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

Federico Rupi,Joerg Schweizer,Cristian Poliziani

doi:10.3390/ijgi8080322

Federico Rupi, Joerg Schweizer + Show 1 more

Open Access

https://doi.org/10.3390/ijgi8080322

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Abstract

This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads.

Highlights

In recent years, due to congestion, air pollution, climate change, energy scarcity, and physical inactivity, an increasing importance has been attributed to sustainable transport modes, and in particular to cycling
The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application
As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route

Summary

Introduction

Due to congestion, air pollution, climate change, energy scarcity, and physical inactivity, an increasing importance has been attributed to sustainable transport modes, and in particular to cycling. Zimmermann et al estimated a link-based bike route choice model from a sample of GPS observations (103 users and 648 trips) in the city of Eugene, Oregon [18]. 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].

Study Area

Estimated Cyclists’ Volumes

Findings

Discussion

Conclusions