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|Title:||Intersection management strategies to realise the benefits of new generation vehicles||Authors:||Wu, Yuanyuan||Keywords:||Engineering::Civil engineering::Transportation||Issue Date:||2021||Publisher:||Nanyang Technological University||Source:||Wu, Y. (2021). Intersection management strategies to realise the benefits of new generation vehicles. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152230||Abstract:||Automation and wireless communication technologies are bringing us the new-generation vehicles: Connected and Automated Vehicles (CAVs). This thesis aims to investigate the impacts and leverage of the advanced features of CAVs on traffic flow at road junctions, and propose corresponding intersection management to realise the promising benefits of CAVs. Conventional intersection managements in terms of controlling traffic, such as signal control, may not necessarily be the optimal strategies when it comes to CAVs environment. The first part of the thesis is devoted to the intersection management optimisation for CAVs. This task is based on the framework of Autonomous Intersection Management (AIM), which is tailored for CAVs aiming at replacing the conventional traffic control strategies. In Chapter 2, using the communication and computation technologies of CAVs, the sequential movements of vehicles through intersections are modelled as multi-agent Markov decision processes, in which vehicle agents cooperate to minimise intersection delay with collision-free constraints. A decentralised coordination multi-agent learning approach is proposed to find the optimal decisions. The effectiveness of the proposed method is demonstrated under a variety of traffic conditions, and compared to three representative benchmarks. The single-objective problem is extended to multiple conflicting-objective problem in Chapter 3, in which traffic efficiency and fairness are comprehensively considered to customise the optimal control strategy of AIM. Deep multi-objective reinforcement learning algorithm is adopted to solve the problem. The outstanding performance of the proposed method in balancing traffic fairness and efficiency is achieved as expected. The second part of the thesis discusses the organisation of CAV trajectories at road junctions. Using communication technologies, CAVs are able to mutually negotiate the right of way at road junctions, engendering the concept of signal-free management for CAVs. Roundabout, as a naturally signal-free strategy, is not receiving enough attention from researchers. The significant difference lies in the organisation of vehicle trajectories. A key motivation of this part is to theoretically compare the performance of signal-free intersection management and roundabout under CAVs environment, and to demonstrate whether roundabout has comparable performance to signal-free intersection. The theoretical formulas of the capacity and average delay of each strategy are derived by applying M/G/1 queueing model. The safety time gaps for two vehicles consecutively and safely passing conflict points are the main variables, which are assumed to be generally distributed in the theoretical derivation, rather than following a specific distribution. To the best of our knowledge, it is the first time the performances of intersection and roundabout in a CAVs environment are directly and theoretically compared. Under the same circumstances, roundabout using first-come-first-served policy has the largest capacity and the least average delay among the three analysed signal-free strategies, revealing the optimal management of CAVs based on circulating trajectory organisation is worthy of in-depth study. The full penetration of CAV technology requires further long-term development and evolution, which makes the mixed traffic composed of both CAVs and Human-driven vehicles (HVs) inevitable. In the third part, to understand how CAV platooning affects the mixed-traffic intersection capacity, an analytical formulation for mixed-traffic intersection capacity based on the Markov chain theory and conflict point technique is proposed, with the spatial distribution of heterogeneous vehicle headways among mixed traffic as the core of the modelling. In the model, not only the CAV penetration rate and the three core platooning parameters (intra-platoon gap, inter-platoon gap and platoon size) are captured, but also the individual willingness of CAVs to form platoons is involved to consider the behavioural aspect of the problem. The influences of platooning parameters and platooning willingness are in line with expectations, while the effect of CAVs penetration rate is not intuitive. It is not certain that higher penetration rate will lead to higher intersection capacity, but further depends on the other analysed parameters.||URI:||https://hdl.handle.net/10356/152230||DOI:||10.32657/10356/152230||Rights:||This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||CEE Theses|
Updated on May 20, 2022
Updated on May 20, 2022
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