Journal of Human Sport and Exercise

Cyclists movement: The results of assessing the intensity using surveillance cameras

Natalia Kireeva, Elena Slepenkova, Dmitry Sobolev



The increase in the number of rides with the use of non-motorized vehicles such as bicycles changes the requirements for urban infrastructure. As a result, there is a growing need for monitoring bicycle traffic. The purpose of this scientific work is to study the traffic intensity of cyclists and users of other non-motorized vehicles and analyse it by type of infrastructure, days of the week, and type of vehicle. For the study, we used data obtained from video surveillance cameras installed on the streets with different types of bicycle infrastructure. A database was formed based on the accounting of the cycle traffic intensity at the counting points. The analysis showed a similarity in the behaviour of cyclists in the conditions of identical infrastructure, which allows us to assess the intensity by the base points for each category of infrastructure.


Bicycle traffic; Video monitoring; Urban bike infrastructure; Traffic estimation; Intensity accounting; Non-motorized vehicles


Annual Cycling Monitoring Report. (2016). Retrieved from

Bahnsen C., Madsen T., Jørgensen A., Lahrmann H., Moeslund T. (2014). Traffic Detector. Denmark: Aalborg University.

Bernardi S., Rupi F. (2015). An Analysis of Bicycle Travel Speed and Disturbances on Off-street and On-street Facilities. Transportation Research Procedia, 5, 82-94.

Bicycle NetWork. (2020). Retrieved from

Bike Data. (2016). Retrieved from

Boss D., Nelson T., Winters M., Ferster C. (2018). Using crowdsourced data to monitor change in spatial patterns of bicycle ridership. Journal of Transport & Health, 9, 226-233.

Chen C., Ma J., Susilo Y., Liu Y., Wang, M. (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transp. Res. C Emerg. Technol., 68, 285-299.

Chen C., Wang H., Roll J., Nordback K., Wang Y. (2020). Using bicycle app data to develop Safety Performance Functions (SPFs) for bicyclists at intersections: A generic framework. Transportation Research Part A: Policy and Practice, 132, 1034-1052.

Esawey M. (2018). Impact of data gaps on the accuracy of annual and monthly average daily bicycle volume calculation at permanent count stations. Computers, Environment and Urban Systems, 70, 125-137.

Heesch K., James B., Washington T., Zuniga K., Burke M. (2016). Evaluation of the Veloway 1: A natural experiment of new bicycle infrastructure in Brisbane, Australia. Journal of Transport & Health, 3, 366-376,

Johnstone D., Nordback K., Lowry M. (2017). Collecting Network-wide Bicycle and Pedestrian Data: A Guidebook for When and Where to Count. WA-RD 875.1. Retrieved from

Kidholm T., Madsen O., Lahrmann H., Comparison of five bicycle facility designs in signalized intersections using traffic conflict studies. (2017). Transportation Research Part F: Traffic Psychology and Behaviour, 46, Part B, 438-450.

Lee K., Sener I. (2020). Emerging data for pedestrian and bicycle monitoring: Sources and applications. Transportation Research Interdisciplinary Perspectives, 100095.

Lee R., Sener I., Mullins J. (2016). An evaluation of emerging data collection technologies for travel demand modeling: from research to practice. Transportation Letters, 8:4, 181-193.

Lin Z., Fan W. (2020). Modeling bicycle volume using crowdsourced data from Strava smartphone application. International Journal of Transportation Science and Technology.

Milne D., Watling D. (2019). Big data and understanding change in the context of planning transport systems. Journal of Transport Geography, 76, 235-244.

Pogodzinska S., Kiec M., D'Agostino C. (2020). Bicycle Traffic Volume Estimation Based on GPS Data. Transportation Research Procedia, 45, 874-881.

Pritchard R., Bucher D., Frøyen Y. (2019). Does new bicycle infrastructure result in new or rerouted bicyclists? A longitudinal GPS study in Oslo. Journal of Transport Geography, 77, 113-125.

Rojas M., Sadeghvaziri E., Jin X. (2016). Comprehensive review of travel behavior and mobility pattern studies that used mobile phone data. Transportation Research Record: Journal of the Transportation Research Board, 2563, 71-79.

Romanillos G., Austwick M., Ettema D., De Kruijf J. (2015). Big Data and Cycling. Transport Reviews, 36, 1-20.

Ryus P., Ferguson E., Laustsen K., Schneider R., F.R. Proulx, T. Hull, Miranda-Moreno L. (2014). Guidebook on pedestrian and bicycle volume data collection NCHRP Report 797, National Cooperative Highway Research Program.

Tianjun L., Buehler R., Mondschein A., Hankey S. (2017). Designing a bicycle and pedestrian traffic monitoring program to estimate annual average daily traffic in a small rural college town. Transportation Research Part D: Transport and Environment, 53, 193-204.

Trofimenko Y., Shashina E. (2017). Methodology and Results of Assessing Safety of Bicycle Infrastructure in Russian Cities. Transportation Research Procedia, 20, 653-658.

Wang Y., Monsere C., Chen C., Wang H. (2018b). Development of a crash risk scoring tool for pedestrian and bicycle projects in Oregon. Transportation Research Record: Journal of the Transportation Research Board, 2672, 30-39.

Wang Z., He S., Leung Y. (2018a). Applying mobile phone data to travel behaviour research: a literature review. Travel Behavior Society, 11, 141-155.

Zhang H., Song X., Long Y., Xia T., Fang K., Zheng J., Huang D., Shibasaki R., Liang Y. (2019). Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis. Applied Energy, 242, 138-147.


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