An empirical analysis of the effect of pedestrian signal countdown timer on driver behavior at signalized intersections.

A pedestrian countdown signal (PCS) is designed to provide additional information to pedestrians at crossings and help their crossing decisions. However, the PCS information can also affect drivers' behaviors when it is visible to drivers. With the countdown information visible to drivers, they can know the timing of the onset of the upcoming yellow and red traffic lights. This unintended information might cause changes in driving behaviors such as early stops, speeding, or abrupt accelerations to cross an intersection before the red light. Current literature has mainly focused on the drivers' crossing decisions or the number of crashes before and after displaying a PCS at intersections. However, there is a paucity of studies that investigate drivers' behaviors when approaching signalized intersections equipped with a PCS. This paper investigates vehicle speed patterns, safety implications, and the factors influencing driving behaviors at intersections before and after displaying the countdown information. To do so, we collected and extracted video-based vehicle trajectory data from 5,000 vehicles at signalized intersections with and without a PCS in the City of Montreal, Canada. The observed data provide the median and 85th centile approaching speed, the intersection entering speed, as well as safety implications regarding the countdown information. The multilevel mixed-effect model and Tukey's test conduct statistical comparisons across intersections and signal phases. The study results demonstrate that drivers cross intersections at a higher speed when the pedestrian countdown information is visible to drivers. Moreover, the vehicles at the same intersection with a PCS show clearly different speed patterns before and after the onset of the countdown timer. After controlling other factors, the mixed-effect model results further indicate displaying a PCS to drivers increase the approaching speed by approximately 11 km/h.

Do stop-signs improve the safety for all road users? A before-after study of stop-controlled intersections using video-based trajectories and surrogate measures of safety.

Converting minor-approach-only stop (MAS) intersections to all-way-stop (AWS) intersections is a prevailing safety countermeasure in North American urban areas. Although the general population positively perceives the installation of stop-signs in residential areas, little research has investigated the impact of AWS on road safety and road user behaviour. This paper investigated the safety effectiveness of converting MAS to AWS intersections using an observational before and after approach and surrogate measures of safety. More specifically, the safety impacts of AWS conversion were investigated using multiple indicators, including vehicle speed measures, vehicle-pedestrian, vehicle-cyclist, vehicles-vehicle interactions as well as yielding rates before and after the treatment implementation. A multi-level regression approach was adopted to determine the effect of stop signs controlling for built environments, traffic exposure, and intersection geometry factors as well as site-specific unobserved heterogeneity. A unique sample of 31 intersections were used in this before-after study. From this sample, video data were collected before and after implementing AWS. In total, 245 h of video were automatically processed and corrected using a specialized computer vision software. More than 68,000 (37,668 before and 31,305 after AWS treatment) road user trajectories were obtained from 104 approaches. The results show that the conversion of MAS to AWS intersections significantly decreased vehicle speed and increased post-encroachment time. This work also shows that implementing AWS significantly increased the yielding rates from 45.7% to 76.7% in MAS conditions and reduced the average speed of motor-vehicles. Using multi-level regression model, it is estimated that when the intersection was converted from MAS to AWS, the minimum speed in the major approaches was reduced by 60.0%.

Massive GNSS data for road safety analysis: Comparing crash models for several Canadian cities and data sources.

Mobile sensors are a useful data source with applications in several transportation fields. Though cost of collection, transmission, and storage has limited studies on driving data and safety, this can be overcome through usage-based insurance (UBI). In UBI programs, drivers are monitored, and their premiums are adjusted based on driver-level surrogate safety measures (SSMs) related to exposure and driving style. Contextual link-level SSMs (volume, speed, or density) could further improve discount calibration. This study quantifies relationships between contextual SSMs and crashes and includes the validation of previous results (correlations between SSMs and crashes and statistical models estimated using smartphone-collected data from Quebec City) and the comparison of three Canadian cities (using UBI data from Quebec City, Montreal, and Ottawa). Extracted SSMs were compared to large volumes of historical crash frequency data using Spearman's Rank Correlation Coefficient and then implemented into spatial Bayesian crash models. Results from the UBI data generally matched those from the previous study, with observed correlations mirroring previous results in direction (braking, congestion, and speed variation are positively associated with crash frequency while mean speed is negatively associated) while correlation strength was slightly higher. Furthermore, these results were consistent between cities. For the crash modelling, repeatability of previous results in Quebec City was moderately good for the UBI data. Importantly for large-scale implementation, models estimated using UBI data were largely consistent between cities. This work provides an important contribution to the existing literature, clearly demonstrating how contextual safety measures could be applied to benefit UBI practices.

Evaluating safety-influencing factors at stop-controlled intersections using automated video analysis.

INTRODUCTION: Although stop signs are popular in North America, they have become controversial in cities like Montreal, Canada where they are often installed to reduce vehicular speeds and improve pedestrian safety despite limited evidence demonstrating their effectiveness. The purpose of this study is to evaluate the impact of stop-control configuration (and other features) on safety using statistical models and surrogate measures of safety (SMoS), namely vehicle speed, time-to-collision (TTC), and post-encroachment time (PET), while controlling for features of traffic, geometry, and built environment. METHODS: This project leverages high-resolution user trajectories extracted from video data collected for 100 intersections, 336 approaches, and 130,000 road users in Montreal to develop linear mixed-effects regression models to account for within-site and within-approach correlations. This research proposes the Intersection Exposure Group (IEG) indicator, an original method for classifying microscopic exposure of pedestrians and vehicles. RESULTS: Stop signs were associated with an average decrease in approach speed of 17.2 km/h and 20.1 km/h, at partially and fully stop-controlled respectively. Cyclist or pedestrian presence also significantly lower vehicle speeds. The proposed IEG measure was shown to successfully distinguish various types of pedestrian-vehicle interactions, allowing for the effect of each interaction type to vary in the model. CONCLUSIONS: The presence of stop signs significantly reduced approach speeds compared to uncontrolled approaches. Though several covariates were significantly related to TTC and PET for vehicle pairs, the models were unable to demonstrate a significant relationship between stop signs and vehicle-pedestrian interactions. Therefore, drawing conclusions regarding pedestrian safety is difficult. Practical Applications: As pedestrian safety is frequently used to justify new stop sign installations, this result has important policy implications. Policies implementing stop signs to reduce pedestrian crashes may be less effective than other interventions. Enforcement and education efforts, along with geometric design considerations, should accompany any changes in traffic control.

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.

Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity.

Crash frequency and injury severity are independent dimensions defining crash risk in road safety management and network screening. Traditional screening techniques model crashes using regression and historical crash data, making them intrinsically reactive. In response, surrogate measures of safety have become a popular proactive alternative. The purpose of this paper is to develop models for crash frequency and severity incorporating GPS-derived surrogate safety measures (SSMs) as predictive variables. SSMs based on vehicle manoeuvres and traffic flow were extracted from data collected in Quebec City. The mixed multivariate outcome is estimated using two models; a Full Bayes Spatial Negative Binomial model for crash frequency estimated using the Integrated Nested Laplace Approximation approach and a fractional Multinomial Logit model for crash severity. Model outcomes are combined to generate posterior expected crash frequency at each severity level and rank sites based on crash cost. The crash frequency model was accurate at the network scale, with the majority of proposed SSMs statistically significant at 95% confidence and the direction of their effect generally consistent with previous research. In the crash severity model, fewer variables were significant, yet the direction of the effect of all significant variables was again consistent with previous research. Correlations between rankings predicted by the mixed multivariate model and by the crash data were adequate for intersections (0.46) but were poorer for links (0.25). The ability to prioritize sites based on GPS data and SSMs rather than historical crash data represents a substantial contribution to the field of road safety.

Observing the observation of (vulnerable) road user behaviour and traffic safety: A scoping review.

Behavioural observation studies in road safety research collect naturalistic data of road users that are not informed (beforehand) of their participation in a research project. It enables the observation of behavioural and situational processes that contribute to unsafe traffic events, while possible behavioural adaptations due to the road users' recognition of being observed are minimized. The literature in this field is vast and diverse, with studies dating back to the 1930s. The aim of this paper is to summarize the research efforts in the domain of road user behavioural observation research to examine trends and developments of this type of research, using a scoping review. After the definition of certain selection criteria, 600 journal articles found in three major online databases were retrieved and included in this review. The number of publications regarding road user behavioural observation studies has increased rapidly during recent years, indicating the importance of behavioural observation studies to study traffic safety. Most studies collected data on car drivers (81%), while vulnerable road users have been observed in 32% of all studies, with pedestrians and (motor)cyclists as the most common road user types. The results showed that the main goal of behavioural observation is to monitor (51%), followed by the evaluation of a specific safety improving measure (38%) and the development of behavioural models (10%). Most topics relate to traffic events where interactions with other road users are necessary, indicating that the examination of behavioural processes underlying single-vehicle crashes has received little attention. The ongoing developments of automated video analysis software tools can be the next methodological step forward in video-based behavioural observation studies, since it enables a more objective data collection and data analysis process.

Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models.

Improving road safety requires accurate network screening methods to identify and prioritize sites in order to maximize the effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models and ranking criteria derived from observed crash data. However, collision databases are subject to errors, omissions, and underreporting. More importantly, crash-based methods are reactive and require years of crash data. With the arrival of new technologies including Global Positioning System (GPS) trajectory data, proactive surrogate safety methods have gained popularity as an alternative approach for screening. GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool. However, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The purpose of this paper is to propose a surrogate safety screening approach based on smartphone GPS data and a Full Bayesian modelling framework. After processing crash data and GPS data collected in Quebec City, Canada, several surrogate safety measures (SSMs), including vehicle manoeuvres (hard braking) and measures of traffic flow (congestion, average speed, and speed variation), were extracted. Then, spatial crash frequency models incorporating the extracted SSMs were proposed and validated. A Latent Gaussian Spatial Model was estimated using the Integrated Nested Laplace Approximation (INLA) technique. While the INLA Negative Binomial models outperformed alternative models, incorporating spatial correlations provided the greatest improvement in model fit. Relationships between SSMs and crash frequency established in previous studies were generally supported by the modelling results. For example, hard braking, congestion, and speed variation were all positively linked to crash counts at the intersection level. Network screening based on SSMs presents a substantial contribution to the field of road safety and works towards the elimination of crash data in evaluation and monitoring.

Vehicle manoeuvers as surrogate safety measures: Extracting data from the gps-enabled smartphones of regular drivers.

Network screening is a key element in identifying and prioritizing hazardous sites for engineering treatment. Traditional screening methods have used observed crash frequency or severity ranking criteria and statistical modelling approaches, despite the fact that crash-based methods are reactive. Alternatively, surrogate safety measures (SSMs) have become popular, making use of new data sources including video and, more rarely, GPS data. The purpose of this study is to examine vehicle manoeuvres of braking and accelerating extracted from a large quantity of GPS data collected using the smartphones of regular drivers, and to explore their potential as SSMs through correlation with historical collision frequency and severity across different facility types. GPS travel data was collected in Quebec City, Canada in 2014. The sample for this study contained over 4000 drivers and 21,000 trips. Hard braking (HBEs) and accelerating events (HAEs) were extracted and compared to historical crash data using Spearman's correlation coefficient and pairwise Kolmogorov-Smirnov tests. Both manoeuvres were shown to be positively correlated with crash frequency at the link and intersection levels, though correlations were much stronger when considering intersections. Locations with more braking and accelerating also tend to have more collisions. Concerning severity, higher numbers of vehicle manoeuvres were also related to increased collision severity, though this relationship was not always statistically significant. The inclusion of severity testing, which is an independent dimension of safety, represents a substantial contribution to the existing literature. Future work will focus on developing a network screening model that incorporates these SSMs.

A novel framework to evaluate pedestrian safety at non-signalized locations.

This paper proposes a new framework to evaluate pedestrian safety at non-signalized crosswalk locations. In the proposed framework, the yielding maneuver of a driver in response to a pedestrian is split into the reaction and braking time. Hence, the relationship of the distance required for a yielding maneuver and the approaching vehicle speed depends on the reaction time of the driver and deceleration rate that the vehicle can achieve. The proposed framework is represented in the distance-velocity (DV) diagram and referred as the DV model. The interactions between approaching vehicles and pedestrians showing the intention to cross are divided in three categories: i) situations where the vehicle cannot make a complete stop, ii) situations where the vehicle's ability to stop depends on the driver reaction time, and iii) situations where the vehicle can make a complete stop. Based on these classifications, non-yielding maneuvers are classified as "non-infraction non-yielding" maneuvers, "uncertain non-yielding" maneuvers and "non-yielding" violations, respectively. From the pedestrian perspective, crossing decisions are classified as dangerous crossings, risky crossings and safe crossings accordingly. The yielding compliance and yielding rate, as measures of the yielding behavior, are redefined based on these categories. Time to crossing and deceleration rate required for the vehicle to stop are used to measure the probability of collision. Finally, the framework is demonstrated through a case study in evaluating pedestrian safety at three different types of non-signalized crossings: a painted crosswalk, an unprotected crosswalk, and a crosswalk controlled by stop signs. Results from the case study suggest that the proposed framework works well in describing pedestrian-vehicle interactions which helps in evaluating pedestrian safety at non-signalized crosswalk locations.

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.

Cross-comparison of three surrogate safety methods to diagnose cyclist safety problems at intersections in Norway.

Relying on accident records as the main data source for studying cyclists' safety has many drawbacks, such as high degree of under-reporting, the lack of accident details and particularly of information about the interaction processes that led to the accident. It is also an ethical problem as one has to wait for accidents to happen in order to make a statement about cyclists' (un-)safety. In this perspective, the use of surrogate safety measures based on actual observations in traffic is very promising. In this study we used video data from three intersections in Norway that were all independently analysed using three methods: the Swedish traffic conflict technique (Swedish TCT), the Dutch conflict technique (DOCTOR) and the probabilistic surrogate measures of safety (PSMS) technique developed in Canada. The first two methods are based on manual detection and counting of critical events in traffic (traffic conflicts), while the third considers probabilities of multiple trajectories for each interaction and delivers a density map of potential collision points per site. Due to extensive use of microscopic data, PSMS technique relies heavily on automated tracking of the road users in video. Across the three sites, the methods show similarities or are at least "compatible" with the accident records. The two conflict techniques agree quite well for the number, type and location of conflicts, but some differences with no obvious explanation are also found. PSMS reports many more safety-relevant interactions including less severe events. The location of the potential collision points is compatible with what the conflict techniques suggest, but the possibly significant share of false alarms due to inaccurate trajectories extracted from video complicates the comparison. The tested techniques still require enhancement, with respect to better adjustment to analysis of the situations involving cyclists (and vulnerable road users in general) and further validation. However, we believe this to be a future direction for the road safety analysis as the number of accidents is constantly decreasing and the quality of accident data does not seem to improve.

Investigating the gender differences on bicycle-vehicle conflicts at urban intersections using an ordered logit methodology.

In the literature, a crash-based modeling approach has long been used to evaluate the factors that contribute to cyclist injury risk at intersections. However, this approach has been criticized as crashes are required to occur before contributing factors can be identified and countermeasures can be implemented. Moreover, human factors related to dangerous behaviors are difficult to evaluate using crash-based methods. As an alternative, surrogate safety measures have been developed to address the issue of reliance on crash data. Despite recent developments, few methodologies and little empirical evidence exist on bicycle-vehicle interactions at intersections using video-based data and statistical analyses to identify associated factors. This study investigates bicycle-vehicle conflict severity and evaluates the impact of different factors, including gender, on cyclist risk at urban intersections with cycle tracks. A segmented ordered logit model is used to evaluate post-encroachment time between cyclists and vehicles. Video data was collected at seven intersections in Montreal, Canada. Road user trajectories were automatically extracted, classified, and filtered using a computer vision software to yield 1514 interactions. The discrete choice variable was generated by dividing post-encroachment time into normal interactions, conflicts, and dangerous conflicts. Independent variables reflecting attributes of the cyclist, vehicle, and environment were extracted either automatically or manually. Results indicated that an ordered model is appropriate for analyzing traffic conflicts and identifying key factors. Furthermore, exogenous segmentation was beneficial in comparing different segments of the population within a single model. Male cyclists, with all else being equal, were less likely than female cyclists to be involved in conflicts and dangerous conflicts at the studied intersections. Bicycle and vehicle speed, along with the time of the conflict relative to the red light phase, were other significant factors in conflict severity. These results will contribute to and further the understanding of gender differences in cycling within North America.

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.

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.

Exposure to lateral collision in signalized intersections with protected left turn under different traffic control strategies.

This paper proposes an original definition of the exposure to lateral collision in signalized intersections and discusses the results of a real world experiment. This exposure is defined as the duration of situations where the stream that is given the right-of-way goes through the conflict zone while road users are waiting in the cross-traffic approach. This measure, obtained from video sensors, makes it possible to compare different operating conditions such as different traffic signal strategies. The data from a real world experiment is used, where the adaptive real-time strategy CRONOS (ContRol Of Networks by Optimization of Switchovers) and a time-plan strategy with vehicle-actuated ranges alternately controlled an isolated intersection near Paris. Hourly samples with similar traffic volumes are compared and the exposure to lateral collision is different in various areas of the intersection and various traffic conditions for the two strategies. The total exposure under peak hour traffic conditions drops by roughly 5 min/h with the CRONOS strategy compared to the time-plan strategy, which occurs mostly on entry streams. The results are analyzed through the decomposition of cycles in phase sequences and recommendations are made for traffic control strategies.