Mobile Phone based Participatory Sensing for Urban Traffics

Current mobile phones are becoming important platforms that serve the ubiquitous sensing and communication needs of people [29, 55]. The sensing and communication modules on mobile phones are usually developed to provide location and context-aware services. Real-time urban traffic conditions are critical to wide populations and serve the needs of many transportation dependent applications. This report presents our experience of building participatory urban traffic informatics systems that exploits the power of bus riders’ mobile phones. In this report, we first present a bus arrival time prediction system based on bus passengers’ participatory sensing. With commodity mobile phones, the bus passengers’ surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. Buses are intelligently recognized and tracked using environmental signal hints. Following the bus prediction system, we present our experience of building a participatory urban traffic monitoring system that exploits the power of bus riders’ mobile phones. The system takes lightweight sensor hints and collects minimum set of cellular data from the bus riders’ mobile phones. It then turns buses into dummy probes, monitors their travel statuses, and derives the instant traffic map of the city. Unlike previous works that rely on intrusive detection or full cooperation from “probe vehicles”, our approach resorts to the crowd-participation of ordinary bus riders, who are the information source providers and major consumers of the final traffic output. The proposed systems solely rely on the collaborative effort of the participating users and is independent from the bus operating companies, so they can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally