A consensus-based distributed trajectory control in a signal-free intersection

Mirheli, Amir; Tajalli, Mehrdad; Hajibabai, Leila; Hajbabaie, Ali

doi:10.1016/j.trc.2019.01.004

Cited by 196 publications

(99 citation statements)

References 61 publications

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“…A constrained optimization model was proposed as an approximation approach to solve the problem efficiently. Similar work was conducted by Mirheli et al (13) and Hu et al ( 14) who considered signal control information as given. On the other hand, some researchers have studied how to optimize signal timing plan and CAV planning jointly, such as the work of Liang et al (15) which proposed a joint traffic signal optimization algorithm based on connected vehicle information to identify optimum signal timing and phasing plans while also providing speed guidance to individual vehicles to minimize the total number of stopping maneuvers.…”

mentioning

confidence: 64%

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

Cheng

Tang

et al. 2020

Transportation Research Record: Journal of the Transportation R

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A model-free approach is presented, based on the Monte Carlo tree search (MCTS) algorithm, for the control of mixed traffic flow of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV), named MCTS-MTF, on a one-lane roadway with signalized intersection control. Previous research has often simplified the problem with certain assumptions to reduce computational burden, such as dividing a vehicle trajectory into several segments with constant speed or linear acceleration/deceleration, which was rather unrealistic. This study departs from the existing research in that minimum constraints on CAV trajectory control were required, as long as the basic rules such as safety considerations and vehicular performance limits were followed. Modeling efforts were made to improve the algorithm solution quality and the run time efficiency over the naïve MCTS algorithm. This was achieved by an exploration-exploitation balance calibration module, and a tree expansion determination module to expand the tree more effectively along the desired direction. Results of a case study found that the proposed algorithm was able to achieve a travel time saving of 3.5% and a fuel consumption saving of 6.5%. It was also demonstrated to run at eight times the speed of a naïve MCTS model, suggesting a promising potential for real-time or near real-time applications.

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mentioning

confidence: 64%

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

Cheng

Tang

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…Besides, it will be interesting, in the future, to explore other applications, for example, using the proposed decomposition approach in problems that incorporate SODTA into facility location and vehicles routing problems as suggested by Hajibabai and Ouyang (). Moreover, it will be very interesting in the future to explore the effects of advanced intersection control methods in the presence of connected vehicles (Islam & Hajbabaie, ) and automated vehicles in a signal‐free network (Mirheli, Hajibabai, & Hajbabaie, ; Mirheli, Tajalli, Hajibabai, & Hajbabaie, ) on system optimal traffic flows.…”

Section: Resultsmentioning

confidence: 99%

A decomposition scheme for parallelization of system optimal dynamic traffic assignment on urban networks with multiple origins and destinations

Mehrabipour

Hajibabai

Hajbabaie

2019

Computer aided Civil Eng

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This paper presents a decomposition scheme to find near‐optimal solutions to a cell transmission model‐based system optimal dynamic traffic assignment problem with multiple origin‐destination pairs. A linear and convex formulation is used to define the problem characteristics. The decomposition is designed based on the Dantzig–Wolfe technique that splits the set of decision variables into subsets through the construction of a master problem and subproblems. Each subproblem includes only a single origin‐destination pair with significantly less computational burden compared to the original problem. The master problem represents the coordination between subproblems through the design of interactive flows between the pairs. The proposed methodology is implemented in two case study networks of 20 and 40 intersections with up to 25 origin‐destination pairs. The numerical results show that the decomposition scheme converges to the optimal solution, within 2.0% gap, in substantially less time compared to a benchmark solution, which confirms the computational efficiency of the proposed algorithm. Various network performance measures have been assessed based on different traffic state scenarios to draw managerial insights.

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“…Based on V2X technology, trajectory information of connected vehicles is not difficult to obtain. In [43,44], the trajectory reconstruction model is established based on location and time information of connected vehicles, and queue length evaluation and trajectory optimization are completed. However, when the penetration rate is low, the sensing results produce large errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Gao

Han

Dong

et al. 2019

Sensors

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With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.

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A consensus-based distributed trajectory control in a signal-free intersection

Cited by 196 publications

References 61 publications

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

A decomposition scheme for parallelization of system optimal dynamic traffic assignment on urban networks with multiple origins and destinations

Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

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