Using Traffic Disturbance Metrics to Estimate and Predict Freeway Traffic Breakdown and Safety Events

2021 ◽  
pp. 036119812110124
Author(s):  
Leila Azizi ◽  
Mohammed Hadi
Keyword(s):  
Traffic Safety ◽  
Management Strategies ◽  
Connected Vehicles ◽  
Freeway Traffic ◽  
Transportation Management ◽  
Time To Collision ◽  
Decision Tools ◽  
Traffic State ◽  
Traffic Performance ◽  
Traffic Disturbance

The introduction of connected vehicles, connected and automated vehicles, and advanced infrastructure sensors will allow the collection of microscopic metrics that can be used for better estimation and prediction of traffic performance. This study examines the use of disturbance metrics in combination with the macroscopic metrics usually used for the estimation of traffic safety and mobility. The disturbance metrics used are the number of oscillations and a measure of disturbance durations in the time exposed time to collision. The study investigates using the disturbance metrics in data clustering for better off-line categorization of traffic states. In addition, the study uses machine-learning based classifiers for the recognition and prediction of the traffic state and safety in real-time operations. The study also demonstrates that the disturbance metrics investigated are significantly related to crashes. Thus, this study recommends the use of these metrics as part of decision tools that support the activation of transportation management strategies to reduce the probability of traffic breakdown, ease traffic disturbances, and reduce the probability of crashes.

Generic Approaches to Estimating Freeway Traffic State and Percentage of Connected Vehicles With Fixed and Mobile Sensing

2021 ◽  
pp. 1-23
Author(s):  
Mingming Zhao ◽  
Claudio Roncoli ◽  
Yibing Wang ◽  
Nikolaos Bekiaris-Liberis ◽  
Jingqiu Guo ◽  
...  
Keyword(s):  
Mobile Sensing ◽  
Connected Vehicles ◽  
Freeway Traffic ◽  
Traffic State

Development of an Evaluation Model of Freeway Traffic Safety Facilities System

2013 ◽  
Vol 734-737 ◽  
pp. 1578-1581
Author(s):  
Yan Yong Guo ◽  
Yao Wu ◽  
Liang Song ◽  
Hui Duan
Keyword(s):  
Traffic Safety ◽  
Evaluation System ◽  
Evaluation Model ◽  
Variation Coefficient ◽  
Freeway Traffic ◽  
Evaluation Result ◽  
Single Index ◽  
Recognition Theory ◽  
Attribute Measure ◽  
Attribute Recognition

This study developed an evaluation model of freeway traffic safety facilities system. Firstly, an evaluation system of freeway traffic safety facility was proposed. Secondly, an evaluation model was proposed based on attribute recognition theory. And the evaluation result was identified according to the attribute measure value of single index and the comprehensive attribute measure value of multiple indexes as well as the confidence criterion. Thirdly, the weight of each indicator was decided by variation coefficient. Finally, A case of TAI-GAN freeway (K1+242~K3+259 segment) was conducted to verify the feasibility and effectiveness of the model.

Empirical Analysis on Time to Collision at Urban Expressway

2014 ◽  
Vol 505-506 ◽  
pp. 1127-1132 ◽  
Author(s):  
Cheng Xu ◽  
Zhao Wei Qu
Keyword(s):  
Empirical Analysis ◽  
Traffic Safety ◽  
Traffic Volume ◽  
T Test ◽  
Descriptive Statistics ◽  
Traffic Condition ◽  
Time To Collision ◽  
Traffic Conditions ◽  
Urban Expressway ◽  
Traffic Conflicts

Traffic safety is of great significance, especially at urban expressway where traffic volume is large and traffic conflicts are highlighted. But little research up to date has discussed in detail how these factors impact the TTC characteristics. In this paper, field Beijing expressway data were collected by video with different locations, lanes, traffic conditions and following vehicle types. Accordingly, some basic descriptive statistics of total TTC samples were shown and analyzed. We then used T-test to analyze the effect of road environments, traffic conditions, and vehicle types on TTC statistically. The results implied three main findings. Firstly, TTC was found to change according to road environments (i.e. TTC on weaving segment is smaller than other locations), secondly, some evidence supported this hypothesis that traffic conditions (especially uncongested traffic condition) affect TTC significantly, and lastly, little correlation was found between TTC means and vehicle types.

Integrated Safety and Operational Analysis of the Access Design of Managed Toll Lanes

2019 ◽  
Vol 2673 (2) ◽  
pp. 127-136 ◽  
Author(s):  
Moatz Saad ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee ◽  
Ling Wang
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Lane Change ◽  
Average Delay ◽  
Microscopic Traffic Simulation ◽  
Delay Performance ◽  
Time To Collision ◽  
Operational Analysis ◽  
Safety Improvement ◽  
Traffic Operation

Managed lanes (MLs) have been implemented as a vital strategy for traffic management and traffic safety improvement. The majority of previous studies involving MLs have adopted a limited scope of examining the effect of MLs segments as a whole, without considering the safety and operational effects of the design of access to the MLs. In the study, several scenarios were tested using microscopic traffic simulation to determine the optimal access design while taking into consideration accessibility levels and weaving lengths. The studied accessibility levels varied from one to three along the studied network. Both safety (i.e., speed standard deviation, time-to-collision, and conflict rate) and operation (i.e., level of service, average speed, average delay) performance measures were included in the analysis. Tobit models were developed for investigating the factors that affect the safety measures. ANOVA and LOS calculations were used to evaluate traffic operation. The results of the safety and operational analysis suggested that one accessibility level is the optimal option in the nine-mile network. A weaving length between 1,000 feet and 1,400 feet per lane change was suggested based on the safety analysis. In addition, from the operation perspective, a weaving length between 1,000 feet and 2,000 feet per lane change was recommended. The results also showed that off-peak periods had better safety and operational performance (e.g., lower conflict rate, less delay) than peak periods. This study has major implications for improving MLs by recommending the optimal accessibility level and weaving length near access zones.

scholarly journals Exploring the Influence of Truck Proportion on Freeway Traffic Safety Using Adaptive Network-Based Fuzzy Inference System

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu ◽  
H. Michael Zhang
Keyword(s):  
Traffic Safety ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Traffic Volume ◽  
Learning Ability ◽  
Adaptive Network ◽  
Freeway Traffic ◽  
Inference System ◽  
Traffic Conflicts ◽  
The Impact

The truck operation of freeway has an impact on traffic safety. In particular, the gradually increasing in truck proportion will inevitably affect the freeway traffic operation of different traffic volume. In this paper, VISSIM simulation is used to supply the field data and orthogonal experimental is designed for calibrate the simulation data. Then, SSAM modeling is combined to analyze the impact of truck proportion on traffic flow parameters and traffic conflicts. The serious and general conflict prediction model based on the Adaptive Network-based Fuzzy Inference System (ANFIS) is proposed to determine the impact of the truck proportion on freeway traffic safety. The results show that when the truck proportion is around 0.4 under 3200 veh/h and 0.6 under 2600 veh/h, there are more traffic conflicts and the number of serious conflicts is more than the number of general conflicts, which also reflect the relationship between truck proportion and traffic safety. Under 3000 veh/h, travel time and average delay increasing while mean speed and mean speed of small car decreases with truck proportion increases. The mean time headway rises largely with the truck proportion increasing above 3000 veh/h. The speed standard deviation increases initially and then fall with truck proportion increasing. The lane-changing decreases while truck proportion increasing. In addition, ANFIS can accurately determine the impact of truck proportion on traffic conflicts under different traffic volume, and also validate the learning ability of ANFIS.

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