WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study
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pISSN 1226-7988, eISSN 1976-3808 www.springer.com/12205
DOI 10.1007/s12205-020-0316-7
Transportation Engineering
WiFi Sensing System for Monitoring Public Transportation Ridership: A Case Study Seunghan Ryu
a
a
, Byungkyu Brian Park
, and Samy El-Tawab
b
Link Lab & Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA College of Integrated Science and Engineering, James Madison University, Harrisonburg, VA 22807, USA
a
b
ARTICLE HISTORY
ABSTRACT
Received 4 March 2020 Revised 21 May 2020 Accepted 26 May 2020 Published Online 5 August 2020
Public transportation system as an essential mode of travel has been investigated by local governments and transportation agencies to capture passengers’ travel behaviors. Despite their efforts, agencies especially in small to medium sized cities could not afford to collect such behaviors data due to significant costs associated with the data collection system. In this study, we presented a WiFi sensing system which makes such data collection feasible with low-cost devices. We demonstrated the WiFi sensing system’s applicability in estimating passengers’ origin-destination (O/D) travel and passengers’ bus stop waiting times via video validation. In addition, WiFi signal strength was analyzed to further improve accuracy of the system. To this end, sliding window algorithm was adopted to mitigate the randomness of mobile devices’ signals. Our small-scale proof of concept experiment was conducted at four bus stops along the main transit corridor in Charlottesville, Virginia. Results indicated that the system was able to re-identify 91% of bus passengers and passengers bus stop waiting time error was as small as 7 seconds. It is expected that the system can be a viable low-cost Internet of Things (IoT) solution for monitoring public transit system performance.
KEYWORDS WiFi MAC address O/D estimation Waiting time Signal strength analysis Public transportation
1. Introduction Public transportation system has helped connecting people with their places of destinations. The system also opens economic opportunities for local businesses, enables students who do not own/use vehicles to reach school or college, and helps the people stay independent. With the importance of the system, many local governments and transportation agencies have tried providing better service to the people in the city through monitoring and collecting big data of transportation performances such as travel time, congestion, and reliability. Those big data are applicable to transportation system management. Nuaimi et al. (2015) introduced that transportation system can greatly benefit from big data to optimize route and schedule, and to accommodate varying traffic demands. As an example, Rathor et al. (2016) stated that real-time congestion data can mitigate potential delay when drivers choose a route and help governmental transportation authorities to control varying traffic conditions. Another application is the automated fare
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