Understanding vehicular routing behavior with location-based service data

Abstract Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated antennas, are route choices, travel modes detection and close encounters. Nowadays, there is a standard large scale set collected over long periods that allows us to characterize these. In this work we thoroughly examine use applications, also referred location-based services (LBS) data, extract understand vehicular choice behavior. Taking Dallas-Fort Worth metroplex an example, first trips simple rules reconstruct origin-destination matrix coupling active LBS users United States census data. We then present method derive commonly used routes individuals traces varying sample rate intervals. further inspect relation between number trip characteristics, including departure time, length time. Specifically, consider time index buffer for taking different routes. Empirical results demonstrate during peak hours, travelers tend reduce impact traffic congestion alternative Overall, proposed analysis framework cost-effective treat sparse generated smartphones inform routing The potential in practice demand management strategies, targeting while generating estimates mitigation.