Using Machine Learning to Estimate Pedestrian and Bicyclist Count of Intersection by Bluetooth Low Energy-Reference-Cited by-同舟云学术

Using Machine Learning to Estimate Pedestrian and Bicyclist Count of Intersection by Bluetooth Low Energy

Published:2022-01 Issue:1 Volume:148 Page:
ISSN:2473-2907
Container-title:Journal of Transportation Engineering, Part A: Systems
language:en
Short-container-title:J. Transp. Eng., Part A: Systems

Author:

Gong Yaobang¹^ORCID,Abdel-Aty Mohamed²^ORCID

Affiliation:

1. Postdoctoral Research Associate, Dept. of Civil and Environmental Engineering, Univ. of Utah, Salt Lake City, UT 84112 (corresponding author). ORCID: .

2. Pegasus Professor and Chair, Dept. of Civil, Environmental and Construction Engineering, Univ. of Central Florida, Orlando, FL 32816. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Transportation,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/JTEPBS.0000619

Reference25 articles.

1. Abedi, N., A. Bhaskar, and E. Chung. 2013. Bluetooth and Wi-Fi MAC address based crowd data collection and monitoring: Benefits, challenges and enhancement. Brisbane: Australasian Transport Research Forum.

2. Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and Wi Fi MAC addresses;Abedi N.;Transp. Res. Part C Emerging Technol.,2015

3. Baas, J., R. Galton, and A. Biton. 2016. FHWA bicycle-pedestrian count technology pilot project: Summary report United States. Washington, DC: Federal Highway Administration.

4. Automated measurement of wait times at airport security: Deployment at Indianapolis international Airport, Indiana;Bullock D. M.;Transp. Res. Rec.,2010

5. Exploration of optimal Wi-Fi probes layout and estimation model of real-time pedestrian volume detection;Du Y.;Int. J. Distrib. Sens. Netw.,2017

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