The role of perceived pedestrian safety on designing safer built environments

Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning

Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection with the behavior of road users. However, safety isn't always as it appears, and there is a scarcity of studies examining the association and mismatch between road traffic safety and road safety perceptions at the city scale, primarily due to the time-consuming nature of data acquisition. In this study, we applied an advanced deep learning model and street view images to predict and map human perception scores of road safety in Manhattan. We then explored the association and mismatch between these perception scores and traffic crash rates, while also interpreting the influence of the built environment on this disparity. The results showed that there was heterogeneity in the distribution of road safety perception scores. Furthermore, the study found a positive correlation between perception scores and crash rates, indicating that higher perception scores were associated with higher crash rates. In this study, we also concluded four perception patterns: "Safer than it looks", "Safe as it looks", "More dangerous than it looks", and "Dangerous as it looks". Wall view index, tree view index, building view index, distance to the nearest traffic signals, and street width were found to significantly influence these perception patterns. Notably, our findings underscored the crucial role of traffic lights in the "More dangerous than it looks" pattern. While traffic lights may enhance people's perception of safety, areas in close proximity to traffic lights were identified as potentially accident-prone regions.

Towards safer streets: A framework for unveiling pedestrians' perceived road safety using street view imagery

Road safety has become a global concern but its impact in low- and middle-income countries is widespread mainly due to lack of appropriate crash database system and under-reporting. In this context, the primary objective of this paper is to provide a scalable framework for unveiling pedestrians' perceived road safety that can also be applied in regions where accessible crash data are limited or near-crashes are left unreported. In the first step of our methodology, a deep learning architecture-based semantic segmentation model (HRNet+OCR) is trained using labeled Google Street View (GSV) images from specific study areas in Dhaka, Bangladesh, which facilitates the identification of both man-made components (such as roads, sidewalks, buildings, and vehicles) and natural elements (including trees and sky). The developed model showed excellent performance in identifying different features in an image by achieving high precision (0.95), recall (0.97), F1-score (0.96), and intersection over union (IoU) (91.86). Secondly, a group of trained raters scored the perceived road safety on an ordinal scale from 0 to 10 (extremely unsafe to extremely safe to walk in terms of road crashes) by assessing the GSV images. Then, several regression models have been used on features extracted from GSV images, and socio-demographic factors (i.e., population density, and relative wealth index) to estimate the perceived road safety, and random forest regression model was found to perform the best. Further, Shapley Additive Explanations (SHAP), a model-agnostic technique has been used for examining feature importance by computing the contribution of each feature to the random forest regression model output. The results show that sidewalk, road, population density, wall, and relative wealth index have higher impact on determining the perceived road safety rating. Additionally, the results of t-tests between the average perceived road safety scores for crash-prone and non crash-prone areas revealed the existence of significant differences. This study also provides perceived road safety rating map on a neighborhood scale, which can be a useful visualization tool for policy-makers and practitioners to identify the road safety deficiencies at specific locations, and formulate appropriate and strategic countermeasures to improve pedestrians' road safety.

Clustering pedestrians' perceptions towards road infrastructure and traffic characteristics

In India, over 25,000 pedestrian fatalities occur due to road crashes every year. While several studies have identified possible causative factors that contribute to these fatalities, little is known about how pedestrians perceive their surrounding environment. This study attempts to bridge this gap by analysing the pedestrian perception of the built environment and traffic-related aspects considering urban roads (arterial and sub-arterial). Fourteen parameters were selected to assess pedestrian perception, and four factors were derived through factor analysis. The obtained factor scores were then subjected to two-step cluster analysis to determine whether pedestrian perception is different for people from different socio-economic demographics with varying travel behaviour. Based on the results obtained from the descriptive analysis, the respondents were most satisfied with the 'quality of streetlights at sidewalks' and 'visibility/sight distances', while they were most dissatisfied with 'pedestrian volume at sidewalks' and 'lighting facilities at crossings'. From the cluster analysis, it can be summarized that female pedestrians walk less frequently than males and perceive a higher probability of collision or near-collision incidents against male pedestrians. The study findings can aid the policymakers in the assessment of the pedestrian perception of the existing road infrastructure and suggest improvements to ensure pedestrian safety.

Traffic as a Barrier to Walking Safely in the United States: Perceived Reasons and Potential Mitigation Strategies

•

Almost a quarter of US adults perceive traffic as a barrier to safe walking.

•

Vehicle speed is the leading perceived traffic characteristic of concern.

•

Respondents perceive sidewalks as the most favorable strategy to mitigate risk.

Motor vehicle traffic is commonly cited as a barrier to walking, but national level perceptions of traffic characteristics that negatively influence walking and potential traffic mitigation strategies remain unclear. The objectives of this study were to describe perceptions of (1) traffic characteristics that make walking unsafe in the United States and (2) potential mitigation strategies to address these concerns among those who report traffic as a barrier to walking. Data were from FallStyles, a nationwide internet panel survey conducted in October 2019 (n = 3,284 adults). Respondents reported if traffic makes walking unsafe where they live; those who answered yes were then asked about traffic characteristics that make walking unsafe (number of vehicles, speed of vehicles, distracted or impaired driving, types of vehicles, and other reasons) and potential mitigation strategies (new or improved sidewalks, crosswalks, pedestrian signals, street lighting, things that slow vehicles down, separating the sidewalk from the road, fewer vehicle lanes, and other). Prevalence of responses was assessed overall and by select sociodemographic and geographic characteristics, and by walking status. Nearly 25% of US adults reported that traffic is a barrier to walking where they live. Of these, 79% selected vehicle speed as a contributing traffic characteristic, and 57% indicated new or improved sidewalks as a potential mitigation strategy. These top responses were shared across all sociodemographic, geographic, and walking behavior subgroups. Speed reduction efforts and built environment enhancements such as sidewalks may alleviate pedestrian safety concerns. Promotion campaigns may be needed to bring awareness to such changes.

Spatio-temporal patterns in pedestrian crashes and their determining factors: Application of a space-time cube analysis model

The number of vehicle accidents involving pedestrians in Korea has decreased gradually since the Pedestrian Safety and Convenience Enhancement Act was enacted in 2012, but the number of serious pedestrian-related crashes per capital remains near the top of a list of such rates for member countries of the Organization of Economic Cooperation and Development. Previous studies of pedestrian safety have been conducted based on various built environments. However, few have analyzed spatio-temporal changes and influential factors over more than 10 years, despite dramatic changes in the built environment during such time spans. Here, we examine big data on pedestrian-related crashes in Seoul from 2009 to 2018 using a space-time cube methodology and binary logistic regression analysis. The results show that the trend in pedestrians killed or severely injured is decreasing with pedestrian environment enhancement projects and pedestrian safety measures in Seoul. Also, the analysis reveals a need to pay more attention to pedestrian safety in areas with a large older population. Pedestrian safety measures should be reinforced in areas of concentrated wholesale and retail businesses. This study also indicates that illegal parking poses a threat to pedestrian safety. Lastly, this study confirms some positive impacts of redeveloped or newly developed areas and pedestrian environment enhancement projects on pedestrian safety.