Chapter
Aug 31, 2020
International Conference on Transportation and Development 2020

Calculating Travel Time across Different Travel Modes Using Bluetooth and WiFi Sensing Data

Publication: International Conference on Transportation and Development 2020

ABSTRACT

Travel times are an important measure for quantifying travel quality across different modes. However, collecting travel time data is a non-trivial and expensive task. Current practice involves using separate data collection methods for each mode. This paper presents a cost-effective and simple way to collect travel time data across multiple modes using media access control (MAC) matching detected by the mobile unit for sensing traffic (MUST) sensor. This technology detects personal electronic devices to determine people’s movement instead of traditional methods which detect singular modes, such as induction loops. This paper proposes a new travel-time calculation method for pedestrian, bicycle, and automobile travelers. A linear model distributes the travel time between different modes by weighting the travel time based on highest, lowest, and most likely speeds. Comparing estimated results for the modal distribution, an accuracy of approximately 83% is achieved, which is acceptable for most applications in transportation engineering.

Get full access to this article

View all available purchase options and get full access to this chapter.

REFERENCES

Belisle, F., Saunier, N., & Audet, O. (2017, November 15). Continuous Tracking: Exploring the Long-Term Value of Bluetooth. 96.
Daamen, W., Yuan, Y., Duives, D., & Hoogendoorn, S. (2016). Comparing Three Types of Real-TimeData Collection Techniques: Counting Cameras, WiFi-Sensors, and GPS Trackers. Proceedings of Pedestrian and Evacuation Dynamics, 568–574. Retrieved October 5,2018.
Day, C. (2012). Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data. https://doi.org/10.5703/1288284314988.
Duives, D., Daamen, W., & Hoogendoorn, S. (2018). How to Measure Static Crowds? Monitoring the Number of Pedestrians at Large Open Areas by Means of WiFi Sensors.
Hainen, A. M., Wasson, J. S., Hubbard, S. M. L., Remias, S. M., Farnsworth, G., & Bullock, D. M. (2011). Estimating Route Choice and Travel Time Reliability with Field Observations of Bluetooth Probe Vehicles. https://doi.org/10.3141/2256-06.
Hidayat, A., Terabe, S., & Yaginuma, H. (n.d.). WiFi Scanner Technologies for Obtaining Travel Data about Circulator Bus Passengers, A Case Study. 97, 1–15.
Kang, L., Qi, B., & Banerjee, S. (n.d.). A Wireless-Based Approach for Transit Analytics. ACM Ditial Library, 75–80. doi:10.1145/2873587.2873589.
Lesani, A., & Miranda-Moreno, L. (2016, January). Development and Testing of a Real Time WiFi-Bluetooth System for Pedestrian Network Monitoring and Data Extrapolation. 95.
Malinovskiy, Y., & Wang, Y. (2012). Pedestrian Travel Pattern Discovery Using Mobile Bluetooth Sensors. Presented at the Transportation Research Board 91st Annual MeetingTransportation Research Board. Retrieved from https://trid.trb.org/view/1129741.
Malinovskiy, Y., Saunier, N., & Wang, Y. (2012). Analysis of Pedestrian Travel with Static Bluetooth Sensors. Transportation Research Record, 2299(1), 137–149. https://doi.org/10.3141/2299-15.
Martchouk, M., Mannering, F., & Bullock, D. (2011). Analysis of Freeway Travel Time Variability Using Bluetooth Detection. Journal of Transportation Engineering, 137(10), 697–704. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000253.
Martin, J., Rye, E., & Beverly, R. (2016). Decomposition of MAC Addressess Structure for Granular Device Inference. ACM Digital Library, 78–88. doi:10.1145/2991079.2991098.
Poucin, G., Patterson, Z., & Farooq, B. (2016). Pedestrian activity pattern mining in WiFi-network connection data. Journal of the Transportation Research Board, 95th. Retrieved October 5, 2018.
Sakhare, R., Mathew, J., Avr, A., Hubbard, S. M. L., Devi, lelitha, & Bullock, D. M. (2018). Comparison of Bluetooth and Bus GPS Data for Estimating Arterial Travel Time and Trip Chaining., 1–10.
Scheuner, J., Mazlami, G., Schoni, D., Stephan, S., De Carli, A., Bocek, T., & Stiller, B. (2016). Probr – A Generic and Passive WiFi Tracking System. IEEE 41st Conference on Local Computer Networks. Retrieved October 15, 2018.
Seer, S., Brandle, N., & Ratti, C. (2014). Kinetics and Human Kinetics: A New Approach for Studying Human Behavior. Transportation Research Part C, 48, 212–228.
Vanhoef M., Matte C., Cunche M., Cardoso L., Piessens F. Why MAC Address Randomization is not Enough: An Analysis of Wi-Fi Network Discovery Mechanisms. ACM AsiaCCS, May 2016, Xi’an, China. 2016, <10.1145/2897845.2897883>. <hal-01282900>.

Information & Authors

Information

Published In

Go to International Conference on Transportation and Development 2020
International Conference on Transportation and Development 2020
Pages: 182 - 193
Editor: Guohui Zhang, Ph.D., University of Hawaii
ISBN (Online): 978-0-7844-8313-8

History

Published online: Aug 31, 2020
Published in print: Aug 31, 2020

Permissions

Request permissions for this article.

Authors

Affiliations

Samuel Ricord [email protected]
1Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Washington, Seattle, WA. Email: [email protected]
John E. Ash [email protected]
2Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Washington, Seattle, WA. Email: [email protected]
Yinhai Wang, Ph.D. [email protected]
3T&DI Past President and Professor, Dept. of Civil and Environmental Engineering, Univ. of Washington, Seattle, WA. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$80.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Paper
$35.00
Add to cart
Buy E-book
$80.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share