Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

Abstract

Over the last decade, demand for active transportation modes such as walking and bicycling has increased. While it is desirable to provide high levels of safety for these eco-friendly modes of travel, unfortunately, the overall percentage of pedestrian and bicycle fatalities increased from 13% to 18% of total road-related fatalities in the last decade. In San Diego County, although the total number of pedestrian and bicyclist fatalities decreased over the same period of time, a similar trend with a more drastic change is observed; the overall percentage of pedestrian and bicycle fatalities increased from 19.5% to 31.8%. This study aims to estimate pedestrian and bicyclist exposure and identify signalized intersections with highest risk for walking and bicycling within the city of San Diego, California, USA. Multiple data sources such as automated pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques, a new sampling strategy, and automated video processing methods were adopted to demonstrate a holistic approach that can be applied to identify facilities with highest need of improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection. Automated pedestrian and bicycle counting models utilized in this study reached a high accuracy, provided certain conditions exist in video data. Results from exposure modeling showed that pedestrian and bicyclist volume was characterized by transportation network, population, traffic generators, and land use variables. There were both similarities and differences between pedestrian and bicycle models, including different spatial scales of influence by mode. Additionally, the study quantified risk incorporating injury severity levels, frequency of victims, distance crossed, and exposure into a single equation. It was found that not all intersections with the highest number of pedestrian and bicyclist victims were identified as high-risk after exposure and other factors such as crash severity were taken into account.

Spatial and Temporal Analysis of Road Traffic Accidents in Major Californian Cities Using a Geographic Information System

“…This study focuses on road traffic accidents in four major cities of California: Los Angeles, Sacramento, San Diego, and San Jose. These cities experienced a high rate of traffic collisions in the past years, as stated in recent studies [15,16,33], which makes them essential locations for our study. Figure 1 shows a graphical representation of the regions under study.…”

“…González et al (2019) examined crashes in the Los Angeles region and the Bay Area, noting significant increases in crashes associated with commercial gentrification [14]. Hasani et al (2019) explored the rise in fatalities among bicyclists and pedestrians, while in San Jose, Kasmalkar and Suckale (2021) identified an increase in accident rates during peak hours [15,16]. Khaghani (2020) reported over 3000 traffic accidents per year in Sacramento [33].…”

“…According to recent data from the National Highway Traffic Safety Administration (NHTSA), traffic accidents caused approximately 42,795 people to die in 2022 [12]. Many researchers, such as those in [13][14][15][16][17], have made an effort to understand and reduce road traffic accidents; however, gaps remain in the comprehensive analysis of such accidents, particularly in areas like California. This state experienced an increase in fatalities of about 7.6% from 2020 to 2021, leading to a Mileage Death Rate of 1.38 [18].…”

“…While significant research has explored road traffic accidents [13][14][15][16][17], this study integrates advanced GIS techniques (space-time cube analysis) alongside non-parametric statistical and spatial techniques (Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Kernel Density Estimation (KDE), the Getis-Ord Gi* method) to conduct a spatio-temporal analysis of traffic accidents across the four major Californian cities: Los Angeles, Sacramento, San Diego, and San Jose. Our objectives were as follows: (1) To observe and analyze the spatio-temporal dynamics of road traffic accidents in these developed cities, capturing patterns across different seasons and years; (2) to study the diverse impacts of the traffic density, weather conditions, and population density on accident frequency and severity; and (3) to provide detailed spatio-temporal analyses of accident hotspots and locations.…”

Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections

“…Traditionally, crash frequency and crash severity have been used as metrics for safety evaluations. Crash data help in identifying high-risk locations, current conditions, necessary safety improvements, and safety evaluation of road users [4,5]. However, unpredictability and irregularity of crash occurrences in the real environment result in the inefficiency of quantitative and qualitative crash determination.…”