Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data

ABSTRACT

Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.

KEYWORDS:

• Crowdsourced data
• Google Map API
• traffic signal optimization
• real-time congestion management
• AIMD based signalling

Acknowledgments

The present work in the paper was not funded by any organization. One of the coauthor, Devanjan Bhattacharya has received funding from UKRI ESRC Impact Acceleration Grant (ES/T50189X/1), and European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie COFUND Grant Agreement No. 801215: TRAIN@Ed: ‘Transnational Research And Innovation Network At Edinburgh.’

Disclosure statement

No potential conflict of interest was reported by the author(s).