Exploiting Near Time Forecasting From Social Network To Decongest Traffic

TL;DR

This paper introduces a framework that leverages social network data to forecast near-term traffic flows, aiming to reduce congestion through simulation and evaluation of management strategies.

Contribution

It presents a modular framework integrating social network insights into traffic simulation and proposes metrics for congestion management evaluation.

Findings

Demonstrated the framework's ability to simulate city traffic with social data

Evaluated multiple congestion avoidance strategies

Showed potential for real-time traffic management improvements

Abstract

Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. Our framework defines metrics to evaluate congestion avoidance strategies. To show utility of the framework, we present experimental studies of few strategies on a public transport system.