Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 4 June 2019 / Approved: 5 June 2019 / Online: 5 June 2019 (10:44:42 CEST)

A peer-reviewed article of this Preprint also exists.

Rupi, F.; Poliziani, C.; Schweizer, J. Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone. ISPRS Int. J. Geo-Inf. 2019, 8, 322. Rupi, F.; Poliziani, C.; Schweizer, J. Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone. ISPRS Int. J. Geo-Inf. 2019, 8, 322.

Journal reference: ISPRS Int. J. Geo-Inf. 2019, 8, 322
DOI: 10.3390/ijgi8080322

Abstract

This research describes numerical methods to analyze the absolute transport demand of cyclists and then 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 and are demonstrated with the city of Bologna, Italy: the dataset is based on approximately 27,500 GPS traces from cyclists, recorded over a period of one month on a volunteer basis using a smartphone application. A first method estimates absolute, city-wide bicycle flows, by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts of the main bikeways of the city. As there is a good correlation between the two sources of flow data, the absolute bike-flows on the entire network have been correctly estimated. A second method describes a novel link-deviation index, 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 link deviation index indicates the planner which road links are contributing most to the total deviation of all cyclists – in this way, repelling and attracting road attributes for cyclists can be identified. This is why the deviation index is of practical help to prioritize bike infrastructure construction on individual road network links.

Subject Areas

GPS traces; cycling volumes; cyclists’ counts; cycling network; deviation index

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