A Road Segment Prioritization Approach for Cycling Infrastructure

Abstract

Understanding the motivators and deterrents to cycling is essential for creating infrastructure that gets more people to adopt cycling as a mode of transport. This paper demonstrates a new approach to support the prioritization of cycling infrastructure and cycling network design, accounting for cyclist preferences and the growing emphasis on 'filtered permeability' and 'Low Traffic Neighborhood' interventions internationally. The approach combines distance decay, route calculation, and network analysis methods to examine where future cycling demand is most likely to arise, how such demand could be accommodated within existing street networks, and how to ensure a fair distribution of investment. Although each of these methods has been applied to cycling infrastructure prioritization in previous research, this is the first time that they have been combined, creating an integrated road segment prioritization approach. The approach, which can be applied to other cities, as shown in the Appendix, is demonstrated in a case study of Manchester, resulting in cycling networks that balance directness against the need for safe and stress-free routes under different investment scenarios. A key benefit of the approach from a policy perspective is its ability to support egalitarian and cost-effective strategic cycle network planning.

Efficient bicycle networks and expansion strategies

“…Our readings have provided articles on computational methods to assess performance by some particular network metrics (Boisjoly et al, 2020;Carl et al, 2013;Mahfouz et al, 2021;Szell et al, 2022;Vybornova et al, 2022), articles that explore empirical cyclist preferences between network qualities (Broach et al, 2012;Buehler & Dill, 2012;Crane et al, 2017;Dill, 2009;Dill & Carr, 2003;Nello-Deakin, 2020;Stinson & Bhat, 2003;Winters et al, 2010), as well as one article providing strategic guidelines for network expansion in different cities (Reggiani et al, 2022). The common finding across most of these articles is that all the previously mentioned network attributes matter, but directness is ultimately the most dominant one due to its effect on travel speed.…”

“…According to Reggiani et al (2022), going from low level of bicycle-maturity to high bicycle-maturity, the preferred order of infrastructure expansion is building direct facilities between most important locations first, then increasing the spatial coverage of the network, then increasing connectivity by bridging missing links, and finally when most of the expansion is done, making capacity and comfort improvements to the existing facilities. According to results of Mahfouz et al (2021), it is plausible that equitable spatial distribution of facilities is not always in conflict with "utilitarian expansion", indicating that could be room for incorporating egalitarian principles…”

“…It provides less direct routes, which compromises the competitiveness of the bicycle mode, and thus reduces some of the user attraction, which is not desired if the city plans to increase the number of cyclists. The minimum spanning tree could also prioritize already developed areas while ignoring under-developed ones (Mahfouz et al, 2021;Szell et al, 2022), which could reinforce the socioeconomic inequalities among these neighbourhoods and contribute to segregation. The resiliency of this network is low because an interruption, such as a roadwork, cuts the entire network into disconnected pieces (Szell et al, 2022).…”

“…In the long-term, it is obviously good to pursue spatial equity, but under uncertain financial outlook in the future and the near-future climate crisis, it is tempting to just maximize the modal shift to cycling. Mahfouz et al (2021) developed a road segment prioritization algorithm for cycling infrastructure, which can be applied either city-wide (utilitarian expansion) or at a community level such that the investment gets distributed proportionally (egalitarian expansion). They found that the egalitarian algorithm, while equalizing the distribution of investment, did not come at any noticeable cost of less connectivity or less city-wide gains.…”

Predicting the impact of urban change in pedestrian and road safety

“…At the same time, many tasks may be automated to some degree by means of urban sensors and big data analysis. In line with other recent advances which take profit of new and extensive data-sets 6,18,29,41 , machine learning 4,5,5,10,43 and their combination with complex networks tools 16,19,28,30,49,61 , our work focuses on the automated assessment and improvement of urban traffic safety, for both pedestrian and vehicles.…”