Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream

Niroumand, Ramin; Tajalli, Mehrdad; Hajibabai, Leila; Hajbabaie, Ali

doi:10.1016/j.trc.2020.102659

Cited by 98 publications

(61 citation statements)

References 26 publications

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“…Connected and automated vehicles (CAVs) have great potential in improving traffic efficiency and reducing traffic congestion and have gained a wide application in the transportation field during the last decade [31]. ese applications mainly focus on CAV-based trajectories planning [22,23,25,26,[32][33][34] and CAV-based signal timing optimization [9,12,16,35] and even further to design traffic signals and CAVs trajectories simultaneously [8,27,29,34,36,37]. ese studies showed that CAVs applications in trajectories planning and signal timing optimization could further reduce gasoline consumption, pollutant emissions, delays, and stops caused by more stable speed change and fewer stops at the intersection [38].…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

Jiang

Zhao

et al. 2021

Journal of Advanced Transportation

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Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.

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Section: Literature Reviewmentioning

confidence: 99%

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

Jiang

Zhao

et al. 2021

Journal of Advanced Transportation

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“…Optimization‐based control formulates the driving task into a minimization (or maximization) objective function with multiple constraints, and the solution is the set of control inputs. In the literature, recent application contexts where this problem has been solved successfully include CAV trajectory planning (Yu et al., 2019), multi‐platoon cooperative control (Du, Chen, Li, Dong, et al., 2020; Li et al., 2019; Y. Li, Chen, Ha, et al., 2020; Schindler et al., 2019), CAVs path control for network‐level performance (S. Chen, Leng, et al., 2020), CAV's proactive decisions at intersections (Mirheli et al., 2019; Zhu & Ukkusuri, 2018), joint control of CAV and traffic signals (Du, Chen, Li, Ha, et al., 2020; Feng et al., 2018; Mirheli et al., 2018; Niroumand et al., 2020). Specifically, Erdman (2013) combined an adaptive control algorithm with green‐light‐optimal‐speed‐advisory to achieve near‐optimal control for a single junction using vehicle‐to‐infrastructure data.…”

Section: Introductionmentioning

confidence: 99%

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Chen

Dong

et al. 2021

Computer aided Civil Eng

138

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A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high‐level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks pose difficultly in achieving cooperative control. The problem is NP‐hard and cannot be efficiently resolved using rule‐based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning‐based algorithm that combines graphic convolution neural network with deep Q‐network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi‐lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane‐changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis‐à‐vis traditional rule‐based and long short‐term memory‐based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane‐change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.

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“…Vehicle trajectory data are often used for the analysis of traffic flow characteristics [21]. Conflicts can be measured with trajectory data through the surrogate safety assessment model (SSAM) [22][23][24][25][26]. Combined with microscopic traffic simulation, precise trajectory data can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Safety Impact of Connected and Autonomous Vehicles with Lane Management on Freeway Crash Hotspots Using the Surrogate Safety Assessment Model

Zhang

Hou

Zhang

et al. 2021

Journal of Advanced Transportation

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One goal for large-scale deployment of connected and autonomous vehicles is to achieve the traffic safety benefit since connected and autonomous vehicles (CAVs) could reduce the collision risk by enhancing the driver’s situation perception ability. Previous studies have analyzed the safety impact of CAVs involved in traffic, but only few studies examined the safety benefits brought by CAVs when approaching high-collision-risk road segments such as the freeway crash hotspots. This study chooses one freeway crash hotspot in Wuhan, China, as an instance and attempts to estimate the safety benefits for differential penetration rates (PRs) of CAVs using the surrogate safety assessment model (SSAM). First, the freeway crash hotspot is identified with kernel density estimation and simulated by VISSIM. Then, the intelligent driver model (IDM) and Wiedemann 99 (a car-following model) are adopted and calibrated to control the driving behaviors of CAVs and human-driven vehicles (HVs) in this study, respectively. The impact that rather CAVs are constrained with or without managed lanes on traffic safety is also discussed, and the PR of CAVs is set from 10% to 90%. The results of this study show that when the PR of CAVs is lower than 50%, there is no significant improvement on the safety measures such as conflicts, acceleration, and velocity difference, which are extracted from the vehicle trajectory data using SSAM. When the penetration rate is over 70%, the experiment results demonstrate that the traffic flow passing the freeway hotspot is with fewer conflicts, smaller acceleration, and smaller velocity difference in the scenario where CAVs are constrained with managed lane compared with the scenario without managed lane control. The safety benefit that CAVs bring needs to be discussed. The lane management of CAVs will also lead to distinct safety impact.

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Joint optimization of vehicle-group trajectory and signal timing: Introducing the white phase for mixed-autonomy traffic stream

Cited by 98 publications

References 26 publications

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Evaluating the Safety Impact of Connected and Autonomous Vehicles with Lane Management on Freeway Crash Hotspots Using the Surrogate Safety Assessment Model

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