Trajectory planning for connected and automated vehicles at isolated signalized intersections under mixed traffic environment
نویسندگان
چکیده
• Optimize both longitudinal and lateral trajectories of CAVs with mixed traffic. Feasible Lane-Changing Gaps are used to reduce solution space heavy Parallel Monte-Carlo Tree Search algorithm is designed for computational efficiency. Rolling horizon implementation dynamic application varying Driving performance traffic improved. Trajectory planning connected automated vehicles (CAVs) has the potential improve operational efficiency vehicle fuel economy in systems. Despite abundant studies this research area, most them only consider trajectory dimension or assume fully CAV environment. This study proposes an approach decentralized at isolated signalized intersection under environment, which consists human-driven (CHVs) CAVs. A bi-level optimization model formulated based on discrete time optimize a single given signal timings information surrounding vehicles. The upper-level optimizes lane-changing strategies. lower-level acceleration profiles strategies from model. Minimization delay, consumption, costs considered objective functions. Strategy (LCST) (PMCTS) solve planned one by according their distance stop bar. rolling scheme applied proposed time-varying conditions. Numerical validate advantages compared benchmark cases without planning. average consumption numbers can be reduced noticeably, especially high demand. delay ~2 s average, limited due fixed timing plans. also reduces CHVs traffic, penetration rates sensitivity analysis shows that control zone length 200 m sufficient ensure satisfactory
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Article history: Received 18 August 2016 Received in revised form 23 February 2017 Accepted 2 April 2017
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ژورنال
عنوان ژورنال: Transportation Research Part C-emerging Technologies
سال: 2021
ISSN: ['1879-2359', '0968-090X']
DOI: https://doi.org/10.1016/j.trc.2021.103309