Congestion Avoidance Routing Based on Large-Scale Social Signals

The emergence of large-scale social signal data has provided unprecedented opportunities to develop techniques for improving transportation systems. In this paper, we use two types of social signal data, namely, mobile phone data and subway card data, to investigate congestion avoidance routing methodologies in the Beijing subway and San Francisco road networks. The social signal data were used to estimate detailed travel demand information and to target sources of congestion, in order to develop intelligent routing models. We study two fundamental routing scenarios, namely, the shortest path (SP) scenario and the minimum cost (MC) scenario, and propose a hybrid routing model that combines SP routing and MC routing. The hybrid model requires only a small fraction of travelers to take MC routes, but achieves nearly the same effect as MC routing. To apply the proposed routing methodologies in practical situations, we develop an information-releasing framework to suggest routes for a small group of travelers whose route adjustments can significantly improve the efficiency of the transportation networks.