Reference
M. Vallati, D. Magazzeni, B. De Schutter, L. Chrpa, and
T. L. McCluskey, "Efficient macroscopic urban
traffic models for reducing congestion: A PDDL+ planning approach,"
Proceedings of the Thirtieth AAAI Conference on Artificial
Intelligence (AAAI-16), Phoenix, Arizona, pp. 3188-3194, Feb. 2016.
Abstract
The global growth in urbanisation increases the demand for services including
road transport infrastructure, presenting challenges in terms of mobility. In
this scenario, optimising the exploitation of urban road networks is a pivotal
challenge. Existing urban traffic control approaches, based on complex
mathematical models, can effectively deal with planned-ahead events, but are
not able to cope with unexpected situations -such as roads blocked due to car
accidents or weather-related events- because of their huge computational
requirements. Therefore, such unexpected situations are mainly dealt with
manually, or by exploiting pre-computed policies. Our goal is to show the
feasibility of using mixed discrete-continuous planning to deal with unexpected
circumstances in urban traffic control. We present a PDDL+ formulation of urban
traffic control, where continuous processes are used to model flows of cars,
and show how planning can be used to efficiently reduce congestion of specified
roads by controlling traffic light green phases. We present simulation results
on two networks (one of them considers Manchester city centre) that demonstrate
the effectiveness of the approach, compared with fixed-time and reactive
techniques.
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BibTeX
@inproceedings{ValMag:15-036,
author = {Vallati, Mauto and Magazzeni, Daniele and De Schutter, Bart and
Chrpa, Luk{\'{a}}{\v{s}} and McCluskey, Thomas L.},
title = {Efficient Macroscopic Urban Traffic Models for Reducing
Congestion: {A} {PDDL+} Planning Approach},
booktitle = {Proceedings of the Thirtieth AAAI Conference on Artificial
Intelligence (AAAI-16)},
address = {Phoenix, Arizona},
pages = {3188--3194},
month = feb,
year = {2016}
}