Jillian Strauss

Estimating Potential Effect of Speed Limits, Built Environment, and Other Factors on Severity of Pedestrian and Cyclist Injuries in Crashes:

Road facilities in urban areas are a major source of injury for nonmotorized road users despite the benefits of nonmotorized transportation. In particular, large Canadian cities such as Montreal face serious problems with pedestrian and cyclist safety. To address these problems, funds are continually allocated through different safety improvement programs such as reduction of speed limits, improvement of intersections, and increased traffic enforcement. However, few analytical tools help to identify and quantify the benefits of countermeasures (e.g., roadway design, speed management strategies, or land use policies) in reducing accident frequency and severity. Injury severity models were developed to determine the effects of road design, built environment, speed limits, and other factors (e.g., vehicle characteristics and movement type) on injury severity levels of pedestrians and cyclists involved in collisions with motor vehicles. Sources of data included police reports describing vehicle–pedestrian and vehicle–cyclist collisions, as well as information on land use, transit network, and road design attributes from the city of Montreal. The impacts of road design, land use, built environment, and other strategies on the injury severity levels of vulnerable road users were investigated. Factors such as darkness, vehicle movement, whether an accident occurred at an intersection, vehicle type, and land use mix affected the severity of pedestrian injuries from collisions. For cyclists, however, only vehicle movement and whether the accident occurred at a signalized intersection had significant effects on the severity of the injury.

Disaggregate Exposure Measures and Injury Frequency Models of Cyclist Safety at Signalized Intersections

This paper proposes a new approach to represent cyclist risk exposure. This approach considers disaggregate motor vehicle and cyclist flows and develops cyclist injury frequency models. Three definitions of risk exposure were used in this research, including aggregated flows, motor vehicle flows aggregated by movement type, and potential conflicts between motor vehicles and cyclists. As an application environment, a large sample of signalized intersections on the island of Montreal, Quebec, Canada, was used, along with data that comprised disaggregate motor vehicle and cyclist flows. Several negative binomial models were fitted to the data. This study showed that cyclist collisions were sensitive to changes in both cyclist and motor vehicle flows. A 10% increase in bicycle flow was associated with a 4.4% increase in the frequency of cyclist injuries. A 10% increase in the total number of motor vehicles that passed through the intersection would result in a 3.4% increase in cyclist injury occurrence. When motor vehicle flows were considered on the basis of movement type, right-turn movements had a great effect on injury occurrence. Similar results, which identified right turns as having the greatest effect on cyclist injuries, were produced when the impact of potential conflicts was determined. The number of bus stops in the proximity of the intersection increased cyclist injury occurrence. Some geometric design factors, such as the presence of a median, parking entrance, and the number of intersection legs, were tested. The effect, however, was found to be statistically nonsignificant.

Mapping cyclist activity and injury risk in a network combining smartphone GPS data and bicycle counts.

In recent years, the modal share of cycling has been growing in North American cities. With the increase of cycling, the need of bicycle infrastructure and road safety concerns have also raised. Bicycle flows are an essential component in safety analysis. The main objective of this work is to propose a methodology to estimate and map bicycle volumes and cyclist injury risk throughout the entire network of road segments and intersections on the island of Montreal, achieved by combining smartphone GPS traces and count data. In recent years, methods have been proposed to estimate average annual daily bicycle (AADB) volume and injury risk estimates at both the intersection and segment levels using bicycle counts. However, these works have been limited to small samples of locations for which count data is available. In this work, a methodology is proposed to combine short- and long-term bicycle counts with GPS data to estimate AADB volumes along segments and intersections in the entire network. As part of the validation process, correlation is observed between AADB values obtained from GPS data and AADB values from count data, with R-squared values of 0.7 for signalized intersections, 0.58 for non-signalized intersections and between 0.48 and 0.76 for segments with and without bicycle infrastructure. The methodology is also validated through the calibration of safety performance functions using both sources of AADB estimates, from counts and from GPS data. Using the validated AADB estimates, the factors associated with injury risk were identified using data from the entire population of intersections and segments throughout Montreal. Bayesian injury risk maps are then generated and the concentrations of expected injuries and risk at signalized intersections are identified. Signalized intersections, which are often located at the intersection of major arterials, witness 4 times more injuries and 2.5 times greater risk than non-signalized intersections. A similar observation can be made for arterials which not only have a higher concentration of injuries but also injury rates (risk). On average, streets with cycle tracks have a greater concentration of injuries due to greater bicycle volumes, however, and in accordance with recent works, the individual risk per cyclist is lower, justifying the benefits of cycle tracks.

Multimodal injury risk analysis of road users at signalized and non-signalized intersections.

This paper proposes a multimodal approach to study safety at intersections by simultaneously analysing the safety and flow outcomes for both motorized and non-motorized traffic. This study uses an extensive inventory of signalized and non-signalized intersections on the island of Montreal, Quebec, Canada, containing disaggregate motor-vehicle, cyclist and pedestrian flows, injury data, geometric design, traffic control and built environment characteristics in the vicinity of each intersection. Bayesian multivariate Poisson models are used to analyze the injury and traffic flow outcomes and to develop safety performance functions for each mode at both facilities. After model calibration, contributing injury frequency factors are identified. Injury frequency and injury risk measures are then generated to carry out a comparative study to identify which mode is at greatest risk at intersections in Montreal. Among other results, this study identified the significant effect that motor-vehicle traffic imposes on cyclist and pedestrian injury occurrence. Motor-vehicle traffic is the main risk determinant for all injury and intersection types. This highlights the need for safety improvements for cyclists and pedestrians who are, on average, at 14 and12 times greater risk than motorists, respectively, at signalized intersections. Aside from exposure measures, this work also identifies some geometric design and built environment characteristics affecting injury occurrence for cyclists, pedestrians and motor-vehicle occupants.

Are signalized intersections with cycle tracks safer? A case-control study based on automated surrogate safety analysis using video data.

Cities in North America have been building bicycle infrastructure, in particular cycle tracks, with the intention of promoting urban cycling and improving cyclist safety. These facilities have been built and expanded but very little research has been done to investigate the safety impacts of cycle tracks, in particular at intersections, where cyclists interact with turning motor-vehicles. Some safety research has looked at injury data and most have reached the conclusion that cycle tracks have positive effects of cyclist safety. The objective of this work is to investigate the safety effects of cycle tracks at signalized intersections using a case-control study. For this purpose, a video-based method is proposed for analyzing the post-encroachment time as a surrogate measure of the severity of the interactions between cyclists and turning vehicles travelling in the same direction. Using the city of Montreal as the case study, a sample of intersections with and without cycle tracks on the right and left sides of the road were carefully selected accounting for intersection geometry and traffic volumes. More than 90h of video were collected from 23 intersections and processed to obtain cyclist and motor-vehicle trajectories and interactions. After cyclist and motor-vehicle interactions were defined, ordered logit models with random effects were developed to evaluate the safety effects of cycle tracks at intersections. Based on the extracted data from the recorded videos, it was found that intersection approaches with cycle tracks on the right are safer than intersection approaches with no cycle track. However, intersections with cycle tracks on the left compared to no cycle tracks seem to be significantly safer. Results also identify that the likelihood of a cyclist being involved in a dangerous interaction increases with increasing turning vehicle flow and decreases as the size of the cyclist group arriving at the intersection increases. The results highlight the important role of cycle tracks and the factors that increase or decrease cyclist safety. Results need however to be confirmed using longer periods of video data.

Cyclist deceleration rate as surrogate safety measure in Montreal using smartphone GPS data.

Urban areas in North American cities with positive trends in bicycle usage also witness a high number of cyclist injuries every year. Previous cyclist safety studies based on the traditional approach, which relies on historical crash data, are known to have some limitations such as the fact that crashes need to happen (a reactive approach). This paper explores the use of GPS deceleration events as a surrogate-proactive measure and investigates the relationship between reported cyclist road injuries and deceleration events. The surrogate safety measure is defined based on deceleration values representing hard breaking situations. This work uses a large sample of GPS cyclist trip data from a smartphone application to extract deceleration rates at intersections and along segments and to explore its relationship with the number of observed injuries and validate deceleration rate (DR) as a surrogate safety measure. Using Spearmans rank correlation coefficient, we compared the ranking of sites based on the expected number of injuries and based on DR. The ranks of expected injuries and dangerous decelerations were found to have a correlation of 0.60 at signalized intersections, 0.53 at non-signalized intersections and 0.57 at segments. Despite the promising results of this study, more granular data and validation work needs to be done to improve the reliability of the measures. The technological limitations and future work are discussed at the end of the paper.