Vehicle Delay Research Articles

A hierarchical intersection system control framework in mixed traffic conditions

Signalized intersections play a crucial role in urban road system and are a significant source of vehicle delays. Conventional intersection control methods struggle to cope with increasingly challenging traffic conditions. Fortunately, the development of artificial intelligence(AI), information communication technology(ICT) and connected and automated vehicle(CAV) technologies has brought new opportunities to enhance traffic performance at intersections. Based on these technologies, in the mixed traffic environment of CAVs and connected human-driven vehicles (CHVs), this paper proposes a hierarchical intersection system control framework, which adopts intersection level distributed control and corridor level centralized control. At the intersection level, considering the impact of CAV-dedicated lanes and using the CAV platoon and “1 + n” platoon consisting of one leading CAV and n following CHVs as control units, the mixed integer quadratic programming problem and optimal control problem are formulated to collaboratively optimize signal timing, lane settings and vehicle trajectories at isolated signalized intersections. At the corridor level, based on the optimization results of the intersection level and the critical path information, the critical path performance function and multi-stage optimal trajectory control formula are constructed to optimize the signal offsets and vehicle trajectories, which are passed to the intersection level, and ultimately minimize vehicle delays in the corridor. Simulation experiments and sensitivity analysis demonstrate the effectiveness and stability of the proposed framework in reducing vehicle delay and improving fuel consumption.

Railway Gate Automation System Using Lidar Technology

Railway gate management technology using LiDAR (Light Detection and Ranging) offers several benefits for enhancing safety and efficiency at level crossings. LiDAR can accurately detect approaching trains and vehicles, providing real-time data on their speed and distance. It can identify obstacles or individuals near the tracks, reducing the risk of accidents. Traffic signals at railway gates can be adjusted dynamically based on real-time data, minimizing delays for vehicles when no trains are approaching. LiDAR sensor emit laser pulses and measure the time it takes for the light to return after reflecting off objects. They create precise 3D maps of the surrounding area. A computer or server that processes the raw data collected by the LiDAR sensors. This system integrates with the railway's existing signalling and control infrastructure. Overall, the deployment of LiDAR technology in railway gate management can significantly enhance safety and operational efficiency while providing valuable data for future improvements.

A method for estimating vehicle delays at parking lot exits in urban networks

Parking lot entrances and exits are places where traffic conflicts are serious and traffic delays often occur. The accurate calculation of vehicle delays is crucial for parking lot entrance and exit management and is beneficial for reducing the interaction between static and dynamic traffic. Field data can be used to determine the vehicle delay, but the process of collecting field data is costly and time-consuming. Therefore, it is necessary to determine the vehicle delay for vehicles at the entrance and exit of the parking lot through mathematical models, as this method is efficient and cost-saving. This paper provides an estimation model based on renewal theory, the Poisson distribution, and the negative exponential distribution to study the delay time of a vehicle preparing to make a right turn at the curb of a parking lot exit (VCPLE). The study focuses on the yielding behaviors of the closest approaching vehicle (CAV) that is travelling in the lane into which the VCPLE is about to merge, and that will be positioned immediately behind the VCPLE once the merge is completed. Furthermore, the headway distance between the VCPLE and the CAV is also considered in addressing the problem. Additionally, a comparative analysis of vehicle delays was conducted via field data alongside predictions from the proposed model and the Cowan M3 model. The results demonstrate the success of the model developed in this study in estimating vehicle delays for the VCPLE. Furthermore, upon comparing the calculation results with those of the M3 model, it becomes evident that the model constructed in this study offers a more precise estimation of vehicle delays at parking lot exits. By calculating the delays experienced by vehicles within the parking lot entrance and exit zones, a comprehensive understanding of the traffic operational status in these areas can be obtained. This, in turn, aids in assessing the efficiency of the transportation system. This analysis enables traffic management authorities to promptly identify and address traffic bottlenecks, ultimately contributing to an improvement in road capacity.

Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment

AbstractThis study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.

Analysis of Signalized Roundabouts with Intelligent Transportation Systems - The Case of Gaziantep Junction 40 (Shell)

This study presents an analysis aimed at optimizing traffic flow at the Shell Intersection in Gaziantep. Due to increasing population and vehicle numbers, traffic problems in the city are growing, especially at central intersections. Using PTV-Vissim micro-simulation software, the current situation was analyzed and alternative scenarios were developed. Performance analyses were conducted on models created using field observations and peak hour traffic counts. The intersection, currently at service level "F", is targeted to be elevated to service levels "B" and "C" through the proposed Single Point intersection design and optimized signal plans. Results indicate that the proposed changes could reduce average vehicle delay by 85-93% and increase average speed by 195-239%. This study aims to contribute to the improvement of urban traffic management through the integration of intelligent transportation systems and geometric arrangements.

Dynamic Network-Level Traffic Speed and Signal Control in Connected Vehicle Environment.

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions.

Intelligent Traffic Control Decision-Making Based on Type-2 Fuzzy and Reinforcement Learning

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow.

Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control

The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors—cautious, normal, aggressive, and platooning—affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.

Optimization Method for Allocating Peak-Period Parking Demand in Hub Parking Lot Clusters

With the expansion of urban scale and the popularization of multi-modal transportation, transportation hubs, as the link of multi-modal travel, are becoming increasingly important in urban development and residents’ lives. In situations of high parking demand, the increase in road traffic volume and parking search delays exacerbates the service pressure on hub parking lots and the traffic congestion on surrounding roads. Therefore, reasonable parking demand allocation is one of the key solutions to this problem. Based on the analysis of the vehicle parking search process, this paper constructs a model for estimating parking search delay on roads outside hub parking lots and proposes an optimization model for parking demand allocation aimed at minimizing the total parking search delay of vehicles. Finally, taking a major transportation hub in Nanjing as a case study, data were obtained through field investigations and simulation experiments to identify peak parking demand periods and calibrate the model parameters. The results show that the average vehicle delay was reduced by 4.5%, with a total reduction of 13,860 s in vehicle delay for parking demands at the hub within one hour. Therefore, by optimizing the allocation of parking demand, the average delay for vehicles searching for parking can be reduced to a certain extent.

A cooperative perception based adaptive signal control under early deployment of connected and automated vehicles

Connected vehicle-based adaptive traffic signal control requires certain market penetration rates (MPRs) to be effective, usually exceeding 10%. Cooperative perception based on connected and automated vehicles (CAVs) can effectively improve overall data collection efficiency and reduce required MPR. However, the distribution of observed vehicles under cooperative perception is highly skewed and imbalanced, especially under very low CAV MPRs (e.g., 1%). To address this challenge, this paper proposes a novel deep reinforcement learning-based adaptive traffic signal control (RL-TSC) method that integrates a traffic flow model, known as the cell transmission model (CTM), denoted as CAVLight. Traffic states estimated from the CTM are integrated with the data collected from the cooperative perception environment to update the states in the CAVLight model. The design of reward function aims for reducing total vehicle delays and stabilizing agent behaviors. Extensive numerical experiments under a real-world intersection with varying traffic demand levels and CAV MPRs are conducted to compare the performance of CAVLight and other benchmark algorithms, including a fixed-time controller, an actuated controller, the max pressure model, and an optimization-based adaptive TSC. Results demonstrate the superiority of CAVLight in performance and generalizability over benchmarks, especially under 1% CAV MPR scenario with high traffic demands. The influence of CTM integration on CAVLight is further explored through RL agent policy visualization and sensitivity analysis in CTM parameters and CAV perception capabilities (i.e., detection range and detection accuracy).

Application of Artificial Intelligence Technology in Optimizing Control Parameters of Traffic Signal Group Systems

On the basis of introducing the concept of intersection signal state phase difference, the author proposes a proportional signal phase difference design scheme for traffic signal system control, on the basis of the HCM signal delay calculation formula, a standardized signal cycle and effective green signal ratio optimization design model were compiled, thus forming a complete set of optimization design modes for the control parameters of the urban main road traffic signal group system. The experimental results indicate that, the entire simulation experiment took 6000 seconds to compare the traditional fuzzy algorithm with the controller implemented by the author's fuzzy neural network, each case was simulated 5 times, the data in the Table is the average of 5 times, compared with traditional fuzzy algorithms, the author's method shows an improvement of 20.8% to 45.67% in average vehicle delay time, especially when the traffic flow is different in the east-west and north-south directions, the improvement is more significant.

Research on Optimizing Low-Saturation Intersection Signals with Consideration for Both Efficiency and Fairness

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections and considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of “sacrificing efficiency for fairness”. Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions.

A Cooperative Optimization Model for Variable Approach Lanes at Signaled Intersections Based on Real-Time Flow.

To resolve the congestion caused by imbalanced traffic at intersections, this paper establishes a model of the average delay deviation with the minimization of the average delay in the approach as the optimization objective. Then, the signal control scheme is further optimized based on the variable approach lanes setting. First, we investigate the threshold conditions for setting the VALs under different flows in a single approach direction. The results show that when the ratio of left-turn traffic exceeds the threshold range of 0.20~0.28, the function of the VALs needs to be changed from straight to left-turn. Then, based on the improved Webster's formula, an optimal timing method that aims at minimizing the average vehicle delay, minimizing the queue length, and maximizing the capacity, is proposed. Finally, taking the actual Huangke intersection in the Hefei demonstration area as an example, three schemes are compared and analyzed in the case of a VAL at the intersection. The results show that under the cooperative optimization scheme proposed in this paper, the travel time and the efficiency of the intersection could be reduced by 18.7% and 9.9%, respectively, when compared with the original and Webster's schemes.

Evaluating the performance of implementing regionally coordinating bus priority signals under different control schemes

Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.

A hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.

Enhancing Work Zone Safety: Evaluating Static Merge Strategies Through Microscopic Traffic Simulation

Background The utilization of merging control has been proposed as a strategy to maximize the capacity of roads in work zone areas. The static Early Merge (EM) and static Late Merge (LM) controls are extensively implemented among these strategies. Although many studies have investigated the efficacy of these controls through the analysis of field data or microscopic traffic simulations, such comparisons are frequently conducted under different work zone conditions, which can result in inconsistent, contradictory conclusions. Materials and Methods A simulation study was carried out using the VISSIM microscopic traffic simulator within a self-developed work zone featuring a 2-to-1 lane closure setup to comprehensively assess and contrast the traffic efficiency of the EM and LM controls. Furthermore, through a comprehensive analysis and comparison of network performance within the designed work zone across various scenarios, including queue length, vehicle delays, and travel time, the significant VISSIM parameters influencing the system's performance were identified. Results The analysis revealed that the parameters CC1 (headway time) and CC2 (longitudinal following threshold) from the car-following model exert more significant influence in the simulated work zone throughput than the Safety Distance Reduction Factor (SDRF) parameter from the lane-changing model. Discussion According to the simulation findings, implementing the EM control is preferable when drivers display aggressive behavior and maintain relatively short safety distances (i.e., low CC1 values). Conversely, opting for the LM control is more advisable in work zone areas where drivers demonstrate cautious driving tendencies and maintain longer safety distances (i.e., high CC1 values). Conclusion The efficacy of static EM and LM was analyzed in a 2-to-1 lane closure work zone on a freeway using the microscopic traffic simulator VISSIM. Simulation results were compared to identify the most relevant VISSIM parameters that influence work zone throughput. Our results indicate that the parameters CC1 and CC2 from the car-following model have a more substantial impact than the SDRF parameter from the lane-changing model. In particular, our comparison results suggest that the work zone throughput decreases in both the EM and LM scenarios as the values of CC1 and CC2 increase. Additionally, SDRF has a relatively negligible effect on the network performance of both merge strategies.

Assessing Traffic Performance: Comparative Study of Human and Automated HGVs in Urban Intersections and Highway Segments

This study conducts a comparative analysis of traf�ic dynamics at urban signalized intersections and on highways, incorporating both human-operated and automated heavy goods vehicles (HGVs) using the PTV VISSIM simulation model. It examines the impacts of automated driving technologies on critical traf�ic performance metrics such as queue length, travel time, vehicle delay, emissions, and fuel consumption. Initial �indings indicate that automation in HGVs enhances traf�ic �low, particularly by reducing queue lengths and vehicle delays. However, varying levels of automation from cautious to aggressive reveal complex trade-offs between operational ef�iciency and environmental impacts. On highways, automated HGVs demonstrate superior performance by reducing travel times and delays while increasing throughput compared to human-driven HGVs. These results underscore the operational bene�its of automated HGVs under diverse traf�ic conditionsand highlight their signi�icant implications for transportation planning and policy-making. This research contributes valuable insights into the integration of automated technologies in transportation systems, facilitating informed decision-making for stakeholders considering the adoption of these advancements in the current infrastructure.

Dynamic Traffic Light Algorithm Incorporating Post-Intersection Space Allocation

This research presents a novel dynamic traffic light algorithm designed to optimize traffic flow and reduce traffic congestion by dynamically allocating green time based on post-intersection space availability. The algorithm employs a three-stage process: input generation, processing, and output. The input stage involves capturing traffic images using cameras strategically placed at intersections, which are then processed using background subtraction, edge detection, and object counting techniques. The processing phase includes vehicle counting using the YOLOv8 algorithm and open space calculation based on the maximum capacity of each road section. The output phase involves dynamically allocating green time to roads based on available post-intersection space and occupancy rates. The algorithm is designed to adapt to changing traffic conditions by continuously monitoring the post- intersection space and adjusting green times accordingly. It also incorporates a reset timer to ensure the algorithm loops back to the initial stage of gathering and processing traffic images. Simulation experiments using a physical model with toy vehicles and a camera setup demonstrated the benefits of this approach. Compared to the density-based approaches[1], this algorithm reduced average vehicle delay by 20-30%, increased overall intersection throughput by 15-25%, and decreased maximum queue lengths in each lane by 25-35%. It also adapted more effectively to fluctuations in traffic conditions, improving performance metrics by 20-30%. These results highlight the potential of incorporating downstream space considerations into traffic light control algorithms to enhance intersection efficiency, reduce traffic congestion, and enable more adaptive and fair traffic management.

Coordinated Control Method for Unequal-Cycle Adjacent Intersections Using Vehicle–Road Collaboration

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.

Evaluating the Performance of Right Turn Lanes at Signalized Intersection Using Traffic Simulation Model

The issue of traffic congestion at signalized intersections is a concern in transportation systems due to the growth of urban areas and increased vehicular transportation. To study the evolution of congestion and evaluate the traffic performance operation of signalized intersections under problematic congested and improved conditions, the microscopic simulation VISSIM software is utilized. The objectives of this paper are to evaluate operational techniques, build a simulation model, and produce a well-calibrated and validated model. The methodology procedure to evaluate the signalized intersection involves the application of a traffic simulation model to observe real-time delays and stopped vehicles. Using the VISSIM software Version 9 to create an intersection model and redesign geometry with an exclusive right turn to enhance the intersection functionality and reduce delay. Our research focused on the Al-Nakhala signalized intersection located in the southern part of Palestine urban street in Baghdad city. This intersection is one of the busiest along the corridor due to significant land-use changes in the study area, including residential, educational, or commercial areas generating daily pressure from additional trips and saturating the absorptive capacity of the intersections. The proposed scenario of an exclusive right–turn could reduce the queue length and vehicle delay at the signalized intersection, resulting in a more efficient traffic operation. As a result of the reduction in vehicle delay, the Level of service (LOS) for the north, west, and east approaches improved from F to D. However, there was only a slight improvement for the south approach, with the LOS changing from E to F. Nonetheless, there was a noticeable reduction in queue length and vehicle delay ranging from 25% to 50%. Doi: 10.28991/CEJ-2024-010-07-010 Full Text: PDF