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

Pazdan Sylwia, Kiec Mariusz

Archives of Transport

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2023

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Vol. 68, iss. 4

77--90

EN

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 esti mated 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 bike sharing 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, contra flow lane) and element of road network (road segment, bicycle crossing with or without traffic signals). Two tailed test for two means was conducted to analyses 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 bike share system or dedicated app.

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Bicycle traffic in the cities

Macioszek Elżbieta, Jurdana Irena

Zeszyty Naukowe. Transport / Politechnika Śląska

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2022

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z. 117

115--127

EN

For many years in many Western Europe countries, cycling has been associated with not only recreation and tourism but has equally gained an important function as a means of transport used in everyday commuting to work, study and entertainment. The bicycle appears to be a very good alternative to motor vehicles that produce exhaust fumes and create congestion on road transport networks. Not only is the bicycle environmentally friendly and takes up little space in road transport networks, but also, the time of bicycle travel is often competitive in relation to travel made by private car or public transport. This article presents the characteristics of the bicycle infrastructure and services offered in selected cities in the world and Poland, as well as the issues of bicycle counters as sources of data on bicycle traffic volume, along with an exemplary analysis of this type of data.

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Szacowanie natężenia ruchu rowerowego na podstawie danych z systemu rowerów miejskich

Pogodzińska S.

Transport Miejski i Regionalny

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2018

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nr 1

5--11

PL

W artykule przedstawiono metodę szacowania natężenia ruchu rowerowego na podstawie danych GPS z systemu rowerów miejskich. Badania wykonano na przykładzie miasta Krakowa, wykorzystując dane o dobowym natężeniu ruchu rowerowego z 5 pętli pomiaru automatycznego oraz dane GPS z systemu rowerów miejskich Wavelo. Na podstawie dwuczynnikowej analizy wariancji (ANOVA) oraz testu post-hoc Tukey’a określono wpływ czynników „lokalizacja” i „dzień tygodnia” na udział rowerów systemu miejskiego w całym potoku rowerzystów. Wykazano, że badany udział zmienia się statystycznie istotnie pomiędzy analizowanymi lokalizacjami. W przypadku udziału szacowanego w poszczególnych dniach tygodnia zmiana jest nieistotna. Wyznaczona zależność pomiędzy ogólnym natężeniem ruchu rowerowego i natężeniem ruchu rowerów systemu Wavelo charakteryzuje się wysokimi współczynnikami determinacji R2 (przekraczającymi wartość 0,90) oraz średnim błędem oszacowania nie większym od 11,5%. Wyniki przeprowadzonych badań wskazują na możliwość szacowania natężenia całości ruchu rowerowego na podstawie danych GPS z systemu rowerów miejskich. Praktyczne wdrożenie takiego sposobu szacowania natężenia ruchu rowerowego wymaga jednak przeprowadzenia pomiarów kontrolnych weryfikujących opracowane zależności wraz z określeniem wpływu lokalizacji przekroju pomiarowego.

EN

The article presents a method of estimation of bicycle traffic flow based on the GPS data from bike share system. Analyses have been made for the city of Krakow, using daily traffic data from 5 automatic counter loops and the GPS data from bike share system called Wavelo. Based on the two-factor analysis of variance (ANOVA) and the Tukey post-hoc test, the influence of „localization” and „day of the week” factors on the share of Wavelo bicycles in the entire bicycle flow was estimated. It has been proved that examined share is not significantly different between individual days of the week, but changes significantly between analyzed locations. Developed models are characterized by the high R2 coefficients (exceeding 0.90) and average error of estimation up to 11.5%. The results of the studies show that bicycle traffic flow can be estimated based on the GPS data provided by bike share system. However, it is necessary to carry out control measurements to verify developed models and their possibilities of application in bicycle traffic flow estimation in other locations.