This page has only limited features, please log in for full access.

Unclaimed
Yuanyuan Wu
School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 23 August 2021 in Sustainability
Reads 0
Downloads 0

The concept of signal-free management at road junctions is tailored for Connected and Automated Vehicles (CAVs), in which the conventional signal control is replaced by various right-of-way assignment policies. First-Come-First-Served (FCFS) is the most commonly used policy. In most proposed strategies, although the traffic signals are replaced, the organization of vehicle trajectory remains the same as that of traffic lights. As a naturally signal-free strategy, roundabout has not received enough attention. A key motivation of this study is to theoretically compare the performance of signalized intersection (I-Signal), intersection using FCFS policy (I-FCFS), roundabout using the typical major-minor priority pattern (R-MM), and roundabout adopting FCFS policy (R-FCFS) under pure CAVs environment. Queueing theory is applied to derive the theoretical formulas of the capacity and average delay of each strategy. M/G/1 model is used to model the three signal-free strategies, while M/M/1/setup model is used to capture the red-and-green light switch nature of signal control. The critical safety time gaps are the main variables and are assumed to be generally distributed in the theoretical derivation. Analytically, I-Signal has the largest capacity benefiting from the ability to separate conflict points in groups, but in some cases it will have higher delay. Among the other three signal-free strategies, R-FCFS has the highest capacity and the least average control delay, indicating that the optimization of signal-free management of CAVs based on roundabout setting is worthy of further study.

ACS Style

Yuanyuan Wu; Feng Zhu. Junction Management for Connected and Automated Vehicles: Intersection or Roundabout? Sustainability 2021, 13, 9482 .

AMA Style

Yuanyuan Wu, Feng Zhu. Junction Management for Connected and Automated Vehicles: Intersection or Roundabout? Sustainability. 2021; 13 (16):9482.

Chicago/Turabian Style

Yuanyuan Wu; Feng Zhu. 2021. "Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?" Sustainability 13, no. 16: 9482.

Journal article
Published: 22 April 2019 in Transportation Research Part C: Emerging Technologies
Reads 0
Downloads 0

Conventional intersection managements, such as signalized intersections, may not necessarily be the optimal strategies when it comes to connected and automated vehicles (CAVs) environment. Autonomous intersection management (AIM) is tailored for CAVs aiming at replacing the conventional traffic control strategies. In this work, using the communication and computation technologies of CAVs, the sequential movements of vehicles through intersections are modelled as multi-agent Markov decision processes (MAMDPs) in which vehicle agents cooperate to minimize intersection delay with collision-free constraints. To handle the huge dimension scale incurred by the nature of multi-agent decision making problems, the state space of CAVs are decomposed into independent part and coordinated part by exploiting the structural properties of the AIM problem, and a decentralized coordination multi-agent learning approach (DCL-AIM) is proposed to solve the problem efficiently by exploiting both global and localized agent coordination needs in AIM. The main feature of the proposed approach is to explicitly identify and dynamically adapt agent coordination needs during the learning process so that the curse of dimensionality and environment nonstationarity problems in multi-agent learning can be alleviated. The effectiveness of the proposed method is demonstrated under a variety of traffic conditions. The comparison analysis is performed between DCL-AIM and the First-Come-First-Serve based AIM (FCFS-AIM), with Longest-Queue-First (LQF-AIM) policy and the signal control based on the Webster’s method (Signal) as benchmarks. Experimental results show that the sequential decisions from DCL-AIM outperform the other control policies.

ACS Style

Yuanyuan Wu; Haipeng Chen; Feng Zhu. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles. Transportation Research Part C: Emerging Technologies 2019, 103, 246 -260.

AMA Style

Yuanyuan Wu, Haipeng Chen, Feng Zhu. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles. Transportation Research Part C: Emerging Technologies. 2019; 103 ():246-260.

Chicago/Turabian Style

Yuanyuan Wu; Haipeng Chen; Feng Zhu. 2019. "DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles." Transportation Research Part C: Emerging Technologies 103, no. : 246-260.