Surrogate Safety Measures Research Articles

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.

Introducing novel statistical-based method of screening and combining currently well-known surrogate safety measures

ABSTRACT Until now various surrogate safety measures (SSM) have been developed and research in this area is ongoing. However, it is unclear which indicators are able to reflect danger more accurately. This paper proposes a novel method of performance comparison by taking the advantage of statistical analyses of SSMs data. This analytical approach is inspired by the dominant behavior of drivers in car-following scenarios. Afterward, those indicators that previously identified as eligible are merged using a recently developed safety indicator, named collision probability (CP). The proposed method is applied to microscopic traffic data collected from the I-80 freeway in Next Generation Simulation (NGSIM) project. The results indicated that the proposed framework helps screen SSMs and calculate the probability of rear-end collisions using a combination of the selected SSMs. The results might be helpful for improving the precision and accuracy of in-vehicle collision avoidance warning system and safety evaluations without crash data.

Complete-Linkage Clustering Analysis of Surrogate Measures for Road Safety Assessment in Roundabouts

This paper presents the findings of a comparative road safety assessment between an existing two-lane roundabout and proposed basic turboroundabout, both designed for the same intersection, to determine which one is safer, based on traffic conflicts and surrogate safety measures. We performed microsimulation models in VISSIM to replicate the fieldobserved traffic operation, and the SSAM to determinate six surrogate measures. We validated the consistency of values obtained by several statistical analyzes. The number of conflicts was 72% lower at the turboroundabout. Through a complete-linkage clustering analysis and Euclidean distances of the surrogate measures, we found that traffic conflicts at the turbo-roundabout tend to cluster in a group, whereas conflicts at the roundabout are scattered, suggesting better organization of traffic flows at the turbo-roundabout. Three-dimensional graphical analysis of clusters and its centroids allowed verifying that surrogate measures point out a safer operation at the turbo-roundabout, even though it presented higher operating speeds. Reducing the dimensionality by principal components analysis, the cumulative variance for the first two components (87.72%) allowed observing results on a two-dimensional graph and their clusters. To endorse conflicts classification, resulting of clusters, we used discriminant analysis. Results validate the methodology and the safety benefits of the turbo-roundabout.

Incorporating Speed in a Traffic Conflict Severity Index to Estimate Left Turn Opposed Crashes at Signalized Intersections

Rigorous evaluation of implemented safety treatments, especially for innovative treatments and those targeted at rare crash types, is challenging to accomplish with conventional crash-based analyses. This paper aims to address this challenge for treatments at urban signalized intersections by providing a methodology that uses surrogate measures of safety obtained from video analytics to predict changes in crashes. To develop this approach, left turn opposed traffic conflicts based on post-encroachment times, along with corresponding conflicting vehicle speeds, are first measured from video observations at signalized intersections. The conflicts are then classified into three severity levels using a risk score function defined by these measures. Multiple linear regression models are developed to relate left turn opposed crashes at the same intersections in the period 2009–2014 to the correspondingly classified conflicts. The results show strong relationships between the classified conflicts and crashes (adjusted [Formula: see text] of 85% and 94% for total and fatal/injury crashes, respectively). The results also reveal that the contribution of conflicts to the risk of crashes varies based on speed dimension of their severity, suggesting that neglecting speed as a factor in conflict severity levels may be at the expense of losing meaningful information. The models can be applied to estimate the change in crashes following a safety treatment by observing, through video analytics, the change in conflicts and speeds and using the crash-conflict-speed model. The methodological approach is viable for quickly evaluating all treatments and, in particular, innovative ones for which knowledge on safety effects is sparse or non-existent.

Safety and mobility effects of installing speed-humps within unconventional median U-turn intersections

The main aim of this paper is to evaluate the effects of installing speed-humps on the safety and mobility of the unconventional median U-turn, three-leg intersections, using the VISSIM microscopic traffic simulation model. The average delay was used as the measure of mobility, while the simulated conflict ratio based on the time-to-collision (TTC) surrogate safety measure, was used as an indicator of safety performance. The surrogate safety assessment model (SSAM) was applied to extract traffic conflicts, with TTC ≤ 1.50 s, which represent less severe conflicts, and TTC ≤ 0.50 s, that represent severe conflicts. The results show that hump speeds of 5.0 km/h significantly increase the intersection average delay by 109–448% and severely increase traffic conflicts, regardless of the hump location. While hump speeds of 20.0 km/h increase the average delay by 2–36%, based on the traffic conditions. Moreover, the 20.0 km/h humps could potentially reduce serve conflicts by about 11–20%.

Section control in Germany – findings for the first setup

ABSTRACT Within the framework of a pilot project, Lower Saxony’s Ministry of the Interior and Sport (Niedersächsisches Ministerium für Inneres und Sport) decided in 2014 to implement section control in Lower Saxony. Germany’s first section control system was installed over five years ago along State Road B6 near Hanover. This article outlines by means of surrogate measures of safety how traffic safety along this road section has increased since the system was set up and since the test period and enforcing period were started. In addition, this article illustrates how traffic safety has decreased to its original state as the result of a court decision, which forced the shutdown of the system. Furthermore, this article shows how traffic safety has increased after switching on the system once again.

Surrogate safety evaluation and validation with crash history for interurban midblock sections under heterogeneous traffic conditions in India

Traditionally, road safety analysis has been conducted by analysing collision records, which has a reactive approach as the analyst waits for collisions to occur. The alternate proactive approach in the form of surrogate safety measures is to study traffic conflicts which are bound to occur more frequently and thus has related them to the possible incidences of collisions. In the present study, traffic conflict indicators have been used to assess the level of safety by considering the typical midblock sections of an interurban corridor using vehicle trajectory data extracted through microscopic simulation. The surrogate safety parameters such as Time to Collision (TTC), Deceleration Rate (DR), change in velocity (Delta V) as well as conflicting vehicle Speed (Max S) have been extracted from trajectory data through the application of numerical elaboration to evaluate safety. Further, an attempt has been made to quantify the traffic conflicts occurring at the midblock of referred study. The proposed threshold values of surrogate safety parameters have been validated using the reported three years’ crash data. The approach presented in the paper has helped in the identification of midblock locations prone to road crashes and hence has served as a proactive alternative as opposed to historical crash based analysis.

Explanatory Analysis of the Safety of Short Passing Zones on Two-Lane Rural Highways

Passing zones afford sufficient passing distance for passing vehicles on two-lane rural highways. This study aimed to assess the effects of geometric and traffic variables on the safety of short passing zones using the rate of passing maneuvers ending in the no-passing zone as a surrogate safety measure. A Poisson regression model was applied to aerial data collected by drone at seven passing zones. The findings showed that increase in the length of the passing zone corresponds to decrease in the rate of passing maneuvers ending in the no-passing zone. The passing rate is also affected by the lane width, the percentage of heavy vehicles in the subject direction, and the directional split in the subject direction. The rate of passing maneuvers ending in no-passing zones reaches a peak as the two-way traffic flow rate increases to 600 vehicles per hour regardless of the directional split, or as the absolute vertical grade increases to 4.8%–6% depending on the percentage of heavy vehicles. After the peak value, the rate decreases. The presented model can serve as a tool for evaluating and improving the safety of short passing zones.

Safety analysis of unsignalized intersections: a bivariate extreme value approach

ABSTRACT Application of extreme value theory (EVT) to road safety analysis is gaining interest, thanks to its ability to produce quick and reliable safety evaluations without the use of crash data. Traditionally applied to single collision types and single extreme variables (i.e. surrogate measures of safety), EVT can be further exploited to simultaneously model multiple collision types, with the use of multiple extreme variables. In this paper two bivariate EVT approaches are applied for the safety evaluation of a three-leg unsignalized intersection, considering: (i) two conflict points and a single surrogate measure of safety; (ii) two surrogate measures of safety collected in a single conflict point. Each bivariate analysis was applied with two EVT methods: Component-wise Maxima (CM) and Excesses Over a Threshold (EOT). Bivariate models produced good results, especially with the EOT method, and were able to significantly improve the univariate benchmark results when the two estimation datasets were correlated.

Analysis of the occurrence and severity of vehicle-pedestrian conflicts in marked and unmarked crosswalks through naturalistic driving study

BackgroundAlthough many studies have been conducted on the safety of pedestrian crossings, few researches have been focused on drivers' behavior in unmarked crosswalk and marked crosswalk areas. Considering that statistics of pedestrian accidents are not the same in the two types of crossing area, based on the last report of the World Health Organization, it is very critical to evaluate driver yielding behavior to determine the differences in the actions of drivers when encountering pedestrians in the two areas. MethodsThis study was conducted based on surrogate measures of safety (SMoS) collected through a Naturalistic Driving Study on 52 participants in Iran. The study was carried out from April 2017 to April 2018 using the installation of cameras in the private vehicle of the participants. The analysis of the recorded films showed that 956 conflicts have occurred in unmarked crosswalks and 392 conflicts in marked crosswalks, respectively. ResultsA model was developed for driver yielding behavior using binary logistic regression, and showed that yielding rates in unmarked crosswalsk were about fifty percent of the yielding rates in marked crosswalks. Based on the model, it is indicated that the aggressive behavior of pedestrians during the crossing, such as running, zigzag and diagonal crossing, as well as the late detection of pedestrians by drivers resulting from high-speed driving in the unmarked crossing areas, will reduce the yielding behavior rate. Also, using the Swedish traffic conflicts technique, the severity of the conflicts was classified into four general categories: encounter, potential, slight, and serious conflict, through 30 different levels on the basis of conflicting speed and time to the accident. The results showed that pedestrians behavior during conflicts of the group “encounter” and drivers’ behavior during conflicts of the groups of “potential”, “slight” and “serious”, were the principal factors in preventing collision through an evasive maneuver. The results showed that increasing the level of conflict severity, which indicates an increase of the conflicting speed and a decrease of the time remaining to point of a possible collision with pedestrian, causes drivers to yield a harsh-maneuver to prevent collision. Soft-maneuvers such as deceleration and acceleration, as well as harsh-maneuvers such as changing the lane/stop during conflicts were most driver yielding behavior during conflict groups of slight and serious. According to the results of the analysis, the behavior of drivers in marked crossing areas is better than in the unmarked crossing area, leading to safer crossing for pedestrians. ConclusionsThis study suggests that the significant differences in driver yielding behavior in the two areas is due to the late detection of pedestrians by drivers and also the less proper action by them in unmarked crosswalk areas. Thus, the probability of accidents in Unmarked Crossing areas is higher than in marked crossing areas. Consequently, the design of improved advanced driver assistance systems to identify the risk of pedestrian accident may improve the driver yielding behavior and thus increase the safety of pedestrians.

Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data

Pedestrian and bicycle safety is a key component in traffic safety studies. Various studies were conducted to address pedestrian and bicycle safety issues for intersections, road segments, etc. However, only a few studies investigated pedestrian and bicycle safety for bus stops, which usually have a relatively larger volume of pedestrians and bicyclists. Moreover, traditional reactive safety approaches require a significant number of historical crashes, while pedestrian and bicycle crashes are usually rare events. Alternatively, surrogate safety measures could proactively evaluate traffic safety status when crash data are rare or unavailable. This paper utilized critical bus driving events extracted from GPS trajectory data as pedestrian and bicycle surrogate safety measures for bus stops. A city-wide trajectory data from Orlando, Florida was used, which contains around 300 buses, 6,700,000 GPS records, and 1300 bus stops. Three critical driving events were identified based on the buses’ acceleration rates and stop time; hard acceleration, hard deceleration, and long stop. The relationships between critical driving events and crashes were examined using Spearman’s rank correlation coefficient. All three events were positively correlated with pedestrian and bicycle crashes. Long stop event has the highest correlation coefficient, followed by hard acceleration and hard deceleration. A Bayesian negative binomial model incorporating spatial correlation (Bayesian NB-CAR) was built to estimate the pedestrian and bicycle crash frequency using the generated events. The results were consistent with the correlation estimation. For example, hard acceleration and long stop events were both positively related to pedestrian and bicycle crashes. Moreover, model evaluation results indicated that the proposed Bayesian NB-CAR outperformed the standard Bayesian negative binomial model with lower Watanabe-Akaike Information Criterion (WAIC) and Deviance Information Criteria (DIC) values. In conclusion, this paper suggests the use of critical bus driving events as surrogate safety measures for pedestrian and bicycle crashes, which could be implemented in a proactive traffic safety management system.

Classifying travelers' driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning

Driving style can substantially impact mobility, safety, energy consumption, and vehicle emissions. While a range of methods has been used in the past for driving style classification, the emergence of connected vehicles equipped with communication devices provides a new opportunity to classify driving style using high-resolution (10 Hz) microscopic real-world data. In this study, location-based big data and machine learning are used to classify driving styles ranging from aggressive to calm. This classification can be used to customize driver assistance systems, assess mobility, crash risk, fuel consumption, and emissions. This study’s main objective is to develop a framework that harnesses Basic Safety Messages (BSMs) generated by connected vehicles to quantify instantaneous driving behavior and classify driving styles in different spatial contexts using unsupervised machine learning methods. To this end, a subset of the Safety Pilot Model Deployment (SPMD) with more than 27 million BSM observations generated by more than 1300 individuals making trips on diverse roadways and through several neighborhoods in Ann Arbor, Michigan, were processed and analyzed. To quantify driving style, the concept of temporal driving volatility, as a surrogate safety measure of unsafe driving behavior, was utilized and applied to vehicle kinematics, i.e., observed speeds and longitudinal/lateral accelerations. Specifically, six volatility measures are extracted and used for classifying drivers. K-means and K-medoids methods are applied for grouping drivers in aggressive, normal, and calm clusters. Clustering results indicate that not only does driving style vary among drivers, but the thresholds for aggressive and calm driving vary across different roadway types due to variations in environment and road conditions. The proportion of aggressive driving styles was also higher on commercial streets than on highways and residential streets. Notably, we propose a Driving Score to measure driving performance consistently across drivers.

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

We present a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate >10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.

Investigation of Crossing Conflicts by Vehicle Type at Unsignalized T-Intersections under Varying Roadway and Traffic Conditions in India

The study attempts to investigate crossing conflicts at unsignalized T-intersections under mixed traffic conditions using post encroachment time (PET) as a surrogate safety measure (SSM). To comprehend the objective, data for four unsignalized T-intersections with varying intersection geometry (with and without Central Island) was collected using videography techniques. Primarily, PET datasets were extracted in the lab from the recorded video and were checked for their probability distributions. Among all the potential distributions, generalized extreme value (GEV) distribution was observed to be the best-fitted distribution. Further investigations revealed that the percentage of critical crossing conflicts (PCCC) was higher with the presence of motorized two-wheelers (2W) and motorized three-wheelers (3W) in the traffic stream. This was followed by cars, buses, LCVs, and trucks. Among vehicle type combinations (conflicting-offending), 2W-2W, 2W-3W, 3W-2W, and 3W-3W were identified as the most critical cases for the subject study locations. Further, it was observed that the volume of conflicting stream and proportion of 2Ws in the conflicting stream has a significant effect on PCCC. The obtained PCCC, when compared among the subject study locations for similar traffic flow characteristics, revealed a significant difference between them, highlighting the effect of intersection geometry on traffic safety. The severity of conflicts was delineated into four levels by correlating PET values with the speed of conflicting vehicles. The developed severity levels, compared with the field-recorded crash data are found to be in close approximation. This establishes PET as a valid SSM.

Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections

Continuous development of urban infrastructure with a focus on sustainable transportation has led to a proliferation of vulnerable road users (VRUs), such as bicyclists and pedestrians, at intersections. Intersection safety evaluation has primarily relied on historical crash data. However, due to several limitations, including rarity, unpredictability, and irregularity of crash occurrences, quantitative and qualitative analyses of crashes may not be accurate. To transcend these limitations, intersection safety can be proactively evaluated by quantifying near-crashes using alternative measures known as surrogate safety measures (SSMs). This study focuses on developing models to predict critical near-crashes between vehicles and bicycles at intersections based on SSMs and kinematic data. Video data from ten signalized intersections in the city of San Diego were employed to train logistic regression (LR), support vector machine (SVM), and random forest (RF) models. A variation of time-to-collision called T2 and postencroachment time (PET) were used to specify monitoring periods and to identify critical near-crashes, respectively. Four scenarios were created using two thresholds of 5 and 3 s for both PET and T2. In each scenario, five monitoring period lengths were examined. The RF model was superior compared to other models in all different scenarios and across different monitoring period lengths. The results also showed a small trade-off between model performance and monitoring period length, identifying models with monitoring period lengths of 10 and 20 frames performed slightly better than those with lower or higher lengths. Sequential backward and forward feature selection methods were also applied that enhanced model performance. The best RF model had recall values of 85% or higher across all scenarios. Also, RF prediction models performed better when considering just the rear-end near-crashes with recalls of above 90%.

Assessment of traffic safety at median openings using surrogate safety measures: a case study in India

Traffic safety is an integral part of transportation engineering. In developing countries, its importance is even more. Additionally, at uncontrolled median openings, the severity of road crashes increase many fold. Conventionally, road crash data were used to analyse safety. However, in developing countries, the accuracy of this data is highly questionable. Therefore, in this study, a new technique in addition to post encroachment time (PET), which is a surrogate safety measure is used to predict the severity of probable road crashes at median openings. After the extraction of PET values from field data, they have been compared with the minimum braking times obtained from calculation of minimum stopping sight distance. The comparison shows that while the number of road crashes may be less at lower traffic volume levels, however the severity of those crashes is much higher as compared to the road crashes occurring at high traffic volumes.

Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data

This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.

Comparing the usefulness of real-time driving aids in a connected environment during mandatory and discretionary lane-changing manoeuvres

Lane-changing manoeuvre is one of the risky manoeuvres performed by drivers either to reach the planned destination (i.e., mandatory lane-changing; MLC) or to achieve better driving conditions (i.e., discretionary lane-changing; DLC). Essentially both lane-changing types require the driver to acquire surrounding traffic information for efficient and safe lane-changing decisions. However, this does not discount the fact that both these lane-changings are fundamentally different from each other as the urgency of lane-changing is much higher during MLC compared to DLC. Real-time driving aids in a connected environment promise to assist during the lane-changing decision-making process, but the differential effectiveness (or usefulness) of real-time driving aids in a connected environment for these two lane-changing types remains unexplored due to the novelty of a connected environment and the consequent scarcity of data. To fill this research gap, this study collected lane-changing data from 78 participants who performed MLC and DLC in the CARRS-Q Advanced Driving Simulator. Participants were asked to drive in three randomised driving conditions: baseline condition (without driving aids), real-time driving aids in a connected environment with perfect communication, and delayed driving aids in a connected environment. While surrogate measures of safety are analysed and compared using descriptive statistics, a hybrid framework of data mining and classical statistical modelling is employed to examine the usefulness of the real-time driving aids in a connected environment for two lane-changing types. We find that the crash risk associated with MLC is significantly reduced in the driving conditions providing driving aids compared to that of DLC. Results also reveal that the probability of engaging in a hard-braking event decreases for both the lane-changing types in the presence of real-time driving aids in the connected environment driving conditions, but a higher decrease in magnitude is found for MLC. Age and gender-related differential impact have been observed where young and male drivers have a higher possibility of engaging in a hard-braking event when driving without driving aids, but the presence of real-time driving aids reduces such risk. This study concludes that the usefulness (or effectiveness) of the real-time driving aids in a connected environment is a function of the urgency of a task, which is evidently higher during MLC, thus providing the maximum advantage during MLC.

Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under different market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.