A spatiotemporal deep learning approach for pedestrian crash risk prediction based on POI trip characteristics and pedestrian exposure intensity.

Pedestrians represent a population of vulnerable road users who are directly exposed to complex traffic conditions, thereby increasing their risk of injury or fatality. This study first constructed a multidimensional indicator to quantify pedestrian exposure, considering factors such as Point of Interest (POI) attributes, POI intensity, traffic volume, and pedestrian walkability. Following risk interpolation and feature engineering, a comprehensive data source for risk prediction was formed. Finally, based on risk factors, the VT-NET deep learning network model was proposed, integrating the algorithmic characteristics of the VGG16 deep convolutional neural network and the Transformer deep learning network. The model involved training non-temporal features and temporal features separately. The training dataset incorporated features such as weather conditions, exposure intensity, socioeconomic factors, and the built environment. By employing different training methods for different types of causative feature variables, the VT-NET model analyzed changes in risk features and separately trained temporal and non-temporal risk variables. It was used to generate spatiotemporal grid-level predictions of crash risk across four spatiotemporal scales. The performance of the VT-NET model was assessed, revealing its efficacy in predicting pedestrian crash risks across the study area. The results indicated that areas with concentrated crash risks are primarily located in the city center and persist for several hours. These high-risk areas dissipate during the late night and early morning hours. High-risk areas were also found to cluster in the city center; this clustering behavior was more prominent during weekends compared to weekdays and coincided with commercial zones, public spaces, and educational and medical facilities.

Do factors associated with older pedestrian crash severity differ? A causal factor analysis based on exposure level of pedestrians.

OBJECTIVE: Older pedestrians are more likely to have severe or fatal consequences when involved in traffic crashes. Identifying the factors contributing to the severity and possible interdependencies between factors in specific exposure areas is the first step to improving safety. Therefore, examining the causal factors' impact on pedestrian-vehicle crash severity in a given area is vital for formulating effective measures to reduce the risk of pedestrian fatalities and injuries. METHODS: This study implements the Thiessen polygon algorithm deployed to define older pedestrians' exposure influence area. Enabling trip characteristics and built environment information as exposure index settings for the background of the pedestrian severity causal analysis. Then, structural equation modeling (SEM) was applied to conduct a factor analysis of the crash severity in high- and low-exposure areas. The SEM evaluates latent factors such as driver risk attitude, risky driving behavior, lack of risk perception among older pedestrians, natural environment, adverse road conditions for driving or walking, and vehicle conditions. The SEM crash model also establishes the relationship between each latent factor. RESULTS: In total, drivers' risky driving behavior (0.270, p < 0.05) in low-exposure areas significantly impacts older pedestrian crash severity more than in high-exposure areas. Lack of risk perception among older pedestrians (0.232, p < 0.05) is the most critical factor promoting crash severity in high-exposure areas. The natural environment (0.634, p < 0.05) in high-exposure areas positively influences older pedestrians' lack of risk perception more than in low-exposure areas. CONCLUSIONS: Significant group differences (p-values ∼ 0.001-0.049) existed between the causal factors of the high-exposure risk areas and the low-exposure risk factors. Different exposure intervals require detailed scenarios based on the critical risks identified. The crash severity promotion measures in different exposure areas can be focused on according to the critical causes analyzed. Those clues, in turn, can be used by transportation authorities in prioritizing their plans, policies, and programs toward improving the safety and mobility of older pedestrians.

Investigating the impact of environmental and temporal features on mobile phone distracted driving behavior using phone use data.

Mobile phone distracted driving (MPDD) is one of the most significant and common factors in distraction-affected crashes. In previous studies, MPDD has been described as a self-selected behavior that affects driving performance, rather than a multidimensionally impacted behavior. In this study, the researchers hypothesized that external environmental features significantly impacted MPDD and tested this hypothesis by structural equation modeling (SEM). Three external latent variables (road, operation, and control factors) were measured at different times during weekdays in urban areas of Texas by integrating a large number of mobile phone sensor data and roadway inventory data. A structural model was developed to test the relationship between the latent variables and the rate of drivers involved in MPDD (MPDDR) on the roadway during different time periods. Finally, the data summary and model results revealed significant temporal effects. Standardized estimates from the SEM results revealed the positive impact of roads factors in the morning peak that broader shoulders, wider medians, and smaller curve radians were correlated with higher MPDDR in the morning peak hours; the negative impact of operation factors that higher average annual daily truck traffic (truck AADT) were associated with lower MPDDR significantly. And the impact of control factors on MPDDR is positive. In other words, the road segments with a large number of traffic signals in urban areas had a higher MPDDR than those without traffic signals. These findings could assist transportation and legislation agencies in the development of appropriate countermeasures or enforcement tactics and implement them effectively to reduce the occurrence of MPDD. In addition, this study provides a novel perspective close to the actual consideration of drivers about using mobile phones while driving, in the context of MPDD research, rather than comparing driver groups and vehicle performance.

Analysis of pedestrian-vehicle crash injury severity factors in Colorado 2006-2016.

This paper investigates factors associated with the severity of pedestrian outcomes from motor vehicle crashes by analyzing a database of all 13,856 reported pedestrian crashes in Colorado over an 11-year period from 2006 to 2016. A total of 14,391 pedestrians were involved in these crashes, resulting in 612 (4.3%) pedestrian fatalities, 11,576 (80.4%) pedestrian injuries, and 2203 (15.3%) property damage only outcomes. The objective is to analyze crash records, as similarly compiled by other states, to show how lives potentially saved by improved factor levels can be estimated as needed for benefit-cost comparisons of alternative countermeasures. Odds ratios of fatal versus non-fatal pedestrian outcomes are computed both independently (unadjusted) and from logistic regression (adjusted) for each factor level accounting for possible correlations between factors. Also computed are odds ratios for fatal plus incapacitating injuries and odds ratios for just 2011-2016 versus all years. This study found that intersection proximity, lighting condition, vehicle type and speed, pedestrian age, pedestrian impairment, and driver impairment by drugs or alcohol were all significant factors associated with the severity of pedestrian outcomes from motor vehicle crashes. Risk ratios from these odds ratios are used to estimate lives potentially saved by having better factor levels present at the time of these crashes. These estimates reflect the relative magnitudes of benefits that might be achieved by potential countermeasures taking into account the number of cases affected.