Analyzing Pedestrian Collision Risk Variables Through Statistical Modeling in San Diego


Book Description

This study aims to evaluate the relationship between 33 variables that fit into the categories of roadway environment, crossing characteristics, population characteristics, and travel behavior with collision risk for pedestrians in City of San Diego using 14 years worth of collision data. The dependent variable, collision risk, measures the ratio of pedestrian collisions to pedestrian volume, controlling for exposure. 60 study sites were disaggregated to approach and departure sides, which resulted in an expanded sample of 342 study cases. This study examines both four-way intersections and mid-block crossings. A preliminary analysis found 13 significant variables, all with weak associations with the pedestrian risk variable. Based on these results, two models were created and analyzed: simple linear regression models with the 13 significant variables, and a multiple linear regression model utilizing all 33 independent variables. The multiple linear regression model found four variables to be significant (sidewalk width, posted speed, curb ramp not present, and informal crosswalk not passable), all with a positive association. The significant variables from both models belong into two categories of variables: variables that increase the amount of time a pedestrian spends crossing the street without separation from vehicle traffic, and variables that decrease visibility. Recommendations for future studies include utilizing a larger sample to increase the probability of achieving statistical significance. This study’s inclusion of both mid-block and intersection sites increases the depth of understanding of pedestrian risk in the City of San Diego by more accurately reflecting the use of both intersection and mid-block crossing locations by pedestrians.




Pedestrian Crash Prediction and Analyzing Contributing Factors Across Texas


Book Description

This study applied tree-based machine learning methods to investigate the contributing factors to both crash frequency and injury severity in vehicle-pedestrian crash events. Vehicle and roadway characteristics, driver and pedestrian attributes, traffic controls and land use conditions, transit provision and weather conditions are used as covariates to predict pedestrian crash frequencies (by roadway segment) and injury severity levels (for pedestrians struck by vehicles on public roadways). In both cases, tree-based models offered significantly more prediction accuracy than traditional statistical models (using negative binomial and ordered probit specifications, with the same covariates). The tree-based models also offer valuable interpretability through the regression tree graph itself (with clear branching based on variable cut-points), variable importance plots (for each covariate), and partial dependence plots to help analysts understand the relationship between contributing factors and the target variable (count or severity). Average daily vehicle-miles travelled (DVMT) on each road segment, population density, segment length, census tract-level job density, distance from nearest K-12 school, transit stop density, and segment speed limits were estimated to be the top contributing factors for increasing pedestrian crash counts. DVMT has been found as the single most responsible factor for vehicle-pedestrian crashes and in a way representing pedestrian exposure to such situations. In terms of predicting injury outcomes, intoxication of the pedestrian and/or driver, higher speed limits at the site, crash location not being in the traffic way, older pedestrian, interstate highway locations, and dark and unlit conditions were predictors for more severe outcomes. Importantly, if the surrounding urban area’s population is reasonably high (over 25,000 persons), the probability of the pedestrian dying falls significantly, which supports the ‘safety in numbers’ idea, for more people available to help save the crash victims, or drivers going more slowly due to crowded conditions, closer hospitals, and so on. While few crash studies have included land use variables and local demographics, including proximity to schools, hospitals, and transit stops, population and jobs density variables appeared to add to crash counts and severity for pedestrians, though this is moderated by the 25,000-population threshold and distance variables







Evaluation of Pedestrian and Bicycle Exposure and Crash Risk at Signalized Intersections in San Diego


Book Description

Over the last decade, demand for active transportation modes such as walking and cycling 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 crash fatalities in the last decade. In San Diego County, although the total number of pedestrian and cyclist 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%. Technological advancement in transportation has been creating new opportunities to explore and investigate new sources of data for the purpose of improving safety planning. This study aims to identify signalized intersections with the highest risk for walking and cycling within the City of San Diego, California, USA. Multiple data sources such as permanent pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques and a new sampling strategy were adopted to demonstrate a holistic approach that can be applied to identify facilities with the highest need for improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection and estimate annual average daily pedestrian and bicyclist (AADP and AADB). Additionally, the study quantified risk incorporating injury severity levels, the 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.




Identifying Collision Parameters in Highway Work Zones Collisions Using Classification and Regression Trees


Book Description

Highway safety modeling represents a means to study interventions that can potentially reduce the thousands of collisions that result in injuries and fatalities each year. Statistical modeling of roadways can identify the roadways where safety interventions are most needed or where the largest effects of such interventions could be felt. Work zone areas are necessary to repair and build new roads that, unfortunately, can increase the risk of collision. Prior to a work zone becoming active, statistical models can estimate the numbers of collisions that will occur as a result of these work zones. Likewise the effectiveness of safety interventions prior to deployment can be used to estimate the reduction in collisions as a result of the interventions.The purpose of this research was to develop a method to estimate the numbers and types of collisions that occur on California roadways. This was achieved by using the classification and regression tree (CART) modeling method to identify key collision factors that result in injuries and fatalities by utilizing data from highway databases. After the factors had been identified, an empirical Bayesian (EB) model was then used to find the number of collisions that would occur on a given road. The CART method provides a weighting factor that can be used to find the number of each type of collision that will occur. Certain types of collisions are more likely to result in a serious injury or fatality can be identified though this method. Cost-benefit analysis could then be used to determine where targeted deployment of California Highway Patrolmen (COZEEP/MAZEEP) would be most effective in reducing these types of collisions.Based on the analysis of the combined CART and EB methods, the types of collisions that are more likely to cause injury/fatality on California highways were identified as well as factors that influence collisions. The methodology developed represents a significant step in highway analysis as it combined information from several established highway databases while also utilizing several highway safety research methods. More accurate prediction models can be used to evaluate where safety interventions, such as COZEEP/MAZEEP, would have the largest impact prior to implementation. These collision models can be used to find the more optimal locations to deploy said safety interventions. Three of four highways identified types of collisions that lead to injury/fatality collisions. Only one highway, I-680, identified the underlying primary factors that are likely to lead to injury/fatality collisions. Cost-benefit analysis was used to calculate the effective cost of deployment of officers based on the numbers and types of collisions reduced. This research provides the framework that can be applied to other roadways within California.




Formulation and Analysis of Pedestrian Safety Problems Using Bayesian Network Model


Book Description

Causes of pedestrian road accident have been a major concern to transportation engineers and other road safety professionals despite all efforts being applied to alleviate this problem. Although studies have aimed at modeling and analyzing the causes of pedestrian road accidents, the bulk of these studies have been found to be too stochastically oriented and more macroscopic than it is necessary. Consequently, the existing models seldom incorporate the interactions between pedestrians and their immediate environment. In this study, pedestrian crossing behavior during spring and summer season has been thoroughly investigated using Bayesian network modeling technique. The model was constructed with variables known to influence pedestrian crossing behavior either directly or indirectly. Stages of the model building process including Graphical Level (GL), Information or Qualitative Level (IL) and Quantitative Level (QL) have been discussed and implemented to extract useful information from both observed data and data elicited from stakeholders' opinion as well as experts' experience. The robustness of the Bayesian network model is compared based on its ability to produce physically meaningful results that truly reflects realistic behavior of a system. The model's results show that pedestrians often exhibit rational crossing behavior than they do irrationally and such an attitude is found to be influenced mostly by their own motives and less by external factors even though roadway environment did not favored them. Also, a sensitivity analysis carried out revealed that signal timing phase length is the most influential parameter that affects pedestrian crossing behavior.




Development of a Methodology for the Evaluation of Active Safety using the Example of Preventive Pedestrian Protection


Book Description

The book reports on a new methodology for optimization and evaluation of traffic safety, which simulates the processes involved in traffic conflicts on the basis of detailed dynamical, human, and technical models. The models incorporate the whole spectrum of human cognitive functions and responses, the responses of an active safety system and the interactions between the human and the system as they occur in a sample of relevant traffic contexts. Using the developed method, the author was able to assess the reduction in accidents and injuries as well as the possible side effects resulting from a preventive pedestrian-protection system. The book provides practical solutions in the area of active safety systems. It represents an interesting source of information for researchers and professionals as well as all stakeholders, including policy makers and consumer advocates, with the common goal of promoting the implementation and adoption of highly efficient systems for preventing accidents and injuries.




Traffic Monitoring Guide


Book Description




Risk Abstracts


Book Description

Provides citations and abstracts to the literature on risks arising from industrial, technological, environmental, and other sources, with an emphasis on assessment of the magnitude and probability of risk and the management of risk. The broad, multidisciplinary coverage of risk-related concerns ranges from public and environmental health to social issues and psychological aspects. Major areas of coverage include review articles, models and forecasting, technological risks, natural hazards, biological risks, environmental risks, medical and environmental health, economics and organization, industrial and labor, policy and planning, sociological factors, psychological aspects.