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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

2023

Vol. 68, iss. 4

77--90

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.

Ocena prędkości rowerzystów na różnych typach infrastruktury rowerowej z wykorzystaniem danych GPS

Kieć M., Pogodzińska S.

Transport Miejski i Regionalny

2018

nr 4

20--24

W artykule oceniono prędkości chwilowe rowerzystów na wybranych elementach infrastruktury rowerowej w Krakowie, którymi były: drogi rowerowe oddzielone od jezdni, drogi rowerowe zlokalizowane przy jezdni, przejazdy rowerowe. Prędkości rowerzystów zostały oszacowane na podstawie przeprowadzonych badań empirycznych oraz analizy danych GPS z systemu rowerów miejskich Wavelo oraz danych z portalu Strava. Wyniki badań wskazują na różnice w prędkości chwilowej roweru dla różnych standardów infrastruktury rowerowej i źródła danych. Wykazano dużą zmienność prędkości rowerzystów dla poszczególnych poligonów, która charakteryzuje się dużym odchyleniem standardowym w próbach badawczych. Wartości prędkości uzyskiwane na podstawie danych z portalu Strava są o około 5,5% wyższe od prędkości notowanych w badaniach empirycznych. W przypadku rowerów miejskich prędkość wynosi około 74,5% prędkości obserwowanych w badaniach empirycznych. Wyższą zgodność wyników uzyskano w przypadku porównania prędkości z dwóch źródeł danych GPS. Prędkość rowerzystów korzystających z rowerów miejskich stanowi około 71% prędkości użytkowników portalu Strava. Przedstawione wyniki wskazują na możliwość wykorzystania danych GPS do oceny rzeczywistej prędkości rowerzystów.

The article presents the cyclists’ speed on selected elements of the bicycle infrastructure in Krakow, including: bicycle paths separated from the roadway, bicycle paths in curb, bicycle crossings. Cyclist’s speed has been estimated on the basis of empirical research and GPS data from the bike sharing system Wavelo and Strava application. The results indicate differences in cyclists’ speed for different standards of bicycle infrastructure and data sources. A high variability of cyclists’ speed for measurement sites was recorded. It is characterized by a value of standard deviation in the research samples. Speed obtained from the Strava application is approximately 5.5% higher than the speed recorded in empirical research. In the case of bike sharing system, the speed is around 74.5% of the speed observed in empirical research. Greater consistency of results was obtained when comparing the speed from two GPS data sources. The speed of Wavelo is approximately 71% of the Strava users speed. The presented results indicate the possibility of using GPS data to evaluate the speed of cyclists.