Ahmed Osama

Evaluating the impact of connectivity, continuity, and topography of sidewalk network on pedestrian safety

With the increasing demand for sustainability, walking is being encouraged as one of the main active modes of transportation. However, pedestrians are vulnerable to severe injuries when involved in crashes which can discourage road users from walking. Therefore, studying factors that affect the safety of pedestrians is important. This paper investigates the relationship between pedestrian-motorist crashes and various sidewalk network indicators in the city of Vancouver. The goal is to assess the impact of network connectivity, directness, and topography on pedestrian safety using macro-level collision prediction models. The models were developed using generalized linear regression and full Bayesian techniques. Both walking trips and vehicle kilometers travelled were used as the main traffic exposure variables in the models. The safety models supported the safety in numbers hypothesis showing a non-linear positive association between pedestrian-motorist crashes and the increase in walking trips and vehicle traffic. The model results also suggested that higher continuity, linearity, coverage, and slope of sidewalk networks were associated with lower crash occurrence. However, network connectivity was associated with higher crash occurrence. The spatial effects were accounted for in the full Bayes models and were found significant. The models provide insights about the factors that influence pedestrian safety and the spatial variability of pedestrian crashes within a city, which can be useful for the planning of pedestrian networks.

A cross-comparison of different techniques for modeling macro-level cyclist crashes

Despite the recognized benefits of cycling as a sustainable mode of transportation, cyclists are considered vulnerable road users and there are concerns about their safety. Therefore, it is essential to investigate the factors affecting cyclist safety. The goal of this study is to evaluate and compare different approaches of modeling macro-level cyclist safety as well as investigating factors that contribute to cyclist crashes using a comprehensive list of covariates. Data from 134 traffic analysis zones (TAZs) in the City of Vancouver were used to develop macro-level crash models (CM) incorporating variables related to actual traffic exposure, socio-economics, land use, built environment, and bike network. Four types of CMs were developed under a full Bayesian framework: Poisson lognormal model (PLN), random intercepts PLN model (RIPLN), random parameters PLN model (RPPLN), and spatial PLN model (SPLN). The SPLN model had the best goodness of fit, and the results highlighted the significant effects of spatial correlation. The models showed that the cyclist crashes were positively associated with bike and vehicle exposure measures, households, commercial area density, and signal density. On the other hand, negative associations were found between cyclist crashes and some bike network indicators such as average edge length, average zonal slope, and off-street bike links.

Evaluating the Impact of Socioeconomics, Land Use, Built Environment, and Road Facility on Cyclist Safety

With the increasing demand for sustainability, the use of cycling as an efficient active mode of transportation is being encouraged. However, the vulnerability of cyclists to severe injuries in crashes can discourage road users from cycling. Therefore, the study of the factors that affect the safety of cyclists is important. This paper describes an investigation of the relationship between cyclist–motorist crashes and various traffic zone characteristics in Vancouver, British Columbia, Canada. The goal was to assess the impacts of socioeconomics, land use, the built environment, and the road facility on cyclist safety through the use of macrolevel collision prediction models. The models were developed by generalized linear regression and full Bayesian techniques. An actual bike exposure indicator (the number of bike kilometers traveled) and the number of vehicle kilometers traveled were used as exposure variables in the models. The safety models showed that cyclist–motorist crashes were nonlinearly associated with an increase in bike, vehicle, and transit traffic as well as socioeconomic variables (i.e., population, employment, and household densities), variables related to the built environment (transit stop, traffic signal, and light pole densities), commercial area density, and the proportion if arterial–collector roads. The models revealed, however, a decline in cyclist–motorist crashes in association with an increase in the proportions of local roads and off-street bike links and an increase in recreational and residential area densities. The spatial effects were accounted for in the full Bayes models and were found to be significant; such a finding implies the importance of consideration of the spatial correlation in the development of macrolevel cyclist safety models.

Developing crash modification functions for pedestrian signal improvement

Pedestrian signals are viable traffic control devices that help pedestrians to cross safely at intersections. Although the literature is extensive when dealing with pedestrian signals design and operations, few studies have focused on the potential safety benefits of installing pedestrian signals at intersections. Most of these studies employed simple before-after (BA) safety evaluation techniques which suffer from methodological and statistical issues. Recent advances in safety evaluation research advocate the use of crash modification functions (CMFunctions) to represent the safety effectiveness of treatments. Unlike crash modification factors (CMFs) that are represented as single values, CMFunctions account for variable treatment location characteristics (heterogeneity). Therefore, the main objective of this study was to quantify the safety impact of installing pedestrian signals at signalized intersections by developing CMFunctions within an observational BA study. The use of observational BA framework to develop the CMFunctions avoids the cross-sectional approach where the functions are derived based on a single time period and no actual treatment intervention. Treatment sites heterogeneity was incorporated into CMFunctions using fixed-effects and random-effects regression models. In addition to heterogeneity, the paper also advocates the use of CMFunctions with a time variable to acknowledge that the safety treatment (intervention) effects do not occur instantaneously but are spread over future time. This is achieved using non-linear intervention (Koyck) models, developed within a hierarchical full Bayes context. The results demonstrated the importance of considering treatment sites heterogeneity (i.e., different circulating volumes and area type among treated locations) and time trends when developing CMFunctions for pedestrian signal improvement.

Crash Modification Functions for Installation of Left-Turn Lanes at Signalized Intersection Approaches

This paper presents the results of a study that developed crash modification (CM) functions for installing left-turn lanes at signalized intersection approaches. CM functions were obtained from a longitudinal before–after safety study that accounted for treatment location characteristics (heterogeneity). This approach for developing CM functions has several advantages over the commonly used cross-sectional evaluations, which have several statistical shortcomings. The developed CM functions incorporate a time variable to acknowledge that the safety treatment effects do not occur instantaneously but are spread over future time; this result was achieved with a nonlinear intervention model with the full Bayes method. Twelve treatment sites were selected for the evaluation, along with 67 comparison sites. The treatment included the addition of one or more LT lanes for each intersection. The analysis showed significant safety improvements for fatal-plus-injury and total collisions but statistically nonsignificant reductions for property-damage-only collisions. The significant covariates included in the CM functions were the time trend, total entering volumes, and category of the new left-turn lanes installation.

A Novel Technique to Identify Hot Zones for Active Commuters’ Crashes

This paper presents an approach to identify and rank accident-prone (hot) zones for active transportation modes. The approach aims to extend the well-known empirical Bayes (EB) potential for safety improvement (PSI) method to cases where multiple crash modes are modeled jointly (multivariate modeling). In this study, crash modeling was pursued with a multivariate model, incorporating spatial effects, using the full Bayes (FB) technique. Cyclist and pedestrian crash data for the City of Vancouver (British Columbia, Canada) were analyzed for 134 traffic analysis zones (TAZs) to detect active transportation hot zones. The hot zones identification (HZID) process was based on the estimation of the Mahalanobis distance, which can be considered an extension to the PSI method in the context of multivariate analysis. In addition, a negative binomial model was developed for cyclist and pedestrian crashes, where the EB PSI for each mode crash was quantified. The cyclist and pedestrian PSIs were combined to detect active transportation hot zones. Overall, the Mahalanobis distance method is found to outperform the PSI method in terms of consistency of results; and discrepancy is observed between the hot zones identified using both approaches.