Modeling Route Choice of Utilitarian Bikeshare Users with GPS Data

Abstract

To understand a bicyclist’s route choice is difficult, given the many factors that influence the attractiveness of different routes. The advent of low-cost GPS devices has made route choice analysis more precise. Bikeshare, with instrumented bikes, allows for better assessment of revealed route preference of a large subpopulation of cyclists. This study used GPS data obtained from 9,101 trips made by 1,866 users of Grid Bikeshare, Phoenix, Arizona. This unique bikeshare system relied on Social Bicycles’ onboard telematics, which allowed nonstation origins and destinations, and operated on a grid street network. The system enabled unique route choice analysis. The trips studied included only direct utilitarian trips. Circuitous trips that could have included multiple destinations or could have been recreational trips were removed. The analysis focused on facility use assessment and route choice behavior. The results were compared between two categories of bikeshare users: registered users and casual users. Registered users made shorter trips on roads with low volume and preferred bike-specific infrastructure. A path size logit model was used to model route choice. Riders were sensitive to travel distance, with little deviation from the shortest path to use more bike-friendly infrastructure. Travel on the bike-specific facilities was equivalent to a decrease in distance by 44.9% (compared with 53.3% for casual users). Left turns imposed higher disutility than right turns did for casual users. The proportion of one-way segments, annual average daily traffic values, and length of trip had a negative influence on route choice, and a number of signalized intersections had a positive influence on route selection. The results were also compared with those of earlier studies.

Data-Driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone

“…These traces correspond to 91.6% of all traces recorded during the considered morning. It is worth mentioning that this is a significantly higher percentage than reported in other studies [14,15]. Starting from these map-matched routes, the bicycle flows (as number of cyclists passing through each network link per hour) have been evaluated.…”

“…In particular, many cities have decided to invest in the construction of quality bikeways with the intention to incentivize people to cycle even medium (and long) distance on a daily basis. Data on cycling volumes help to support this decision making; researchers have investigated the factors that influences ridership [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. This data can be collected by the use of traditional manual or instrumental counts [3,11], which are characterized by some drawbacks.…”

“…Instrumental and permanent count stations do provide continuous data, but cover typically only a small number sections of the road network. More recently, the widespread use of smartphones and mobile applications for self-localization and navigation has increased the availability of observed cyclists' data [8][9][10][12][13][14][15]. This type of data provides detailed information about the origin/destination of the trips as well as the chosen routes.…”

“…One of the main problems with GPS data is the representativeness of the data, because data collection is usually provided on a volunteer basis, which is not necessarily representative for the entire population [13]. Another problem is the level of detail of the network: in many cases, the success of identifying the correct network links from GPS points is limited if the bike network model is not sufficiently detailed [14,15]. In the present work, care has been taken to obtain a realistic model of the bicycle network.…”

Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone

“…This type of problem has been highlighted by Jestico et al [22], who used data provided by strava.com to quantify how well crowdsourced fitness app data represent ridership through a comparison with manual cycling counts in Victoria, British Columbia. Another problem is the level of detail of the network: in many cases, the success of identifying the correct network links from GPS points is limited if the bike network model is not sufficiently detailed [23,24]. This paper explains how to estimate the city-wide bicycle flows and how to identify weak points of the road network in terms of bicycle friendliness.…”