Vehicle Delay Research Articles

Comparative Evaluation of Vehicle Delays at Midblock Rectangular Rapid Flashing Beacon and Pedestrian Hybrid Beacon Pedestrians’ Crossings

Comparative Evaluation of Vehicle Delays at Midblock Rectangular Rapid Flashing Beacon and Pedestrian Hybrid Beacon Pedestrians’ Crossings

Time–Space Analysis of Transport Infrastructures: A Pilot Study of Shibuya-Type Crossings for Signalized Intersections in Developing Cities

Cities are growing larger, and congestion is becoming a major issue. Walking is increasingly becoming an important mode of transport in developing cities. One of the reasons for the high death toll is the lack of pedestrian facilities at transport infrastructures such as signalized intersections, where a conflict arises between vehicles turning right and pedestrians crossing at different speeds. This conflict, known as the vehicle–pedestrian conflict (in right-driving jurisdictions), occurs when the green light for vehicles is shared with the green pedestrian light. Additionally, if the intersection is congested due to high pedestrian flow, vehicles will turn right only during the yellow light, trying to find a gap in the pedestrian flow. As a result, delays increase for both pedestrians and vehicles, reducing the intersection’s capacity. To reduce the vehicle–pedestrian conflict, various pedestrian facilities can be implemented, such as Shibuya-type crossings, which include an exclusive pedestrian phase and diagonal crossing. When applying this type of solution, vehicle delays are reduced up to 81% on average, increasing the efficiency and safety of the space used at the intersection. However, pedestrian delays might increase up to 5 times, due to the need to wait for the third exclusive phase for them to cross the intersection in all directions. The method is applied in a case of study in Valparaiso, Chile, and therefore can be expanded in further research to other developing cities in Chile and specifically Latin America.

Satisfaction-Based Optimal Lane Change Modelling of Mixed Traffic Flow and Intersection Vehicle Guidance Control Method in an Intelligent and Connected Environment

The information interaction characteristics of connected vehicles are distinct from those of non-connected vehicles, thereby exerting an influence on the conventional traffic flow model. The original lane-changing model for non-connected vehicles is no longer applicable in the context of the new traffic flow environment. The modelling of the new hybrid traffic flow, comprising both connected and ordinary vehicles, is set to be a pivotal research topic in the coming years. The objective of this paper is to present a methodology for optimal mixed traffic flow dynamic modelling and cooperative control in intelligent and connected environments (ICE). The study utilizes the real-time perception and information interaction of connected vehicles for traffic information, taking into account the access characteristics of both connected and non-connected vehicles. The satisfaction-based free lane-changing and mandatory lane-changing models of connected vehicles are designed. Secondly, a mixed traffic flow lane-changing model based on influence characteristics is constructed for the influence area of connected vehicles. This model takes into account the degree of influence that connected vehicles have on non-connected vehicles, with different distances being considered respectively. Subsequently, a vehicle guidance strategy for mixed traffic flows comprising grid-connected and conventional vehicles is proposed. A variety of speed guidance scenarios are considered, with an in-depth analysis of the speed optimization of connected vehicles and the movement law of non-connected vehicles. This comprehensive analysis forms the foundation for the development of a vehicle guidance strategy for mixed traffic flows, with the overarching objective being to minimize the average delay of vehicles. In order to evaluate the effectiveness of the proposed method, the intersection of Gaota Road and Fangshui North Street in Yanqing District, Beijing, has been selected for analysis. The results of the study demonstrate that by modifying the density of the mixed traffic flow, the overall average speed of the mixed traffic flow declines as the density of vehicles increases. The findings reported in this study reflect the role of connected vehicles in enhancing road capacity, maximizing intersection capacity and mitigating the occurrence of queuing phenomena, and improving travel speed through the mixed traffic flow lane-changing model based on impact characteristics. This study also provides some guidance for future control of the mixed traffic flow formed by emergency vehicles and social vehicles and for realizing a smart city.

Traffic and Vehicle Management in Roundabouts Through Systems Based on Dedicated Short-Range Communications and Visible Light Communications

This study introduces an innovative system integrating visible light communication (VLC) and Dedicated Short-Range Communication (DSRC) technologies to enhance traffic flow and safety in urban roundabouts. The approach optimizes vehicle coordination and minimizes collision risks through advanced traffic management algorithms and infrastructure-to-vehicle communication. Simulations conducted on nine roundabouts in Suceava municipality demonstrate that the hybrid VLC–DSRC system improves traffic efficiency by over 80% and significantly reduces vehicle delays and congestion. Results emphasize the potential of this integrated solution in modernizing urban traffic systems while ensuring a high level of road safety.

Collaborative optimization of signals and ecological driving speed guidance for buses without dedicated bus lanes in the connected environment

The transit signal priority, as an effective method to address public transport operation issues, has been widely applied. With the continuous advancement of connected technology, research on developing transit signal priority strategies using vehicle-to-everything technology is gaining increasing attention. However, current traffic signal priority studies primarily focus on optimizing bus speeds on dedicated bus lanes, neglecting the adverse impacts of private vehicle queuing on priority strategies, as well as the carbon emissions resulting from speed fluctuations. To more effectively evaluate the optimization effect of bus priority in the absence of dedicated bus lanes, this paper proposes a cooperative control method combining signal control and eco-driving speed guidance in a connected environment. The objective is to maximize the reduction in delays for both buses and private vehicles at intersections and optimize carbon emission reduction. Initially, an Extended Kalman Filter is employed to predict the arrival time of buses at intersections and the signal status. Building upon this, optimal timings for phase adjustments and the optimization bus trajectories are calculated using signal control and eco-driving speed guidance models. Then a Genetic Algorithm is used to solve the model. Finally, the effectiveness of the proposed model is verified using Zhengzhou city as a case study and compared against scenarios involving NTSP, TSP, and speed guidance. The results demonstrate that, in the absence of dedicated bus lanes, the proposed method not only ensures the stability of bus operations but also achieves significant bus priority and carbon emission benefits while mitigating the adverse impact of bus priority on private vehicles to a certain extent.

Intelligent Traffic Lights Control System: Riyadh City

Traffic congestion is one of the main problems facing Riyadh city's growth, which results in significant environmental pollution and makes transportation difficult. These factors not only generate material losses but also make daily living difficult for people. The principal reasons of traffic congestion in Riyadh include, but are not limited to, the rapid growth of the population. By 2030, the Kingdom of Saudi Arabia aims to have a population of 50-60 million, which will make traffic congestion worse if preventive measures are not taken. The development of an intelligent traffic lights control system is one strategy to address traffic issues. The purpose of traffic lights is to efficiently control vehicle traffic at intersections. These traffic signals typically operate in a preset order based on a fixed cycle period. Due to this design, vehicles on one side of the road must wait even while there are no automobiles on the other, making them intrinsically unable to effectively regulate traffic flow during atypical conditions. As a result, it becomes necessary to simulate traffic control algorithms in order to reduce the amount of stops, vehicle delays, etc. There is a chance to create a system that will meet these needs with fuzzy logic. We use fuzzy logic toolbox in MATLAB to design the intelligent traffic lights control system for an isolated traffic junction.

Median U-Turn Intersection Critical Parameter Research and Operational Performance Evaluation

As one of the unconventional intersection designs, the median U-turn (MUT) intersection design has been confirmed to have the potential to improve intersection efficiency and produce lower vehicle delays, and it has been widely applied to many urban networks. Most existing studies have successfully evaluated the operational performance of MUT intersections by analyzing the overall traffic flow, particularly for intersection delay and capacity. However, the impact of the critical parameters and behavior of individual vehicles in MUT intersection operations has not been adequately studied. These limitations cannot provide a complete and objective evaluation of MUT intersections. Therefore, a comprehensive cellular automata model is proposed to simulate vehicle dynamic variations, considering road channelization information and vehicle random movements. This model will be compared to the VISSIM model to validate rationality by using field data, and it shows a good agreement between the simulation results of both models. In addition, sensitive experiments reveal that properly adjusting the elements, including the separation distance, the proportion of left-turn vehicles, and green phase percentage, can improve the operation of MUT intersections to varying degrees. By appropriately improving intersection features and conducting reasonable evaluations, the overall performance and sustainability of the MUT intersections in Xi’an city can be enhanced. Finally, this paper offers a valuable guideline for the evaluation and modeling strategies of MUT intersections.

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.