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An automatic vehicle classification (AVC) system designed from either still images or videos has the potential to bring significant benefits to the development of a traffic control system. On AVC, numerous articles have been published in the literature. Over the years, researchers in this domain have created and used a variety of datasets, but most often, these datasets may not reflect the exact scenarios of the Indian subcontinent due to specific peculiarities of the road conditions, road congestion nature, and vehicle types usually seen in Indian subcontinent. The primary goal of this paper is to create a new still image dataset, called JUIVCDv1, which contains 12 different local vehicle classes that are collected using mobile cameras in a different way for developing an automated vehicle management system. We have also discussed the characteristics of the current datasets, and various other factors taken into account while creating the dataset for the Indian scenario. Apart from this, we have benchmarked the results on the developed dataset using eight state-of-the-art pre-trained convolutional neural network (CNN) models, namely Xception, InceptionV3, DenseNet121, MobileNetV2, and VGG16, NasNetMobile, ResNet50 and ResNet152. Among these, the Xception, InceptionV3 and DenseNet121 models produce the best classification accuracy scores of 0.94, 0.93 and 0.92 respectively. These models are further utilized to make an ensemble model to enhance the performance of the overall categorization model. Majority voting-based ensemble, Weighted average-based ensemble, and Sum rule-based ensemble approaches are used as ensemble models that give accuracy scores of 0.95, 0.94, and 0.94, respectively. [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]

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 paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators. [ABSTRACT FROM AUTHOR]

This paper presents a cooperative highway platooning strategy that integrates Multi-Agent Reinforcement Learning (MARL) with Proximal Policy Optimization (PPO) to effectively manage the complex task of merging. In modern transportation systems, platooning—where multiple vehicles travel closely together under coordinated control—promises significant improvements in traffic flow and fuel efficiency. However, the challenge of merging, which involves dynamically adjusting the formation to incorporate new vehicles, remains challenging. Our approach leverages the strengths of MARL to enable individual vehicles within a platoon to learn optimal behaviors through interactions. PPO ensures stable and efficient learning by optimizing policies balancing exploration and exploitation. Simulation results show that our method achieves merging with safety and operational efficiency. [ABSTRACT FROM AUTHOR]

SD-WANs are an innovative software-defined network (SDN) technology used to reinvent networks, services, and applications in wide area network (WANs). The development of SD-WANs ranges from network optimization in the past to service provision platforms at present and distributed computing systems in the future. The existing surveys on SD-WANs are fragmented, covering specific problems only, and are not comprehensive with detailed research directions. This paper seeks to provide a systematic survey on SD-WANs by introducing major research directions and stating specific problems. Therefore, four major research directions related to traffic engineering, network optimization and systems, service orchestration, and the security issues of SD-WANs are sequentially introduced, along with detailed statements relating to specific problems and the classification of state-of-the-art research. Finally, the trends and challenges regarding SD-WANs are summarized and our future work is described. [ABSTRACT FROM AUTHOR]

Railway interlocking systems are essential safety components in rail transportation, designed to prevent train collisions. They regulate the transitions between sections of a railway station using rail traffic control elements. An interlocking system can assess whether the configuration of these control elements poses a collision risk. Over the years, researchers have developed various algebraic models to tackle this issue, highlighting the potential use of computer algebra systems in implementing interlocking systems. In this work, we aim to enhance these systems' capabilities. Not only will they indicate whether a situation is dangerous, but if it is, they will also provide guidance on how to configure certain rail traffic control elements to ensure safety. In this paper, we introduce an algebraic model that represents the railway station through polynomials. This approach transforms the task of identifying dangerous situations into calculating the residue of a polynomial over a set of polynomials. The monomials contained in this residue polynomial encode all possible configurations that would render the situation safe. [ABSTRACT FROM AUTHOR]

Spread of parking difficulty can be regarded as a special cascading failure process of urban parking systems. A comprehensive understanding of this process can be greatly helpful to build a more robust parking system. Parking network, a specified complex network, is proposed to model, simulate, and analyse the failure process of urban parking systems in this paper. This model is applied to the analysis of parking systems in an abstract city grid and the downtown area of Luohu, Shenzhen. The results demonstrate that the parking network can capture subtle variations among various parking cruising behaviours or strategies from a network perspective. To enhance the utility of the parking network, an auxiliary indicator named "Parking Difficulty Index" is introduced to help assess the failure degree of urban parking system, estimate the optimal timing for parking guidance intervention, and evaluate the effectiveness of various guidance strategies in mitigating parking difficulties. [ABSTRACT FROM AUTHOR]

Connected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic. [ABSTRACT FROM AUTHOR]

In urban areas, emergency vehicles (EVs) need efficient traffic signal preemption to ensure timely responses during peak hours. While emergency vehicle traffic signal preemption (EVTSP) has garnered significant attention in the literature, the issues of queue spillbacks and negative impacts on non‐priority traffic have been relatively underreported. These issues are particularly critical during peak hours, notably in densely populated urban areas. This study presents an EVTSP system that considers queue spillbacks on approaches along the routes of EVs and the negative impacts of signal preemption on non‐priority traffic. The proposed control system has four key components: (1) a queue length management algorithm to ensure that an EV will not be impeded by excessive queues, particularly on its initial approaches along its route; (2) a signal preemption algorithm to guarantee uninterrupted passage of an EV even for approaches experiencing queue spillbacks; (3) a traffic status recovery algorithm to alleviate the extra waiting time for non‐priority vehicles after an EV crosses each intersection; and (4) a signal plan recovery algorithm to smoothly transit traffic signals to normal operation. The experimental results confirm that the proposed system considerably improves the travel time of an EV and mitigates the negative impacts on non‐priority traffic. [ABSTRACT FROM AUTHOR]

Explicit signal coordination carries prior knowledge of traffic engineering and is widely accepted for global implementation. With the recent popularity of reinforcement learning, numerous researchers have turned to implicit signal coordination. However, these methods inevitably require learning coordination from scratch. To maximize the use of prior knowledge, this study proposes an explicit coordinated signal control (ECSC) method using a soft actor–critic for cycle length determination. This method can fundamentally solve the challenges encountered by traditional methods in determining the cycle length. Soft actor–critic was selected among various reinforcement learning methods. A single agent was administered to the arterials. An action is defined as the selection of a cycle length from among the candidates. The state is represented as a feature vector, including the cycle length and features of each leg at every intersection. The reward is defined as departures that indirectly minimize system vehicle delays. Simulation results indicate that ECSC significantly outperforms the baseline methods, as evident in system vehicle delay across nearly all demand scenarios and throughput in high demand scenarios. The ECSC revitalizes explicit signal coordination and introduces new perspectives on the application of reinforcement learning methods in signal coordination. [ABSTRACT FROM AUTHOR]

Pollution of the environment by microplastics is a problem that is increasingly visible and worrisome, with tyre wear particles (TWPs) being considered, after several studies, as one of the major sources of microplastics. The aim of this paper is to present a review of the current knowledge on microplastics generated by road traffic, more specifically in TWPs. Generation and pathways depend on different factors like characteristics of the tyre and road or physical properties of the particles, among others. Currently, there are some studies on TWPs, both, carried out at the laboratory and under real scale conditions. So far, it has been possible to identify the presence of TWPs in samples, but their quantification involves time-consuming and expensive methods. The big knowledge gap is to find a standardised and practical method for sampling, detection and quantification, so more research on TWPs and their dispersion in the environment is needed. [ABSTRACT FROM AUTHOR]

Reducing the speed of the vehicle can reduce the risk and severity of the collision. Automatic means of vehicle traffic operation control, such as automatic speed cameras, is one of the main methods to solve traffic violations such as speeding. This study takes the actual operation of the automated hardware complex for vehicle traffic control in the Russian Federation as the research objective, and discusses the impact of the application of automated hardware complex for vehicle traffic control on traffic safety. In the process of building the automated traffic flow monitoring system of urban agglomeration, it is necessary to form a set of methodology to evaluate the hardware complex of traffic infrastructure. According to the research results of the influence of vehicle traffic automation and control system on road safety, a model, method and algorithm were developed to evaluate the efficiency of automated hardware complex for vehicle traffic control. The relevant factors introduced into the intelligent transportation system for the overall evaluation of the application efficiency of the vehicle traffic operation control automation hardware complex were determined. A life cycle model of automated hardware complex for vehicle traffic operation control was established to evaluate its operation efficiency in the development stage of intelligent transportation system, and on this basis, the maneuverability standard of urban agglomeration traffic monitoring system management is determined. The results show that the number of hardware complexes used in vehicle traffic operation control has a positive impact on the road traffic accident rate. It is suggested that the standard of one hardware complex for every 6.5 thousand registered vehicles in this area should be used as an indicator of the equipment level of the integrated automated system for vehicle traffic control in this area. [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]

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]

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]

In areas with significant changes in traffic demand and high vehicle dispersion at adjacent intersections, such as the surrounding roads of large shopping malls and schools, traffic problems are prone to occur. This is due to the unequal signal cycle lengths used at upstream and downstream intersections, which lead to periodic phase offsets as the cycles progress. To address this, we propose a multi-strategy integrated vehicle–road coordinated control method to tackle traffic flow operational issues caused by the offset characteristics of unequal-cycle adjacent intersections. A multi-strategy combined algorithm and control logic is established, which includes downstream intersection coordinated phase green extension, dynamic offset adjustment, and transitional queue speed guidance. The proposed method can substantially minimize the offset from falling into an incompatible threshold, effectively reducing queuing and early arrival of vehicles in the straight-through direction. It enables arriving vehicles to pass through the intersection without or with minimal stopping. Finally, the effectiveness of the method is validated using simulation experiments. A vehicle–road coordinated simulation verification platform was established, and comparative experiments were designed. The results indicate that the multi-strategy combined vehicle–road coordinated control method proposed in this paper, while ensuring the original through capacity for straight movements, can effectively reduce queue lengths, the number of stops, average vehicle delay, and travel time for single-direction straight lanes. This improvement enhances the efficiency of coordinated movements in the unequal–cycle adjacent intersections. [ABSTRACT FROM AUTHOR]

Fast and accurate ship target detection technology plays an important role in improving driving safety, rescue at sea, marine environmental protection, and sea traffic control. It is also one of the key technologies for the development of ship informatization and intelligence. However, the current ship target detection models used at different scales in multiple scenarios exhibit high complexity and slow inference speed. The trade-off between model detection speed and accuracy limits the deployment of ship target detection models on edge devices. This study proposes a lightweight multi-scale ship target detection model based on the Yolov5s model. In the proposed model, the lightweight EfficientnetV2 and C3Ghost networks are integrated into the backbone and neck networks of the Yolov5s model to compress the computational and parametric quantities of the model and improve the detection speed. The Shuffle Attention mechanism is embedded in the neck network component of the model to enhance the representation of important feature information, suppress irrelevant feature information, and improve the model's detection performance. The improved method is trained and verified on the dataset collected and labeled by the authors. Compared with the baseline model, the inference speed of the proposed model increased by 29.58%, mAP0.5 improved by 0.1%, and the parameters and floating-point operations decreased by 42.82% and 68.35%, respectively. The file size of the model is 8.02MB, which is 41.46% lower than the baseline model. Compared with other lightweight models, the method proposed in this study is more favored in edge computing. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

• 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]

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]

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]

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]

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]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study evaluates the impact of city spatial development patterns on the spread of airborne pandemics, focusing on Mashhad, Iran. Using ArcGIS spatial statistics, 91 spatial indices were examined between March 5, 2020, and October 2, 2021, during five distinct waves of the virus, revealing that private vehicle ownership and limited use of public transportation did not significantly reduce COVID-19 infection rates in Mashhad. Instead, factors such as proximity to transportation hubs, healthcare facility polarization, and retail shop distribution had a significant impact. Surprisingly, demographic vulnerability and conventional population-related factors had relatively minor effects. The study also highlights the influence of building concentration with high floor area ratios (FAR) and the impact of local authorities' optimism in reducing social distancing measures, especially in travel and traffic control, on the increased spread of the virus during recent waves. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method. [ABSTRACT FROM AUTHOR]

To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-network-based traffic signal control optimization method under the MPC framework is proposed. A dynamic correlation matrix is introduced in the predictive model to adapt to the dynamic changes in correlations between nodes over time. The signal control optimization strategy is solved using DRL, where the agent explores the optimal control strategy based on pre-set constraints in the future road environment. The geometric structure and traffic flow data of a real intersection were selected as the simulation validation environment, and a joint simulation was conducted using Python and SUMO. The experimental results indicate that in low-traffic scenarios, the queue length is reduced by more than 2 vehicles compared to the selected comparison methods; in high-traffic scenarios, the queue length is reduced by an average of 17 vehicles. Under the actual traffic data of the intersection, the average speed is increased by 6.4% compared to the fixed timing method; compared to the inductive signal control method, it increases from 9.76 m/s to 11.69 m/s, an improvement of 19.7%, effectively enhancing the intersection signal control performance. [ABSTRACT FROM AUTHOR]

Efficient traffic flow management at intersections is vital for optimizing urban transportation networks. This paper presents a comprehensive approach to refining traffic flow by analyzing the capacity of roads and integrating fuzzy logic-based traffic light control systems. We examined the capacity of roads connecting intersections, considering factors such as road vehicle capacity, vehicle speed, and traffic flow volume, through detailed mathematical modeling and analysis. Control is determined by the maximum capacity of each road segment, providing valuable insights into traffic flow dynamics. Building upon this capacity and flow analysis, the research introduces a novel intelligent traffic light controller (ITLC) system based on fuzzy logic principles. By incorporating real-time traffic data and leveraging fuzzy logic algorithms, our ITLC system dynamically adjusts traffic light timings to optimize vehicle flow at two intersections. The paper discusses the design and implementation of the ITLC system, highlighting its adaptive capabilities in response to changing traffic conditions. Simulation results demonstrate the effectiveness of the ITLC system in improving traffic flow and reducing congestion at intersections. Furthermore, this research provides an analysis of the mathematical models used to calculate road capacity, offering insights into the underlying principles of traffic flow optimization. Through the simulation, we have validated the accuracy and reliability of our controller. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the daily berth planning problem for Xiamen Hai-Tian Container Terminal (XHCT) at the Port of Xiamen, China, and propose the development and implementation of a berth planning system. The aim of the berth planning problem is to optimize daily berth plans by considering various decisions, including berth allocation, quay crane assignment, fairway traffic control, and berthing safety requirements. Among these decisions, the berthing safety requirement is a novel but practical problem in berth allocation that concerns the resource allocation related to berthing safety and interrelation with other decisions. A mathematical programming-driven methodological framework is designed with a 0-1 integer linear programming model for problem formulation and a highly efficient decomposition heuristic algorithm for solving the problem. This framework establishes the core for the berth planning system. The adoption of the berth planning system contributes to the increase of container throughput and berth capacity by transforming the planning process of XHCT. Moreover, the mathematical programming-driven daily berth planning informs further intelligent operations development in the Port of Xiamen and other container ports. History: This paper was refereed. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72394360, 72394362, 72025103, 71831008, 72361137001, 72071173, and 72371221]. [ABSTRACT FROM AUTHOR]

This article presents an integrated educational module for undergraduate traffic engineering at the microscale level. When dealing with a complex transportation system, the education program should cultivate students' acquisition of both in‐depth traffic specialization and breadth of engineering knowledge. For the educational instruction of the signal phase design, this article presents an integrated design that includes several engineering subjects: signal phase design, circuits and electronics, and object‐oriented programming (OOP). Due to the lengthy evolution of signal phase design and the high‐end industrial standards for this issue, this well‐structured problem requires an instructional design to adopt a worked example that integrates varied engineering domains. The integrated design session is intended to deliver education through designing a miniature signal controller while creating an immersive situation and encouraging social teamwork. Feedback from participating students has been positive, indicating the achievement of the planned learning objectives and better mastery of engineering practices. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Connected and automated vehicles (CAVs) are being developed and designed to operate on existing roads. Their safe and efficient operation during roadworks, where traffic management measures are often introduced, is crucial. Two alternative measures are commonly applied during roadworks on motorways: (i) closing one or multiple lanes (ii) narrowing one or all lanes. The former can cause delays and increased emissions, while the latter can pose safety risks. This study uses a VISSIM‐based traffic microsimulation to compare the effectiveness of these two strategies on traffic efficiency and safety, considering various market penetration rates (MPR) of CAVs. The model was calibrated and validated with the data collected from M1 motorway in the United Kingdom. Results show that average delays per vehicle‐kilometre‐travelled decreased from 102.7 to 2.5 s (with lane closure) and 23.6 to 0.6 s (with narrow lanes) with 0% and 100% CAV MPR, respectively. Moreover, safety in narrow lanes improved by 4.8 times compared to 1.5 times improvement in lane closure with a 100% CAV MPR; indicating that narrow lanes would result in better safety performance. These findings could assist transport authorities in designing temporary traffic management measure that results in better CAV performance when navigating through roadworks. [ABSTRACT FROM AUTHOR]

Guide signs have a very good role in guiding people's travel, and good design of guide signs can improve the efficiency of drivers. The amount of sign information at the nodes in the road network affects the driver's reading of the sign information, which in turn affects the access efficiency of the whole road network. Therefore, by introduced the amount of guidance sign information from nodes as a penalty coefficient, the cost of the road segment was calculated, and a guidance path planning model between origin to destination (OD) was established, Combined with the A∗ algorithm, the entire road network was traversed to obtain the optimal guidance path between ODs. By added guidance information on the guidance path, the coherence of guidance sign information between ODs was improved, guiding drivers can make better path, and reduce traffic pressure in the road network. At last, Select the area from the railway station ( point O) to the People's Hospital ( point D) in Qingyang City, Gansu Province for practical application and analysis. [ABSTRACT FROM AUTHOR]