Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data

Abstract

Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the applicability of BLOS rating schemes for the estimation of whole-journey route choice. The methods evaluated share a common trait of being applicable for mixed traffic urban environments: Bicycle Compatibility Index (BCI), Bicycle Stress Level (BSL), Sixth Edition Highway Capacity Manual (HCM6), and Level of Traffic Stress (LTS). Routes are generated based on BLOS-weighted networks and the suitability of these routes is determined by finding the percentage overlap with empirical route choices. The results show that BCI provides the best match with empirical route data in all five origin–destination pairs, followed by HCM6. BSL and LTS which are not empirically founded have a lower match rate, although the differences between the four methods are relatively small. By iterating the detour rate that cyclists are assumed to be willing to make, it is found that the best match with modelled BLOS routes is achieved between 15 and 21% additional length. This falls within the range suggested by existing empirical research on willingness to deviate from the shortest path, however, it is uncertain whether the method will deliver the comparable findings in other cycling environments.

Human-Centric Data Science for Urban Studies

“…Cycling. Four issue papers focused on cycling, exploring the stress challenges experienced in urban environments by the vulnerable demographic of cyclists [49][50][51][52]. Werner et al [49] and Pajarito and Gould [52] focused on improving livability through cycling, investigating cyclists' stress sensations through route analysis.…”

“…Werner et al [49] and Pajarito and Gould [52] focused on improving livability through cycling, investigating cyclists' stress sensations through route analysis. Pritchard et al [50] also studied cycling stress, but considered a mix of indicators for assessing bicycle level of service. Zhang et al [51] took a different approach, leveraging public bicycle-sharing data and machine learning methods to identify land use.…”

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

“…Empirical data on detour rates do exist. Detours have been calculated in different ways, see for example Pritchard et al [12] and Griffin and Jiao [13].…”

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

“…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.…”