored technology roadmaps across numerous sectors, the viewpoint of roadmap development guided by government entities is frequently neglected despite the role of these entities in shaping technological policies, underwriting research and development initiatives, and driving nationwide innovation strategies. In this study, a comprehensive framework is proposed for developing technology roadmaps tailored for systems and technologies led by governmental entities. This framework has been adapted from the Advanced Technology Roadmap Architecture (ATRA) and brought original adjustments thereby addressing the research gap. The study also presents strategic recommendations for the ATSCS implementation in South Korea, integrating systems engineering principles for a holistic approach to technological advancements. The framework can be replicated to serve as a guide for governments seeking to implement effective and efficient technology roadmaps for public infrastructure systems. [ABSTRACT FROM AUTHOR]

During road work, lane closures significantly reduce road capacity and negatively impact traffic safety in the upstream segments. This study introduces a merge control strategy for the work zone on freeway that aims to alleviate severe congestion and improve flow efficiency in environments where manual vehicles (MVs) and connected automated vehicles (CAVs) coexist. Using a short‐term prediction model combined with a proportional‐integral‐derivative (PID) controller, this strategy dynamically adjusts merging behavior based on real‐time traffic conditions. The PID controller calculates error values as the difference between current and target states, adjusting responses through proportional, integral, and derivative terms. The predictions of the traffic state based on the density of open lanes in each segment guide the controller's decision to initiate a "Merge" or "No Merge" guidance. When merging is deemed necessary, the controller estimates the optimal number of vehicles to merge for each segment, using the severe congestion threshold as a reference point. This approach was tested using a microscopic simulation tool on a calibrated real‐world network under mixed traffic conditions. The results indicate that the proposed strategy effectively disperses merging upstream, increases merging speeds, and maintains lane density below critical congestion levels, thus enhancing operation efficiency and safety in work zone areas. [ABSTRACT FROM AUTHOR]

The severity of crashes on national/provincial roads, urban roads, and rural roads was analyzed. Random parameters logit models with heterogeneity in means and variances under different time periods of the day and roadway classifications were employed. Analysis of factors influencing injury severity of single-vehicle crashes in the driver, vehicle, road, and environment to explore correlation and difference between influencing factors and crash severity. The results showed that the effect of factors that determine injury severity varied significantly across different roadway classifications. Specifically, driver age (<30) and summer were only significantly associated with single-vehicle crashes on urban roads. Dawn/dusk was only significant in single-vehicle crashes on rural roads. Meanwhile, overall spatiotemporal instability was observed in the study. While several indicators were also reported to show relative spatial or temporal stability such as male, driver age (>55), motorcycle, traffic control, and visibility (<100 m). [ABSTRACT FROM AUTHOR]

Short-time traffic flow prediction can not only help urban traffic management to complete the control and induce but also reduce the degree of urban congestion and improve the efficiency of urban operation. At the same time, improving the effect of short-time traffic flow prediction is one of the key points of traffic control and guidance development. Currently, there are many different models for short-term traffic flow prediction, and different prediction models have their different advantages and disadvantages. In this paper, we review the current status of research on short-time traffic flow prediction based on Long Short-Term Memory (LSTM) neural network: first, we analyze the research method in this paper; second, we briefly review the research and application effects of common prediction methods in traffic flow prediction; finally, the optimization method based on LSTM and its application effect are summarized and analyzed from the aspect of LSTM network and its combination model. In addition, short-time traffic flow prediction still faces challenges due to the problems of the data itself and the complexity of real-world traffic flow impact factors. [ABSTRACT FROM AUTHOR]

Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process (GP), trust region, and Thompson sampling are employed in the TuRBO and contribute considerably to performance in terms of computational speed, solution quality, and scalability. Empirical studies are carried out using data from Mudanjiang and Chengdu, China. The performance of TuRBO is compared with that of Bayesian optimization (BO), genetic algorithm and random sampling. The results show that TuRBO converges the fastest because of its ability to balance exploration and exploitation through the trust region and Thompson sampling. Meanwhile, because TuRBO enables more efficient exploitation through the local GP, the solution quality of TuRBO outperforms others significantly. The average waiting time achieved by TuRBO was 2.84% lower than that achieved by BO. Finally, the method has been successfully extended to a large network with 233‐dimensional spaces and 122 signalized intersections, demonstrating that the developed methodology can deal with high‐dimensional traffic signal control effectively for real case applications. [ABSTRACT FROM AUTHOR]

Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced "white phase." CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do‐not‐walk intervals for pedestrian movements. A receding horizon‐based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%. [ABSTRACT FROM AUTHOR]

For proper planning, design and operation of an electronic highway toll facility, the capacity of the toll lane is an important aspect that needs to be considered. Although the capacities of roads and intersections are generally referred to as stochastic in nature, toll lane capacity is still considered as a deterministic value. A study was undertaken into the variation in service headway and thus reliability related to capacity values for toll lanes. Videographic traffic data were collected at three electronic toll plazas in India. The service headway of cars was found to be lower compared to vehicles with trailers. A Monte Carlo simulation was used to generate the different variations that may occur in the service headway owing to supply-side and demand-side changes. Finally, the capacity for 80%, 90% and 95% reliability was found as 494, 554 and 608 passenger cars per hour, respectively. The developed capacity values can be used to check the volume-to-capacity ratio in a mixed traffic lane and can be used to develop the level-of-service thresholds for electronic toll collection lanes in future studies. [ABSTRACT FROM AUTHOR]

Time headway between vehicles is considered as an important microscopic traffic flow parameter that affects the safety and capacity of highway facilities. This research work intends to provide a field study of vehicle time headway distribution on Petra multilane highway that connects Irbid and Ramtha cities in Jordan. The main objective of this study is to integrate the utilization of Computer Vision (CV) and Artificial Intelligence (AI) to extract time-headway data, as well as to investigate the suitability of the negative exponential distribution for the random headway state and the normal distribution for the constant headway state. Time headway data was videotaped for moving traffic over two different periods, one having medium traffic volume and the other representing the rush hour. Five hundred observations of time headway were extracted through an AI Python code developed specifically for this task and vehicle image detection. 50% of the observations represented the random headway state and the other 250 observations represented the constant headway state. The developed regression analysis model for the extracted time headway data versus their associated frequencies shows a tendency toward negative exponential distribution (R2=0.98) for the random headway state, while showing a normal distribution relationship (R2=0.99) with chi-square test at a level of significance of 0.01. This indicates the successful integration between Computer Vision (CV) and AI in investigating suitable models for headway distributions. This finding will open the door for the usage of AI and computer vision in numerous applications of traffic and transportation engineering disciplines. [ABSTRACT FROM AUTHOR]

Summary: Plasmodesmata are plasma membrane‐lined connections that join plant cells to their neighbours, establishing an intercellular cytoplasmic continuum through which molecules can travel between cells, tissues, and organs. As plasmodesmata connect almost all cells in plants, their molecular traffic carries information and resources across a range of scales, but dynamic control of plasmodesmal aperture can change the possible domains of molecular exchange under different conditions. Plasmodesmal aperture is controlled by specialised signalling cascades accommodated in spatially discrete membrane and cell wall domains. Thus, the composition of plasmodesmata defines their capacity for molecular trafficking. Further, their shape and density can likewise define trafficking capacity, with the cell walls between different cell types hosting different numbers and forms of plasmodesmata to drive molecular flux in physiologically important directions. The molecular traffic that travels through plasmodesmata ranges from small metabolites through to proteins, and possibly even larger mRNAs. Smaller molecules are transmitted between cells via passive mechanisms but how larger molecules are efficiently trafficked through plasmodesmata remains a key question in plasmodesmal biology. How plasmodesmata are formed, the shape they take, what they are made of, and what passes through them regulate molecular traffic through plants, underpinning a wide range of plant physiology. See also the Commentary on this article by Burch-Smith, 243: 5–6. [ABSTRACT FROM AUTHOR]

Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement. [ABSTRACT FROM AUTHOR]

Evaluating arterial operational performance plays a pivotal role in arterial system management and enhancement. As a fundamental reference guide for traffic engineers, the Highway Capacity Manual (HCM) provides a series of analysis methodologies for evaluating multimodal operations on urban streets, highways, freeways, and off-street pathways. However, the efficient use of the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM can be restricted by a large amount of traffic volume collections at signalized and access point intersections, roadway segment geometric data inputs, and complicated calculation procedures on urban street segments. Further, the use of alternative tools, such as vehicle trajectory data, remains to be explored to supplement the urban street facility calculation framework in the HCM. To bridge the gap, this research utilizes vehicle trajectory data as an alternative tool and proposes a combined vehicle trajectory data and HCM calculation framework to implement and assess the existing performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. A case study was carried out on three major signalized arterials in Sparks, Nevada, for HCM methodology implementation and performance evaluation. Based on evaluation results, it was found that vehicle trajectory data can be utilized as an alternative tool to implement the performance evaluation methodology of urban street facilities for motorized vehicles in the HCM 7th Edition. It is recommended to combine arterial through-vehicle trajectories, the time-space diagram, arterial level of service (LOS), and the poorest segment LOS for arterial operational performance evaluation and further enhancement. The proposed automobile traveler perception score (ATPS) in the HCM was found to be insensitive to reflecting traveler’s perception of service quality. Additional traveler perception-based factors are recommended to be included for further ATPS development and enhancement. [ABSTRACT FROM AUTHOR]

Decentralized traffic signal controls, such as max-pressure (MP) control, also known as back-pressure (BP) control, have received increased attention recently. MP signal control has been analytically proven to maximize the network throughput and stabilize vehicle queue lengths whenever possible. However, previous work on MP signal control with cyclic and noncyclic phases did not include pedestrian access, which may increase pedestrians’ travel time, and delay or even encourage some dangerous behaviors like jaywalking. Because the movement of pedestrians is a nonnegligible factor in traffic management, and many urban planning researchers have found that walking space and walking continuously have significant health, safety, and environmental impacts, a pedestrian-friendly MP signal control policy is needed. Here, we propose a novel pedestrian-friendly MP signal control, Ped-MP, that considers pedestrian access in an urban network to achieve both maximum stability for private vehicles and a comfortable, safe walking experience. This study modifies the original MP control to include pedestrians’ access for the first time. Furthermore, this policy still inherits the decentralized property of original MP control, which means it only relies on the local information of individual intersections. Simulation studies are implemented on a popular benchmark network, the Sioux Falls network, with added pedestrians network. The results indicate that, although considering pedestrians’ access may reduce the stable region for vehicles, the pedestrians’ travel time and delay can be reduced significantly. [ABSTRACT FROM AUTHOR]

As the change of the traffic environment, the design of level intersections on some urban roads can no longer meet the current traffic situation, which cannot withstand the current traffic flow load, making the operation efficiency of the intersections low. Based on the optimization theory and the design guidelines of intersections on urban roads, the optimal signal period was screened, the optimal optimization scheme was matched, and the road traffic capacity model of intersections was established, through the optimal quantitative analysis on the traffic capacity, saturation and service level of intersections. Taking the intersection of Jingcheng Road and Fangyuan Road in Chengdu as an example, the traffic flow characteristics of the intersection were analyzed by video observation method and field investigation method, and the model was verified with VISSIM software. The research results show that compared with the original intersection, the optimized intersection can separate the traffic flow in time and space, to improve the operation efficiency. The research results can be used as a reference for the optimal design of level intersections. [ABSTRACT FROM AUTHOR]

Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control. [ABSTRACT FROM AUTHOR]

The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]

• Infrastructure design influences cycling and overtaking behaviour. • Cyclists feel more distracted when engaging in cognitive tasks. • Cognitive distraction does not impair cycling behaviour in simple situations. • A simulator study assessing hands-free cognitive distraction while cycling. • The distraction and complexity levels were too low to lead to behavioural adjustments. Previous studies using a survey or field observational approach show that secondary task engagement negatively impacts cyclists' performance. Prohibiting handheld phone use, while allowing hands-free use via headphones, is applied to reduce safety critical impact of secondary task engagement. Hands-free secondary task engagement limits visual-motor distraction, but cognitive distraction might still impact cycling performance. Therefore, the purpose of this study was to investigate the behavioural effects of hands-free cognitive secondary task engagement while riding alone on different kinds of cycle paths as well as when overtaking other cyclists and reacting to typical events like stopping at a traffic light or evading a pedestrian or an obstacle on the cycle way. Using the cycling simulator at the Department of Traffic and Engineering Psychology at the TU-Braunschweig, a mixed design was used with three levels of secondary task engagement as the independent between-factor: no task (NT), podcast task (PC), acoustic speech task (AS). Additionally, three types of lane markings of the cycle way and three events were varied as within-factors. N = 58 participants (36 female, 22 male) completed the experiment. In none of the situations and none of the parameters examined, an effect of secondary task engagement was found, although cyclists subjectively felt more distracted. This was not due to a lack of sensitivity to the parameters measured, as the types of infrastructure significantly influenced cycling and overtaking behaviour. In line with multiple resource theory, results suggest that secondary tasks requiring cognitive but not visual-motor resources can be done while cycling without adverse effects on behaviour. However, this might also be an effect of task difficulty and may change when the cycling task or the secondary task becomes more complex. Thus, additional studies including more complex traffic situations, are relevant. [ABSTRACT FROM AUTHOR]

In current signal control schemes, the prevailing control method is still the arterial coordinated control, which involves coordinating a group of adjacent traffic signals along a major road. However, during the actual implementation process, technicians have observed substantial variations in the effectiveness of mainline coordinated control across different road segments. These variations are attributed to different signal control strategies, giving rise to an ongoing debate regarding the alignment of the beginning or end of the green phase. The impact of these two alignment methods on bandwidth and road capacity in arterial coordinated control remains a subject of controversy. Therefore, this paper aims to compare the two methods using a real-world scenario. Based on the inherent characteristics of coordinated control, a traffic capacity model for coordinated segments was established. Subsequently, the influence of green start alignment and green end alignment on the capacity of green wave segments and the improvement of existing traffic conditions was analyzed using the proposed capacity model. To validate the analytical model, simulations were executed using VISSIM, and data on delay and the number of stops were collected. The conclusion indicates that from the perspective of benefits and safety, green start alignment outperforms green end alignment. This research provides insights for parameter settings in signal coordinated control, with the potential to significantly reduce the number of vehicle stops, and has practical significance for energy conservation and emission reductions. [ABSTRACT FROM AUTHOR]

Rapid and precise detection of significant data streams within a network is crucial for efficient traffic management. This study leverages the TabNet deep learning architecture to identify large-scale flows, known as elephant flows, by analyzing the information in the 5-tuple fields of the initial packet header. The results demonstrate that employing a TabNet model can accurately identify elephant flows right at the start of the flow and makes it possible to reduce the number of flow table entries by up to 20 times while still effectively managing 80% of the network traffic through individual flow entries. The model was trained and tested on a comprehensive dataset from a campus network, demonstrating its robustness and potential applicability to varied network environments. [ABSTRACT FROM AUTHOR]