Surrogate Safety Measures Research Articles

Impact of Splitter-Island on Pedestrian Safety at Roundabout Using Surrogate Safety Measures: A Comparative Study

This study aimed to investigate the effects of the splitter-island on pedestrian safety at roundabouts, considering the interaction between pedestrians and vehicles. Traffic accident statistics have traditionally been used to assess traffic safety. This method has severe limitations when used to investigate the impact of a particular feature of traffic facilities on safety at a microscopic level. Most previous research on surrogate safety measures (SSMs) had, on the other hand, focused on studying the safety of traffic operation conditions. The impact of a particular geometric feature of a roundabout on safety has so far received little attention in the literature. Therefore, we evaluated pedestrian safety in the presence and absence of physical splitter-islands by taking advantage of comparative statistical analysis (SA) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) methods. The Towa-Cho roundabout in Nagano prefecture in Japan was chosen for this study because it allows for five approaches, three with a splitter-island and two without. From 33-hr video data, a total of 343 and 183 potential pedestrian-vehicle conflicts were detected in three crosswalks with splitter-islands and two crosswalks without splitter-islands, respectively. The minimum time-to-collision (TTCmin), post-encroach-time (PET), maximum speed (MaxS), and maximum deceleration-to-safety (DTS) measures were monitored using particle tracking velocimetry (PTV) software for potential pedestrian-vehicle conflicts. It was found that geometric differences were significantly reflected by SSMs, and TTCmin was the most efficient among all. It was concluded that the splitter-island had a better safety performance than the roundabout approach.

A revise on using surrogate safety measures for rear-end crashes

Traffic safety is among the most serious topics of transportation engineering studies. Different methods have been proposed for safety evaluations. Applying surrogate safety measures (SSM) to predict the crash likelihood and severity is a relatively new approach, which needs microscopic traffic data instead of crash data. Numerous SSMs have been introduced in the literature. Now, it is somehow hard to decide about the selection of suitable SSMs for safety assessments based on microscopic traffic data. In this paper, first a review on SSMs relating to rear-end conflicts introduced in the high-quality papers is done. Then, with the help of microscopic traffic data of NGSIM in I-80 freeway, SSMs are calculated for each car-following scenario. Because of the diversity of SSMs and contradicting results it was tried to propose a method for combining the results of these indicators to report one number as the rear-end collision risk. The efficiency of the new indicator is evaluated in comparison to individual SSMs. The proposed methodology can be beneficial to improve the efficiency of In-vehicle collision avoidance warning system (IVCAWS) and enhancing highway safety evaluations based on SSMs.

Observed sidewalk autonomous delivery robot interactions with pedestrians and bicyclists

The recent deployment of sidewalk autonomous delivery robots (SADRs) across university campuses has offered students, staff, and faculty a convenient option for food delivery to their residences or workplaces. However, these low-speed automated food delivery services, which were first commercially deployed on American campuses in early 2019 and continued to offer an important contactless delivery service during the height of the Covid-19 pandemic, traverse campuses on pathways originally designed for pedestrians and bicyclists, creating a potential for conflicts among the different pathway users and potentially unsafe transportation conditions. This study examines one week of field-recorded video from ten locations across the Northern Arizona University campus to help understand the prevalence and severity of SADR-involved interactions with pedestrians and bicyclists. The severity of SADR-involved interactions was quantified by using the surrogate safety measure of post-encroachment time, which was then modeled as a function of conflict- and site-level characteristics to identify predictors of moderate or dangerous conflicts between SADRs and human pathway users. Findings from this study, which provides initial real-world insights into the impacts of SADRs sharing pathways with pedestrians and bicyclists, are intended to help inform facility management strategies capable of supporting the safe introduction of this emerging autonomous freight technology on shared-use facilities in current and potential future settings.

Safety evaluation of horizontal curves on two lane rural highways using machine learning algorithms: A priority-based study for sight distance improvements

Objective Current Road geometric design processes disregard stochastic aspects, resulting in inadequate traffic safety considerations. In addition, the primary sources of crash data are obtained from police department, insurance agency and hospitals, where detailed investigation is not carried out from a transportation perspective. So, the data obtained from these sources may or may not be reliable. The main objective of this study is to account uncertainties using reliability as a tool that considers slow down the vehicle while they maneuver the curve and to develop thresholds of reliability index associated with sight distance based on operating speed design consistent measure as a surrogate safety measure rather than using crash data. Methodology This study proposes a thresholds of reliability index associated with sight distance for various operating speed ranges based on design consistent measure. In addition, established the relationship between the consistency levels, geometric characteristics and vehicle characteristics. In this study, classical topography survey was carried out on the field using total station. The data collected comprises speed data and geometric data of 18 horizontal curves (Lane based analysis was carried out). A total of 3042 free flowing vehicle speeds have been extracted from the video graphic survey and used in the analysis. Results The threshold values of reliability indices associated with sight distance is higher as the operating speed increases for the section to be a consistent design section. The results from the Binary Logit Model show that the consistency level is significantly affected by deflection angle and operating speed. Deflection angle was negatively correlated with the in-consistency level, and the operating speed was positively correlated with in-consistency level. Conclusions From Binary Logit Model (BLM) results, we can conclude that an increase in the value of deflection angle will significantly decrease the probability of In-Consistent level, which indicates uncertainties that cause drivers to change the path of vehicle or rate of deceleration of the vehicle while maneuvering the curve will be decreased. Whereas, increase in the operating speed will significantly increase the probability of In-Consistency level.

Exploiting Surrogate Safety Measures and Road Design Characteristics towards Crash Investigations in Motorway Segments

High quality data on road crashes, road design characteristics, and traffic are typically required to predict crash frequency. Surrogate Safety Measures (SSMs) are an alternative category of indicators that can be used in road safety analyses in order to quantify various unsafe traffic events. The objective of this research is to exploit road geometry data and SSMs toward various road crash investigations in motorway segments. To that end, for this analysis, a database containing data on injury and property-damage-only crashes, road design characteristics, and SSMs of 668 segments was compiled and utilized. The results of the developed negative binomial regression model revealed that crash frequency is positively correlated with the average annual daily traffic volume, the length of the segment, harsh accelerations, and harsh braking. Moreover, four distinct clusters representing crash risk levels of the examined segments emerged from the hierarchical clustering procedure, ranging from more risk-prone, potentially unsafe locations to more safe locations. These four clusters also formed the response variable classes of a random forest model. This classification model used various road geometry data and SSMs as predictors and achieved high classification performance for all classes, averaging more than 88% correct classification rates.

Modelling and Mitigating Secondary Crash Risk for Serial Tunnels on Freeway via Lighting-Related Microscopic Traffic Model with Inter-Lane Dependency.

This paper models and mitigates the secondary crash (SC) risk for serial tunnels on the freeway which is incurred by traffic turbulence after primary crash (PC) occurrence and location-heterogeneous lighting conditions along serial tunnels. A traffic conflict approach is developed where SC risk is quantified using a surrogate safety measure based on the simulated vehicle trajectories after PC occurs from a lighting-related microscopic traffic model with inter-lane dependency. Numerical examples are presented to validate the model, illustrate SC risk pattern over time, and evaluate the countermeasures for SC, including adaptive tunnel lighting control (ATLC) and advanced speed and lane-changing guidance (ASLG) for connected vehicles (CVs). The results demonstrate that the tail of the stretching queue on the PC occurrence lane, the adjacent lane of the PC-incurred queue, and areas near tunnel portals are high-risk locations. In serial tunnels, creating a good lighting condition for drivers is more effective than advanced warnings in CVs to mitigate SC risk. Combined ATLC and ASLG is promising since ASLG informs CVs of an immediate response to traffic turbulence on the lane where PC occurs and ATLC alleviates SC risks on adjacent lanes via smoothing the lighting condition variations and reducing inter-lane dependency.

Expected Safety Performance of Different Freeway Merging Strategies in an Environment of Mixed Vehicle Technologies

This study evaluates different proposed merging solutions that reduce the conflict between merging vehicles and mainline traffic within a mixed traffic environment using a safety measure to see which strategy might work better than others under specific traffic conditions. The mixed traffic includes various percentages of driver-operated vehicles (DVs) and connected autonomous vehicles (CAVs). The probability of noncompliance (PNC) is selected as a surrogate safety measure to assess the strategies. A MATLAB program is developed to simulate various traffic conditions at a merging area and to calculate the PNC merging for the different merging strategies. In addition, to examine the relationship between PNC and collision frequency at the merging area, the collision data at 15 merging ramps in Ottawa were collected to examine the relationship between PNC values obtained from the simulation for the case of a full-DV vehicle fleet and no management strategy (current conditions) and actual safety performance. The results confirmed the validity of PNC as a surrogate safety measure that is correlated to expected collision frequency at merge areas. By simulating all proposed merging management strategies, the results of this study showed a general trend of decreasing PNC and, hence, improved safety performance since the CAV penetration rate increases even when no management strategy is used or under the do-nothing option. However, most merging strategies had better expected safety performance than the do-nothing option, which indicates the value of implementing a merging management strategy, especially during the period of transition from a full-DV to a full-CAV fleet.

A conflict-based safety assessment technique for rear-end crash risk at signalized intersections in a lower-middle-income country: A comparison between homogeneous and heterogeneous traffic conditions

Rear-end crashes at signalized intersections are attributed to different traffic behavior and operational characteristics. However, the influences of homogenous traffic conditions in developed countries and heterogeneous traffic conditions in low and middle-income developing countries like India are rarely explored. This study aims to examine the spatial–temporal characteristics of rear-end crash risk at signalized intersections characterized by lane-disciplined homogeneous and non-lane-based heterogeneous traffic conditions. In particular, this study has applied a conflict-based safety assessment framework to compare the rear-end crash risks across these traffic conditions. Vehicular trajectory data of vehicles at four signalized intersections, one in the homogeneous and three in heterogeneous traffic conditions, is analyzed. Rear-end conflicts position and time of occurrence in the upstream and downstream section of the stop-line are identified and modeled by extreme value models. Results show that the vehicles in heterogeneous traffic conditions are more aggressive, resulting in a higher number of conflicts and probable crashes. The presence of smaller-sized vehicles in dominating proportions and disregard of lane discipline with a higher degree of lateral movement are responsible for more traffic conflicts. The estimated extreme value models suggest that the minimum rear-end crash risk probability at signalized intersections with heterogeneous traffic conditions is about three times higher than that at signalized intersections with homogeneous traffic conditions. The observation is similar for crash risks estimated from both surrogate safety measures used in this study. The weak lane discipline and irregular lateral movement of vehicles are responsible for higher crash risks in heterogeneous traffic conditions.

Explicitly incorporating surrogate safety measures into connected and automated vehicle longitudinal control objectives for enhancing platoon safety

The concepts of Connected and Automated Vehicles (CAV) and vehicle platooning have generated high expectations regarding the safety performance of future transportation systems. Existing CAV longitudinal control research primarily focuses on efficiency and control stability, by considering different inter-vehicle spacing policies. In very few cases, safety was also considered as a constraint, but not in the main control objectives. Theoretically, stability can only guarantee that CAV platoons eventually achieve an equilibrium state but is unable to promise safety along the process of achieving equilibrium. It is important to note that CAV does not mean absolutely safe, and its longitudinal or platoon control safety performance depends on how the control algorithms are designed, how accurately it can detect and predict its lead vehicle’s (could be a human-driven vehicle) next move, and other practical factors such as control and communication delays. To optimize CAV platoon safety, this study integrates surrogate safety measures (SSM) and model predictive control (MPC) into CAV longitudinal control for trajectory optimization. SSM has been widely adopted for modeling the safety consequences of various vehicle control strategies and identifying near-crash events from either simulated or field-captured traffic data. This study directly incorporates three typical SSM into the longitudinal control objectives of CAV and constructs a state-space MPC algorithm to model how these SSM vary as a result of CAV dynamics. Numerical examples are provided to show the performance of these SSM-based optimal CAV longitudinal control methods under traffic flow perturbations. To further confirm the necessity of explicitly considering SSM in CAV longitudinal control and its effectiveness in reducing rear-end collision risk, the proposed methods are compared with three classical longitudinal control models that do not consider SSM based on microscopic traffic simulation. It is noted that all SSM-based optimal control methods perform better than others as manifested by some key risk indicators, demonstrating the importance of explicitly considering SSM and safety in CAV longitudinal control.

Application of Connected Vehicle Data to Assess Safety on Roadways

Using surrogate safety measures is a common method to assess safety on roadways. Surrogate safety measures allow for proactive safety analysis; the analysis is performed prior to crashes occurring. This allows for safety improvements to be implemented proactively to prevent crashes and the associated injuries and property damage. Existing surrogate safety measures primarily rely on data generated by microsimulations, but the advent of connected vehicles has allowed for the incorporation of data from actual cars into safety analysis with surrogate safety measures. In this study, commercially available connected vehicle data are used to develop crash prediction models for crashes at intersections and segments in Salt Lake City, Utah. Harsh braking events are identified and counted within the influence areas of sixty study intersections and thirty segments and then used to develop crash prediction models. Other intersection characteristics are considered as regressor variables in the models, such as intersection geometric characteristics, connected vehicle volumes, and the presence of schools and bus stops in the vicinity. Statistically significant models are developed, and these models may be used as a surrogate safety measure to analyze intersection safety proactively. The findings are applicable to Salt Lake City, but similar research methods may be employed by researchers to determine whether these models are applicable in other cities and to determine how the effectiveness of this method endures through time.

Gate violation prediction at highway-rail grade crossings using tree-based ensemble techniques

Highway-rail grade crossing (HRGC) safety is one of the priority areas in the United States transportation system that requires for greater research efforts not just limited to crash analysis, but also to gain a deeper understanding of surrogate safety measures such as driver behavior-based traffic violations at HRGCs. This paper uses vehicle profile data to identify the key variables and develop prediction models for gate violations and examine the relationship between model accuracy and the key input variables. A data set of 256 vehicle-train events was collected at two HRGC testbeds in Lincoln, Nebraska. Among them, 76 events are gate violations, and 180 events are non-violations. Two tree-based ensemble techniques, the bootstrap forest and the boosted tree, were applied to the data set. It was found that once a vehicle is within 190 feet from the HRGC stop line, the model was approximately 80 percent accurate in predicting a gate violation. It was also found that as vehicles came closer to the HRGC, the prediction error decreased. With the advent of vehicle profile data collection, tree-based ensemble techniques are ideal for safety studies as they can utilize the highly non-linear vehicle profiles and relate these to safety surrogate metrics.

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data is scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as Autonomous Vehicles (AVs), Connected Vehicles (CVs), and Connected-Autonomous Vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs, and SSM-based models used for mixed traffic safety evaluation. It reviews the current relevant literature and applies a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.

Surrogate safety measures using bicycle vehicle models

Surrogate Safety Measures (SSM) allow for analysis of crashes without having access to actual crash data as those are difficult to obtain, being of rare event nature. This article, for the first time, uses kinematic and dynamic bicycle models for vehicles for trajectory analysis to compute surrogate safety measures. The model has two versions, with and without the consideration of adhesion coefficients depending on the road conditions. Previously, only longitudinal models have been used and recently, a combined longitudinal and lateral model has been used by the authors but with a single wheel model, which is being enhanced by using the bicycle model in this paper.

A Framework for Proactive Safety Evaluation of Intersection Using Surrogate Safety Measures and Non-Compliance Behavior

A Framework for Proactive Safety Evaluation of Intersection Using Surrogate Safety Measures and Non-Compliance Behavior

The use of microscopic simulators for safety assessment in automated and partially automated scenarios: a comparison

In the field of safety assessments, the use of simulators to predict unknown scenarios is becoming crucial, due to the ability of predicting conflict points. The microscopic simulators, accountable for modeling single-vehicle interactions, differ for the type of car-following and lane-changing model, hence for the number of definable parameters. The possibility of modifying all the parameters inside the model equations makes them suitable for simulating autonomous vehicles too. To evaluate the road safety of a site, the output of traffic simulators can become the input of the SSAM (Surrogate Safety Assessment Model), which relies on surrogate safety measures (SSMs). Thus, the choice of the simulator is fundamental. The focus of this dissertation concerns the comparison of the car-following and lane-changing models at the basis of the most used commercial microscopic simulators (VISSIM, AIMSUN, PARAMICS) and the related SSAM outputs in the automated and partially automated traffic environment. The Gipps Model, valid for the car-following and the lane-changing model, aims at always achieving the safety distance, intervening on kinematic parameters like the acceleration, speed, and distance. It is possible to intervene on a limited number of parameters of this model. The Wiedemann model belongs to the family of psycho-physical one and it is governed by several parameters, which makes it useful to remodel complex situations, simulating the human mental workload in driving. The Gipps model and the Wiedemann one (implemented respectively in AIMSUN and VISSIM) have already been used for safety purposes, while the Fritszche one (implemented in PARAMICS) has been currently used only for environmental issues related to automation. It is possible to modify up to 16 parameters in the Gipps and Wiedemann model to simulate the automated and partially autonomous vehicles, even if these models belong to different car-following model family. Based on the site variables, each simulator showed potential strengths. Scenarios involving automated vehicles, which do not account for human behaviors, might be better represented by the Gipps model, which aims at reproducing only the safety distance and not the human mental behavior. Thus, this model could also be the most suitable for the general purpose of crash analysis above mentioned.

Reckoning Level of Risk and Severity (LORS) Based on the Gap Acceptance at a Three-Legged Uncontrolled Intersection of a Rural Highway

Traffic safety assessment is an essential part of traffic planning and engineering. At uncontrolled intersections, the severity of crashes has increased manifolds. This is due to the fact of not following the priority rule during the right-turning movements. In conventional methods, past accident records were used to analyze traffic safety. However, the drawbacks of the traditional method to estimate the severity require a lot of accident data for analysis to draw any conclusion and suggest safety measures. The accuracy of the record of accidents and details is highly uncertain. Therefore, surrogate safety measures are being used to analyze the safety. In the present paper, the gap acceptance time (GT) is used to examine the risk and the speed and gap values define the level of risk and severity (LORS). Moreover, using the value of the critical gap, the probability of risk is estimated. Further, the level of risk and severity is computed based on the gap and speed data of vehicles at an uncontrolled intersection. Hence, with the speed and gap data, the level of risk and severity is classified as low risk, high risk and low severe, and high severe. The methodology implemented is validated by using the actual accident data.

Assessing the Impact of Increasing Tractor-Trailer Speed Limit on the Safety and Mobility of Three-Lane Highways in Egypt

Currently, the speed limit for tractor-trailers (TTs) on Egyptian highways is 30 km/h lower than the speed limit for passenger cars (PCs). The main purpose of this article is therefore to evaluate the effects of an increase in TT speed limit on both the traffic safety and mobility of Egyptian three-lane highways, with speed limits of 90 and 100 km/h for PCs. A SUMO simulation model was used in this analysis. Average vehicle delay was used as a measure of mobility, while the simulated conflict ratio based on time to collision (TTC) and deceleration rate required to avoid crash (DRAC) surrogate safety measures were used as performance indicators of safety. All simulated vehicles were equipped with surrogate safety measurement (SSM) devices to extract traffic conflicts, with TTC ≤ 2.50 representing less sever conflicts; TTC ≤ 1.50 s and DRAC ≥ 3.35 m/s2 representing potential conflicts; and TTC ≤ 0.50 s and DRAC ≥ 6.0 m/s2 representing severe conflicts. The results show that increasing the speed limit for TTs would significantly reduce the average delay by approximately 27–76% for traffic volumes 500–2000 vehicle/hr/lane, with TTs comprising 2.5–15% of total traffic volume. Furthermore, in most cases, when comparing the situation after the speed limit increase for TTs with the base case (i.e., TTs with a speed limit of 60 km/h and 70 km/h for highways, and a speed limit of 90 km/h and 100 km/h for PCs), the results show no statistically significant change in the conflict ratio more than 1.0, suggesting that there is no safety change. Furthermore, due to the increase in speed limit for TTs, in some cases and for different thresholds, there is a statistically significant reduction in the conflict ratio, indicating improvements in terms of safety.

Driver behavior indices from large-scale fleet telematics data as surrogate safety measures

Large-scale telematics data enable a high-resolution inference of road network’s safety conditions and driver behavior. Although many researchers have investigated how to define meaningful safety surrogates and crash predictors from telematics, no comprehensive study analyzes the driver behavior derived from large-scale telematics data and relates them to crash data and the road networks in metropolitan cities. This study extracts driver behavior indices (e.g., speed, speed variation, hard braking rate, and hard acceleration rate) from large-scale telematics data, collected from 4000 vehicles in New York City five boroughs. These indices are compared to collision frequencies and collision rates at the street level. Moderate correlations were found between the safety surrogate measures and collision rates, summarized as follows: (i) When normalizing crash frequencies with traffic volume, using a traffic AADT model, safety-critical regions almost remain the same. (ii) The correlation magnitude of hard braking and hard acceleration varies by road types: hard braking clusters are more indicative of higher collision rates on highways, whereas hard acceleration is a stronger hazard indicator on non-highway urban roads. (iii) Locations with higher travel times coincide with locations of high crash incidence on non-highway roads. (iv) However, speeding on highways is indicative of collision risks. After establishing the spatial correlation between the driver behavior indices and crash data, two prototype safety metrics are proposed: speed corridor maps and hard braking and hard acceleration hot-spots. Overall, this paper shows that data-driven network screening enabled by telematics has great potential to advance our understanding of road safety assessment.

Analysis of lane-changing conflict between cars and trucks at freeway merging sections using UAV video data

The freeway on-ramp merging section is often identified as a crash-prone spot due to the high frequency of traffic conflicts. Cars and trucks have different sizes and operation characteristics, but very few traffic conflict analysis studies considered different vehicle types at freeway merging sections. Thus, the main objective of this study is to analyze lane-changing conflicts between different vehicle types at freeway merging sections. Vehicle trajectories are extracted from the Unmanned Aerial Vehicle (UAV) video data which are collected in Shanghai, China. Time-to-collision (TTC) is utilized as the surrogate safety measure (SSM) to analyze lane-changing conflicts. Results show that TTC values of car-car conflicts are the smallest, while truck-truck conflicts have the largest TTC values. Although traffic conflicts frequently occur at the on-ramp and additional rightmost lane, the spatial distribution of lane-changing conflicts is significantly different between different vehicle types. The findings of this study are useful for transportation management agencies to design proper strategies to improve traffic safety at freeway merging sections.

Investigating and modeling the influence of PET-types on crossing conflicts at urban unsignalized intersections in India

Un-signalized intersections in India witnessed the maximum number of crashes and fatalities in 2019. The nature of the crash investigation is still largely reactive, where the need for accurate and reliable crash data for effective safety diagnosis is pivotal. In India, crash records are unscientific, and critical details are missing. Therefore, a proactive approach using surrogate safety measures is more promising and prudent in analyzing traffic safety. The present study investigates and models crossing conflicts at un-signalized intersections under mixed traffic conditions. Traffic video data for 14 un-signalized intersections (eight un-signalized three-legged intersections and six un-signalized four-legged intersections) were collected under normal weather conditions. The crossing conflicts were identified and characterized as critical and noncritical conflicts based on the values of post-encroachment time (PET). Conflicts with PET values between −1 s and 1 s were identified as critical conflicts. The observation revealed the existence of both positive and negative PET values. The investigation revealed that crossing conflicts with negative PET values are riskier and more unsafe than conflicts with positive ones. Therefore, the crossing conflicts with positive and negative PETs were modeled separately. The positive and negative PET-based critical crossing conflicts are modeled as a function of traffic flow and intersection geometry-related characteristics using truncated negative binomial regression under a full Bayesian modeling framework. K-fold cross-validation with fivefold was employed to calibrate the model, and RMSE was used to find the best model. The modeling results revealed that the volume and traffic composition of the offending and conflicting stream and intersection geometry significantly influence the number of positive and negative PET-based critical crossing conflicts. The developed models can interest engineers and safety experts to analyze traffic safety and identify critical intersections in urban road networks.