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Anomaly Detection and Performance Visualization of Truck Parking Information and Management Systems

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Abstract

The shortage of truck parking is one of the main challenges facing drivers, fleet managers, and owner operators in the US. To help drivers make safer and more efficient parking decisions and better utilize existing truck parking facilities, Truck Parking Information Management Systems (TPIMS) have been deployed in several states to provide real-time parking information to truck drivers through various communication channels. This paper presents an anomaly detection method to identify sensor failures by tracking structural changes in time series parking data. In addition, a data dashboard is developed to evaluate and visualize the performance of the TPIMS through multidimensional aggregation of parking flow data. In particular, truck parking data collected from rest areas and truck stops along I-80 in Iowa are analyzed in this study. The Pruned Exact Linear Time (PELT) method is adopted to identify sensor failure and it can detect the anomaly in the parking flow time series data within two weeks in twelve different parking sites. Performance measures in terms of utilization, demand cycles, and system reliability are quantified and visualized in a data dashboard. It can be seen from the dashboard that after the implementation of TPIMS, the utilization among parking facilities along I80 is more evenly distributed. Such intuitive tools can help agencies assess the performance of the system and make informed decisions.

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Data Availability

TPIMS data can be accessed from WisTransPortal http://transportal.cee.wisc.edu/. The puck transmission log data is obtained through private correspondence.

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Funding

This research is funded by Iowa Department of Transportation.

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Authors and Affiliations

  1. Department of Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, 50011, USA

    Yilun Yang & **g Dong-O’Brien

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  1. Yilun Yang
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  2. **g Dong-O’Brien
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Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by YY. The draft of the manuscript was written by both YY and JD-O’B. All authors read and approved the final manuscript.

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Correspondence to **g Dong-O’Brien.

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Yang, Y., Dong-O’Brien, J. Anomaly Detection and Performance Visualization of Truck Parking Information and Management Systems. Data Sci. Transp. 5, 21 (2023). https://doi.org/10.1007/s42421-023-00084-9

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