Projects per year
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.
|Number of pages||12|
|Early online date||13 Mar 2022|
|Publication status||E-pub ahead of print - 13 Mar 2022|
- crowdsourced data
- Google Map API
- traffic signal optimization
- real-time congestion management
- AIMD based signal
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- 1 Finished
MobilitySafe: Increasing safety and sustainability of micro-mobility modes in pandemic, UKRI ESRC ES/T50189X/1
1/12/20 → 31/08/21
Project: University Awarded Project Funding