The interactions between accessibility and crash risk from a social equity perspective: A case study at the Rotterdam-The Hague metropolitan region.

PROBLEM: Transport policies generally prioritize improving safety and accessibility levels, as they are regarded as the most important indicators of the quality of the transport system serving the public. However, inequalities associated with safety and accessibility issues are generally overlooked in these policies. Despite the importance and necessity of transport policies to address equity issues, there is still scarce knowledge on the interactions between equity, safety, and accessibility. This research aims to address this gap in the literature by creating a better understanding of the relationships between accessibility levels and traffic safety with a focus on social equity perspectives. METHOD: A crash risk evaluation method and a Gravity model are utilized to analyze cycling safety and accessibility to jobs by bicycle. Two linear regression models (LM) were conducted to investigate the statistical correlations between cycling crash risk and accessibility. Moreover, the Bivariate local Moran's I method was employed to assess the spatial inequalities of distribution of crash risk and job accessibility over different income-level populations. RESULTS: The analyses showed that low-income people are not only disadvantaged in terms of job accessibility by bicycle but are also exposed to higher cycling crash risks, compared to high-income groups. Furthermore, most disadvantaged zones that have the highest need for road safety and accessibility improvements are identified as areas where low-income populations are exposed to higher crash risk and/or have lower access to jobs by bicycle. SUMMARY: This study contributes to the transport literature by investigating the interactions between safety and accessibility and the impacts on transport equity. The findings of the statistical and spatial analysis are beneficial for the decision-makers, considering the probable mutual implications of land-use and transport developments and projects aiming to improve safety, accessibility, or both for different population groups.

A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas.

Built-environment factors potentially alleviate or aggravate traffic safety problems in urban areas. This paper aims to investigate the relationships of these factors with vehicle-bicycle and vehicle-vehicle property damage only (PDO) and killed and severe injury (KSI) crashes in urban areas. For this purpose, an area-level analysis using 100x100m2 cells, along with a Spatial Hurdle Negative Binomial regression model were employed. The study area is composed of a selection of municipalities in the Netherlands-Randstad Area where major land-use developments have occurred since the 1970s. The study was conducted by developing a rich dataset composed of various national and local databases. The findings reveal that built-environment factors and land-use policies have substantial impacts on safety, which cannot be neglected. The factors explaining the land-use density and diversity in the area (e.g., urbanity and function mixing levels), as well as the land-use design characteristics (indicated by average age of the neighborhoods), traffic and road network characteristics, and proximity to different destinations influence the probability, frequency, and severity of crashes in urban areas. Furthermore, low socioeconomic levels are associated with a higher frequency of traffic crashes.

Post COVID-19 teleworking and car use intentions. Evidence from large scale GPS-tracking and survey data in the Netherlands.

This study examines the changes in teleworking during the lockdown in April 2020 and the intention to change commuting behaviour after COVID-19 in the Netherlands. Survey data of 1,515 Dutch employees and large-scale smartphone-based GPS-data of the same participants before and during COVID-19 is used. The probability of increasing teleworking during COVID-19 is estimated using an ordinal logistic regression model, considering sociodemographic characteristics, the initial travel behaviour and the initial work situation as determining factors. Two binary logistic regression models are developed to analyse whether employees expect to continue teleworking after the COVID-19 pandemic and whether they will decrease car use for commuting. Both models consider teleworking and car use intentions in the context of behavioural changes during COVID-19. The main factors that influenced teleworking during the lockdown are job characteristics. Office workers and teaching staff were more likely to increase the amount of time spent working from home and showed a higher chance of changes in daily commuting routines. After COVID-19, office workers expect to increase teleworking. The results suggest that employees with a relatively large change in teleworking during the early lockdown expect to work from home more frequently after COVID-19. This effect is strengthened further by positive experiences with teleworking (i.e. more pleasure and higher productivity) and supporting policy measures by the employer, such as sufficient ICT facilities. The main conclusion related to intended changes in mode choice is that car use for commuting is expected to decrease after COVID-19, mostly because of an increase in teleworking.