Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model

• Published in: Accident analysis and prevention Vol. 200; p. 107533
• Main Authors: Costa, Miguel, Lima Azevedo, Carlos, Siebert, Felix Wilhelm, Marques, Manuel, Moura, Filipe
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2024
• Subjects: Accident severity model; Built environment typology; Cycling safety; Gaussian–Bernoulli mixture; Latent class discrete outcome model; Accident severity model; Gaussian–Bernoulli mixture; Built environment typology; Cycling safety; Latent class discrete outcome model
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2024.107533

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle. This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian–Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information. We jointly estimate model components, combining machine learning and econometric approaches, allowing for more intricate and flexible representations while maintaining interpretability. Results show the potential of our approach. Risk factors are indexed depending on where accidents occurred and their contribution. We can discover complex relations between specific built environments and accident characteristics and uncover differences in the impact of certain accident factors on one environment typology but not others. Using multiple data sources also proves helpful as an additional layer of knowledge, providing unique value to understand and model cycling accidents. Another critical aspect of our approach is the potential for simulation, where locations can be examined through simulated accident features to understand the inherent risk of various locations. These findings highlight the ability to capture heterogeneity in accidents and their relation to the built environment. Capturing such relations allows for more direct countermeasures to risky situations or policies to be designed, simulated, and targeted. •A latent class discrete outcome model was used to analyze cycling accidents.•Modeling used a two-part process via a joint machine learning and econometric tool.•Results show a relation between the built environment and accident-contributing factors.•Results underscore a holistic influence of the built environment on the severity of accidents.•Authoritative accident records were augmented with volunteered geographic information.