Hidden Markov map matching through noise and sparseness
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Modeling urban traffic dynamics in coexistence with urban data streams
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
coRide: carpool service with a win-win fare model for large-scale taxicab networks
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
WRENMining: Large-Scale Data Collection for Human Contact Network Research
Proceedings of First International Workshop on Sensing and Big Data Mining
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Traffic congestion, volumes, origins, destinations, routes, and other road-network performance metrics are typically collected through survey data or via static sensors such as traffic cameras and loop detectors. This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above. In this paper we conduct a case study that demonstrates that it is possible to accurately infer traffic volume through data collected from a roving sensor network of taxi probes that log their locations and speeds at regular intervals. Our model and inference procedures can be used to analyze traffic patterns and conditions from historical data, as well as to infer current patterns and conditions from data collected in real-time. As such, our techniques provide a powerful new sensor network approach for traffic visualization, analysis, and urban planning.