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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]

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]

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 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]

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]

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]

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]

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing. [ABSTRACT FROM AUTHOR]

Traffic flow prediction can provide important reference data for managers to maintain traffic order, and can also be based on personal travel plans for optimal route selection. On account of the development of sensors and data collection technology, large-scale road network historical data can be effectively used, but their high non-linearity makes it meaningful to establish effective prediction models. In this regard, this paper proposes a dual-stream cross AGFormer-GPT network with prompt engineering for traffic flow prediction, which integrates traffic occupancy and speed as two prompts into traffic flow in the form of cross-attention, and uniquely mines spatial correlation and temporal correlation information through the dual-stream cross structure, effectively combining the advantages of the adaptive graph neural network and large language model to improve prediction accuracy. The experimental results on two PeMS road network data sets have verified that the model has improved by about 1.2% in traffic prediction accuracy under different road networks. [ABSTRACT FROM AUTHOR]

In recent years, the problem of traffic congestion has become increasingly serious, and research on traffic system control has become a new hotspot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable support points can alleviate traffic congestion from a new perspective. This article improves the full speed differential model considering strong wind models from the perspective of bifurcation control to adjust traffic flow. This article theoretically proves the existence conditions of Hopf bifurcation and saddle node bifurcation in the model and finds the stability mutation point of the transportation system stability. A nonlinear system feedback controller was designed for unstable bifurcation points using Chebyshev polynomial approximation and random feedback control methods. Without changing the system equilibrium point, the advance, delay, and elimination of Hopf bifurcation were achieved, and the abrupt behavior of the transportation system was controlled, thereby alleviating traffic congestion. This article explains the changes in the stability of complex transportation systems from the perspective of bifurcation analysis, which can better capture the characteristics of traffic flow. By adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the transportation system is fully described, and the influence of unstable focal points and saddle points on the system is suppressed, thereby slowing down the traffic flow. In addition, unstable bifurcation points can be eliminated, and the Hopf bifurcation can be controlled to advance, delay, and disappear, thereby achieving control over the stable behavior of the transportation system. This helps alleviate traffic congestion and also helps describe actual traffic phenomena. [ABSTRACT FROM AUTHOR]

Emergency vehicles (EVs) must reach their destination within the shortest amount of time considering the importance of their service in saving lives and reducing damage to property. The aim of this research was to reduce the delay experienced by these vehicles through the alteration of traffic control signals to give preferential treatment to EVs upon detection. Specifically, this study compared three algorithms with concepts of EV pre-emption, longest queue first and emergency vehicle signal priority (EVSP) based on average general and EV delays through a traffic simulation approach using the Vissim software program and its component object model interface. The performance evaluation was applied at Tuk Chai intersection in Bangkok, Thailand as a case study. The second algorithm of longest queue first with pre-emption was found to be the best approach for both peak hour traffic conditions and 0.8 (peak) traffic conditions (i.e. peak hour traffic volume reduced by 20%), performing better than the do-nothing condition by 69.70% for stochastic EV input and 79.14% for fixed EV input. The findings also show that the algorithm of preferential treatment using EVSP strategies produced a competitive and robust performance with EV delay values better than the do-nothing condition by 75.96, 77.27 and 45.67% in 0.8 (peak), peak hour and 1.2 (peak) traffic conditions, respectively (the last of these three being peak hour traffic volume increased by 20%). [ABSTRACT FROM AUTHOR]

The conflicting traffic volume (CTV) is one of the crucial parameters for the estimation of U-turn capacity at a mid-block median opening (MBMO). Therefore, in this work, CTVs at MBMOs were estimated. A simple yet practical methodology was developed and a parameter called the spatiotemporal conflict factor was introduced. In order to estimate the spatial conflict factor, the entire carriageway width was divided into two zones – the spatial conflict zone and the spatial no-conflict zone. Spatial conflict factors were estimated by studying the placement characteristics of approaching through traffic and U-turning traffic. Subsequently, temporal conflict factors were estimated based on the no-conflict time gap and possible slowdown sections. The spatiotemporal conflict factors and the resulting CTVs were then calculated for MBMOs in six-lane and four-lane roads. The tangible outcome of this investigation is the ability to estimate the realistic capacity values of median openings, which will be beneficial for traffic planners in the efficient management of traffic for improved levels of service and safety. [ABSTRACT FROM AUTHOR]

This study integrates travel time and delay with spot speed study, utilizing the floating car method. The correlation between increased traffic flow and elevated delay times is underscored. Notably, a significant portion of vehicles operating below anticipated speeds emerges as a pivotal factor contributing to congestion. The average journey time is approximately 4 minutes, with an average stopped delay ranging from 0.17 to 0.11 minutes. Spot speed study in four directions reveals nuanced speed dynamics. In the East-West direction, the 15th, 85th, and 98th percentile speeds are 39.14 km/h, 62.27 km/h, and 85.00 km/h and similar patterns are observed in the other directions. These results transcend routine traffic studies, offering actionable insights for urban planning, traffic regulation, and economic assessments. The study delves beyond statistical compilations, providing concrete evidence of the intricate interplay between traffic flow, delay times, and speed dynamics. It aspires to guide traffic engineers, urban planners, and decision-makers into the realms of future urban development. The comprehensive understanding derived ensures informed decision-making in transportation management and infrastructure planning. [ABSTRACT FROM AUTHOR]

Due to the limited internal space of urban tunnels and the inability to manoeuvre the vehicles in them, critical situations will be created for vehicle traffic in accidents. Under these situations, traffic management strategies should be utilized for improving traffic status. The common and applicable strategies in these situations include traffic flow direction, line management, and ramp control. Accordingly, this research is conducted to determine the impact of each of these strategies in the occurrence of the most critical accident in the Niayesh and Resalat tunnels of London. Therefore, the studied areas of tunnels are initially simulated by software and traffic data at the peak hours of the morning in 2013, and then the amounts of traffic flow parameters, the total travel time, delay time, stop time, flow density, and the average velocity of each strategy are studied by defining four different scenarios. The results of conducted survey and analyses indicate that adopting the target strategies of this paper improves the conditions of traffic functional parameters; and according to the comparisons, the traffic flow direction strategy has the highest efficiency in Niayesh tunnel and the ramp control strategy has the highest efficiency in Resalat tunnel. [ABSTRACT FROM AUTHOR]

In this work, the connected vehicle's messages are used to create an enhanced adaptive traffic signal control (ATSC) system for improved traffic flow. Few existing studies use connected and automated vehicles (CAVs) to develop traffic signal control algorithms under hybrid connected and autonomous conditions. The proposed approach focuses on a four‐phase traffic intersection with both CAVs and human‐driven vehicles (HVs). CAVs share real‐time state information, and a model called Roads Dynamic Segmentation estimates queuing procedures and vehicle fleet numbers on dynamic road sections. This information is used in the Store and Forward Model (SFM) to predict intersection queuing length. The ATSC system, based on model predictive control (MPC), aims to minimize intersection queue length while considering traffic constraints (undersaturated, saturated, and oversaturated) and avoiding free‐flow problems due to queue overflow. To reduce computational complexity, a linear‐quadratic‐regulator (LQR) is used. Real‐world vehicle trajectories and the SUMO tool are used for experimental purposes. Results show that the proposed method reduces average delay by 38.50% and 33.42% compared to fixed timing and traditional MPC in cases of oversaturated traffic flow with 100% CAV penetration. Even with a penetration rate of only 20%, average delay decreases by 13.65% and 6.50%, respectively. This study showcases not only the potential benefits of CAV in enhancing traffic, but also enables the optimal utilization of green duration in signalized intersection control systems. This helps prevent traffic congestion and ensures the efficient and smooth movement of traffic flow. [ABSTRACT FROM AUTHOR]

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management. [ABSTRACT FROM AUTHOR]

Forecasting traffic flow is crucial for Intelligent Traffic Systems (ITS), traffic control, and traffic management systems. Complex spatial and temporal interactions of traffic networks make traffic forecasting tasks challenging. Recently, Graph Convolutional Network (GCN) has attracted researchers' attention as it can better represent graph-shaped road networks and extract spatial features of traffic. However, traditional GCN has some drawbacks since it uses a static adjacency matrix which is unable to capture the time-varying features of traffic propagation. To overcome this, we represent the traffic road network as a dynamic graph and use a probabilistic spatiotemporal adjacency matrix to identify the time-varying impacts of adjacent roads on target roads in GCN. In addition, to find the similarity among the nonadjacent nodes, we have employed node-specific learning in GCN rather than sharing parameters in traditional GCN. This node-specific learning helps our model to learn detailed characteristics of road networks. For temporal feature extractions, we used a Gated Recurrent Unit (GRU) that captures the local trend of traffic flow and an attention mechanism to capture the global trend of traffic flow. We compared the performance of our model with baseline models using two real-world datasets. Experimental results show that our model is effective in forecasting both short and long-term traffic flow. Source code of our model is available at https://github.com/atkia/PSTGCN [ABSTRACT FROM AUTHOR]

In this paper is present the procedure for introducing the coordination of light signals at five intersections on the Boulevard "St. Kliment Ohridski" in Skopje. The primary purpose of a traffic signal is to assign the right-of-way to intersecting traffic streams for the purpose of ensuring that all streams are served safely and without excessive delay. A properly designed and timed signalized intersection will minimize fuel consumption, delay, and stops without having an adverse effect on safety. Travelers will realize one or more of the following benefits at intersections where the traffic signal is needed, properly designed, and well timed: orderly movement of traffic, increase in the traffic-carrying capacity of the intersection, reduction in the frequency and severity of certain types of crashes, progressed traffic when traveling in a coordinated signal system, and interruption of heavy traffic flow to provide safe opportunities for minor movements. Coordinated signal timing synchronizes traffic movements and manages the progression speed of specific modes where uninterrupted flow is desired along a corridor. While traditionally applied to increase vehicular traffic flow and reduce peak-hour delay, coordinated signal timing can also be optimized for slower speeds, creating an uninterrupted flow for bicyclists or low vehicle progression speeds for a pedestrian-friendly downtown. Signals may also be timed to coordinate transit headways along routes where regular transit service is consistent and has low variability. Coordination of traffic signals can reduce the number of stops along a corridor and provide for a continuous flow of traffic at the target speed. Progression speeds should be set at or below the target speed, rather than existing 85th percentile speeds. [ABSTRACT FROM AUTHOR]

Network traffic classification is a fundamental and intricate component of network management in the modern, high-tech era of 5G architectural design, planning of resources, and other areas. Investigation of traffic classification is a key responsibility of traffic engineering in SDN. SDN is a network programmability technology used in 5G networks that divides the control plane from the data plane. It also points the way for autonomous and dynamic network control. SDN needs data from the classification system's flow statistics to apply the appropriate network flow policies. To control the volume of heterogeneous network traffic data in 5G network service, the network administrator must implement a carefully supervised traffic investigation system. This study uses machine learning techniques to examine alternative ways of handling heterogeneous network traffic. The suggested approach is Ensemble Learning for Automated Network Traffic Categorization. i.e., CatBoosting for Automated network traffic classification for multiclass (Cat-ANTC) predicts traffic categorization and offers a higher prediction accuracy than individual models and a more regularized model formalization to decrease over-fitting and boost efficiency. The Cat-ANTC is evaluated using benchmark network traffic datasets that are openly accessible and contrasted with current classifiers and optimization methods. It is clear that when compared to the currently used ensemble techniques, the suggested ensemble methodology produces promising outcomes. Additionally, the proposed method is tested and shown to perform better than the classification of traffic flow using the current model. [ABSTRACT FROM AUTHOR]

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network's operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model's correlation. [ABSTRACT FROM AUTHOR]

When a work zone is located at an intersection, it greatly reduces the capacity and change the traffic flow characteristics. However, the impacts of work zones have not attracted much attention, and the signal control method of the intersection with a work zone has not been investigated yet. This paper focuses on a specific type of work zone, which is located within the area of an intersection. The saturation flow rate model, traffic wave model, traffic delay model, and emptying time model are proposed to capture the traffic flow characteristics of the intersection with an island work zone, and a signal control model is further put forward. The real data is collected to calibrate, validate, and evaluate the proposed models. The results indicate that the proposed signal control model can reduce about 15.6% of queue length and 17.2% of traffic delay for the intersection with an island work zone. [ABSTRACT FROM AUTHOR]

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system. [ABSTRACT FROM AUTHOR]

Large enterprise networks typically rely on expensive, high-speed backbone links to connect multiple campuses across diverse regions. As the volume of traffic traversing these backbone links increases, traffic engineering techniques are employed to filter or redirect traffic flows. Nevertheless, simple rerouting strategies can introduce business disruptions such as packet reordering, which significantly impact the user experience. To address this issue, we introduce an enhanced traffic scheduling algorithm named Critical Flow Rerouting with Weight- Reinforcement Learning(CFRW-RL), which builds upon the critical flow rerouting-reinforcement learning (CFR-RL) algorithm. CFRW-RL incorporates the principles of reinforcement learning, accounting for both the weights and classifications of data flows. This approach enables the algorithm to prioritize flows with lower weights for rerouting. The simulation results demonstrate that CFRW-RL significantly minimizes the rerouting of high-priority business flows and reduces business interference compared with the CFR-RL algorithm and that it maintains a similar computational complexity. [ABSTRACT FROM AUTHOR]

To reduce the risk of traffic conflicts in merging area, driver's behavior pattern was analyzed to provide a theoretical basis for traffic control and conflict risk warning. The unmanned aerial vehicle (UAV) was used to collect the videos in two different types of merging zones: freeway interchange and service area. A vehicle tracking detection model based on YOLOv5 (the fifth version of You Only Look Once) and Deep SORT was constructed to extract traffic flow, speed, vehicle type, and driving trajectory. Acceleration/deceleration distribution and vehicle lane-changing behavior were analyzed. The influence of different vehicle models on vehicle speed and lane-changing behavior was summarized. Based on this data, the mean and standard deviation of velocity, acceleration, and variable acceleration were selected as the characteristic variables for driving style clustering. To avoid redundant information between features, principal component dimensionality reduction was performed, and the dimensionality reduction data was used for K-means and K-means++ clustering to obtain three driving styles. The results show that there are obvious differences in the driving behaviors of vehicles in different types of merging areas, and the characteristics of different areas should be fully considered when conducting traffic conflict warnings. [ABSTRACT FROM AUTHOR]

Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas. [ABSTRACT FROM AUTHOR]

Due to recent advancements in connected and autonomous vehicles (CAVs), freeway traffic control (FTC) has become a trendy area of research. Combining CAVs and control measures has opened more efficient traffic flow possibilities. However, while there is extensive research on how traffic control and CAVs can enhance traffic flow performance, their safety benefits should be evaluated more comprehensively. It is challenging to distinguish their contributions to traffic flow safety. Studies on the safety of CAVs in traffic flow indicate that traffic flow safety improves with higher market penetration rates (MPR) of CAVs. However, this finding only sometimes aligns with the traffic flow performance effects of CAVs on freeways. This paper introduces a framework for assessing the safety effects of FTC strategies in mixed traffic scenarios involving human-driven vehicles and CAVs. The proposed framework is tested through a microsimulation-based case study in Istanbul, Turkey. The results show that, despite varying MPRs, the safety effects of CAVs and FTC methods do not consistently reduce the number of conflicts in the traffic flow context. [ABSTRACT FROM AUTHOR]

Using historical traffic flow information to accurately and real-time predict future traffic flow information is particularly important for the management and control of traffic scenarios, and the guidance and planning of traffic travel. Short-term traffic flow prediction requires a large amount of forecast data and complex changes in traffic flow in space and time. However, the current prediction model cannot meet the requirements of real-time and accuracy of short-term traffic flow prediction. This paper proposes two traffic flow prediction models based on the changing trend and correlation of short-term traffic flow in time and space, combined with deep learning related theories. Firstly, this paper takes the traffic flow of the expressway as the research object, explores the basic parameters of traffic flow-flow, speed occupancy rate in time and space distribution law and change trend, and selects the speed as the research object, analyzes its temporal and spatial characteristics and correlation to provide data support for this study. Then, this paper introduces the related theories and frameworks of deep learning, mainly including neural network theory and hyperparameter optimization theory, as well as the deep learning model design framework. Pytorch and hyperparameter optimization framework Optuna, so as to provide theoretical foundation and technical support for this research. Finally, experiments of short-term traffic prediction based on single detection point and short-term traffic prediction based on multiple detection points are carried out on the PeMS dataset. Firstly, a multi-lane spatiotemporal convolutional network model (MSCTAN) based on traffic flow prediction at a single detection point is proposed to capture the spatiotemporal correlation of multi-lane traffic at a single detection point. Spatiotemporal Convolution and Spatiotemporal Attention Network Model (MSCSAN). For the two models proposed in this paper, comparative experiments were designed, and the mean absolute error (MAE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE) were used as evaluation indicators to verify their performance at a single detection point and multiple detection points. The results show that, whether it is a single detection point experiment or a multi-detection point experiment, the two models achieve the best performance compared with the comparison model. [ABSTRACT FROM AUTHOR]

The integrated design of traffic signal control (TSC) and reversible lane (RL) is an effective way to solve the problem of tidal congestion with uncertainty at isolated intersections, because of its advantage in making full use of temporal‐spatial transportation facilities. Considering the contradiction between the dynamic TSC scheme and the fixed RL scheme in one period, a two‐stage optimization method based on improved mean‐standard deviation (MSD) model for isolated intersections with historical and real‐time uncertain traffic flow is proposed. In the first stage, applying the same‐period historical data of multiple days, a robust optimal traffic signal control model with reversible lane based on MSD model (MSD‐RTR model) is put forward to obtain the fixed RL scheme and the compatible initial TSC scheme. A double‐layer nested genetic algorithm (DN‐GA) is designed to solve this model. In the second stage, applying real‐time period data and multi‐day same‐period historical data, a robust optimal dynamic traffic signal control model based on MSD model (MSD‐RDT model) is put forward to obtain the dynamic TSC scheme. Three modes which reflect the different weights of historical period and real‐time period in this MSD‐RDT model are presented to improve the model stability, and a multi‐mode genetic algorithm (MM‐GA) is designed. Finally, a case study is presented to demonstrate the efficiency and applicability of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

Given the role of road tunnels in a road network, it is relevant to quantitatively assess their resilience due to disruptive events. In this respect, the travel speed of traffic flow, as a metric of resilience, in the event of traffic accidents in road tunnels has been scarcely used. This represents a gap of knowledge that this paper intends to fill. For the purpose, the research method applied involves the development of a traffic macro-simulation model, and the analysis of results using the average travel speed and its spatial profile as a resilience metric. Particularly, we have evaluated the resilience of a twin-tube motorway tunnel when a traffic accident occurs in a tube. The findings showed how the best functionality level of the system, expressed in terms of average vehicles' speed, with its related greatest resilience index, is associated with the partial closure of the disrupted tube rather than the complete one. Further benefits might be obtained by activating Variable Message Signs (VMSs) that alert only the Heavy Goods Vehicles (HGVs) to exit the motorway before entering the tunnel and to use an alternative itinerary identified in the nearby transportation network. In this respect, we found that by means of the activation of VMSs as a traffic control strategy, the resilience index increased by about 7–17% when the traffic accident caused the partial closure of the disrupted tube for 1–3 h. Improvements might also be achieved by rapidly opening the two traffic by-passes at the tunnel portals that allow for the use of the adjacent undisrupted tube for two-way traffic. The unavailability of an alternative itinerary in the nearby transportation network along which to re-route the HGVs is also examined, finding a reduction in the functionality conditions of the system. The results obtained might serve as a support tool in the choice of functional recovery strategies in the case of the temporary partial or complete block of a tunnel tube due to a traffic accident. [ABSTRACT FROM AUTHOR]

With the escalating demand for efficient traffic management and the increasing complexity of traffic control, diverse sensor technologies have been implemented to measure traffic in real-time. The road-side LiDAR emerges as a novel technology addressing the data gap in multimodal traffic analyses. LiDAR sensing return time to precisely capture distance and reflectivity, generating point cloud data encompassing all traffic trajectory information. It overcomes challenges posed by illumination conditions like light, dust and fog, which often affect camera sensor performance. In addition, LiDAR sensing minimises the effect of changing object position and angles, simplifying object detection and recognition.This paper tackles the challenges of analysing LiDAR-derived traffic data by proposing a method for traffic trajectory data enrichment. The methodology followed includes creating a semantic map, bridging the physical space and raw data, transforming from a local to a standard Coordinate Reference System (CRS) and enriching data trajectory representation. Three use cases are presented based on the dataset obtained after enrichment: object classification, permissible directions violation detection, and traffic flow density. The proposed method is validated using traffic data from a LiDAR system of 6 sensors located in one of the busiest intersections in Sofia, Bulgaria. The raw sensor data is processed by a fusion box called the Augmented LiDAR Box, delivering time series frames with labelled moving objects in.osef format. The results prove that the proposed data enrichment method successfully transforms the trajectories into semantic sequences, opening up new avenues for trajectory analysis and intersection traffic micro-modelling. [ABSTRACT FROM AUTHOR]

Recently, the use of traffic monitoring applications is very common. Many of these, for example, have algorithms that seek the fastest way for the driver to complete the journey. Vehicular networks can be used to collect information in real time, such as traffic flow, accidents and congestion, helping to decongest the already chaotic traffic in cities. Thus, the use of vehicular ad-hoc networks (VANETs) in this case adds greater reliability, robustness and automation to traffic control systems. Practical experiments using VANETs are scarce; therefore, it is of great importance to validate simulations and ensure that they are explored and studied by researchers in this area. According to our surveys, works that address real measurements are fewer in number compared to software simulations. One of these factors, for example, is the development of drivers and software for the equipment and the high complexity of making such experiments available. Another point that also justifies the low number of actual measurements is the high cost of the equipment that supports the IEEE 802.11p standard, as it is still not widespread in the market. The main objective of this work is to carry out a study on vehicle ad-hoc network simulators and identify the advantages and disadvantages of each simulator covered. Particularly, a VANET network capable of operating in the IEEE 802.11p standard is simulated, evaluating performance in certain traffic characteristics. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect. [ABSTRACT FROM AUTHOR]

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight's remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios. [ABSTRACT FROM AUTHOR]

Since its outbreak in December 2019, COVID-19 has spread rapidly across the world. To slow down the spread of the pandemic, various countries have implemented a series of policies and measures. The transportation system is not only an important carrier for COVID-19, but also a vital means for the prevention and control of the spread of the pandemic. Therefore, most anti-pandemic measures are based on travel restrictions, thereby slowing down the spread of the pandemic. As a result, because of the impact of the pandemic and corresponding control measures, the transportation system has undergone tremendous changes. By analyzing the evolution of the transportation system in response to the influence of COVID-19, it is possible to better understand socioeconomic changes and the changes in residents' daily life. Based on rich license plate recognition data, the characteristics of urban motorized travel under the influence of COVID-19 has been analyzed. According to the processes associated with the control of the pandemic and the resumption of work and production, the analysis period is divided into four stages. The changes in indicators of macroscopic traffic status are analyzed for each stage. The three types of typical motor vehicle groups (i.e., non-localized operating vehicles, taxis, and localized operating vehicles) are characterized by the traffic flow they contribute, the number of vehicles in transit, the average travel intensity, the average daily travel time of a vehicle, the average daily travel distance of a vehicle, and the spatiotemporal distributions of origins and destinations of trips. These data clarify the spatiotemporal evolution characteristics of peoples' travel behavior at different stages of the pandemic. The results of data analysis show that COVID-19 has deeply changed the motorized travel behavior of urban residents. In the initial stage of resumption of work and production, the willingness to engage in motorized travel had decreased significantly compared with that in the first stage. This willingness gradually resumed until the third and fourth stages, but still did not fully reach the level before the onset of the pandemic. Specifically, the traffic status during morning and evening peaks has basically recovered, and has even increased beyond the level before the pandemic; however, a certain gap was still found between off-peak hours. There were also significant differences in the extent to which different types of vehicles were affected by the pandemic. Among these, taxis were impacted the most by the pandemic. In the fourth stage (at the end of April), the average daily travel time of a vehicle and the average daily travel distance of a vehicle still decreased by 29.25% and 22.63% compared with the first stage, respectively. The operating time of many taxis was shortened from 22:00 PM to 19:00 PM. The spatiotemporal characteristics of vehicles show that the reduction of flexible travel demand (e.g., shopping, catering, and entertainment) is key to the reduction of the travel demand of the road network. This research provides data support for the implementation of traffic control measures under future grave public health events and enables the formulation of urban traffic policies in the post COVID-19 era. [ABSTRACT FROM AUTHOR]

In this study, a traffic queue model is employed to simulate traffic parameters for optimization purposes. The primary objective is to enhance the average queue length, cumulative delay, and cumulative reward for varying numbers of vehicles generated using an innovative approach. The proposed methodology integrates deep Reinforcement Q-Learning techniques implemented through a realistic simulator, Simulation of Urban MObility, based on OpenStreetMap. This simulator is used to balance the flow of traffic by monitoring traffic conditions and adjusting signals (Red/Yellow/Green) accordingly. The research extensively evaluates simulation outcomes across different scenarios involving 50, 100, 500, and 1000 vehicles, with episodes 10, 50 and 100. The results provide valuable insights into training times, simulation durations, and total rewards obtained, indicating improvements in mitigating traffic congestion. The introduction of a traffic control strategy enhances model effectiveness through the utilization of four-layer deep neural network architectures with 400 neurons in hidden layers. However, the study also highlights persistent challenges related to real-time implementation and complexities in state-action-agent relationships. The progress highlights improvements in reducing congestion and enhancing efficiency at junctions by dynamically adjusting signal phases while leveraging historical data. The results highlight the potential of dynamic traffic management strategies in effectively addressing urban traffic challenges. [ABSTRACT FROM AUTHOR]

With recent technological advancements, the commercialization of autonomous vehicles (AVs) is expected to be realized soon. However, it is anticipated that a mixed traffic of AVs and human-driven vehicles (HVs) will persist for a considerable period until the Market Penetration Rate reaches 100%. During this phase, AVs and HVs will interact and coexist on the roads. Such an environment can cause unpredictable and dynamic traffic conditions due to HVs, which results in traffic problems including traffic congestion. Therefore, the routes of AVs must be controlled in a mixed traffic environment. This study proposes a multi-objective vehicle routing control method using a deep Q-network to control the driving direction at intersections in a mixed traffic environment. The objective is to distribute the traffic flow and control the routes safely and efficiently to their destination. Simulation results showed that the proposed method outperformed existing methods in terms of the driving distance, time, and waiting time of AVs, particularly in more dynamic traffic environments. Consequently, the traffic became smooth as it moved along optimal routes. [ABSTRACT FROM AUTHOR]

Level 3 automated driving systems could introduce challenges to traffic systems as they require a specific lead time in their procedures to ensure the safe return of vehicle control to the driver. These processes, called 'transitions of control', may particularly pose complications in accelerating traffic flows when regulations mandate control transitions due to an operational speed limitation of 60 km/h as established in recent certification processes based on UNECE regulations from 2021. To investigate these concerns, we conducted a comprehensive simulation study to examine potential safety implications arising from control transitions within mixed-autonomy traffic. The simulation results indicate adverse safety impacts due to increased safety-relevant interactions between vehicles caused by transitions of control in dynamic traffic flow conditions. Our findings also reveal that those effects could become stronger once string unstable ACC controllers are deployed as well. [ABSTRACT FROM AUTHOR]

Accurately obtaining accurate information about the future traffic flow of all roads in the transportation network is essential for traffic management and control applications. In order to address the challenges of acquiring dynamic global spatial correlations between transportation links and modeling time dependencies in multi-step prediction, we propose a spatial linear transformer and temporal convolution network (SLTTCN). The model is using spatial linear transformers to aggregate the spatial information of the traffic flow, and bidirectional temporal convolution network to capture the temporal dependency of the traffic flow. The spatial linear transformer effectively reduces the complexity of data calculation and storage while capturing spatial dependence, and the time convolutional network with bidirectional and gate fusion mechanisms avoids the problems of gradient vanishing and high computational cost caused by long time intervals during model training. We conducted extensive experiments using two publicly available large-scale traffic data sets and compared SLTTCN with other baselines. Numerical results show that SLTTCN achieves the best predictive performance in various error measurements. We also performed attention visualization analysis on the spatial linear transformer, verifying its effectiveness in capturing dynamic global spatial dependency. [ABSTRACT FROM AUTHOR]

With the increasing number of urban vehicles, as well as the current situation of non-intelligent traffic control systems, spatiotemporal non-uniform traffic resource occupation, and limited traffic planning and design, existing urban traffic planning methods cannot effectively solve problems such as frequent traffic congestion and uncontrollable commuting time for residents. In order to solve the above problems, this paper first constructs a multi-queue, multi-server queuing model based on the server vacation and a multi-hop cascaded queuing model from the perspective of local intersections and global commuting paths. We analyze the theoretical changes in passage delay costs at local intersections and on global commuting paths as a function of traffic flow and the random duration of traffic signals. On this basis, this article proposes a collaborative intelligent traffic planning algorithm based on artificial intelligence, which utilizes traffic sensors to dynamically perceive traffic congestion status and collaboratively plans the optimal duration of traffic signals and the optimal driving path of vehicles from both local and global perspectives, thereby maximizing the on-time arrival ratio of vehicles while ensuring the required commuting delay. The simulation results show that the proposed method can increase the on-time arrival ratio of vehicles by at least 20% compared to contrast methods while meeting the requirements relating to commuting delays. This verifies that our method can provide support for the improvement in efficiency in future Internet of vehicles. [ABSTRACT FROM AUTHOR]

The alleviation of traffic congestion relies on efficient traffic control and traffic guidance, which are based on real-time short-term traffic flow prediction. In this paper, the stacked autoencoder (SAE) deep learning model with powerful feature learning capability is selected to predict the traffic flow on road sections. The process of training SAE includes the pre-training phase and the fine-tuning phase, which mainly apply the BP algorithm. However, the process of training SAE is time-consuming and cannot meet the real-time performance of modern application systems. This paper proposes a parallel training strategy for the SAE prediction model based on data parallel mode. The gradient solution process in our algorithm satisfies the conditions of parallel computing, so the training process can be designed in a parallel manner. The original dataset is distributed to some computing nodes, which are work nodes. The work node is responsible for gradient calculation using the local data. The task of the sole master node is to synthesize the gradient calculation results and then broadcast the updated gradient to each work node. The simulation results show that the SAE-based prediction model achieves better results than the traditional model, and the parallel algorithm reduces the running time of training processes. [ABSTRACT FROM AUTHOR]

Identification and evaluation of traffic congestion are critical for better management of urban traffic, especially at merge sections where two different traffic streams converge and generate shockwaves. This study evaluates the travel time reliability measure (travel time index (TTI)) as a reliable congestion measure by analysing the impact of traffic flow parameters such as traffic volume, capacity, road geometrics and conflicting traffic flows at merge sections on TTI under mixed traffic conditions. The data required for congestion analysis were collected through video recording. Statistical models were developed to: (a) evaluate the relationship between road geometric characteristics and capacity of merge sections; (b) estimate traffic density and its evolution with time and compare it against occupancy values; and (c) correlate the relationship between TTI and traffic behaviour at merge sections and determine the variations in TTI for different levels of service of merge sections. The models developed are statistically validated using secondary data sets from the same study section, as well as from data collected from other multi-lane merge sections. The developed model successfully estimated recurring congestion by considering both space and time. This study highlights the potential usage of TTIs in capturing travel time variations more accurately at merge sections under mixed traffic conditions. [ABSTRACT FROM AUTHOR]

Considering the traffic flow of the inner and outer intersection approach of urban arterial roads, an optimization control model of traffic arterial road signal parameters with signal cycle, green light time, and phase offset as the optimization variables is proposed. In order to solve the optimization model of timing parameters, the swarm evolution mechanism of artificial bee colony algorithm is improved and applied. The case study shows that the optimization model of timing parameters can collaboratively optimize the signal cycle, green light time and phase offset of the arterial coordinated control system, and the timing results of the optimization model by comprehensive consideration of inner and outer approach traffic flow are more beneficial to the improvement of traffic efficiency than the decoupling optimization algorithm. The proposed optimization model and solution algorithm can improve the effect of arterial coordinated control. [ABSTRACT FROM AUTHOR]