Post-encroachment Time Research Articles

Safety assessment of pedestrian-vehicle interaction at signalized intersections: An observational study

Road safety is a crucial aspect of global policies and management. Surrogate Safety Measures (SSMs) have gained attention in the study of pedestrian safety. This study aims to establish an effective SSM methodology to analyze driver-pedestrian interactions. The analysis relies on SSM indicators, without the need for an initial classification of driver-pedestrian interactions into specific interaction patterns. The proposed methodology offers several advantages, including the accurate identification of conflicts through an affordable approach making it easily accessible for public administrations and authorities to assess pedestrian safety at road intersections. A dataset comprising 270 driver-pedestrian interactions, observed at three road intersections in Rome, Italy, was examined. The severity level of each event was assessed through a preliminary classification of each interaction into three patterns: high, low, and none. Subsequently, the severity levels were evaluated using three methods, employing Time-to-Collision (TTC), Post-Encroachment Time (PET), and a combination of TTC and PET. A comparison between the severity levels identified by the two approaches was conducted. The findings reveal that Method 2, utilizing PET, consistently identifies conflicts. Additionally, a binomial logistic regression analysis was performed to identify the variables that influence the likelihood of an interaction escalating into a conflict. The results demonstrate that the probability of conflict increases with the duration of a red signal, particularly for younger pedestrians.

Proposing an effective approach for traffic safety assessment on heterogeneous traffic conditions using surrogate safety measures and speed of the involved vehicles

Objective The vehicular traffic in the cities is becoming more complex in developing countries like India due to the rising population and rapid urbanization. With the annually increasing road accidents, a study on the effective safety assessment of heterogeneous traffic conditions is needed. The study aims to evaluate the safety of heterogeneous traffic by spotting the critical conflicts with respect to the speed of the involved vehicles. Methods The current study proposed Critical Following Speed instead of using a single threshold value for safety assessment in mixed traffic. Critical Following Speed was proposed by comparing the stopping and the available distances between the involved vehicles and used to identify the critical conflicts. With this, the study uses the speeds of both the leading and the following vehicles to judge the nature of the conflict. Three unsignalized four-legged intersections (S-1, S-2, and S-3) and two straight road sections (S-4 and S-5) were selected as the study area in Trichy, India. Post Encroachment Time (PET) and Time to Collison (TTC) were used as surrogate indicators to assess the crossing or merging and rear-end conflicts, respectively. Results The average PET and TTC values were between 1.25 and 1.73 s in the study locations. The overall percentage of critical conflicts indicated the non-safer crossing and merging maneuvers in three locations. The other two locations were experiencing safe rear-end conflicts, as the percentages of critical conflicts were below 4%. Various combinations of leading and following vehicle types were examined for the contribution of critical conflicts. The proposed methodology was validated with the 4-year accident data and a good relation was obtained. Conclusions Fast-moving vehicles were responsible for the less safe maneuvers with a higher collision probability in all the study locations. The correlation between critical conflicts and road accidents shows the effectiveness of the proposed approach in the traffic safety assessments for mixed traffic. This approach could be employed even in countries with homogenous traffic conditions instead of using a single threshold value. The correlations also show the potential of the proposed Critical Following Speed as a surrogate safety indicator for safety evaluation in the future.

A Bayesian extreme value theory modelling framework to assess corridor-wide pedestrian safety using autonomous vehicle sensor data

Pedestrians are a vulnerable road user group, and their crashes are generally spread across the network rather than in a concentrated location. As such, understanding and modelling pedestrian crash risk at a corridor level becomes paramount. Studies on pedestrian crash risks, particularly with the traffic conflict data, are limited to single or multiple but scattered intersections. A lack of proper modelling techniques and the difficulties in capturing pedestrian interaction at the network or corridor level are two main challenges in this regard. With autonomous vehicles trialled on public roads generating massive (and unprecedented) datasets, utilising such rich information for corridor-wide safety analysis is somewhat limited where it appears to be most relevant. This study proposes an extreme value theory modelling framework to estimate corridor-wide pedestrian crash risk using autonomous vehicle sensor/probe data. Two types of models were developed in the Bayesian framework, including the block maxima sampling-based model corresponding to Generalised Extreme Value distribution and the peak over threshold sampling-based model corresponding to Generalised Pareto distribution. The proposed framework was applied to autonomous vehicle data from Argoverse—a Ford Motors subsidiary. This autonomous vehicle fleet of Agro AI (owner of Argoverse dataset) is equipped with two 64 beams synchronised LiDAR sensors, a cluster of seven high-resolution cameras, and a pair of stereo-vison high-resolution cameras to capture surrounding road users’ information within a range of 200 meters. A subset of the Argoverse dataset, focussing on an arterial corridor in Miami, USA, was used to extract pedestrian and vehicle trajectories. From these trajectories, vehicle–pedestrian conflicts were identified and measured using post encroachment time. The non-stationarity of extremes was captured by vehicle volume, pedestrian volume, average vehicle speed, and average pedestrian speed in the extreme value model. Both block maxima and peak over threshold sampling-based models were found to provide a reasonable estimate of historical pedestrian crash frequencies. Notably, the block maxima sampling-based model was more accurate than the peak over threshold sampling-based model based on mean crash estimates and confidence intervals. This study demonstrates the potential of using autonomous vehicle sensor data for network-level safety, enabling an efficient identification of pedestrian crash risk zones in a transport network.

Risk assessment of pedestrian red-light violation behavior using surrogate safety measures: Influence of human, road, vehicle, and environmental factors

Pedestrian red-light violation is one of the crucial causes of pedestrian crashes at urban intersections, which cause considerable injuries and casualties to this vulnerable road group of road users. The objective of this study is to evaluate the risk of pedestrian-vehicle collisions by clustering the pedestrians' red-light violations using surrogate safety measures. The present study utilized surveillance camera footage to collect data on pedestrians' red-light violations at two urban intersections in Babol City. Based on critical thresholds of post-encroachment time (PET), Time to Collision (TTC), and Gap Time (GT), three different risk levels of red-light violations were identified through the use of a K-means algorithm. Moreover, structural equation models were developed for each of the risk levels considering variables that are associated with four major components: human, environment, road, and vehicle. Lastly, policy insights into amending pedestrian behavior and promoting traffic safety culture were proposed, with an overarching emphasis on the human factor, due to its identified greater influence on the propensity for red-light violations.

Before-after safety evaluation of part-time protected right-turn signals: An extreme value theory approach by applying artificial intelligence-based video analytics

Extreme value theory models have opened doors for before-after safety evaluation of engineering treatments using traffic conflict techniques. Recent advancements in automated conflict extraction technologies have further expedited conflict-based safety evaluation as a potential alternative to traditional crash-based methods. However, the suitability of extreme value theory models in the before-after evaluation of engineering treatments needs to be rigorously tested. As such, this study proposes a traffic conflict-based before-after evaluation of a novel part-time protected right-turn signal strategy for right-turn or opposing-through crashes at signalised intersections. A part-time protected right-turn signal strategy refers to a signal arrangement where permissive and fully protected right-turn phasings are operated during peak and off-peak hours, respectively. A deep neural network-based computer vision technique was applied to extract the conflicts from a total of 654 h of video recordings (before period: 266 h and after period: 388 h) over seven treated approaches, and four matching control approaches at five signalised intersections in the city of Cairns, Australia. Using post encroachment time and post-collision velocity difference as traffic conflict measures, non-stationary bivariate generalised extreme value models were developed to estimate the severe and non-severe opposing-through crashes at signal cycle levels. The odds ratio analysis of model-predicted crash risks suggests that part-time protected right-turn signals reduce 67% and 81% of severe and non-severe opposing-through crashes at signalised intersections, respectively. Part-time protected right-turn signal strategy offers a good safety solution without precipitating need for capacity upgrades to accommodate queued right turners at signalised intersections.

INFLUENCE OF SIGHT DISTANCE CHARACTERISTICS ON SAFETY OF INTERSECTIONS USING SURROGATE SAFETY MEASURES

Recent road accident statistics indicate that more than 30% of accidents occur at intersections. Whereas, total percentage of intersections present in the entire road network is less than 2%. From design point of view, one way to ensure reduction of accidents at intersections is to provide sufficient sight distance and clear sight triangles. However, it was observed that even when minimum sight distance is provided, it does not ensure non-occurrence of accidents because available sight distance at intersection changes with time. Hence there is a need to understand the effect of sight distance characteristics on safety at uncontrolled intersections. From safety studies, it was observed that crash data is highly unreliable as it is generally underreported. Hence in the present study, surrogate safety measures were used instead of crash data. To accomplish the study objectives, six uncontrolled three-legged intersections were selected. Geometric data such as sight distance was measured as per IRC SP 41 (1994) guidelines. Traffic data collection at each intersection was carried out using videography method. From the recorded videos, volume data, speed data, and Post Encroachment Time (PET) data were extracted manually. The effect of various geometric and traffic factors on the critical conflict rate was studied using correlation and regression analysis. From the models developed, it is observed that for every 1 unit increase in logarithm of major and minor road sight distance, critical conflict rate decreased by 18.14% and 33.56%, respectively. Also, it was found that, the available sight distance is far shorter than the design/minimum sight distance specified by IRC SP 41 (1994).

Crash frequency prediction based on extreme value theory using roadside lidar-based vehicle trajectory data

Crash prediction models (CPMs) are mostly developed using statistical or data-driven methods that rely on observed crashes. However, the historical crash records can be unreliable due to availability and data quality issues. Near-crashes based CPMs offer a proactive approach to predict crash frequencies prior to the occurrence of crashes. Surrogate safety measures can be used to identify near-crashes from road user trajectories. Roadside LiDAR offers an innovative approach to collect vehicle trajectory data at a microscopic resolution with high accuracy providing detailed information of all road user movements. This study presents a methodology to identify near-crashes from Roadside LiDAR based vehicle trajectory data using the surrogate indicators: TTC (Time to Collision), PET (Post Encroachment Time), ACT (Anticipated Collision Time) and MaxD (Maximum Deceleration). Additionally, time-based, and evasive-action-based surrogate measures are combined as different pairs to obtain crash probabilities using extreme value theory (EVT). The study results show that the bivariate EVT model displays a better fit to conflict extremes, predicting crash frequencies better than the univariate model. Likewise, while the bivariate model with ACT and MaxD pair performed the best in terms of accuracy, the TTC and MaxD pair was able to reflect the relative threat levels at the study intersections. Overall, the methodology lays ground for using roadside lidar based trajectory data for proactive safety analysis of signalized intersections.

A generalized driving risk assessment on high-speed highways using field theory

This study presents a new safety measure derived from field theory. It evaluates the risk arising from the various concurrent conflicts within a platoon that can occur on high-speed highway driving situations, such as car-following, yielding, and lane changing. We defined the risk field as a finite scalar field produced by traveling vehicles on the road, and we defined the conflict field as the overlapping risk field between any vehicles in proximity on the roadway. The study used a probabilistic trajectory prediction model to construct risk fields and an approximation method to reduce the computational time for real-time applications. To demonstrate the applicability of the proposed new measure, we applied it to real-world trajectory data (NGSIM data from US Highway 101). We compared the results with three traditional conflict-based safety measures: post-encroachment time (PET), modified time-to-collision (MTTC), and deceleration rate to avoid a crash (DRAC). The new measure produced seamless and continuous risk estimations even during time windows when the other measures could not estimate the risk between vehicles. This is a major advantage over traditional measures. The study also developed visual displays of the estimated conflict fields to provide safety analysts with an intuitive and fast understanding of the results of the safety assessments made using the conflict field measure. We conclude that the proposed new safety measure provides a robust, reliable, and improved assessment of the risk involved in expected future mixed-traffic environments that involve both human-driven vehicles and automated vehicles in the future.

An integrated approach of machine learning and Bayesian spatial Poisson model for large-scale real-time traffic conflict prediction

The use of traffic conflicts in road safety evaluation is gaining considerable popularity as it plays a vital role in developing a proactive safety management strategy and allowing for real-time safety analysis. This study proposes an integrated approach that combines a machine learning (ML) algorithm and a Bayesian spatial Poisson (BSP) model to conduct large-scale real-time traffic conflict prediction by considering traffic states as the explanatory variables. Traffic conflicts are measured by two indicators, the Time to Collision (TTC) and the Post-Encroachment Time (PET). Based on both TTC and PET, traffic conflict severity is classified into five categories. For each conflict severity category, a binary variable (conflict occurrence) and a count variable (conflict frequency) are developed, respectively. In addition to conflict variables, traffic state parameters are extracted from a large-scale high-resolution trajectory dataset. The traffic parameters include volume, density, speed, and the corresponding space-based and space–time-based measures within a 30-second interval. Eight ML-based classifiers are applied to predict conflict occurrence, and the best classifier is selected. A binary logistic regression is developed to explore the potential linkages between traffic states and conflict occurrence. As well, a resampling technique Borderline-SMOTE is used to mitigate the sparsity caused by the predefined short interval. The BSP model is utilized to predict the specific number of conflicts. Further, the BSP model can also explain the relationship between traffic states and conflict frequency, and thus the significant influencing traffic states are identified. The results show that random forest outperforms the other MLs in terms of conflict occurrence prediction accuracy. Further, the proposed integrated approach achieves a high performance of conflict frequency prediction with RMSE values of 0.1384 ∼ 0.1699, MAPE values of 9.25% ∼ 36.99%, and MAE values of 0.0087 ∼ 0.6398. The finding emphasizes the need for separately predicting the occurrence and frequency of conflicts with different severities.

Ultra-Wideband-Based Time Occupancy Analysis for Safety Studies.

This study investigates the use of ultra-wideband (UWB) tags in traffic conflict techniques (TCT) for the estimation of time occupancy in a real-world setting. This study describes UWB technology and its application in the TCT framework. Many experiments were conducted to evaluate the accuracy of the occupancy time measurement using a UWB-based tag. The UWB performance was measured using data from UWB tags as well as a video camera system by subtracting the time occupancy within a conflict zone. The results show that the UWB-based system can be utilized to estimate occupancy time with a mean absolute error difference from ground truth measurements of 0.43 s in the case of using two tags and 0.06 s in the case of using one tag in an 8 m × 8 m study area with double-sided two-way communication. This study also highlights the advantages and limitations of using UWB technology in TCT and discusses potential applications and future research directions. The findings of this study suggest that the UWB-based localization of multiple tags needs further improvements to enable consistent multi-tag tracking. In future work, this technology could be utilized to estimate post-encroachment time (PET) in various traffic scenarios, which could improve road safety and reduce the risk of collisions.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety-Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both. This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1,140-min of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, weaving segments, expressway merge/diverge segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multivideo stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based trajectory datasets, CitySim had significantly more safety-critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations’ three-dimensional base maps and signal timings.

Interaction Patterns of Motorists and Cyclists at Intersections: Insight from a Vehicle–Bicycle Simulator Study

At intersections, road users need to comprehend the intentions of others while also implicitly expressing their own intentions using dynamic information. Identifying patterns of this implicit communication between human drivers and vulnerable road users (VRUs) at intersections could enhance automated driving functions (ADFs), enabling more human-like communication with VRUs. To this end, we conducted a coupled vehicle–bicycle simulator study to investigate interactions between right-turning motorists and crossing cyclists. This involved 34 participants (17 pairs of motorists and cyclists) encountering each other in a virtual intersection. The analysis focused on identifying interaction patterns between motorists and cyclists, specifically aiming to discern which patterns were more likely to be accepted by both parties. We found that in CM (vehicles overtaking), the post-encroachment time (PET) and the average speed of vehicles were higher than in the other two interaction patterns: C (bicycles always in front) and CMC (bicycles overtake). However, subjective ratings indicated that CM was viewed as more critical and less cooperative. Furthermore, this study unveiled the influence of crossing order and overtaking position on subjective ratings through ordered logistic regressions, suggesting that earlier overtaking could improve cyclists’ acceptance of the interaction. These findings may contribute to the optimization of communication strategies for ADF, thereby ensuring safety in interactions with VRUs.

Analyzing traffic conflicts and the behavior of motorcyclists at unsignalized three-legged and four-legged intersections in Cartagena, Colombia

IntroductionThe global motorcycle market has grown significantly, with over 770 million vehicles estimated to be in use worldwide. Motorcycle-related road traffic deaths in low and middle-income countries (LMICs) like Colombia are concerning, comprising 30% of all reported fatalities. Cartagena has an average of 70 motorcycle-related deaths annually between 2019 and 2022, making it a high-risk area for motorcyclists. ObjectiveThe study aimed to identify factors associated with motorcyclist safety at unsignalized three-legged and four-legged intersections in Cartagena by observing the behavior of the motorcyclists and the analysis of the potential traffic conflicts. The observational analysis focused on the access of motorcyclists from a secondary road to a main road since it is the behavior offered by the most significant road interaction and the potential risk of traffic conflicts due to crossing. MethodsThe observational process was consolidated at ten three-legged intersections and seven four-legged intersections. Thirty-six hours of videos were collected considering different time slots and weekdays randomly distributed during September 2019 and March 2020. The selection of the intersections included different vehicular flows and road safety conditions. The variables considered in the study were: interaction with other road users, motorcyclist behavior, vehicle handling, potential distractors, and safety elements. The study used the Swedish Traffic Conflict Technique to analyze conflict analysis, incorporating the Post Encroachment Time (PET) measurement. The analysis was developed with descriptive and inferential statistical techniques. The collected variables were analyzed individually (frequency analysis), and contrasts were conducted with the PET values. The study evaluated associations between motorcycles and other motorized road actors at intersections about behaviors and crossroads. ResultsIn the Records, 10,281 motorcycle accesses at three and four-Legged Intersections were interactions with other road users, where 2417 and 1903 resulted in potential traffic conflicts, respectively. Average potential conflicts per hour were 115 and 127 at three and four-legged intersections. At the two intersections, the average PET values in motorcycles were between 2.09 and 2.10 s, while in the other motorized road users, it averaged around 2.67 to 2.71 s. In the road conditions, it was identified that intersections with a traffic flow of<10,000 vehicles/day and poor visibility to the left of the intersection lead to more unsafe conditions for motorcyclists. Motorcycle taxi drivers were the user group most frequently involved in traffic conflicts. Actions on the part of motorcyclists, such as risky behaviors, not using helmets, not using turn signals, and not waiting patiently for access, showed a relationship with the potential for traffic conflicts. Finally, turns to the left, particularly the indirect turn to the left on the opposite road, showed a greater risk of traffic conflicts. ConclusionsThe study found that motorcycles exhibit more severe traffic conflicts than motorized vehicles at intersections. Infrastructure conditions significantly impact the risk of intersection conflicts. Individual behaviors such as not stopping at intersections and driving recklessly increased the risk of traffic conflicts. The study recommends improving infrastructure such as visibility and signaling and implementing separators to reduce travel speed and traffic conflicts for motorcycles.

Evaluating the traffic safety performance of left-turn waiting areas at signalized intersections

Left-turn waiting area (LWA) is an innovative traffic design that is popularly applied to improve the traffic capacity of signalized intersections in China. The traffic safety impacts of the LWA, however, have not been fully discussed in previous studies. Thus, the study aims to evaluate the safety performance of the LWA by means of the traffic conflict technique. A field investigation was conducted to collect the post-encroachment time (PET) of conflicts and relevant variables at the signalized intersections in Jinhua, China. The Chi-square and two sample t-tests were adopted to examine the difference in conflict distribution between the intersections with and without LWA. The random parameter ordered logit model was employed to identify the factors contributing to the risks of vehicular collisions. Results indicate that (1) intersections with LWA are generally associated with more merging conflicts; (2) there are no significant discrepancies in the PET values between intersections with and without LWA; and (3) factors such as the number of left-turn lanes, number of receiving lanes, conflict type, vehicle type, driving direction, stopping outside LWA and overtaking behavior are identified to significantly impact the traffic conflicts. The findings serve to develop the countermeasures to ensure the safe operation of LWA.

An exploration of the effects of various street lighting conditions on driver behaviors and safety at stop-controlled intersections: A driving simulator experiment

Lack of visibility is one of the challenges that impact drivers’ ability to perform driving tasks at night. Although prior studies indicated that lighting increases nighttime safety, lighting at stop-controlled intersections is not mandatory according to many U.S. states’ guidelines. This paper aims to investigate the impact of different lighting conditions on safety-related driving behavior during nighttime at stop-controlled intersections. The data were collected using a driving simulator experiment where participants drove through several intersections during daytime, nighttime without lighting, nighttime with partial lighting, and nighttime with full lighting. Collected measures included speed, time-to-collision (TTC), and post-encroachment time (PET). TTC/PET were used as safety surrogate measures to evaluate the impact of lighting on drivers’ safety. The results indicated that TTC/PET were higher during daytime and nighttime with partial/full lighting. Furthermore, the findings revealed that lower speeds were recorded at nighttime with partial/full lighting. Kruskal–Wallis test revealed that TTC/PET medians for nighttime without lighting were statistically significantly different from TTC/PET medians of the remaining lighting conditions. Moreover, negative binomial modeling indicated that lack of lighting and speed had inverse correlation with TTC. In summary, the results revealed that street lighting at stop-controlled intersections provided 37% increase in TTC, 15% increase in PET, and 15% reduction in speed.

Effect of signal timing on vehicles’ near misses at intersections

Driving characteristics often vary between the different states of the signal. During red and yellow phase, drivers tend to speed up and reduce the following distance which in turn increases the possibility of rear end crashes. Intersection safety, therefore, relies on the correct modelling of signal phasing and timing parameters, and how drivers respond to its changes. This paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned aerial vehicle (UAV) video data has been used to study a major intersection. Post encroachment time (PET) between vehicles was calculated from the video data as well as speed, heading and relevant signal timing parameters such as all red time, red clearance time, yellow time, etc. Random parameter ordered logit model was used to model the relationship between PET and signal timing parameters. Overall, the results showed that yellow time and red clearance time is positively related to PETs. The model was also able to identify certain signal phases that could be a potential safety hazard and would need to be retimed by considering the PETs. The odds ratios from the models also indicate that increasing the mean yellow and red clearance times by one second can improve the PET levels by 10% and 3%, respectively.

Do Traffic Countermeasures Improve the Safety of Vulnerable Road Users at Signalized Intersections? A Combination of Case-Control and Cross-Sectional Studies Using Video-Based Traffic Conflicts

Driven by the vision of eliminating road fatalities, Vision Zero initiatives have been widely adopted by many cities around the world, with significant investment of resources in various safety countermeasures. However, there is still a lack of reliable quantitative evidence on the effectiveness of those countermeasures on the traffic conflict frequency at intersections. This research attempts to address this challenge with a combination of case-control and cross-sectional studies, aiming at quantifying the safety effects of three commonly applied Vision Zero countermeasures, namely, leading pedestrian interval, no right turn on red, and installation of a dedicated bicycle lane. A case study was conducted using video trajectory data from 10 signalized intersections in the City of Toronto, Canada. The traffic interactions between vehicles and vulnerable road users were extracted using a video data processing platform, and two surrogate measures of safety, including post-encroachment time and conflict speed, were obtained, and then used to classify the conflict severity into different levels. A comparative analysis using mixed-effects negative binomial regression was conducted to quantify the impacts of different treatments on the frequency of traffic conflicts under specific road weather and traffic conditions. The results show that these three types of traffic countermeasures can effectively reduce the frequency of high-risk and moderate-risk traffic conflicts, moderated by various traffic exposure and weather and environmental conditions and accessible pedestrian signals. These findings could help road safety engineers and decision makers make better informed decisions on their road safety initiatives and projects.

Surrogate Safety Evaluation at Uncontrolled Intersection in Non-Lane Base Traffic Conditions

Every year, road accidents cost billions of dollars and injure millions of people around the world. The current study implements traffic conflict techniques to examine vehicular safety at three-arm uncontrolled intersections. The traffic conflict technique is designed to be proactive, not solely rely on crashes, and require shorter observation time periods to create acceptable safety assessments. Various surrogate safety measures based on spatial and temporal proximity between road users, like Post Encroachment Time (PET), Time to Collision (TTC), Deceleration Rate (DR), etc., are being used to study road safety. The study focused on crossing conflicts by right-turning and through traffic, as they are considered severe among other conflicts. PET and conflicting vehicle speeds in through traffic are used to determine critical conflicts. However, Delta V and Encroachment Time (ET) are taken as surrogate indicators to identify severity levels of through and right turning movements, respectively, using the clustering technique..

Traffic safety assessment for roundabout intersections using drone photography and conflict technique

Road design deficiencies and improper driver behavior at roundabout intersections may result in traffic bottlenecks, irregular traffic patterns, and potential crashes. Thus, road safety inspection is conducted to identify potential safety hazards and propose safety measures. The traditional safety inspection depends on unreliable traffic collision data visual data collection and superficial analysis. In this regard, surrogate safety assessment approaches are utilized to overcome the limitations found in traditional approaches. This paper employs an innovative surrogate approach for such a process by analyzing videos captured by a drone. A video processing technique is applied to determine the vehicle trajectories and extract conflict points. Accordingly, the conflict data are analyzed in terms of location, direction, and post-encroachment time (PET) as a safety measure to identify potential safety problems related to intersection geometry and driver behavior. This methodology is applied to an intersection case study in New Cairo City, Egypt. The findings of this study confirm the interaction between intersection geometry, drivers’ behavior, and road safety on the examined safety measures.