Surrogate Safety Assessment Model Research Articles

Safety of cyclists interacting with autonomous vehicles: A combined microscopic simulation and SSAM analysis

The aim of this paper is to investigate the safety of cyclists while interacting with autonomous vehicles (AVs) in an uncontrolled T-intersection using PTV Vissim microsimulation combined with the Surrogate Safety Assessment Model (SSAM). For the microsimulation, three cycling behaviours were constructed (cautious, normal, and aggressive), and two AV behaviours (cautious and normal) were adopted. 87 h of microsimulation resulted in 230 conflicts. The type and nature of these conflicts, as well as two temporal indicators and four speed-related indicators were analysed. Nearly 96% of the conflicts were rear-end collisions, and almost 70% were conflicts where the cyclist was the first to arrive at the conflict point. Interestingly, the least involved cycling behaviour in conflicts was aggressive and the most involved was cautious. A significant relationship was found between severity clusters of temporal indicators and cycling behaviour. It is also concluded that an important future research direction would be the construction of a two-dimensional conflict diagram with severity thresholds specifically for bicycle-vehicle interactions.

Simulated surrogate measures to assess the effectiveness of countermeasures at signalized intersections

The traditional method for assessing safety conditions at signalized intersections depends on historical crash data. Difficulty and long waits for data collection as well as lack of reliability, represent some limitations. As a result of safety evaluation using traditional methods, countermeasures may be proposed to improve the degree of safety. This paper aims to assess the effectiveness of countermeasures at signalized intersections using micro-simulation model (VISSM10) software and the Surrogate Safety Assessment Model (SSAM) to deal with traffic conflicts as surrogate measures rather than crash data. The study relied on VISSIM10 to create a trajectory file as input of SSAM to conduct a traffic safety assessment using traffic conflict indicators of time to collision (TTC). Four four-legged signalized intersections in the city of Diwaniya are chosen to assess safety and then propose appropriate countermeasures. Different countermeasures are tested through simulation to estimate their effectiveness using two measures: the increase in time to collision and the percentage reduction in traffic conflicts. The results showed that model calibration reduced the mean absolute error of prevention (MAPE) and improved the fit between both the actual conflicts and simulated conflicts. A validation simulation has been performed compared with the observed conflict. According to the linear regression the number that simulated conflicts which highly related to the number of actual conflicts. Additionally, R2 can be described by the difference in simulated conflicts. Results go with effectiveness based on crash data and promising for unknown ones.

Dynamic Risk Assessment and Control Framework for Work Zone and Its First Implementation under Simulation Environment

The number of road maintenance operations in China is increasing. Work zones can interfere with everyday driving and bring significant safety hazards to the throughput vehicles and construction personnel. However, there is currently a lack of effective methods to conduct a dynamic risk assessment of work zones and provide control guidance when necessary. Therefore, this study proposes a dynamic risk assessment and control framework for the work zones. The framework adopts a closed-loop control and dynamic monitoring architecture. A massive amount of microscopic data on vehicles’ behavior is obtained based on advanced data collection technologies (including multicamera multiobject tracking, naturalistic driving, and microscopic traffic simulation). Traffic conflicts between single vehicle and two- or multivehicle are detected using vehicle behavior analyzing technology and the surrogate safety assessment model. A comprehensive assessment index, i.e., UTECN (unit total equivalent conflict number), is established that can consider the severity and possibility of accidents caused by conflicts based on the vehicle collision energy theory, probability theory, and risk management theory. A risk assessment standard is established based on the level of safety service. The framework is first implemented in a simulation environment, and its feasibility and effectiveness are verified by taking a work zone of Shanghai Waihuan Expressway S20 as a case study. The result shows that the framework has good practicality. Under a moderate traffic level, the risk of the work zone can be assessed within 20 minutes. It can be quickly and effectively controlled at an acceptable level after several iterations, which is of great significance for ensuring the safety of life and property of throughput vehicles and construction personnel within the work zone.

Surrogate safety evaluation of curve sections on an interurban highway under heterogeneous traffic conditions in India

<p class="H1">Road traffic safety is a global concern. Conventionally, road safety evaluation is carried out by analyzing historical crash data, which is a reactive approach to safety analysis. The safety analyst has to wait for sufficient time to accumulate crash data before taking up any safety analysis as a sample of substantial crashes is required for analysis. But in safety analysis with a proactive approach no crash data is required, which is replaced with surrogate “conflicts”, which can be obtained from the new techniques of traffic conflict technique and surrogate safety parameters. Other approaches can be applied in traffic safety evaluation in anticipation of the crash occurrence. The advent of traffic conflict techniques, i.e., use of traffic conflicts in place of crashes and microsimulation methods like modeling of the traffic flow and pattern in traffic streams on a road network, started to apply as a method of analyzing microscopic simulation models and traffic conflict techniques to determine the safety issues in traffic systems and correlate them to the probable incidences of collisions. In this regard, surrogate safety parameters have been used to determine the level of safety on the typical curve sections of an interurban highway namely, Faridabad-Gurgaon four-lane divided highway in Haryana, India. This is accomplished by the use of vehicle trajectory data extracted through microscopic simulation in Verkher in Staedten Simulation (VISSIM) and analysis in Surrogate Safety Assessment Model (SSAM) for the above-referred corridor. Further, efforts have been made to present the intensity of traffic conflicts happening at the curved sections. The surrogate safety measures time to collision (TTC), deceleration rate (DR), and change in velocity (<em>ΔV</em>), as well as conflicting vehicle speed (Max <em>S</em>), are obtained by analysis from the SSAM model for all the curve sections and are validated using the reported crash data on the curve sections of the candidate corridor for 3 years. With the help of statistical elaboration, the critical threshold for TTC with the heterogeneous traffic movement is found 1.6 s, meaning any conflict occurring less than this time would invariably lead to a fatal crash. Similarly, the critical deceleration rate is observed as 0.569 m/s<sup>2</sup>, implying that any conflict with more than this value may lead to a fatal crash. Further, the Delta<em>V</em> values deduced for the study corridor on interurban curve sections catering to heterogeneous traffic movement is 4.1 m/s. Again, any conflict more than this value can turn into a potential crash.</p>

A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions

Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven – 40% 1st generation AVs– 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

CAV-enabled data analytics for enhancing adaptive signal control safety environment

Given the connected and autonomous vehicle (CAV) generated trajectories as a “floating sensor” data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision.

A Conflict-Based Safety Diagnosis of SCI Roundabouts Using a Surrogate Safety Measure Model

Recently, the surrogate safety assessment model has been considered for the safety performance analysis of roundabouts. This model can perform a detailed analysis of conflicts based on the trajectory data of vehicles inside the roundabout. The Separated Central Island (SCI) roundabout, as one of the roundabouts with distinct geometrical characteristics, holds the potential for safety evaluation using the conflict-based method. Therefore, in this study, a safety diagnosis of an SCI roundabout was conducted for the first time. In this study, a safety diagnosis procedure for SCI roundabout was first defined; for this purpose, vehicle trajectory data were obtained using an Unnamed Aerial Vehicle (UAV) and then entered into Surrogate Safety Measure Model software (SSAM 3.0). A case study was conducted on the only SCI roundabout in Iran, located in Mashhad. The parameters of Time-to-Collision (TTC), maximum speed difference of two vehicles involved in a collision (MaxΔV), and maximum speed of vehicles in collision (MaxS) were utilized to determine the type and severity of conflicts and risk analysis. The results of roundabout risk analysis showed that the severity level of conflicts is mainly of the injury type and that the lowest severity of conflicts is related to fatality. In addition, the highest frequency of injury conflicts is associated with lane-change conflict and the lowest frequency is linked to rear-end conflict. The highest and lowest frequency of damage conflicts are related to crossing and lane-change conflicts, respectively. After overall risk scoring, the severity level of conflicts is mainly related to injury type and the lowest severity of conflicts is associated with fatality; 31% of the total conflicts obtained are of the damaging type, and 69% are of the injury type. Finally, comparing the results of the conflict data with the 8-year crash data in such roundabouts confirms that in the absence of crash data in such roundabouts traffic engineers can use the roundabout analysis based on this study to predict the safety situation of such roundabouts before implementing engineering processes.

Simulator and Microsimulation Study of Transportation Network Company Pickup/Dropoff Area Designs

The advent of transportation network companies (TNCs), also known as commercial ridesharing providers, has disrupted transportation systems leading to new facility designs. Dedicated pickup/dropoff areas can facilitate the future shift to autonomous vehicles. In this study, angled stalls and parallel curbside parking, along with signage and education, were investigated using pedestrian and wheelchair simulators. Human behavior was examined with regard to safety, efficiency, and accessibility. Microsimulation and Surrogate Safety Assessment Model were used to examine the effect of increased demand on design capacity and number of conflicts. In simulations, both stall and curbside designs performed comparably in crosswalk usage, but the stall design was safer when passengers waited longer for vehicle arrival. It is preferable to wait to approach the loading area until the TNC vehicle arrives. Stall design was more accessible but curbside design had greater efficiency because of shorter vehicle waiting time (−7.31 s [ p < 0.01]). Signage and education were found to be effective in increasing sidewalk usage, decreasing deviations from the crosswalk, and reducing vehicle waiting time. Post-simulator surveys showed similar ratings for safety (80.65%), with stall design scoring higher in accessibility (90% versus 52%) and curbside design slightly higher in efficiency (77% versus 68%). Participants reported almost equal preference for stall or curbside designs (48% versus 52%), and over 90% found the signage and education effective. Microsimulation indicated that stall design had higher capacity (500 vehicles per hour) than curbside design (350 vehicles per hour) within 180 linear feet, and fewer overall conflicts at 300 loadings.

Assessing bicycle-vehicle conflicts at urban intersections utilizing a VR integrated simulation approach

Animosity between drivers and cyclists has existed on urban road networks for many years. Conflicts between these two groups of road users are exceptionally high in the shared right-of-way environments. Benchmarking methods of conflict assessments are mostly based on statistical analysis with limited data sources. The actual crash data would be valuable to understand the features of bike-car collisions, however the available data are spatially and temporally sparse. To this end, this paper proposes a simulation-based bicycle-vehicle conflict data generation and assessment approach. The proposed approach uses a three-dimensional visualization and virtual reality platform, integrating traffic microsimulation to reproduce a naturalistic driving/cycling-enabled experimental environment. The simulation platform is validated to reflect the human-resembled driving/cycling behaviors under different infrastructure designs. Comparative experiments are carried out on bicycle-vehicle interactions under different conditions, with data collected from a total of 960 scenarios. Based on the results of the surrogate safety assessment model (SSAM), the obtained key insights include: (1) scenarios of a high conflict probability do not lead to actual crashes, which suggests that the classic SSM-based measurements such as TTC or PET values may not sufficiently reflect real cyclist-driver interactions; (2) the major cause of conflicts is variation in vehicle acceleration, which suggests that drivers are considered to be the main party responsible for bicycle-vehicle conflict/crash occurrence; (3) the proposed approach is able to generate near-miss events and reproduce interaction patterns between cyclists and drivers, facilitating experiments and data collections which would be typically unavailable for this type of study.

Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration

Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.

Studying Traffic Safety During the Transition Period Between Manual Driving and Autonomous Driving: A Simulation-Based Approach

Connected and Autonomous Vehicles (CAVs) are becoming a reality and are progressively penetrating the markets level by level. CAVs are a promising solution for traffic safety. However, robust studies are needed to explore and assess the expected behavior. This study attempts to evaluate traffic safety resulting from a near-real introduction of CAVs with different levels of automation (from Level 1 to Level 4). The investigation consisted of modeling different CAV levels using Gipps’ model, followed by the simulation of nine mixed fleets at a motorway segment. Subsequently, the Surrogate Safety Assessment Model was used for safety analysis. According to the results: (1) the gradual penetration of CAV levels led to a progressive reduction in traffic conflicts, ranging from 18.9% when the penetration of high levels of automation (Level 3 and Level 4 vehicles) is 5%, to 94.1% when all the vehicles on the traffic flow are Level 4; (2) human-driven vehicles and vehicles with low levels of automation (Level 1 and Level 2 vehicles) are more frequently involved in conflicts (as follower vehicles) than vehicles with high automation levels. E.g. human-driven vehicles are involved in conflicts from 8% to 122% more, while vehicles with high automation levels are involved in conflicts from 80% to 18% less than their sharing percentages, respectively, depending on different mixed fleets. This study confirms the theory and conclusions from previous literature that indicate a safety gain due to CAV penetration. Moreover, it provides a broader perspective and support for the introduction of CAVs levels.

From conflicts to crashes: Simulating macroscopic connected and automated driving vehicle safety

Safety evaluation is a critical aspect through the future stages of automation development. Since there is a lack of historical and generalizable safety data in high levels of Connected and Autonomous Vehicles (CAVs), a possible approach to follow is the microscopic simulation method. Through microsimulation, vehicle trajectories are able to be exported and traffic conflicts to be identified using the Surrogate Safety Assessment Model (SSAM). Therefore, it is crucial to develop techniques in order to analyze conflict data extracted from microsimulation and evaluate crash data aiming to support road safety applications of automation technologies. This paper attempts to propose a safety evaluation approach for estimating crash rate of CAVs through microsimulation. For this purpose, the city center of Athens (Greece) was modelled using the Aimsun Next software paying attention to the calibration and validation of the model using real data of traffic characteristics. Moreover, different scenarios were formulated concerning different market penetration rates (MPRs) of CAVs and two fully automated generations (1st and 2nd generation) were simulated for modelling them. Subsequently, the SSAM software was used in order traffic conflicts to be identified and then converted to crash rate. Analysis of the outputs along with traffic data and network geometry characteristics were then conducted. The results indicated that in higher CAV MPRs, crash rates will be significantly lower as well as when the following-vehicle in the occurred conflict is a 2nd generation CAV. Lane change conflicts caused the highest crash rates compared to rear-end conflicts, which presented the lowest rates.

Construction of Real-time Dynamic Reversible Lane Safety Control Model in Intelligent Vehicle Infrastructure Cooperative System

ABSTRACT Reversible lanes play an important role in alleviating urban traffic congestion and improving the utilization of road resources. However, most of the reversible lanes currently implemented are timed and fixed, and there is little research on the safety of reversible lanes without isolation facilities. In view of the existing problems of reversible lanes, this paper proposed a real-time dynamic reversible lane safety control model. The influencing factors of reversible lane safety control are analyzed first, and then determine model parameters of the no-entry zone and the buffer zone in the conflict area. Moreover, model parameters of emptying, exit, and recovery zones in the operation process were determined as well. Additionally, this paper discussed the length of reversible lanes and the timing of signals. The surrogate safety assessment model (SSAM) is adopted to analyze vehicle trajectory data for traffic simulation under different traffic flows. The experiment results show that the proposed reversible lane safety control scheme can keep the vehicle’s traffic conflict rate below 5%, which can effectively ensure the safe operation of the reversible lane.

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.

Safety Performance Evaluation of Continuous Flow Intersections in the Era of Connected Vehicles: A Microsimulation Modelling Approach

This study employed Federal Highway Administration’s Surrogate Safety Assessment Model (SSAM) to investigate the safety of implementing Connected Vehicles (CVs) at the Continuous Flow Intersection (CFI), by reproducing a real-world corridor, that has multiple successive implementations of CFIs, in VISSIM. Econolite’s ASC/3 Software-in-the-Loop signal controllers and Python-programmed Vehicle to Infrastructure (V2I) communications were embedded in VISSIM. Additionally, the effect of CV-Market Penetration Rate (CV-MPR) on safety is taken into consideration. The study shows that CV deployments at partial and full CFIs leads to notable reductions in crash likelihoods and severities. The total number of conflicts, rear-end and lane change conflicts dropped by 23.8%, 23.6% and 24.4%, respectively at full CFIs and 100% MPR, whereas those were reduced by 6.4%, 4.8% and 17.9%, respectively at partial CFIs and 100% MPR. It was also found that at least a 50% MPR of CVs is required for safety improvements to be influential.

Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function

The left hard shoulder plays an important role in the event of an emergency on the inside of a multi-lane highway, but past studies have not been able to clarify the criteria for its installation or quantify the safety impact of its installation on the left side. In order to study the influence of the left hard shoulder on the safety of vehicles traveling on multi-lane highways, based on past studies that only studied the situation of four-lane highways, this paper firstly constructs a multi-lane highway simulation model under different numbers of lanes based on the VISSIM traffic simulation and uses Surrogate Safety Assessment Model (SSAM) to study the conflict characteristics of multi-lane highway vehicles under different numbers of lanes. Based on the above findings, this paper introduces the Safety Performance Function (SPF) to construct a multi-lane freeway accident prediction model, calibrates the model by adding the indexes affected by the left side hard shoulder to the basic prediction mode, and uses the historical accident data of the Badou-Shihu section of the Guangdong Northern Second Ring Highway as the basis to study the differences in accident rates of the investigated section before and after setting the left hard shoulder. The study showed that the average Time to Collision (TTC) increased by 57.2%, Maximum Deceleration (MaxD) increased by 19.2%, and Delta Speed (DeltaS) increased by 15.3% after setting hard shoulders on the left side of multi-lane freeways, and traffic conflicts on multi-lane freeways were significantly reduced, and safety was improved considerably. In addition, the rear-end conflict rate decreased by 0.17%, 0.75%, and 4.6% after setting hard shoulders on the left side of one-way three, four, and five lanes, respectively, indicating that hard shoulders on the left side are the most effective in improving the safety of one-way five-lane freeways. The accident prediction results show that within the reasonable setting range of the left hard shoulder width (0~4 m), the accident rate decreases by about 1.5% for every 0.5 m increase if only the influence of the left hard shoulder width is considered. Without considering other factors, increasing the width of the hard shoulder on the left side can reduce the number of accidents. This indicates a significant safety improvement for a one-way five-lane highway after setting the hard shoulder on the left side, and the conclusion is consistent with the simulation results. In this paper, based on past research, the research object is extended to one-way three-, four-, and five-lane highways. The findings of this paper can help the road authorities develop specifications for installing hard shoulders on the left side of multi-lane freeways and adopt strategies to improve the traffic safety level of multi-lane freeways. In addition, the models and methods used in this paper can also help build a framework for future intelligent networked vehicle avoidance systems and promote the development of intelligent networked technologies.

Detection Method for All Types of Traffic Conflicts in Work Zones

Traffic conflict technology (TCT) is widely used to assess the safety of work zones. The current TCT is temporal and (or) spatial proximity defined based, which can only detect two-vehicle or multi-vehicle conflicts, and is not competent for single-vehicle conflicts. However, single-vehicle accidents in work zones are also severe. This study proposes a detection method for all types of traffic conflicts in work zones. Based on vehicle micro-behavior data, evasive behavior is detected by automatic segmentation, Support Vector Machine (SVM)-based behavior identification, and threshold-based judgment methods. Two-vehicle or multi-vehicle conflicts are detected by classical proximity defined-based method, i.e., the surrogate safety assessment model (SSAM). By comparing the analysis results of the evasive behavior with the one of SSAM, single-vehicle conflicts can be detected. Taking a practical work zone as an example, the effectiveness of this method in detecting all types of traffic conflicts in work zones is verified. The single-vehicle conflict can be subdivided into 10 types according to basic behavior types, such as straight-line driving and decelerating. The two or multi-vehicle conflicts can be subdivided into three types, such as rear-end conflict. The example also verifies the applicability of this method under different traffic volume scenarios.

A Simulation-Based Study of the Influence of Low-Speed Vehicles on Expressway Traffic Safety

To reveal the impact mechanism of low-speed vehicles (LSVs) on expressway traffic safety, this paper uses the polynomial fitting method to establish evolution models of traffic density and average speed at different LSV speeds in order to explore the queuing and dissipation characteristics of vehicles affected by LSVs and investigate the impact range of LSVs on expressways. Based on the findings above, this paper builds a Surrogate Safety Assessment Model (SSAM)-based model to quantify driving safety and further explore the differences in vehicle conflicts when an LSV moves in different lanes at the same speed. The simulation experiment is conducted based on the field data from the Inner Ring North Road located along the Nanjing Inner Ring High Speed Road. The results show that the evolutionary features of lane traffic density and average speed under different LSV speeds satisfy the octuple polynomial law, reflecting the spatial heterogeneity of vehicle distribution at different LSV driving speeds. Meanwhile, LSVs with different speeds produced the most significant negative impact on the roadway within 400 m of the expressway entrance. The lower the speed of the LSV, the more significant the adverse effect. In addition, this paper finds that when an LSV travels in different lanes at the same speed, the inner, middle, and outer lanes have the highest number of total conflicts, rear-end conflicts, and lane-change conflicts, respectively. Meanwhile, vehicles in the outer lane are the most significantly affected by LSVs, while vehicles in the middle lane are the least affected with the highest traffic efficiency. Additionally, the Maximum Speed (MaxS) and Difference in Vehicle Speed (DeltaS) for the middle lane are 47.9% and 60.5% higher than the outer lane, respectively. Nevertheless, based on the Probability of Unsuccessful Evasive Actions, i.e., P(UEA), vehicles in the middle lane have the highest probability of potential traffic conflicts. The methods used in this paper will have positive implications for establishing autonomous vehicle risk avoidance systems which can improve the safety levels of expressways.

Safety assessment of coordinated signalized intersections in a connected vehicle environment: a microsimulation approach

In this study, the safety impact of the coupled implementation of signal coordination and connected vehicles (CVs) is examined in a microsimulation environment created in VISSIM. The Surrogate Safety Assessment Model (SSAM) was implemented to generate results of surrogate safety measures. The findings provided evidence that CVs can improve the safety performance at all market penetration rates (MPRs) of CVs in terms of all performance metrics. In addition, further safety improvements were achieved at higher CV MPRs. It was observed that coordinated signals had lower likelihoods of experiencing collisions compared to uncoordinated signals. Specifically, coordinated signals showed significantly higher time-to-collision (TTC) and post-encroachment time (PET) values when compared to uncoordinated signals at the 100% CV MPR only. Moreover, the impact of CV technologies on reducing the total number of conflicts (TNC) would be stronger than that of traffic signal coordination alone while both would lead to reductions in the TNC.