Luis F. Miranda-Moreno

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. For six cycle tracks and comparable reference streets, vehicle/bicycle crashes and health record injury counts were obtained and use counts conducted. The relative risk (RR) of injury on cycle tracks, compared with reference streets, was determined. Overall, 2.5 times as many cyclists rode on cycle tracks compared with reference streets and there were 8.5 injuries and 10.5 crashes per million bicycle-kilometres. The RR of injury on cycle tracks was 0.72 (95% CI 0.60 to 0.85) compared with bicycling in reference streets. These data suggest that the injury risk of bicycling on cycle tracks is less than bicycling in streets. The construction of cycle tracks should not be discouraged.

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. Different model settings were attempted as part of a model sensitivity analysis. Among other results, it was found that the BE in the proximity of an intersection has a powerful association with pedestrian activity but a small direct effect on pedestrian-vehicle collision frequency. This suggests that the impact of BE is mainly mediated through pedestrian activity. In other words, strategies that encourage densification, mix of land uses and increase in transit supply will increase pedestrian activity and may indirectly, with no supplementary safety strategies, increase the total number of injured pedestrians. In accordance with previous research, the number of motor vehicles entering a particular intersection is the main determinant of collision frequency. Our results show that a 30% reduction in the traffic volume would reduce the total number of injured pedestrians by 35% and the average risk of pedestrian collision by 50% at the intersections under analysis. Major arterials are found to have a double negative effect on pedestrian safety. They are positively linked to traffic but negatively associated with pedestrian activity. The proposed framework is useful for the identification of effective pedestrian safety actions, the prediction of pedestrian volumes and the appropriate safety design of new urban developments that encourage walking.

Alternative risk models for ranking locations for safety improvement

Many types of statistical models have been proposed for estimating accident risk in transport networks, ranging from basic Poisson and negative binomial models to more complicated models, such as zero-inflated and hierarchical Bayesian models. However, little systematic effort has been devoted to comparing the performance and practical implications of these models and ranking criteria when they are used for identifying hazardous locations. This research investigates the relative performance of three alternative models: the traditional negative binomial model, the heterogeneous negative binomial model, and the Poisson lognormal model. In particular, this work focuses on the impact of the choice of two alternative prior distributions (i.e., gamma versus lognormal) and the effect of allowing variability in the dispersion parameter on the outcome of the analysis. From each model, two alternative accident estimators are computed by using the conditional mean under both marginal and posterior distributions. A sample of Canadian highway-railway intersections with an accident history of 5 years is used to calibrate and evaluate the three alternative models and the two ranking criteria. It is concluded that the choice of model assumptions and ranking criteria can lead to considerably different lists of hazardous locations.

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. In this model, the highway-railway crossings are assigned probabilistically to different segments based on their attributes with a separate injury severity component for each segment. The validity and strength of the formulated collision consequence model is tested using the US Federal Railroad Administration database which includes inventory data of all the railroad crossings in the US and collision data at these highway railway crossings from 1997 to 2006. The model estimation results clearly highlight the existence of risk segmentation within the affected grade crossing population by the presence of active warning devices, presence of permanent structure near the crossing and roadway type. The key factors influencing injury severity include driver age, time of the accident, presence of snow and/or rain, vehicle role in the crash and motorist action prior to the crash.

Weather or Not to Cycle

This study investigated the relationship between weather conditions and cycling ridership, as well as the hourly, daily, monthly, and yearly trends for use of urban bicycle facilities. A unique data set of cyclist ridership, collected at five automatic counting stations on primarily utilitarian bike facilities in the city of Montreal, Canada, was used. Absolute and relative ridership models were used to analyze the direct and lagging effects of weather variables and extreme weather conditions on hourly cyclist volumes. Precipitation, temperature, and humidity had significant effects on bicycle ridership. After other factors were controlled for, when the temperature doubled, a 43% to 50% increase in ridership could be expected; however, the temperature had a negative effect when it was higher than 28°C and humidity was greater than 60%. The results also showed that bicycle volumes in a given hour were significantly affected not only by the presence of rain in the same hour but also by the presence of rain in the previous 3 h or in the morning only. Daily bicycle volumes were 65% to 89% lower on weekend days than on Monday, the weekday with lowest ridership. This finding confirmed that the analyzed facilities were primarily utilitarian. Further, bicycle volumes peaked in the summer months, with an additional ridership of 32% to 39% with respect to April. Finally, bicycle volumes increased by approximately 20% to 27% in 2009 and 35% to 40% in 2010 with respect to 2008 in the cycling facilities under analysis.

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. Data from various data sources, such as weather, road condition observations, traffic counts and accidents, are integrated and used to test three event-based models including the Negative Binomial model, the generalized NB model and the zero inflated NB model. These models are compared for their capability to explain differences in accident frequencies between individual snow storms. It was found that the generalized NB model best fits the data, and is most capable of capturing heterogeneity other than excess zeros. Among the main results, it was found that the road surface condition index was statistically significant influencing the accident occurrence. This research is the first showing the empirical relationship between safety and road surface conditions at a disaggregate level (event-based), making it feasible to quantify the safety benefits of alternative maintenance goals and methods.

The impact of traffic volume, composition, and road geometry on personal air pollution exposures among cyclists in Montreal, Canada

Cyclists may experience increased exposure to traffic-related air pollution owing to increased minute ventilation and close proximity to vehicle emissions. The aims of this study were to characterize personal exposures to air pollution among urban cyclists and to identify potential determinants of exposure including the type of cycling lane (separated vs on-road), traffic counts, and meteorological factors. In total, personal air pollution exposure data were collected over 64 cycling routes during morning and evening commutes in Montreal, Canada, over 32 days during the summer of 2011. Measured pollutants included ultrafine particles (UFPs), fine particles (PM2.5), black carbon (BC), and carbon monoxide (CO). Counts of diesel vehicles were important predictors of personal exposures to BC, with each 10 vehicle/h increase associated with a 15.0% (95% confidence interval (CI): 5.7%, 24.0%) increase in exposure. Use of separated cycling lanes had less impact on personal exposures with a 12% (95% CI: −43%, 14%) decrease observed for BC and smaller decreases observed for UFPs (mean: −1.3%, 95% CI: −20%, 17%) and CO (mean: −5.6%, 95% CI: −17%, 4%) after adjusting for meteorological factors and traffic counts. On average, PM2.5 exposure increased 7.8% (95% CI: −17%, 35%) with separate cycling lane use, but this estimate was imprecise and not statistically significant. In general, our findings suggest that diesel vehicle traffic is an important contributor to personal BC exposures and that separate cycling lanes may have a modest impact on personal exposure to some air pollutants. Further evaluation is required, however, as the impact of separate cycling lanes and/or traffic counts on personal exposures may vary between regions.

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. The study is based on a robust database resulting from a sampling campaign carried out in about 50 DS locations, monitored bi-weekly over a three-year period. Models for explaining and predicting HPC levels were based on both one-level and multi-level Poisson regression techniques. The latter take into account the nested structure of data, the possible spatiotemporal correlation among HPC observations and the fact that sampling points, months and/or distribution sub-systems may represent clusters. Models show that the best predictors for spatiotemporal occurrence of HPC in the DS are: free residual chlorine that has an inverse relation with the HPC levels, water temperature and water ultraviolet absorbance, both having a positive impact on HPC levels. A sensitivity analysis based on the best performing model (two-level model) allowed for the identification of seasonal-based strategies to reduce HPC levels.

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. It is found that factors such as visibility, precipitation intensity, air temperature, wind speed, exposure, month of the winter season, and storm hour have statistically significant effects on winter road safety. Most importantly, road surface conditions are identified as one of the major contributing factors, representing the first contribution showing the empirical relationship between safety and road surface conditions at such a disaggregate level. The applicability of the modeling framework is demonstrated using several examples, such as quantification of the benefits of alternative maintenance operations and evaluation of the effects of different service standards using safety as a performance measure.

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.