Abstract
Current transportation management systems rely on physical sensors that use traffic volume and queue-lengths. These physical sensors incur significant capital and maintenance costs. The ubiquity of mobile devices has made possible access to accurate and cheap traffic delay data. However, current traffic signal control algorithms do not accommodate the use of such data. In this paper, we propose a novel parsimonious model to utilize real-time crowdsourced delay data for traffic signal management. We demonstrate the versatility and effectiveness of the data and the proposed model on seven different intersections across three cities and two countries. This signal system provides an opportunity to leapfrog from physical sensors to low-cost, reliable crowdsourced data.
Highlights
Congestion has literally put our cities at the “crossroads.” Signals are a common strategy to manage traffic at junctions through prioritization of traffic movements while ensuring the efficient and safe flow of traffic
A more recent and novel algorithms such as max-pressure [5, 6] used for adaptive signal systems at a network level rely on queue length and volume counts that require physical sensors
We demonstrate the use of these data and the versatility of the model for real-time signal optimization at seven different intersections across three cities and two countries. This adaptive traffic signal system provides an opportunity for developing countries with widespread mobile devices to leapfrog from physical sensors to crowdsourced data for traffic management
Summary
Congestion has literally put our cities at the “crossroads.” Signals are a common strategy to manage traffic at junctions through prioritization of traffic movements while ensuring the efficient and safe flow of traffic. The high cost and limited access to delay data meant that most adaptive traffic signal systems relied on volume and queue length data. We demonstrate the use of these data and the versatility of the model for real-time signal optimization at seven different intersections across three cities and two countries This adaptive traffic signal system provides an opportunity for developing countries with widespread mobile devices to leapfrog from physical sensors to crowdsourced data for traffic management. It is possible to have a queue length of multiple vehicles with an average delay of τij(t) = 1 These situations are likely to represent unusual realizations of stochastic demand and turning proportions, especially when the signal control is responsive to delay. The traffic network is defined to be stable if the queue lengths remain bounded in expectation, i.e. there exists a κ < 1 such that for all T,
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