An exploratory study of spatial patterns of cycling in Tel Aviv using passively generated bike-sharing data. (April 2019)

• Record Type: Journal Article
• Authors: Levy, Nadav; Golani, Chen; Ben-Elia, Eran
• Is Part Of: Journal of transport geography. Volume 76(2019)
• Page Start: 325
• Page End: 334
• Publication Date: 2019-04
• Subjects: Big data -- Bike-sharing -- Traffic assignment -- Spatially adjusted regression -- GTFS -- Tel Aviv; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2017.10.005
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: Investments in bike-sharing and cycling infrastructures are justified for contributing towards more sustainable mobility in cities. Harvesting data from passive sources has important potential for better understanding the spatial patterns of human movements in urban areas including cycling. We explore data obtained from the Tel Aviv bike-sharing system and corresponding GTFS data, to understand the spatial patterns of cycling in the city and its relation to bus travel. Using a combination of transportation and geostatistical models including spatially adjusted regression, and all-or-nothing traffic assignment, we show that cycling movements are not well balanced and different behaviors are associated with the length of trips. Shorter trips are more concentrated in the city center and seem to complement bus travel. Longer trips are more focused on links with dedicated bicycle lanes and do not show strong correlations with bus travel, possibly indicating a weak substitution effect. The implications of data-driven studies for transport policy and spatial inquiries of urban mobility are further discussed. Highlights: Bike-sharing data is harvested to analyze the spatial movements of cyclists in Tel Aviv. Behavioral insights are gained by combining spatial analysis and transportation modeling methods. Preference to bicycle lanes is visible in the data and correlated with trip length. Spatially adjusted regression models are estimated to study the association between cycling and public transport.