Investigating and modelling the relationship between traffic volume and extracts from cellphone activity data

The high penetration rate of mobile phones among drivers and passengers indicates an opportunity for obtaining detailed information on vehicle spatial movement and estimating traffic state at a lower cost than traditional traffic monitoring techniques. In this study, we examine and assess the use of cellphones as probes to measure vehicle speeds. Network-based wireless location technology is adopted and calibrated, and the cellphone signalling data is used for traffic state estimation. Then, we introduce the relationship between the cellular network and road network, and explain how it can be used to estimate link-based average travel speed. Cellular probe-based measurements are compared with those obtained by microwave detectors for each five-minute time interval on busy road links along a freeway in Zhejiang, China, on weekdays, weekends, and holidays respectively. The analysis results show that the proposed cellphone-based system can effectively estimate travel speed on freeways with high cellphone penetration rates.

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Investigating and modelling the relationship between traffic volume and extracts from cellphone activity data

Cited by 4 publications

References 23 publications

Deep LSTM Recurrent Neural Networks for Arterial Traffic Volume Data Imputation

Deep LSTM Recurrent Neural Networks for Arterial Traffic Volume Data Imputation

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Evaluation of freeway travel speed estimation using anonymous cellphones as probes: a field study in China

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