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.g. through cyclists and right turning vehicles. However, road users perform maneuvers that are more varied at a high spatiotemporal resolution such as a range of sharp to wide turning movements. These maneuvers (motion patterns) have not been considered in past studies as a basis for analysis to identify, among a range of possible motion patterns in each direction of travel, which ones are safer, and which are more likely to result in a crash. This paper presents a novel movement-based probabilistic SMoS approach to evaluate the safety of road users' trajectories based on clusters of trajectories representing the various movements. This approach is applied to cyclist-vehicle interactions at two locations of cycling network discontinuity and two control sites in Montréal. The Kruskal-Wallis and Kolmogorov-Smirnov tests are used to compare the time-to-collision (TTC) distribution between motion patterns in each site and between sites with and without a discontinuity. Results demonstrate the insight provided by the new approach and indicate that cyclist interactions are more severe and less safe at locations with a cycling network discontinuity and that cyclists following different movements have statistically different levels of safety.

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 Spearman's 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.

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. Federal Railroad Administration's (FRA) HRGC accident database for the period 2007-2013 to identify VRU injury severity factors at HRGCs.

RESULTS

The results show that train speed is a key factor influencing injury severity. Further analysis illustrated that the presence of illumination does not reduce the severity of accidents for high-speed trains. In addition, there is a greater propensity toward fatal accidents for elderly road users compared to younger individuals. Interestingly, at night, injury accidents involving female road users are more severe compared to those involving males.

CONCLUSIONS

The ordered probit model was the primary technique, and CART and association rules act as the supporter and identifier of interactions between variables. All 3 algorithms' results consistently show that the most influential accident factors are train speed, VRU age, and gender. The findings of this research could be applied for identifying high-risk hotspots and developing cost-effective countermeasures targeting VRUs at HRGCs.

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.

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.

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. The proposed methodology allows for the analysis of speed data using hourly observations. Therefore, the entire daily profile of speed is considered. Furthermore, it accounts for the entire distribution of speed in contrast to the traditional approach of considering only a point estimate such as 85th percentile speed. Different reference speeds were used to examine variations in the treatment effectiveness in terms of speeding rate and frequency. In addition to comparing rates of vehicles exceeding reference speeds of 40 km/h and 50 km/h (speeding), we verified how the implemented treatment affected "excessive speeding" behaviors (exceeding 80 km/h). To model operating speeds, two Bayesian generalized mixed linear models were utilized. These models have the advantage of addressing the heterogeneity problem in observations and efficiently capturing potential intra-site correlations. A variety of site characteristics, temporal variables, and environmental factors were considered. The analyses indicated that variables such as lane width and night hour had an increasing effect on speeding. Conversely, roadside parking had a decreasing effect on speeding. One-way and lane width had an increasing effect on excessive speeding, whereas evening hour had a decreasing effect. This study concluded that although the treatment was effective with respect to speed references of 40 km/h and 50 km/h, its effectiveness was not significant with respect to excessive speeding-which carries a great risk to pedestrians and cyclists in urban areas. Therefore, caution must be taken in drawing conclusions about the effectiveness of speed limit reduction. This study also points out the importance of using a comparison group to capture underlying trends caused by unknown factors.

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.

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. The present research stems from the fact that studies related to roundabout safety have generally focused on drivers, while overlooking the importance of safety as it relates to vulnerable users, especially pedestrians. Moreover, it handles this particular issue through an approach that has not been used so far in this context; the Stated Preference (SP) survey. As such, there are two main goals (and contributions) of this work. First, to show how SP surveys can be used to investigate the importance of different design and operational features to pedestrian perceptions of safety in roundabouts. This allows us, for example, to quantify how some features of roundabouts (e.g. high traffic volume) can be compensated for by design features such as pedestrian islands. This is useful in helping to design roundabouts that pedestrians prefer and will hopefully use, to help encourage active transport. Second, to demonstrate how traffic simulation software can be successfully used to include difficult-to-communicate attributes in SP surveys.

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. Although the Highway Safety Manual (2010) (HSM) and other research studies provide calibration and updating procedures, the data requirements can be very taxing. This paper presents a practical and sound Bayesian method to estimate and/or update safety performance function (SPF) parameters combining the information available from limited data with the SPF parameters reported in the HSM. The proposed Bayesian updating approach has the advantage of requiring fewer observations to get reliable estimates. This paper documents this procedure. The adopted technique is validated by conducting a sensitivity analysis through an extensive simulation study with 15 different models, which include various prior combinations. This sensitivity analysis contributes to our understanding of the comparative aspects of a large number of prior distributions. Furthermore, the proposed method contributes to unification of the Bayesian updating process for SPFs. The results demonstrate the accuracy of the developed methodology. Therefore, the suggested approach offers considerable promise as a methodological tool to estimate and/or update baseline SPFs and to evaluate the efficacy of road safety countermeasures under limited data conditions.

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. Montreal, Quebec, Canada is the application environment, using an extensive inventory of a large sample of signalized intersections containing disaggregate motor-vehicle traffic volumes and bicycle flows, geometric design, traffic control and built environment characteristics in the vicinity of the intersections. Cyclist injury data for the period of 2003-2008 is used in this study. Also, manual bicycle counts were standardized using temporal and weather adjustment factors to obtain average annual daily volumes. Results confirm and quantify the effects of both bicycle and motor-vehicle flows on cyclist injury occurrence. Accordingly, more cyclists at an intersection translate into more cyclist injuries but lower injury rates due to the non-linear association between bicycle volume and injury occurrence. Furthermore, the results emphasize the importance of turning motor-vehicle movements. The presence of bus stops and total crosswalk length increase cyclist injury occurrence whereas the presence of a raised median has the opposite effect. Bicycle activity through intersections was found to increase as employment, number of metro stations, land use mix, area of commercial land use type, length of bicycle facilities and the presence of schools within 50-800 m of the intersection increase. Intersections with three approaches are expected to have fewer cyclists than those with four. Using Bayesian analysis, expected injury frequency and injury rates were estimated for each intersection and used to rank corridors. Corridors with high bicycle volumes, located mainly in the central neighbourhoods of Montreal, have lower risk of injury. These results may reflect the "safety in numbers" hypothesis or cyclist preference towards safer intersections and corridors. Despite these corridors having a lower individual risk, they are nevertheless associated with a greater number of injuries.

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. The proposed model is an ordered response formulation of a fractional split model. The ordered nature of the speed variable allows us to propose an ordered variant of the fractional split model in the literature. The proposed formulation allows us to model the proportion of vehicles traveling in each speed interval for the entire segment of roadway. We extend the model to allow the influence of exogenous variables to vary across the population. Further, we develop a panel mixed version of the fractional split model to account for the influence of site-specific unobserved effects. The paper contributes substantively by estimating the proposed model using a unique dataset from Montreal consisting of weekly speed data (collected in hourly intervals) for about 50 local roads and 70 arterial roads. We estimate separate models for local roads and arterial roads. The model estimation exercise considers a whole host of variables including geometric design attributes, roadway attributes, traffic characteristics and environmental factors. The model results highlight the role of various street characteristics including number of lanes, presence of parking, presence of sidewalks, vertical grade, and bicycle route on vehicle speed proportions. The results also highlight the presence of site-specific unobserved effects influencing the speed distribution. The parameters from the modeling exercise are validated using a hold-out sample not considered for model estimation. The results indicate that the proposed panel mixed ordered probit fractional split model offers promise for modeling such proportional ordinal variables.

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).

METHOD

A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas.

RESULTS

This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis.

CONCLUSIONS

As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification.

IMPACT ON INDUSTRY

The results show the benefits of incorporating prior information when working with limited crash data in road safety studies.

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. We used bicycle counts and crash data to estimate crash rates.

RESULTS

A bicycle facility guideline written in 1972 endorsed cycle tracks but American Association of State Highway and Transportation Officials (AASHTO) guidelines (1974-1999) discouraged or did not include cycle tracks and did not cite research about crash rates on cycle tracks. For the 19 US cycle tracks we examined, the overall crash rate was 2.3 (95% confidence interval = 1.7, 3.0) per 1 million bicycle kilometers.

CONCLUSIONS

AASHTO bicycle guidelines are not explicitly based on rigorous or up-to-date research. Our results show that the risk of bicycle-vehicle crashes is lower on US cycle tracks than published crashes rates on roadways. This study and previous investigations support building cycle tracks.

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.

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.

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.

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.

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.

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.

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. Following the recent developments in the literature, two testing procedures are studied under a Bayesian framework: Bayesian test with weights (BTW) and a Bayesian test controlling for the posterior false discovery rate (FDR) or false negative rate (FNR). The hypotheses tests are implemented on the basis of two random effect or Bayesian models, namely, the hierarchical Poisson/Gamma or Negative Binomial model and the hierarchical Poisson/Lognormal model. A dataset of highway-railway grade crossings is used as an application example to illustrate the proposed procedures incorporating both the posterior distribution of accident frequency and the posterior distribution of ranks. Results on the effects of various decision parameters used in hotspot identification procedures are discussed.