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Title: Incorporating Queue Dynamics into Schedule-Driven Traffic Control
Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10 − 15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios.  more » « less
Award ID(s):
2038612
NSF-PAR ID:
10292800
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
International Joint Conference on Artificial Intelligence
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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