On the Challenges of Mobile Crowdsensing for Traffic Estimation

Abstract

Traffic congestion adversely impacts our lives. Traffic estimation resorting to mobile (crowdsensing) probes is a challenging task. We present key challenges for accurate and real-time traffic estimation resorting to crowdsensing data, namely data sparsity, user trip diversity, population bias, data quality, among others. We propose solutions to address some of these issues and demonstrate the relevance of others through an exploratory data analysis.