Daniela Socas Gil
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.
- Javed Aslam, Sejoon Lim, Xinghao Pan, and Daniela Rus. 2012. City-scale Traffic Estimation from a Roving Sensor Network. In Proc. of the 10th ACM Conference on Embedded Network Sensor Systems (SenSys '12). New York, NY, USA, 141--154. Google Scholar
Digital Library
- J. Rodrigues, A. Aguiar, and J. Barros. 2014. SenseMyCity: Crowdsourcing an Urban Sensor. ArXiv e-prints (Dec. 2014). arXiv:cs.CY/1412.2070Google Scholar
- J. Rodrigues, J. Perreira, and A. Aguiar. 2017. Impact of Crowdsourced Data Quality on Travel Patterns Estimation. In Proc. of the ACM Workshop On Mobile Crowdsensing Systems And Applications. New York, NY, USA. Google ScholarDigital Library
Index Terms
- On the Challenges of Mobile Crowdsensing for Traffic Estimation
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