Abstract
Information about truck stops in highways is essential for trip planning, monitoring and other applications. GPS data of truck movement can be very useful to extract information that helps us understand our highway network better. In this paper, we present a method to identify truck stops on highways from GPS data, and subsequently characterize the truck stops into clusters that reflects their functionality. In the procedure, we extract the truck stoppage locations from the GPS data and cluster the stoppage points of multiple trips to obtain truck stops. We construct arrival time distribution and duration distribution to identify the functional nature of the stops. Subsequently, we cluster the truck stops using the above two distributions as attributes. The resultant clusters are found to be representative of different types of truck stops. The characterized truck stoppages can be useful for dynamic trip planning, behavior modeling of drivers and traffic incident detection.
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Acknowledgement
The study is supported under the project ‘Real Time Traffic Prediction and Traffic Management (RTT)’ by the Ministry of Human Resource Development, Government of India, and IIT Kharagpur. The data was provided by eTrans Solutions Pvt Ltd.
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Aziz, R. et al. (2016). Identifying and Characterizing Truck Stops from GPS Data. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_13
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DOI: https://doi.org/10.1007/978-3-319-41561-1_13
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