scholarly journals Estimating Rear-End Accident Probabilities with Different Driving Tendencies at Signalized Intersections in China

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Weijie Wang ◽  
Yingshuai Li ◽  
Jian Lu ◽  
Yaping Li ◽  
Qian Wan
Keyword(s):  
Clustering Analysis ◽  
Logistic Regression Model ◽  
Probability Model ◽  
Primary Objective ◽  
Signalized Intersections ◽  
Simulation Framework ◽  
Dilemma Zone ◽  
Accident Probability ◽  
Quantitative Results ◽  
Accident Probabilities

Rear-end accidents are the most common accident type at signalized intersections because of the different driving tendencies in the dilemma zone (DZ), where drivers are faced with indecisiveness of making “stop or go” decisions at yellow onset. In various researches, the number of vehicles in the DZ has been used as a safety indicator—the more the vehicles in the DZ, the higher the probability of rear-end accidents. However, the DZ-associated rear-end accident potential varies depending on drivers’ driving tendencies and the situations (position and speed) at the yellow onset. This study’s primary objective is to explore how the driving tendency impacts the DZ distribution and the probability of rear-end accidents. To achieve this, three types of driving tendencies were classified using K-means clustering analysis based on driving variables. Further, the boundary of the DZ is determined by logistic regression model of drivers’ stop/go decision. Then, we proposed the conditional probability model of rear-end accidents and developed a Monte Carlo simulation framework to calculate the model. The results indicate that the rear-end accident probability is dependent on the driving tendency even at the same position with the same speed in the DZ. The aggressive type has the highest risk probability followed by conservative and then the normal types. The quantitative results of the study can provide the basis for rear-end accident assessments.

scholarly journals Research on Driver Behavior in Yellow Interval at Signalized Intersections

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Xiujuan Tian ◽  
Wei Wang ◽  
Xiyang Zhou ◽  
Hongmei Liang
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Driver Behavior ◽  
Signalized Intersections ◽  
Fuzzy Decision ◽  
Fuzzy Decision Tree ◽  
Tree Model ◽  
Dilemma Zone

Vehicles are often caught in dilemma zone when they approach signalized intersections in yellow interval. The existence of dilemma zone which is significantly influenced by driver behavior seriously affects the efficiency and safety of intersections. This paper proposes the driver behavior models in yellow interval by logistic regression and fuzzy decision tree modeling, respectively, based on camera image data. Vehicle’s speed and distance to stop line are considered in logistic regression model, which also brings in a dummy variable to describe installation of countdown timer display. Fuzzy decision tree model is generated by FID3 algorithm whose heuristic information is fuzzy information entropy based on membership functions. This paper concludes that fuzzy decision tree is more accurate to describe driver behavior at signalized intersection than logistic regression model.

How do Optimistic Individuals Affect Insurance Advertisements?

2020 ◽  
Vol 14 (2) ◽  
Author(s):  
Yoichiro Fujii ◽  
Michiko Ogaku ◽  
Mahito Okura ◽  
Yusuke Osaki
Keyword(s):  
Maximum Level ◽  
Optimal Level ◽  
Insurance Premium ◽  
Free Rider ◽  
Free Rider Problem ◽  
Accident Probability ◽  
Insurance Firms ◽  
The Difference ◽  
Accident Probabilities ◽  
Equilibrium Number

AbstractSome people have optimistic expectations regarding their accident probability, and thus, refrain from purchasing adequate insurance. This study investigates how insurance firms use advertisements to lower the ratio of optimistic individuals in the market. The main results are as follows: first, the optimal level of advertisements is maximized when the insurance premium is moderate. Second, the maximum level of advertisement varies according to the degree of optimism, which is measured by the difference between accurate and optimistic accident probabilities. Third, the advertisement decision is affected by the free-rider problem, and the equilibrium number of insurance firms with advertisement is always larger than that of firms without advertisement in a competitive insurance market.

Accounting for Nonrandom Arrivals in Estimate of Delay at Signalized Intersections

1996 ◽  
Vol 1555 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Janice Daniel ◽  
Daniel B. Fambro ◽  
Nagui M. Rouphail
Keyword(s):  
Field Data ◽  
Empirical Studies ◽  
Primary Objective ◽  
Signalized Intersections ◽  
Degree Of Saturation ◽  
Delay Model ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Research Show

The primary objective of this research was to determine the effect of nonrandom or platoon arrivals on the estimate of delay at signalized intersections. The delay model used in the 1994 Highway Capacity Manual (HCM) accounts for nonrandom arrivals through the variable m, which can be shown to be equal to 8kI, where k describes the arrival and service distributions at the intersection and I describes the variation in arrivals due to the upstream intersection. The 1994 HCM delay model m-values are a function of the arrival type, where the arrival type describes the quality of progression at the intersection. Although an improvement to the fixed k I-value used in the 1985 delay model, the 1994 m values are based on empirical studies from limited field data and do not account for the decrease in random arrivals as the volume approaches capacity at the downstream intersection. This research provides an estimate of the variable kI for arterial conditions. An analytical equation was developed as a function of the degree of saturation, and a separate equation was developed for each signal controller type. The results from this research show that the proposed kI's provide delay estimates closer to the measured delay compared with the delay estimates using the kI-values in the 1994 HCM delay model.

Safety-Based Left-Turn Phasing Decisions for Signalized Intersections

2017 ◽  
Vol 2619 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Kiriakos Amiridis ◽  
Nikiforos Stamatiadis ◽  
Adam Kirk
Keyword(s):  
Urban Areas ◽  
Primary Objective ◽  
Signalized Intersections ◽  
Safety Performance ◽  
Signal Timing ◽  
Left Turn ◽  
Crash Data ◽  
Intersection Safety ◽  
Traffic Volumes

The efficient and safe movement of traffic at signalized intersections is the primary objective of any signal-phasing and signal-timing plan. Accommodation of left turns is more critical because of the higher need for balancing operations and safety. The objective of this study was to develop models to estimate the safety effects of the use of left-turn phasing schemes. The models were based on data from 200 intersections in urban areas in Kentucky. For each intersection, approaches with a left-turn lane were isolated and considered with their opposing through approach to examine the left-turn–related crashes. This combination of movements was considered to be one of the most dangerous in intersection safety. Hourly traffic volumes and crash data were used in the modeling approach, along with the geometry of the intersection. The models allowed for the determination of the most effective type of left-turn signalization that was based on the specific characteristics of an intersection approach. The accompanying nomographs provide an improvement over existing methods and warrants and allow for a systematic and quick evaluation of the left-turn phase to be selected. The models used the most common variables that were already known during the design phase, and they could be used to determine whether a permitted or protected-only phase would suit the intersection when safety performance was considered.

Performance of Advance Warning for End of Green System for High-Speed Signalized Intersections

2005 ◽  
Vol 1925 (1) ◽  
pp. 176-184 ◽  
Author(s):  
Srinivasa R. Sunkari ◽  
Carroll J. Messer ◽  
Hassan Charara
Keyword(s):  
High Speed ◽  
Red Light ◽  
Signalized Intersections ◽  
Traffic Signal ◽  
Traffic Operations ◽  
Dilemma Zone ◽  
Green Approach ◽  
High Speeds ◽  
Approach Speed ◽  
Green System

A major difficulty with traffic signal operation on high-speed approaches is the dilemma faced by approaching motorists when the downstream signal turns yellow. Should the motorists stop or proceed through the intersection? Crashes that may occur at these intersections result in excessive property damage and personal injury because of the high speeds involved. The Texas Transportation Institute has developed a new system named the Advance Warning for End of Green System (AWEGS) for application at high-speed signalized intersections. Typically, dilemma zone detection strategy is based on a certain approach speed (typically the 85th percentile). AWEGS provides protection for the majority of motorists who are not covered by the dilemma zone treatment. AWEGS provides advance warning to motorists by using signs mounted on the roadside. These signs (Be Prepared To Stop When Flashing) would flash a beacon about 5 to 6 s before the onset of the yellow signal for high-speed approaches. Similar systems have been implemented in Canada and in a few U.S. states that use the trailing-green approach, which results in loss of dilemma zone protection every cycle. AWEGS, however, is almost completely independent of the traffic signal controller, and hence the signal controller would continue to provide the dilemma zone protection for which it was designed. The system was implemented at two sites in Waco and Brenham, Texas. Results of AWEGS implementation illustrated an improvement in traffic operations. AWEGS consistently enhanced the dilemma zone protection at intersections and reduced red light running by about 40%.

Modeling Pedestrian Crossing Behavior and Safety at Signalized Intersections

2018 ◽  
Vol 2672 (31) ◽  
pp. 76-86 ◽  
Author(s):  
S. Marisamynathan ◽  
P. Vedagiri
Keyword(s):  
Probability Model ◽  
Pearson Correlation ◽  
Personal Characteristics ◽  
Signalized Intersections ◽  
Contributing Factors ◽  
Safety Level ◽  
Traffic Crashes ◽  
User Perceptions ◽  
Binary Logit ◽  
Pedestrian Crossing

Pedestrian noncompliance behavior is one of the most critical causes of pedestrian involved traffic crashes at intersections in India. Thus, the objectives of this study are to examine how various factors affect pedestrian crossing behavior and to propose models for pedestrian crossing behavior and level of safety at signalized intersections which will be useful to regulate pedestrian flow. The data were collected with video and a user perceptions survey at six selected signalized intersections in Mumbai, India. The differences between pedestrian crossing behavior with respect to personal characteristics, socioeconomic attributes, and existing crossing facilities were identified using Pearson correlation and odd ratio tests. Furthermore, the major reasons for noncompliance behavior were obtained by analysis of field data to prevent noncompliance behavior and enhance pedestrian safety. The results showed that a significant number of the pedestrians violated the traffic signal to save time and for convenience (46%). A binary logit model was developed to evaluate the impacts of contributing factors on pedestrian crossing behavior. Further, an ordered probability model was established to evaluate and estimate the pedestrian level of safety at signalized intersections. Two models were validated, and their statistical results show that the models predict the pedestrian crossing behavior and safety level more precisely. Developed models and study outcomes can help transport planners and designers understand pedestrian crossing behavior on crosswalks at signalized intersections and thus create a safer crossing environment for all pedestrians.

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