Data-Driven Analysis of Bicycle Sharing Systems as Public Transport Systems Based on a Trip Index Classification

Bicycle Sharing Systems (BSSs) are exponentially increasing in the urban mobility sector. They are traditionally conceived as a last-mile complement to the public transport system. In this paper, we demonstrate that BSSs can be seen as a public transport system in their own right. To do so, we build a mathematical framework for the classification of BSS trips. Using trajectory information, we create the trip index, which characterizes the intrinsic purpose of the use of BSS as transport or leisure. The construction of the trip index required a specific analysis of the BSS shortest path, which cannot be directly calculated from the topology of the network given that cyclists can find shortcuts through traffic lights, pedestrian crossings, etc. to reduce the overall traveled distance. Adding a layer of complication to the problem, these shortcuts have a non-trivial existence in terms of being intermittent, or short lived. We applied the proposed methodology to empirical data from BiciMAD, the public BSS in Madrid (Spain). The obtained results show that the trip index correctly determines transport and leisure categories, which exhibit distinct statistical and operational features. Finally, we inferred the underlying BSS public transport network and show the fundamental trajectories traveled by users. Based on this analysis, we conclude that 90.60% of BiciMAD’s use fall in the category of transport, which demonstrates our first statement.

[1]  Jian Chen,et al.  Mode Choice Model for Public Transport with Categorized Latent Variables , 2017 .

[2]  Simon Washington,et al.  Bike share's impact on car use: evidence from the United States, Great Britain, and Australia , 2014 .

[3]  Yu-Jun Zheng,et al.  The impact of a public bicycle-sharing system on urban public transport networks , 2018 .

[4]  Robert Weibel,et al.  Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth , 2018, EPJ Data Science.

[5]  H. Hahn Sur quelques points du calcul fonctionnel , 1908 .

[6]  Antoni Domènech,et al.  A GIS-Based Evaluation of the Effectiveness and Spatial Coverage of Public Transport Networks in Tourist Destinations , 2017, ISPRS Int. J. Geo Inf..

[7]  Rubén Fernández Pozo,et al.  Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing , 2020, Sensors.

[8]  Shashi Shekhar,et al.  Identifying K Primary Corridors from urban bicycle GPS trajectories on a road network , 2016, Inf. Syst..

[9]  Judea Pearl,et al.  Heuristics : intelligent search strategies for computer problem solving , 1984 .

[10]  Yu Zheng,et al.  Citywide Bike Usage Prediction in a Bike-Sharing System , 2020, IEEE Transactions on Knowledge and Data Engineering.

[11]  Felipe González,et al.  A combined destination and route choice model for a bicycle sharing system , 2016 .

[12]  Yamir Moreno,et al.  A Multilayer perspective for the analysis of urban transportation systems , 2016, Scientific Reports.

[13]  Jerry E. Mueller AN INTRODUCTION TO THE HYDRAULIC AND TOPOGRAPHIC SINUOSITY INDEXES1 , 1968 .

[14]  Ricardo Lüders,et al.  Temporal Performance Analysis of Bus Transportation Using Link Streams , 2019, Mathematical Problems in Engineering.

[15]  Ruchuan Wang,et al.  The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey , 2018, Sensors.

[16]  Simon Washington,et al.  Shortest path and vehicle trajectory aided map-matching for low frequency GPS data , 2015 .

[17]  Cecilia Mascolo,et al.  Comparing cities’ cycling patterns using online shared bicycle maps , 2015 .

[18]  Naveen Eluru,et al.  Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system , 2015 .

[19]  Armando Bazzani,et al.  Understanding the variability of daily travel-time expenditures using GPS trajectory data , 2015, EPJ Data Science.

[20]  Carlo Ratti,et al.  Understanding individual mobility patterns from urban sensing data: A mobile phone trace example , 2013 .

[21]  Wei Xiong,et al.  An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold , 2017, ISPRS Int. J. Geo Inf..

[22]  Philip S. Yu,et al.  Bicycle-Sharing System Analysis and Trip Prediction , 2016, 2016 17th IEEE International Conference on Mobile Data Management (MDM).

[23]  J. Dávila,et al.  Shaping cities for health: complexity and the planning of urban environments in the 21st century , 2012, The Lancet.

[24]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[25]  Jesfis Peral,et al.  Heuristics -- intelligent search strategies for computer problem solving , 1984 .

[26]  S. Benhamou,et al.  Spatial analysis of animals' movements using a correlated random walk model* , 1988 .

[27]  Federico Chiariotti,et al.  A Dynamic Approach to Rebalancing Bike-Sharing Systems , 2018, Sensors.

[28]  Helmut Alt,et al.  Measuring the resemblance of polygonal curves , 1992, SCG '92.

[29]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[30]  F. Koppelman,et al.  Alternative nested logit models: structure, properties and estimation , 1998 .

[31]  Jennifer Dill,et al.  Where do cyclists ride? A route choice model developed with revealed preference GPS data , 2012 .

[32]  Marta C. González,et al.  A data science framework for planning the growth of bicycle infrastructures , 2020 .

[33]  Michael Batty,et al.  Mining bicycle sharing data for generating insights into sustainable transport systems , 2014 .

[34]  Casey J. Wichman,et al.  Bicycle Infrastructure and Traffic Congestion: Evidence from DC's Capital Bikeshare , 2015 .

[35]  H. Mannila,et al.  Computing Discrete Fréchet Distance ∗ , 1994 .