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Intersection traffic signal optimisation considering the impact of upstream curbside bus stops
Author(s) -
Li Rui,
Zheng Changjiang,
Wang Hua,
Zhao De,
Ran Bin,
Xue Xin
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0660
Subject(s) - intersection (aeronautics) , upstream (networking) , transit (satellite) , transport engineering , real time computing , traffic flow (computer networking) , signal (programming language) , computer science , process (computing) , engineering , simulation , computer network , public transport , programming language , operating system
Transit‐oriented travel mode is considered as one of the most effective travel strategies for improving the level of travel service and reducing travel time and delay. Curbside bus stops are often built to support the operation of the transit system, which leads to new bottlenecks of road segments near such stops and exerts a negative impact on traffic flows of neighbouring intersections. The authors propose a traffic signal optimisation model with the objective to minimise total travel delay by considering the interaction of traffic flows between the intersections and upstream curbside bus stops. First, transits’ following and lane‐changing behaviours are explored by analysing their trajectories collected by unmanned vehicle aerial video detectors. Second, transits’ occupancy time at curbside bus stops is statistically analysed by using automatic vehicle location data. They then use an equivalent‐flow analysis method to quantify the impact caused by transits’ actions of lane‐changing and moving into/out stop basins (including the waiting process). Such impact is integrated into the algorithm of intersection traffic signal optimisation. They finally validate the proposed signal optimisation model based on real‐time traffic data collected from Nanjing, China. It is revealed that the improved model can reduce vehicle travel delay by 4.3% at the tested intersection.

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