Is that move safe? Case study of cyclist movements at intersections with cycling discontinuities.

The cycling safety research literature has proposed methods to analyse safety and case studies to better understand the factors that lead to cyclist crashes. Surrogate measures of safety (SMoS) are being used as a proactive approach to identify severe interactions that do not result in an accident and interpreting them for a safety diagnosis. While most cyclist studies adopting SMoS have evaluated interactions by counting the total number of severe events per location, only a few have focused on the interactions between general directions of movement e.

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.

Analyzing injury severity factors at highway railway grade crossing accidents involving vulnerable road users: A comparative study.

Objective: The main objective of this study is to identify the main factors associated with injury severity of vulnerable road users (VRUs) involved in accidents at highway railroad grade crossings (HRGCs) using data mining techniques.

Methods: This article applies an ordered probit model, association rules, and classification and regression tree (CART) algorithms to the U.S.

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.

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.

Speed limit reduction in urban areas: a before-after study using Bayesian generalized mixed linear models.

In fall 2009, a new speed limit of 40 km/h was introduced on local streets in Montreal (previous speed limit: 50 km/h). This paper proposes a methodology to efficiently estimate the effect of such reduction on speeding behaviors. We employ a full Bayes before-after approach, which overcomes the limitations of the empirical Bayes method.

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.

Pedestrian preferences with respect to roundabouts -- a video-based stated preference survey.

Research on user behavior and preferences has been a helpful tool in improving road safety and accident prevention in recent years. At the same time, there remain some important areas of road safety and accident prevention for which user preferences, despite their importance, have not been explored. Most road safety research has not explicitly addressed vulnerable user (pedestrians and cyclists) preferences with respect to roundabouts, despite their increasing construction around the world.

Bayesian methodology to estimate and update safety performance functions under limited data conditions: a sensitivity analysis.

In road safety studies, decision makers must often cope with limited data conditions. In such circumstances, the maximum likelihood estimation (MLE), which relies on asymptotic theory, is unreliable and prone to bias. Moreover, it has been reported in the literature that (a) Bayesian estimates might be significantly biased when using non-informative prior distributions under limited data conditions, and that (b) the calibration of limited data is plausible when existing evidence in the form of proper priors is introduced into analyses.

Bayesian road safety analysis: incorporation of past evidence and effect of hyper-prior choice.

Problem: This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites).

Modeling vehicle operating speed on urban roads in Montreal: a panel mixed ordered probit fractional split model.

Vehicle operating speed measured on roadways is a critical component for a host of analysis in the transportation field including transportation safety, traffic flow modeling, roadway geometric design, vehicle emissions modeling, and road user route decisions. The current research effort contributes to the literature on examining vehicle speed on urban roads methodologically and substantively. In terms of methodology, we formulate a new econometric model framework for examining speed profiles.

Cyclist activity and injury risk analysis at signalized intersections: a Bayesian modelling approach.

This study proposes a two-equation Bayesian modelling approach to simultaneously study cyclist injury occurrence and bicycle activity at signalized intersections as joint outcomes. This approach deals with the potential presence of endogeneity and unobserved heterogeneities and is used to identify factors associated with both cyclist injuries and volumes. Its application to identify high-risk corridors is also illustrated.

Bicycle guidelines and crash rates on cycle tracks in the United States.

Objectives: We studied state-adopted bicycle guidelines to determine whether cycle tracks (physically separated, bicycle-exclusive paths adjacent to sidewalks) were recommended, whether they were built, and their crash rate.

Methods: We analyzed and compared US bicycle facility guidelines published between 1972 and 1999. We identified 19 cycle tracks in the United States and collected extensive data on cycle track design, usage, and crash history from local communities.

A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level.

This research presents a disaggregated modeling approach for investigating the link between winter road collision occurrence, weather, road surface conditions, traffic exposure, temporal trends and site-specific effects. This approach is unique as it allows for quantification of the safety effects of different winter road maintenance activities at an operational level. Different collision frequency models are calibrated using hourly data collected from 31 different highway routes across Ontario, Canada.

A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings.

In this paper, we aim to identify the different factors that influence injury severity of highway vehicle occupants, in particular drivers, involved in a vehicle-train collision at highway-railway grade crossings. The commonly used approach to modeling vehicle occupant injury severity is the traditional ordered response model that assumes the effect of various exogenous factors on injury severity to be constant across all accidents. The current research effort attempts to address this issue by applying an innovative latent segmentation based ordered logit model to evaluate the effects of various factors on the injury severity of vehicle drivers.

The link between built environment, pedestrian activity and pedestrian-vehicle collision occurrence at signalized intersections.

This paper studies the influence of built environment (BE) - including land use types, road network connectivity, transit supply and demographic characteristics - on pedestrian activity and pedestrian-vehicle collision occurrence. For this purpose, a two-equation modeling framework is proposed to investigate the effect of built environment on both pedestrian activity and vehicle-pedestrian collision frequency at signalized intersections. Using accident data of ambulance services in the City of Montreal, the applicability of our framework is illustrated.

Risk of injury for bicycling on cycle tracks versus in the street.

Most individuals prefer bicycling separated from motor traffic. However, cycle tracks (physically separated bicycle-exclusive paths along roads, as found in The Netherlands) are discouraged in the USA by engineering guidance that suggests that facilities such as cycle tracks are more dangerous than the street. The objective of this study conducted in Montreal (with a longstanding network of cycle tracks) was to compare bicyclist injury rates on cycle tracks versus in the street.

Quantifying safety benefit of winter road maintenance: accident frequency modeling.

This research presents a modeling approach to investigate the association of the accident frequency during a snow storm event with road surface conditions, visibility and other influencing factors controlling for traffic exposure. The results have the premise to be applied for evaluating different maintenance strategies using safety as a performance measure. As part of this approach, this research introduces a road surface condition index as a surrogate measure of the commonly used friction measure to capture different road surface conditions.

Modeling of heterotrophic bacteria counts in a water distribution system.

Heterotrophic plate count (HPC) constitutes a common indicator for monitoring of microbiological water quality in distribution systems (DS). This paper aims to identify factors explaining the spatiotemporal distribution of heterotrophic bacteria and model their occurrence in the distribution system. The case under study is the DS of Quebec City, Canada.

Bayesian multiple testing procedures for hotspot identification.

Ranking a group of candidate sites and selecting from it the high-risk locations or hotspots for detailed engineering study and countermeasure evaluation is the first step in a transport safety improvement program. Past studies have however mainly focused on the task of applying appropriate methods for ranking locations, with few focusing on the issue of how to define selection methods or threshold rules for hotspot identification. The primary goal of this paper is to introduce a multiple testing-based approach to the problem of selecting hotspots.