Microscopic Traffic Modeling Inside Intersections: Interactions Between Drivers

Zhao, Jing; Knoop, Victor L.; Wang, Meng

doi:10.1287/trsc.2022.1163

Cited by 55 publications

(28 citation statements)

References 77 publications

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“…Modeling trafc fow at intersections is essential for the design, control, and management of intersections. But modelling vehicle trajectories within intersections is challenging because there are an infnite number of possible paths in two-dimensional space, and drivers can simultaneously adjust their speeds [21,22]. Terefore, this paper assumes that the 2D movement of the intersection is not considered.…”

Section: Simulation and Results Analysismentioning

confidence: 99%

Deep Q‐Learning Network Model for Optimizing Transit Bus Priority at Multiphase Traffic Signal Controlled Intersection

Zhong

Liu²,

2023

Mathematical Problems in Engineering

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When multiple bus vehicles send priority requests at a single intersection, the existing fixed-phase sequence control methods cannot provide priority traffic request services for multiphase bus vehicles. In view of the conflict of multiphase bus priority requests at intersections, the priority vehicle traffic sequence is determined, which is the focus of this study. In this paper, a connected vehicle-enabled transit signal priority system (CV-TSPS) has been proposed, which uses vehicle-infrastructure communication function (V2I) technology to obtain real-time vehicle movement, road traffic states, and traffic signal light phase information. By developing a deep Q-learning neural network (DQNN), especially for optimizing traffic signal control strategy, the public transit vehicles will be prioritized to improve their travel efficiency, while the overall delay of road traffic flow will be balanced to ensure the safe and orderly passage of intersections. In order to verify the validity of the model, the SUMO traffic analysis software has been applied to simulate real-time traffic control, and the experimental results show that compared with the traditional timing signal control, the loss time of vehicles is reduced by nearly 40%, and the cumulative loss time per capita is reduced by nearly 43.5%, and a good control effect is achieved. In the case of medium and low densities, it is better than the solid scheduled traffic control scheme.

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Section: Simulation and Results Analysismentioning

confidence: 99%

Deep Q‐Learning Network Model for Optimizing Transit Bus Priority at Multiphase Traffic Signal Controlled Intersection

Zhong

Liu²,

2023

Mathematical Problems in Engineering

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“…The movement of vehicles at intersections is twodimensional. Using the car-following only means that it is assumed that all vehicles run along the traffic lanes [24]. In the model, the vehicle can reverse.…”

Section: Fig 2 Navigation Algorithm Flowmentioning

confidence: 99%

Unmanned Driving Technology in Coal Mine Based on Semantic Information Method

Zhang

Lin

et al. 2023

IEEE Access

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Aiming at the transportation scheduling problem of unmanned trucks in open-pit mines, an optimization model for transportation scheduling of unmanned trucks in open-pit mines is established, taking the minimum sum of fuel cost, fixed start-up cost, fault maintenance cost and network base station construction and maintenance cost as the objective function, and taking mining amount, crushing amount in crushing field, truck number and truck transportation work as the bundle reduction conditions. By adjusting the position of the encapsulated individual and the optimal encapsulated individual through the parameter position adaptive updating mechanism, the search range of the solution space can be enlarged, so that the algorithm can jump out of the local optimum. This paper analyzes the influence of vehicle clearance on coordinated traffic at intersections under clearance theory, and puts forward the minimum vehicle clearance to meet coordinated traffic. Generate trajectories with behavioral semantics through hierarchical online selection of primitives; Based on the model predictive control algorithm, the motion control module fully considers the execution ability, tracking deviation and control stability of the platform drive motor, and updates the weight coefficient of the control objective function in real time with the help of the semantic attributes of the behavioral motion primitives given by the motion planning, so as to reduce the lateral error, heading error and speed error of the trajectory tracking. Finally, a real vehicle is verified by a medium-sized hybrid unmanned crawler platform, which proves that the proposed intersection coordinated traffic model is feasible in theory, reflecting that the traffic density is a factor that affects the transportation efficiency. Under the condition of meeting the production requirements, reduce the intersection conflict and improve the traffic capacity of the intersection by adopting large-load unmanned mine trucks as much as possible, reducing the number of unmanned mine trucks and the interference between equipment.

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“…Redon et al [12] proposed the detection and recognition framework of YOLOv2, optimized the detection accuracy, detection speed, and detection category of the network model, and improved the running speed of the feature extraction network and the convergence speed of the model. Zhao et al [13] proposed an integrated microscopic trafc fow model to describe human-driven vehicle maneuvers under interactions and verifed that the model can accurately describe vehicle passing orders of interacting maneuvers, paths, and speeds against empirical data. Zhao et al [14] proposed a novel method to model the trajectories of vehicles in two-dimensional space and speed, and the descriptive power, plausibility, and accuracy of the proposed model are investigated by comparing the calculated results under several cases, which can be solved from symmetry or analytically.…”

Section: Introductionmentioning

confidence: 99%

Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision

Wang

Li-ming

Zhu

et al. 2023

Journal of Advanced Transportation

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This article aims to explore an effective method for reducing vehicle collisions at unsignalized intersections. First, a monocular-binocular vision switching system is built to enable machine vision-based detection of obstacle vehicles in the left and right front directions. Then, the motion state and trajectory of each obstacle vehicle are predicted, and the intersection points of the trajectories of the obstacle vehicle and the ego vehicle are calculated. On this basis, a cross-conflict judgment model based on trajectories and collision times and a safety assessment model based on safety distance are established. Finally, the conflict judgment and safety assessment for the obstacle vehicles are simulated. The results of the simulation demonstrate that the monocular-binocular vision switching system proposed in this article can achieve a detection accuracy of 95%, a ranging accuracy of 96%, and a cross-conflict detection accuracy of 97%, while ensuring a maximum detection area, which can meet the requirements of traffic safety assurance at unsignalized intersections.

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Microscopic Traffic Modeling Inside Intersections: Interactions Between Drivers

Cited by 55 publications

References 77 publications

Deep Q‐Learning Network Model for Optimizing Transit Bus Priority at Multiphase Traffic Signal Controlled Intersection

Deep Q‐Learning Network Model for Optimizing Transit Bus Priority at Multiphase Traffic Signal Controlled Intersection

Unmanned Driving Technology in Coal Mine Based on Semantic Information Method

Conflict Judgment and Safety Assessment at Unsignalized Intersections Based on Machine Vision

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