A composite zonal index for biking attractiveness and safety

Kamel, Mohamed Bayoumi; Sayed, Tarek; Bigazzi, Alexander

doi:10.1016/j.aap.2020.105439

Cited by 24 publications

(15 citation statements)

References 52 publications

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“…The same data set was used previously to demonstrate the importance of accounting for mediation in safety models and to develop a comprehensive zone-based index to represent both biking attractiveness and bike crash risk (Kamel et al., 2019, 2020), modeling Bike Kilometers Traveled as a function of network characteristics (Osama et al., 2017), and accounting for measurement error in traffic exposure variables (e.g. BKT) in bike–vehicle crash modeling (Kamel and Sayed, 2020).…”

Section: Data Collectionmentioning

confidence: 99%

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

Kamel

Sayed

2020

Environment and Planning B: Urban Analytics and City Science

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There has been recent interest in the use of network analysis to quantify bike network features and their impact on biking levels and safety. However, limited bike network indicators have been evaluated. This study introduces a list of network indicators to quantify the bike network and study its effect on bike kilometers traveled and bike–vehicle crashes. Data from the city of Vancouver, Canada, are used as a case study. Full Bayesian modeling incorporating spatial effects is employed to develop Bike Kilometers Travelled (BKT) and bike–vehicle crash models. The developed BKT models show that the bike network centrality, assortativity, and weighted slope have negative associations with BKT, while the bike network directness, length, complexity and development, and connectivity have positive associations with BKT. The developed crash models show that the bike network length, centrality, assortativity, and continuity have negative associations with bike–vehicle crashes. On the other hand, the bike network complexity and development, connectivity, and linearity have positive associations with bike–vehicle crashes. The models provide insights that can be useful for planning bike networks to increase bike traffic and improve bike safety. The models also show that some changes to a bike network to increase bike traffic should be accompanied by crash risk-mitigating measures. As well, the models can be used to identify zones within a city that require safety improvements.

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Section: Data Collectionmentioning

confidence: 99%

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

Kamel

Sayed

2020

Environment and Planning B: Urban Analytics and City Science

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“…Consistent with prior research, we demonstrated that the length of the bicycle network was positively associated with various measures of bicycle ridership. 11,12,27 A surprising finding was variation in the direction of association between degree centrality and bicycle ridership. In the innercity region of Inner Melbourne, the finding of centrality being negatively associated with bicycle ridership is logical; high network centrality indicates low inter-connectivity and accessibility of the network.…”

Section: Discussionmentioning

confidence: 99%

Association between network characteristics and bicycle ridership across a large metropolitan region

Beck¹,

Pettit²,

Winters³

et al. 2021

Preprint

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Background: Numerous studies have explored associations between bicycle network characteristics and bicycle ridership. However, the majority of these studies have been conducted in inner metropolitan regions and as such, there is limited knowledge on how various characteristics of bicycle networks relate to bicycle trips within and across entire metropolitan regions, and how the size and composition of study regions impact on the association between bicycle network characteristics and bicycle ridership.Methods: We conducted a retrospective analysis of household travel survey data and bicycle infrastructure in the Greater Melbourne region, Australia. Seven network metrics were calculated and Bayesian spatial models were used to explore the association between these network characteristics and bicycle ridership (measured as counts of the number of trips, and the proportion of all trips that were made by bike). Results: We demonstrated that bicycle ridership was associated with several network characteristics, and that these characteristics varied according to the outcome (count of the number of trips made by bike or the proportion of trips made by bike) and the size and characteristics of the study region.Conclusions: These findings challenge the utility of approaches based on spatially modelling network characteristics and bicycle ridership when informing the monitoring and evaluation of bicycle networks. There is a need to progress the science of measuring safe and connected bicycle networks for people of all ages and abilities.

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“…More than half of the listed papers take into account user demand characteristics (e.g., gender, age and population density) and mobility patterns [21,23,30,31,37,42]. These data are usually difficult to estimate in a design phase and to obtain when the BSS is operational.…”

Section: Input Datamentioning

confidence: 99%

“…Ref. [25] fit a model to predict the potential for accidents based on the presence of both types of road users, i.e., drivers and cyclists, when there are shared cycle lanes, and, recently, Kamel et al [42] developed the Bike Safety Index (BSI), a crash prediction model that includes variables such as vehicle kilometres travelled in the area, cyclist kilometres travelled in the same area, average link length and signal density.…”

Section: Safety Versus Securitymentioning

confidence: 99%

Attractiveness of Bike-Sharing Stations from a Multi-Modal Perspective: The Role of Objective and Subjective Features

Nogal

Jiménez

2020

Sustainability

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When analysing the performance of bike-sharing scheme (BSS) stations, it is common to find stations that are located in specific points that capture the interest of users, whereas nearby stations are clearly underused. This uneven behaviour is not totally understood. This paper discusses the potential factors influencing station attractiveness, supported by the related literature on cyclists’ and pedestrians’ preferences and the characteristics of the stations themselves. The existing literature addresses these topics independently, while this work unites them by proposing a non data-extensive methodology that allows the attractiveness of BSS stations to be assessed. Attractiveness in this context is understood as the set of physical, environmental and service-related features of a bike station that make it more appealing for BSS users than nearby stations. Special attention is paid to differentiating objective features, based on facts, from subjective features, those influenced by personal perceptions. This classification becomes important in this context because subjective aspects can change from one geographical location to another, making the findings related to these aspects difficult to apply to other regions. Moreover, the assessment of the stations’ levels of safety and security is included. Thus, the proposed measure of attractiveness of BSS stations provides a balanced overview of several features. The consideration of station attractiveness when designing BSS layouts will help to refine the design of new layouts and will assist in conducting an appropriate diagnostic evaluation of the existing ones. This tool will allow urban and transportation planners to reduce re-balancing costs and to maximise user satisfaction at a low cost, which have a direct impact on improving the urban sustainability. The proposed method is applied to the Dublin bike sharing scheme, Dublinbikes, with good performance results.

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A composite zonal index for biking attractiveness and safety

Cited by 24 publications

References 52 publications

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

Association between network characteristics and bicycle ridership across a large metropolitan region

Attractiveness of Bike-Sharing Stations from a Multi-Modal Perspective: The Role of Objective and Subjective Features

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