Assessing the collision risk of mixed lane-changing traffic in the urban inter-tunnel weaving section using extreme value theory

Urban inter-tunnel weaving (UIW) sections are characterized by short lengths and frequent lane-changing behaviors in the area, commonly used for fast through traffic. These features increase the likelihood of collisions, however, collision risk assessment in this area has been inadequate. The aim of this study was to evaluate the potential collision risk of urban inter-tunnel weaving (UIW) sections in mixed lane-changing traffic conditions in morning rush hours, utilizing surrogate safety measures. The investigation involved the collection of trajectory data via an unmanned aerial vehicle (UAV). Time to collision (TTC) and extended time to collision (ETTC) were chosen as surrogate safety indicators. The estimation of collision risk was conducted using Extreme Value Theory (EVT) by means ofsurrogate safety indicators. It was found that the threshold of TTC and ETTC in this area was 1.25 s. Furthermore, a comprehensive evaluation of collision risks associated with various vehicle types was performed, revealing an inverse relationship between thecollisions riskof vehicles in mixed traffic and their size. It was worth noting that while heavy vehicles exhibit a lower collision risk, they resulted in the highest energy loss and inflicted greater harm in the event of a collision. By an examination of the distribution features pertaining to conflict types during the operation of heavy vehicles, it showed that the highest likelihood of conflict with heavy vehicles occurred when adjacent lanes are involved. Consequently, the implementation of assisted driving technology for heavy vehicles was imperative in order to mitigate the risk associated with side collisions.

Safety performance evaluation of freeway merging areas under autonomous vehicles environment using a co-simulation platform

Merging areas serve as the potential bottlenecks for continuous traffic flow on freeways. Traffic incidents in freeway merging areas are closely related to decision-making errors of human drivers, for which the autonomous vehicles (AVs) technologies are expected to help enhance the safety performance. However, evaluating the safety impact of AVs is challenging in practice due to the lack of real-world driving and incident data. Despite the increasing number of simulation-based AV studies, most relied on single traffic/vehicle driving simulators, which exhibit limitations such as inaccurate description of AV behavior using pre-defined driving models, limited testing modules, and a lack of high-fidelity traffic scenarios. To this end, this study addresses these challenges by customizing different types of car-following models for AVs on freeway and developing a software-in-the-loop co-simulation platform for safety performance evaluation. Specifically, the environmental perception module is integrated in PreScan, the decision-making and control model for AVs is designed by Matlab, and the traffic flow environment is established by Vissim. Such a co-simulation platform is supposed to be able to reproduce the mixed traffic with AVs to a large extent. By taking a real freeway merging scenario as an example, comprehensive experiments were conducted by introducing a single AV and multiple AVs on the mainline of freeway, respectively. The single AV experiment investigated the performance of different car-following models microscopically in the case of merging conflict. The safety and comfort of AVs were examined in terms of TTC and jerk, respectively. The multiple AVs experiment examined the safety impact of AVs on mixed traffic of freeway merging areas macroscopically using the developed risk assessment model. The results show that AVs could bring significant benefits to freeway safety, as traffic conflicts and risks are substantially reduced with incremental market penetration rates.

Conflict-Based safety evaluations at unsignalized intersections using surrogate safety measures

Conflict-based road safety assessments may provide a deeper insight into the processes leading to crashes compared to assessments solely based on field crash data. The evaluation of road safety is conducted on specific road segments using different surrogate measure of safety indicators, such as temporal, spatial, and kinematic proximity measures, depending on the relevant context and applicability of these measures. Therefore, this study endeavored to develop a methodology by adopting safety measures such as post encroachment time (PET) and conflicting speeds of through vehicles for crossing maneuvers and time to collision (TTC) for rear-end collisions at five unsignalized intersections in urban mixed traffic conditions. Critical conflicts are calculated by calculating a speed variable known as the critical speed, which is based on the braking distance. A study found that the motorized two wheeler (MTW) categories involve the highest proportion of critical conflict with right-turning vehicles, followed by cars, autos, and light commercial vehicle (LCVs). Furthermore, crossing conflicts were modeled as a function using the generalized linear regression approach. The findings revealed that the most significant factors were traffic volume and vehicular composition in a conflicting stream. The unsupervised classification technique k-mean clustering was used to determine the defined severity level threshold for rear-end maneuvers. The result observed was that a TTC threshold of less than 1.15 s was identified as high-risk vehicular interaction. Additional investigation indicated that presence of certain moving vehicle categories, including MTWs and cars, led to a higher proportion of critical crossing conflicts. The conceptualized safety framework can be applied to evaluate safety at unsignalized intersections in the mixed traffic scenarios.

A review of surrogate safety measures on road safety at unsignalized intersections in developing countries

In recent times, the assessment of unsignalized intersection safety has received significant research attention because of the complex and diverse traffic movements and driving behaviour at such locations. However, priority traffic regulations are not well followed in comparison to the unsignalized junctions, which leads to more conflicts. Additionally, the severity of conflicts increases with continuous traffic manoeuvres, including right-turns and through traffic, combined with different driving behaviours. Several studies have compared crash-based analysis to proactive traffic safety measures. Current research outcomes imply that surrogate safety measures (SSMs) have the potential to elucidate the sequence of events that result in collisions, their underlying causes, and their outcomes. Therefore, to further understand the appropriateness of SSMs, further study is required based on heterogeneity in traffic along with driver behaviour that incorporates turning vehicle factors. This study presents an all-inclusive evaluation of the recent advancements in SSMs and their practical implementation, with a particular emphasis on unsignalized intersections in developing nations. The findings of this investigation would be helpful in identifying the appropriate safety indicators for evaluating traffic safety at unsignalized intersections.

Advances and applications of computer vision techniques in vehicle trajectory generation and surrogate traffic safety indicators

The application of Computer Vision (CV) techniques massively stimulates microscopic traffic safety analysis from the perspective of traffic conflicts and near misses, which is usually measured using Surrogate Safety Measures (SSM). However, as video processing and traffic safety modeling are two separate research domains and few research have focused on systematically bridging the gap between them, it is necessary to provide transportation researchers and practitioners with corresponding guidance. With this aim in mind, this paper focuses on reviewing the applications of CV techniques in traffic safety modeling using SSM and suggesting the best way forward. The CV algorithms that are used for vehicle detection and tracking from early approaches to the state-of-the-art models are summarized at a high level. Then, the video pre-processing and post-processing techniques for vehicle trajectory extraction are introduced. A detailed review of SSMs for vehicle trajectory data along with their application on traffic safety analysis is presented. Finally, practical issues in traffic video processing and SSM-based safety analysis are discussed, and the available or potential solutions are provided. This review is expected to assist transportation researchers and engineers with the selection of suitable CV techniques for video processing, and the usage of SSMs for various traffic safety research objectives.

Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation

This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.

Development of a conflict risk evaluation model to assess pedestrian safety in interaction with vehicles

Interactions of motorised vehicles with pedestrians have always been a concern in traffic safety. The major threat to pedestrians comes from the high level of interactions imposed in uncontrolled traffic environments, where road users have to compete over the right of way. The interactions become more complex with the variety of user types and their available conflict resolution strategies. In this research, a conflict risk evaluation model is developed to assess the safety level of pedestrian conflict with other road users. Surrogate safety indicators are employed to measure road users' temporal and spatial proximity during a conflict. The thresholds are determined through the application of various methods (i.e., intersection point, p-tile, maximum between-class variance, and minimum cross-entropy method) to separate potential critical conflicts against normal traffic conditions, on the basis of the conflict risk evaluation model. An F-score method is used to select the optimal threshold given by various applied methods. Two data sets of shared space and mid-block were used to develop and validate conflict risk evaluation models for the interaction of pedestrians with vehicles (passenger cars) and light vehicles (two- or three-wheel vehicles) separately. The proposed model can potentially be used as a real-time conflict risk evaluation model to improve traffic safety.

Investigating the effects of travel lane configuration and lane width on traffic safety where powered-two-wheelers (PTWs) share with larger vehicles: A micro perspective

The design of travel lane configuration and lane width is crucial to traffic safety, especially in an urban mixed traffic environment where Powered-Two-Wheelers (PTWs) are prevalent and share the same roads with larger vehicles such as cars, buses, and trucks. However, there have been limited studies on the effects of the design of travel lane configuration and lane width on safety in such a mixed traffic environment. It's true the above-mentioned research question can be evaluated simply in terms of the number of crashes. However, doing so not only requires a few years of crash and traffic data, but limited insight can be gained in terms of how driver and rider behaviours are affected, and this has implications for further improvement in road safety. This study examines the changes in driving/riding behaviours and surrogate events before and after the adjustments of travel lane configuration and lane width by proposing a micro perspective approach as a complement to conventional studies. A before-and-after site-based investigation was conducted at two study sites which had opposite adjustments for travel lane configuration and lane widths: at one site the number of lanes was reduced, thereby widening the lane width in the outer lane on one road section, and at the second site the number of lanes was increased, thereby narrowing lane width in the outer lane on the other road section. The results showed that an increase in lane width resulted in a considerable increase in the number of speeding events as well as unsafe driving/riding behaviours and surrogate events related to lane splitting, lane sharing, and overtaking.

Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters

Most existing efforts to assess safety performance require sufficient crash data, which generally takes a few years to collect and suffers from certain limitations (such as long data collection time, under-reporting issue and so on). Alternatively, the surrogate safety measure (SSMs) based approach that can assess traffic safety by capturing the more frequent "near-crash" situations have been developed, but it is criticized for the potential sampling and measurement errors. This study proposes a new safety performance measure-Risk Status (RS), by fusing crash data and SSMs. Real-world connected vehicle data collected in the Safety Pilot Model Deployment (SPMD) project in Ann Arbor, Michigan is used to extract SSMs. With RS treated as a latent variable, a structural equation model with conditional autoregressive spatial effect and corridor-level random parameters is developed to model the interrelationship among RS, crash frequency, risk identified by SSMs, and contributing factors. The modeling results confirm the proposed interrelationship and the necessity to account for both spatial autocorrelation and unobserved heterogeneity. RS can integrate both crash frequency and SSMs together while controlling for observed and unobserved factors. RS is found to be a more reliable criterion for safety assessment in an implementation case of hotspot identification.

Prediction of rear-end conflict frequency using multiple-location traffic parameters

Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating -for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.

Evaluating pedestrian vehicle interaction dynamics at un-signalized intersections: A proactive approach for safety analysis

The present research demonstrates the use of advanced trajectory based data to analyze road user interactions at an un-signalized intersection under heterogeneous traffic complexities. This study demonstrates an improvement over the conventional grid-based analysis to estimate surrogate safety measures (SSM). An advanced pattern-based approach to categorize pedestrian-vehicle interactions based on the road user behavior is proposed in the study. A concept of a two-interaction pattern has been applied, which deals with the responsive and non -responsive behavior of the road users, respectively. The behavior-based patterns were categorized based on the SSM like Speed, Time to Collision, and Gap Time profiles of the pedestrian and vehicle interacting on an un-signalized intersection. On conducting a variable importance test, i.e., k-fold test, it was comprehended that, for pattern-1, Time to collision (TTC), and for pattern-2 both TTC and Post Encroachment Time (PET) were showing required importance. Further, Import Vector Machine (IVM) approach was used to classify the severity levels based on selected indicators computed from 1486 events, occurring at three Un-Signalized intersections in India. The proposed severity levels will help to test and evaluate various infrastructure and control improvements for making urban intersections safe for road users. It was observed from the severity levels of both the patterns that, events involving non-evasive behavior can also result in critical interaction. Overall, the research provides an advanced framework for evaluating and improving the safety of the uncontrolled intersections.

Longitudinal safety evaluation of connected vehicles' platooning on expressways

Connected vehicles (CV) technology has recently drawn an increasing attention from governments, vehicle manufacturers, and researchers. One of the biggest issues facing CVs popularization associates it with the market penetration rate (MPR). The full market penetration of CVs might not be accomplished recently. Therefore, traffic flow will likely be composed of a mixture of conventional vehicles and CVs. In this context, the study of CV MPR is worthwhile in the CV transition period. The overarching goal of this study was to evaluate longitudinal safety of CV platoons by comparing the implementation of managed-lane CV platoons and all lanes CV platoons (with same MPR) over non-CV scenario. This study applied the CV concept on a congested expressway (SR408) in Florida to improve traffic safety. The Intelligent Driver Model (IDM) along with the platooning concept were used to regulate the driving behavior of CV platoons with an assumption that the CVs would follow this behavior in real-world. A high-level control algorithm of CVs in a managed-lane was proposed in order to form platoons with three joining strategies: rear join, front join, and cut-in joint. Five surrogate safety measures, standard deviation of speed, time exposed time-to-collision (TET), time integrated time-to-collision (TIT), time exposed rear-end crash risk index (TERCRI), and sideswipe crash risk (SSCR) were utilized as indicators for safety evaluation. The results showed that both CV approaches (i.e., managed-lane CV platoons, and all lanes CV platoons) significantly improved the longitudinal safety in the studied expressway compared to the non-CV scenario. In terms of surrogate safety measures, the managed-lane CV platoons significantly outperformed all lanes CV platoons with the same MPR. Different time-to-collision (TTC) thresholds were also tested and showed similar results on traffic safety. Results of this study provide useful insight for the management of CV MPR as managed-lane CV platoons.

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.

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.

In search of the severity dimension of traffic events: Extended Delta-V as a traffic conflict indicator

Most existing traffic conflict indicators do not sufficiently take into account the severity of the injuries resulting from a collision had it occurred. Thus far, most of the indicators that have been developed express the severity of a traffic encounter as their proximity to a collision in terms of time or space. This paper presents the theoretical framework and the first implementation of Extended Delta-V as a measure of traffic conflict severity in site-based observations. It is derived from the concept of Delta-V as it is applied in crash reconstructions, which refers to the change of velocity experienced by a road user during a crash. The concept of Delta-V is recognised as an important predictor of crash outcome severity. The paper explains how the measure is operationalised within the context of traffic conflict observations. The Extended Delta-V traffic conflict measure integrates the proximity to a crash as well as the outcome severity in the event a crash would have taken place, which are both important dimensions in defining the severity of a traffic event. The results from a case study are presented in which a number of traffic conflict indicators are calculated for interactions between left turning vehicles and vehicles driving straight through a signalised intersection. The results suggest that the Extended Delta-V indicator seems to perform well at selecting the most severe traffic events. The paper discusses how the indicator overcomes a number of limitations of traditional measures of conflict severity. While this is a promising first step towards operationalising an improved measure of traffic conflict severity, additional research is needed to further develop and validate the indicator.

Evaluation of pedestrian safety at intersections: A theoretical framework based on pedestrian-vehicle interaction patterns

Pedestrians are the most vulnerable road users, and pedestrian safety has become a major research focus in recent years. Regarding the quality and quantity issues with collision data, conflict analysis using surrogate safety measures has become a useful method to study pedestrian safety. However, given the inequality between pedestrians and vehicles in encounters and the multiple interactions between pedestrians and vehicles, it is insufficient to simply use the same indicator(s) or the same way to aggregate indicators for all conditions. In addition, behavioral factors cannot be neglected. To better use information extracted from trajectories for safety evaluation and pay more attention on effects of behavioral factors, this paper develops a more sophisticated framework for pedestrian conflict analysis that takes pedestrian-vehicle interactions into consideration. A concept of three interaction patterns has been proposed for the first time, namely "hard interaction," "no interaction," and "soft-interaction." Interactions have been categorized under one of these patterns by analyzing profiles of speed and conflict indicators during the whole interactive processes. In this paper, a support vector machine (SVM) approach has been adopted to classify severity levels for a dataset including 1144 events extracted from three intersections in Shanghai, China, followed by an analysis of variable importance. The results revealed that different conflict indicators have different contributions to indicating the severity level under various interaction patterns. Therefore, it is recommended either to use specific conflict indicators or to use weighted indicator aggregation for each interaction pattern when evaluating pedestrian safety. The implementation has been carried out at the fourth crosswalk, and the results indicate that the proposed method can achieve a higher accuracy and better robustness than conventional methods. Furthermore, the method is helpful for better understanding underlying levels of safety from the behavioral perspective, which can also provide evidence for targeted traffic education on proper behaviors.

Simulation of safety: a review of the state of the art in road safety simulation modelling

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come. This paper sets out to assess the state of the art in the use of computer models to simulate and assess the level of safety in existing and future traffic systems. It reviews developments in the area of road safety simulation models. In particular, it reviews computer models of driver and vehicle behaviour within a road context. It focuses on stochastic numerical models of traffic behaviour and how reliable these are in estimating levels of safety on the traffic network. Models of this type are commonly used in the assessment of traffic systems for capacity, delay and general performance. Adding safety to this assessment regime may allow more comprehensive assessment of future traffic systems. To date the models have focused primarily on vehicular traffic that is, cars and heavy vehicles. It has been shown that these models have potential in measuring the level of conflict on parts of the network and the measure of conflict correlated well with crash statistics. Interest in the prediction of crashes and crash severity is growing and new models are focusing on the continuum of general traffic conditions, conflict, severe conflict, crash and severe crashes. The paper also explores the general data types used to develop, calibrate and validate these models. Recent technological development in in-vehicle data collection, driver simulators and machine learning offers considerable potential for improving the behavioural base, rigour and application of road safety simulation models. The paper closes with some indication of areas of future development.