Bicycle free-flow speed estimation based on GPS data – comparison of bikesharing system and Strava data

Sylwia Pazdan; Mariusz Kiec

doi:10.61089/aot2023.w6hjz713

Authors

Sylwia Pazdan Cracow University of Technology, Faculty of Civil Engineering, Cracow, Poland Author https://orcid.org/0000-0001-6106-7768
Mariusz Kiec Cracow University of Technology, Faculty of Civil Engineering, Cracow, Poland Author https://orcid.org/0000-0001-9193-7269

DOI:

https://doi.org/10.61089/aot2023.w6hjz713

Keywords:

bicycle speed, bikesharing system, Strava, big data

Abstract

The increasing number of cyclists in cities around the world results in a greater focus on bicycle traffic. Next to traffic volume, the main characteristic of traffic used in road safety analysis, infrastructure planning, design, etc. is its speed. Bicycle speed is strongly affected by the type of bicycle facility, motor vehicle traffic parameters (volume, speed, share of heavy vehicles), trip motivation, weather conditions, etc., and therefore it is difficult to estimate. Traditionally, bicycle speed is determined directly using speed radar or indirectly, as a quotient of measurement base length and travel time calculated using a stopwatch or video technique. There are also researches where bicycle speed was estimated based on GPS sources, mainly mobile apps. However, depending on the GPS source and the group of cyclists, bicycle speed gained from GPS data can be different from the speed of regular cyclists (due to different levels of experience or types of bicycle). In the paper, the relationships between bicycle speed obtained from empirical measurements and two different GPS sources, which were bikesharing system (Wavelo) and Strava app, were analysed. In total 18 research sites were selected different in terms of bicycle facility (bicycle path, shared pedestrian/bicycle path, contraflow lane) and element of road network (road segment, bicycle crossing with or without traffic signals). Two-tailed test for two means was conducted to analyse the statistical significance of differences in bicycle speed estimated based on GPS data and empirical measurements using video technique. It showed that Wavelo and Strava speeds are by 17.4% lower are by 23.1% higher than the speeds of regular cyclists respectively. Two linear regression models describing relationships between bicycle speeds from empirical measurements and GPS data were developed. The results show that the variance of bicycle speed is almost 80% described by the variance of Wavelo speed and 60% described by the variance of Strava speed, which suggests that bicycle free-flow speed can be estimated based on GPS data either from bikeshare system or dedicated app.

References

Alhomaidat, F., & Eljufout, T. (2021). Perception of cycling risks and needs associated with skill level, gender, and age. Archives of Transport, 59(3), 113-227. https://doi.org/10.5604/01.3001.0015.2390.

Allen, D. P., Rouphail, N., Hummer, J. E., & Milazzo II, J. S. (1998). Operational Analysis of Uninterrupted Bicycle Facilities. Transportation Research Record, 1636, 29–36. https://doi.org/10.3141/1636-05.

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. https://doi.org/10.1016/j.trpro.2015.01.004.

Brown, M.J., Scott, D.M., & Páez, A. (2022). A spatial modeling approach to estimating bike share traffic volume from GPS data. Sustainable Cities and Society, 76, Article 103401. https://doi.org/10.1016/j.scs.2021.103401.

Buck, D., Buehler, R., Happ, P., Rawls, B., Chung, P., & Borecki, N. (2013). Are Bikeshare Users Different from Regular Cyclists? A First Look at Short-Term Users, Annual Members, and Area Cyclists in the Washington, DC Region. Transportation Research Record, 2387, 112–119. https://doi.org/10.3141/2387-13.

Castro, G.P., Johansson, F., & Olstam, J. (2022). How to Model the Effect of Gradient on Bicycle Traffic in Microscopic Traffic Simulation, Transportation Research Record, 2676(11), 609-620. https://doi.org/10.1177/03611981221094300.

Clarry, A., Faghih Imani, A., & Miller, E. J. (2019). Where we ride faster? Examining cycling speed using smartphone GPS data. Sustainable Cities and Society, 49, Article 101594. https://doi.org/10.1016/j.scs.2019.101594.

Cubells, J., Miralles-Guasch, C., & Marquet, O. (2023). Gendered travel behaviour in micromobility? Travel speed and route choice through the lens of intersecting identities. Journal of Transport Geography, 106, Article 103502. https://doi.org/10.1016/j.jtrangeo.2022.103502.

El-Geneidy, A., Krizek, K. J., & Iacono, M. (2007). Predicting Bicycle Travel Speeds Along Different Facilities Using GPS Data: A Proof of Concept Model. In Proceedings of 86th Annual Meeting of the Transportation Research Board, Washington, D.C.

Fishman, E., & Schepers, P. (2016). Global bike share: What the data tells us about road safety. Journal of Safety Research, 56, 41–45. https://doi.org/10.1016/j.jsr.2015.11.007.

Imani, A. F., Eluru, N., El-Geneidy, A. M., Rabbat, M., & Haq, U. (2014). How does land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. Journal of Transport Geography, 41, 306–314. https://doi.org/10.1016/j.jtrangeo.2014.01.013.

Jensen, P., Rouquier, J.B., Ovtracht, N., & Robardet, C. (2010). Characterizing the speed and paths of shared bicycle use in Lyon. Transportation Research Part D: Transport and Environment, 15(8), 522–524. https://doi.org/10.1016/j.trd.2010.07.002.

Joo, S., Oh, C., Jeong, E., & Lee, G. (2015). Categorizing bicycling environments using GPS-based public bicycle speed data. Transportation Research Part C: Emerging Technologies, 56, 239–250. https://doi.org/10.1016/j.trc.2015.04.012.

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45. https://doi.org/10.1115/1.3662552.

Knight, A., & Charlton, S.G. (2022). Protected and unprotected cycle lanes' effects on cyclists' behaviour. Accident Analysis & Prevention, 171, https://doi.org/10.1016/j.aap.2022.106668.

Kovaceva, J., Wallgren, P. & Dozza, M. (2022). On the evaluation of visual nudges to promote safe cycling: Can we encourage lower speeds at intersections?. Traffic Injury Prevention, 23(7), 428-433. https://doi.org/10.1080/15389588.2022.2103120.

Krukowicz, T., Firląg, K., Sobota, A., Kołodziej, T., & Novačko, L. (2021). The relationship between bicycle traffic and the development of bicycle infrastructure on the example of Warsaw. Archives of Transport, 60(4), 187-203. https://doi.org/10.5604/01.3001.0015.6930.

Ling, H., & Wu, J. (2004). A study on cyclist behavior at signalized intersections. IEEE Transactions on Intelligent Transportation Systems, 5(4), 293–299. https://doi.org/10.1109/TITS.2004.837812.

Murgano, E., Caponetto, R., Pappalardo, G., Cafiso, S.D., & Severino, A. A. (2021). Novel Acceleration Signal Processing Procedure for Cycling Safety Assessment. Sensors, 21, 4183. https://doi.org/10.3390/s21124183.

Parkin, J., & Rotheram, J. (2010). Design speeds and acceleration characteristics of bicycle traffic for use in planning, design and appraisal. Transport Policy, 17(5), 335–341. https://doi.org/10.1016/j.tranpol.2010.03.001.

Pazdan, S. (2020). The impact of weather on bicycle risk exposure. Archives of Transport, 56(4), 89-105. https://doi.org/10.5604/01.3001.0014.5629.

Pazdan, S., Kiec, M., & D’Agostino, C. (2021). Impact of environment on bicycle travel demand - Assessment using bikeshare system data. Sustainable Cities and Society, 67, Article 102724, 874–881. https://doi.org/10.1016/j.scs.2021.102724.

Pogodzinska, S., Kiec, M., & D’Agostino, C. (2020). Bicycle Traffic Volume Estimation Based on GPS Data. Transportation Research Procedia, 45, 874–881. https://doi.org/10.1016/j.trpro.2020.02.081.

Poliziani, C.; Rupi, F. & Schweizer, J. (2022). Traffic surveys and GPS traces to explore patterns in cyclist’s in-motion speeds. Transportation Research Procedia, 60, 410–417. https://doi.org/10.1016/j.trpro.2021.12.053.

Rios, A. C., Alvarado, E. S., Lima, M. S. & De La Cruz, F. C. (2021). Evaluation of a bicycle lane as a sustainable means of transport in cities with an excessive presence of motorcycle taxis. In Proceedings of Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI), Bogotá, Colombia. https://doi.org/10.1109/CONIITI53815.2021.9619755.

Romanillos, G., & Gutiérrez, J. (2020). Cyclists do better. Analyzing urban cycling operating speeds and accessibility. International Journal of Sustainable Transportation, 14(6), 448-464. https://doi.org/10.1080/15568318.2019.1575493.

Saunier, N., & Chabin, V. (2020). Should I Bike or Should I Drive? Comparative Analysis of Travel Speeds in Montreal. Findings. https://doi.org/10.32866/001c.11900.

Strauss, J., & Miranda-Moreno, L. F. (2017). Speed, travel time and delay for intersections and road segments in the Montreal network using cyclist Smartphone GPS data. Transportation Research Part D: Transport and Environment, 57, 155–171. https://doi.org/10.1016/j.trd.2017.09.001.

Thompson, D. C., Rebolledo, V., Thompson, R. S., Kaufnan, A., & Rivara, F. P. (1997). Bike speed measurements in a recreational population: Validity of self reported speed. Injury Prevention, 3, 43–45. https://doi.org/10.1136/ip.3.1.43.

Toljic, M., Brezina, T., & Emberger, G. (2021). Influence of surface roughness on cyclists’ velocity choices. Proceedings of the Institution of Civil Engineers: Municipal Engineer, 174(1), 2 – 13. https://doi.org/10.1680/jmuen.18.00058.

Yaqoob, S., Cafiso, S., Morabito, G., & Pappalardo, G. (2023). Detection of anomalies in cycling behavior with convolutional neural network and deep learning. Eur. Transp. Res. Rev., 15(1): 9. https://doi.org/10.1186/s12544-023-00583-4.

Zaki, M. H., Sayed, T., & Cheung, A. (2013). Automated collection of cyclist data using computer vision techniques. Transportation Research Record, 2387, 10–19. https://doi.org/10.3141/2387-02.

Zhou, J., Guo, Y., Sun, J., Yu, E., & Wang, R. (2022). Review of bike-sharing system studies using bibliometrics method. Journal of Traffic and Transportation Engineering, 9(4), 608-630. https://doi.org/10.1016/j.jtte.2021.08.003.