Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surroun...Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions,thus providing insights into roadway safety improvement countermeasures.This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions.In order to remove the outliers and white noise existing in the raw data,vehicle trajectories were reconstructed by apply-ing discrete wavelet transform and Kalman filtering(KF).Generalized time-to-collision was adopted to detect and measure the severity of conflicts,by which 1127 conflict events were extracted.Path analysis(PA)models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity.Various roadway and environmental characteristics such as traffic flow average speed,percentage of trucks,and intersection skew angle were included in the models.The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity.In the indirect effects,the kinematics of conflicting vehicles such as the average and standard deviation of speed,plays an intermediate role in linking roadway factors and conflict outcome.The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.展开更多
Right-turn collisions at intersections are one of the most dominant crash types in suburban areas,especially at unsignalized intersections.There is,however,a lack of comprehensive research on the speed patterns of veh...Right-turn collisions at intersections are one of the most dominant crash types in suburban areas,especially at unsignalized intersections.There is,however,a lack of comprehensive research on the speed patterns of vehicles during right-turn manoeuvres and their impacts on crashes.To provide an in-depth investigation of the factors determining the safety of right-turn manoeuvres,driving behavior data were collected through an instrumented vehicle study.Using this data,binary logistic regression models were developed to identify the factors affecting the probability of vehicle-vehicle(V-V)and vehicle-pedestrian(V-P)conflicts at six suburban intersections in Babol,Iran,during right-turn stage manoeuvres.In total,1456 V-V and V-P conflicts were identified from the data analysis.The results from the logistic regression model showed that the vehicle speed,the distance between road users,as well as driver and pedestrian distractions were associated with a higher risk for V-V or V-P conflicts.To estimate the safe right-turn speeds to be selected by drivers at different stages of the right turn,i.e.,at the start,during,and end of the movement,linear regression models were developed.The results showed that participants adjust their driving behaviors the same way toward pedestrians as they do toward vehicles.The findings of this study can be leveraged for the development of a robust advanced driving assistance system,the use of which can further improve the safety performance of right-turn manoeuvres.展开更多
The relationship between the opposing left-turn conflict and the traffic participants was analyzed in this study. Based on the traffic conflict technology, the image data were collected in a real traffic situation. Th...The relationship between the opposing left-turn conflict and the traffic participants was analyzed in this study. Based on the traffic conflict technology, the image data were collected in a real traffic situation. The relationship was investigated under two different conditions. The number of opposing left-turn conflicts was positively correlated with the number of left-turn vehicles while the ratio of left-turn vehicles to opposing vehicles was less than 1, and showed a positive correlation with the number of opposing-through vehicles when the ratio of left-turn vehicles to opposing vehi- cles was more than 1. In other words, the opposing left-turn risk was positively correlated with the number of the minor traffic participants, which had a negative effect on the whole traffic system op- eration.展开更多
The traffic conflict technique （TCT） was developed as ＂surrogate measure of road safety＂ to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applicati...The traffic conflict technique （TCT） was developed as ＂surrogate measure of road safety＂ to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applications of TCT focus on a specific site by the way of manually or automated supervision. Nowadays the development of in-vehicle （IV） technologies pro- vides new opportunities for monitoring driver behavior and interaction with other road users directly into the traffic stream. In the paper a stereo vision and GPS system for traffic conflict investigation is presented for detecting conflicts between vehicle and pedestrian. The system is able to acquire geo-referenced sequences of stereo frames that are used to provide real time information related to conflict occurrence and severity. As case study, an urban bus was equipped with a prototype of the system and a trial in the city of Catania （Italy） was carried out analyzing conflicts with pedestrian crossing in front of the bus. Experimental results pointed out the potentialities of the system for collection of data that can be used to get suitable traffic conflict measures. Specifically, a risk index of the conflict between pedestrians and vehicles is proposed to classify collision probability and severity using data collected by the system. This information may be used to develop in-vehicle warning systems and urban network risk assessment.展开更多
Road intersections are important nodes for the convergence,turning,and diversion of traffic flows in the urban road network,but at the same time,due to the large traffic volume and conflict points at the intersection,...Road intersections are important nodes for the convergence,turning,and diversion of traffic flows in the urban road network,but at the same time,due to the large traffic volume and conflict points at the intersection,it has become a traffic congestion and accident-prone area,which seriously affects road traffic safety and vehicle traffic efficiency.Therefore,it is of great significance to study the collaborative control strategy of urban intersections.This paper analyzes and summarizes the methods of intersection cooperative control based on intelligent connected vehicles in recent years,and looks forward to the future development trend and prospects of the combination of intersection cooperative control and Vehicle-Infrastructure Cooperation System.展开更多
With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The...With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.展开更多
Affected by the limited interchange spacing,the operational risk of vehicles in expressway small-spacing interchanges(SSIs)is more complex compared to other interchanges.In this study,unmanned aerial vehicle(UAV)measu...Affected by the limited interchange spacing,the operational risk of vehicles in expressway small-spacing interchanges(SSIs)is more complex compared to other interchanges.In this study,unmanned aerial vehicle(UAV)measurements were integrated with joint simulation data to explore the risk characteristics of SSIs with the help of traffic conflict theory.Seven traffic flow parameters,including mainline traffic volume,were selected to evaluate their impact on traffic conflicts.The distribution of four traffic conflict indicators,such as time to collision(TTC),was analyzed,and their severity was categorized using cumulative frequency analysis and minibatch K-means clustering.By varying the spacing,the study scrutinized trends in traffic conflicts,emphasizing the influence of various traffic flow parameters,distinctions in conflict indicators,and the ratio of severe conflicts to total conflicts.Additionally,an analysis of the spatial distribution of severe conflicts was conducted.The results suggested that traffic conflicts in SSIs are influenced by multiple factors,with mainline and entry traffic volumes being the most significant.Heavy vehicle proportions and entry ramp speeds had notable effects under certain spacing conditions.Considerable variations were observed in conflict indicators across different spacings,with the maximum conflict speed being the most affected by spacing,while TTC was the least.As spacing increased,the proportion of severe conflicts decreased,with severe TTC dropping from 18%to 10%.High-density conflict zones were identified near merging points in the second and third lanes.With larger spacing,the conflict zone range narrowed while the density of conflict points intensified.展开更多
A new safety assessment method for parallel routes is presented. From the aspects of safety guard system of air traffic control（ATC） and considering the flight conflict as causing event of air collision accidents, t...A new safety assessment method for parallel routes is presented. From the aspects of safety guard system of air traffic control（ATC） and considering the flight conflict as causing event of air collision accidents, this paper fosters a four-layer safety guard of controller command, short-term conflict alerts （STCAs）, pilot visual avoidance, and traffic alert collision avoidance system（TCAS）. Then, the problem of parallel routes collision risk is divided into two parts：the calculation of potential flight conflict and the analysis of failure probability of the four-layer safety guard. A calculation model for controller interference times is induced. By using cognitive reliability and error analysis method（CREAM）,the calculation problem to failure probability of controller sequencing flight conflicts is solved and a fault tree model of guard failure of STCA and TCAS is established. Finally, the Beijing-Shanghai parallel routes are taken as an example to be calculated and the collision risk of the parallel routes is obtained under the condition of radar control. Results show that the parallel routes can satisfy the safety demands.展开更多
Conflicts are common phenomena in the field of traffic engineering, while are also the main cause of all the traffic problems. The vehicle access driveway of large shopping mall is often the conflict area of urban roa...Conflicts are common phenomena in the field of traffic engineering, while are also the main cause of all the traffic problems. The vehicle access driveway of large shopping mall is often the conflict area of urban roads, traffic engineers and city managers face a great challenge as to make the traffic enter and exit the shopping mall safely and or-derly. This paper studies the issues of access management of large shopping mall with a focus on Carrefour shopping mall in Shuangjin district, Beijing. This shopping mall attracts and generates lots of traffic during peak hours, those huge numbers of vehicles come in and out of the shopping mall through the access driveway, resulting in a lot of conflicts to the urban road traffic flow and deteriorating highly smooth movement of traffic on the urban roadway. The impacts of the existing design and layout of access driveway on surrounding traffic operation are also studied in this paper. Some countermeasure of improvements had proposed to decrease traffic conflicts and make sure that the traffic conditions get better off, all the improvement countermeasures are based on the access management principles. Results from this study can be a good reference on other similar driveways at large shopping mall areas.展开更多
A methodology for calibrating and validating VISSIM simulation model is presented that allows to replicate the observed vehicles conflicts. A roundabout case study has been selected to test the usefulness of a combine...A methodology for calibrating and validating VISSIM simulation model is presented that allows to replicate the observed vehicles conflicts. A roundabout case study has been selected to test the usefulness of a combined approach of VISSIM simulation package and the surrogate safety assessment model(SSAM) for providing reliable estimates of traffic conflicts. Safety performance has been assessed from the field by video-recording vehicle interactions at the roundabout, and then expressed in terms of time to collision(TTC)values.The proposed calibration procedure has been performed by a multistage methodology involving microscopic drivers' car following behavior parameters to enhance the correlation between observed and simulated queue lengths at the roundabout's entries. The calibration procedure is based on a statistical screening of inputs leading to a linear expression relating significant parameters to the queue length. The best estimates of the model's parameters have been determined using a genetic algorithm technique.The spatial distribution of the rear-end conflicts and the TTC values determined by SSAM have been finally compared with the observed ones to analyze the capability of the model of replicating rear-end conflicts.The results suggest to this calibration procedure impacts positively on the estimate of the safety performance measures obtained through the simulation processes.Notwithstanding the good results in the evaluation of the model's accuracy, the simulation seems to fail in reproducing the traffic phenomena linked to unusual driving behavior, and therefore it is not able to replicate forced drivers' maneuvers that can lead to a conflict situation.展开更多
Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value models.Two sampling techniques,namely,block maxima and peak...Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value models.Two sampling techniques,namely,block maxima and peak over threshold,form the core of these models.Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical use.Recently,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity aspect.To address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level.In particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level.Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of data.Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were developed.The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters simultaneously.The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model.Overall,the modeling framework presented in this study advances conflict-based safety assessment,where the severity dimension can be captured via bivariate hybrid models.展开更多
基金sponsored by the National Natural Science Foundation of China(No.52372335)the Belt and Road Cooperation Program under the 2023 Shanghai Action Plan for Science,Technology and Innovation(No.23210750500).
文摘Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions,thus providing insights into roadway safety improvement countermeasures.This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions.In order to remove the outliers and white noise existing in the raw data,vehicle trajectories were reconstructed by apply-ing discrete wavelet transform and Kalman filtering(KF).Generalized time-to-collision was adopted to detect and measure the severity of conflicts,by which 1127 conflict events were extracted.Path analysis(PA)models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity.Various roadway and environmental characteristics such as traffic flow average speed,percentage of trucks,and intersection skew angle were included in the models.The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity.In the indirect effects,the kinematics of conflicting vehicles such as the average and standard deviation of speed,plays an intermediate role in linking roadway factors and conflict outcome.The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.
文摘Right-turn collisions at intersections are one of the most dominant crash types in suburban areas,especially at unsignalized intersections.There is,however,a lack of comprehensive research on the speed patterns of vehicles during right-turn manoeuvres and their impacts on crashes.To provide an in-depth investigation of the factors determining the safety of right-turn manoeuvres,driving behavior data were collected through an instrumented vehicle study.Using this data,binary logistic regression models were developed to identify the factors affecting the probability of vehicle-vehicle(V-V)and vehicle-pedestrian(V-P)conflicts at six suburban intersections in Babol,Iran,during right-turn stage manoeuvres.In total,1456 V-V and V-P conflicts were identified from the data analysis.The results from the logistic regression model showed that the vehicle speed,the distance between road users,as well as driver and pedestrian distractions were associated with a higher risk for V-V or V-P conflicts.To estimate the safe right-turn speeds to be selected by drivers at different stages of the right turn,i.e.,at the start,during,and end of the movement,linear regression models were developed.The results showed that participants adjust their driving behaviors the same way toward pedestrians as they do toward vehicles.The findings of this study can be leveraged for the development of a robust advanced driving assistance system,the use of which can further improve the safety performance of right-turn manoeuvres.
基金Supported by the Programme of Introducing Talents of Discipline to Universities (B12022)
文摘The relationship between the opposing left-turn conflict and the traffic participants was analyzed in this study. Based on the traffic conflict technology, the image data were collected in a real traffic situation. The relationship was investigated under two different conditions. The number of opposing left-turn conflicts was positively correlated with the number of left-turn vehicles while the ratio of left-turn vehicles to opposing vehicles was less than 1, and showed a positive correlation with the number of opposing-through vehicles when the ratio of left-turn vehicles to opposing vehi- cles was more than 1. In other words, the opposing left-turn risk was positively correlated with the number of the minor traffic participants, which had a negative effect on the whole traffic system op- eration.
基金the Italian Ministry of Economic Development for the financial support of this research within the program"Industria 2015"
文摘The traffic conflict technique （TCT） was developed as ＂surrogate measure of road safety＂ to identify near-crash events by using measures of the spatial and temporal proximity of road users. Traditionally applications of TCT focus on a specific site by the way of manually or automated supervision. Nowadays the development of in-vehicle （IV） technologies pro- vides new opportunities for monitoring driver behavior and interaction with other road users directly into the traffic stream. In the paper a stereo vision and GPS system for traffic conflict investigation is presented for detecting conflicts between vehicle and pedestrian. The system is able to acquire geo-referenced sequences of stereo frames that are used to provide real time information related to conflict occurrence and severity. As case study, an urban bus was equipped with a prototype of the system and a trial in the city of Catania （Italy） was carried out analyzing conflicts with pedestrian crossing in front of the bus. Experimental results pointed out the potentialities of the system for collection of data that can be used to get suitable traffic conflict measures. Specifically, a risk index of the conflict between pedestrians and vehicles is proposed to classify collision probability and severity using data collected by the system. This information may be used to develop in-vehicle warning systems and urban network risk assessment.
基金Tinajin Research Tnnovation Project for Postgraduate Students:Research on multi-sensor fusion vehicle detection algorithm in complex weather conditions(2020YJSS086).
文摘Road intersections are important nodes for the convergence,turning,and diversion of traffic flows in the urban road network,but at the same time,due to the large traffic volume and conflict points at the intersection,it has become a traffic congestion and accident-prone area,which seriously affects road traffic safety and vehicle traffic efficiency.Therefore,it is of great significance to study the collaborative control strategy of urban intersections.This paper analyzes and summarizes the methods of intersection cooperative control based on intelligent connected vehicles in recent years,and looks forward to the future development trend and prospects of the combination of intersection cooperative control and Vehicle-Infrastructure Cooperation System.
文摘With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.
基金supported in part by the National Natural Science Foundation of China(No.52172340).
文摘Affected by the limited interchange spacing,the operational risk of vehicles in expressway small-spacing interchanges(SSIs)is more complex compared to other interchanges.In this study,unmanned aerial vehicle(UAV)measurements were integrated with joint simulation data to explore the risk characteristics of SSIs with the help of traffic conflict theory.Seven traffic flow parameters,including mainline traffic volume,were selected to evaluate their impact on traffic conflicts.The distribution of four traffic conflict indicators,such as time to collision(TTC),was analyzed,and their severity was categorized using cumulative frequency analysis and minibatch K-means clustering.By varying the spacing,the study scrutinized trends in traffic conflicts,emphasizing the influence of various traffic flow parameters,distinctions in conflict indicators,and the ratio of severe conflicts to total conflicts.Additionally,an analysis of the spatial distribution of severe conflicts was conducted.The results suggested that traffic conflicts in SSIs are influenced by multiple factors,with mainline and entry traffic volumes being the most significant.Heavy vehicle proportions and entry ramp speeds had notable effects under certain spacing conditions.Considerable variations were observed in conflict indicators across different spacings,with the maximum conflict speed being the most affected by spacing,while TTC was the least.As spacing increased,the proportion of severe conflicts decreased,with severe TTC dropping from 18%to 10%.High-density conflict zones were identified near merging points in the second and third lanes.With larger spacing,the conflict zone range narrowed while the density of conflict points intensified.
基金Supported by the National High Technology Research and Development Program of China("863"Program)(2006AA12A105)~~
文摘A new safety assessment method for parallel routes is presented. From the aspects of safety guard system of air traffic control（ATC） and considering the flight conflict as causing event of air collision accidents, this paper fosters a four-layer safety guard of controller command, short-term conflict alerts （STCAs）, pilot visual avoidance, and traffic alert collision avoidance system（TCAS）. Then, the problem of parallel routes collision risk is divided into two parts：the calculation of potential flight conflict and the analysis of failure probability of the four-layer safety guard. A calculation model for controller interference times is induced. By using cognitive reliability and error analysis method（CREAM）,the calculation problem to failure probability of controller sequencing flight conflicts is solved and a fault tree model of guard failure of STCA and TCAS is established. Finally, the Beijing-Shanghai parallel routes are taken as an example to be calculated and the collision risk of the parallel routes is obtained under the condition of radar control. Results show that the parallel routes can satisfy the safety demands.
文摘Conflicts are common phenomena in the field of traffic engineering, while are also the main cause of all the traffic problems. The vehicle access driveway of large shopping mall is often the conflict area of urban roads, traffic engineers and city managers face a great challenge as to make the traffic enter and exit the shopping mall safely and or-derly. This paper studies the issues of access management of large shopping mall with a focus on Carrefour shopping mall in Shuangjin district, Beijing. This shopping mall attracts and generates lots of traffic during peak hours, those huge numbers of vehicles come in and out of the shopping mall through the access driveway, resulting in a lot of conflicts to the urban road traffic flow and deteriorating highly smooth movement of traffic on the urban roadway. The impacts of the existing design and layout of access driveway on surrounding traffic operation are also studied in this paper. Some countermeasure of improvements had proposed to decrease traffic conflicts and make sure that the traffic conditions get better off, all the improvement countermeasures are based on the access management principles. Results from this study can be a good reference on other similar driveways at large shopping mall areas.
文摘A methodology for calibrating and validating VISSIM simulation model is presented that allows to replicate the observed vehicles conflicts. A roundabout case study has been selected to test the usefulness of a combined approach of VISSIM simulation package and the surrogate safety assessment model(SSAM) for providing reliable estimates of traffic conflicts. Safety performance has been assessed from the field by video-recording vehicle interactions at the roundabout, and then expressed in terms of time to collision(TTC)values.The proposed calibration procedure has been performed by a multistage methodology involving microscopic drivers' car following behavior parameters to enhance the correlation between observed and simulated queue lengths at the roundabout's entries. The calibration procedure is based on a statistical screening of inputs leading to a linear expression relating significant parameters to the queue length. The best estimates of the model's parameters have been determined using a genetic algorithm technique.The spatial distribution of the rear-end conflicts and the TTC values determined by SSAM have been finally compared with the observed ones to analyze the capability of the model of replicating rear-end conflicts.The results suggest to this calibration procedure impacts positively on the estimate of the safety performance measures obtained through the simulation processes.Notwithstanding the good results in the evaluation of the model's accuracy, the simulation seems to fail in reproducing the traffic phenomena linked to unusual driving behavior, and therefore it is not able to replicate forced drivers' maneuvers that can lead to a conflict situation.
基金This research is funded by the Queensland University of Technology,iMOVE CRC,and supported by the Cooperative Research Centres program,an Australian Government initiative.
文摘Traffic conflict techniques rely heavily on the proper identification of conflict extremes,which directly affects the prediction performance of extreme value models.Two sampling techniques,namely,block maxima and peak over threshold,form the core of these models.Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches,as their crash estimates are too imprecise,hindering their widespread practical use.Recently,anomaly detection techniques for sampling conflict extremes have been used,but their application has been limited to estimating crash frequency without considering the crash severity aspect.To address this research gap,this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level.In particular,modified time-to-collision(MTTC)and expected post-collision change in velocity(Delta-V orΔV)have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level.Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane,Australia,using four days of data.Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques(isolation forest and minimum covariance determinant)were developed.The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters,including location,scale,and both location and scale parameters simultaneously.The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records,thereby demonstrating the viability of the proposed approach.A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model.Overall,the modeling framework presented in this study advances conflict-based safety assessment,where the severity dimension can be captured via bivariate hybrid models.
