A Social Media Based Approach for Route Planning During Urban Events

Abstract

Traffic congestion is a major issue in most big cities, resulting in longer travel time and increased greenhouse gas emission. Various factors can cause traffic congestion, and includes not only traffic events on roads (e.g., car accidents) but also urban events (e.g., football games, concerts, and festivals), where a large number of human activities happen in a certain place and at a certain time. The technology of connected vehicles (CV) has provided a crowd-souring platform enabling communication between vehicles and surrounding information share to be more timely and effective. Taking the advantage of that, in this paper we focus on navigation during urban events, and present an approach to find feasible routes avoiding traffic congestion caused by the different types of events. Using 12-month geo-tagged tweets, we create a human activity network to capture certain types of human activities across cities. Based on that, an event estimation algorithm is developed to find the possible events that would occur in the near future, and to estimate their probabilities. These detected events are represented in the form of obstacle polygons with timestamps, and are used by the routing algorithm to generate congestion avoidance routes. We apply our approach to the road network of Toronto, Ontario, Canada, and the experimental results show the capability of our approach in supporting routing during urban events.