Adaptive Traffic Signal Control for Developing Countries Using Fused Parameters Derived from Crowd-Source Data

TL;DR

This paper presents an adaptive traffic signal control method for developing countries that uses crowd-sourced traffic data to dynamically manage congestion and reduce waiting times at intersections.

Contribution

It introduces a novel approach that fuses heterogeneous traffic parameters from crowd-source data for real-time traffic signal control in developing countries.

Findings

Effective congestion reduction at intersections in Delhi

Decreased vehicle waiting times in simulations

Sensitive response to real-time traffic variations

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 inexpensive alternative for traffic sensing and tracking technologies.