scholarly journals An Assessment Method of Urban Traffic Crash Severity Considering Traveling Delay and Non-Essential Fuel Consumption of Third Parties

2020 ◽  
Vol 12 (17) ◽  
pp. 6806
Author(s):  
Yi Cao ◽  
Shiwen Li ◽  
Chuanyun Fu
Keyword(s):  
Fuel Consumption ◽  
Assessment Method ◽  
Current Approach ◽  
Third Parties ◽  
Urban Traffic ◽  
Crash Severity ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Comprehensive Index ◽  
Severity Classification

Urban traffic crashes may lead to only a few casualties, but may generate severe negative impacts on the surrounding traffic, such as evidently increasing traveling delay and non-essential fuel consumption of third parties (i.e., vehicles not involved in the crash). Such detrimental consequences of urban traffic crashes are usually ignored by the traditional crash severity evaluation approaches. Therefore, this study attempts to classify urban traffic crash severity by considering the traveling delay and non-essential fuel consumption of third parties in addition to casualties and property damages. Based on the losses of traveling delay and non-essential fuel consumption of third parties, the losses of crash casualties, and property damages, a comprehensive index of urban traffic crash severity was developed. Moreover, the thresholds of the proposed comprehensive index for urban crash severity classification were determined based on the crash data from 2013 to 2014 collected from Harbin, China. The developed comprehensive index was applied to a case study, which also compared the crash severity classification outcomes from the developed method and the current approach. The results indicate that the developed method of urban traffic crash severity classification is more reasonable than the existing approach. Such superiority of the proposed urban crash severity classification method is due to considering the traveling delay and non-essential fuel consumption of third parties caused by a crash.

scholarly journals The Model of Severity Prediction of Traffic Crash on the Curve

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Jian-feng Xi ◽  
Hai-zhu Liu ◽  
Wei Cheng ◽  
Zhong-hao Zhao ◽  
Tong-qiang Ding
Keyword(s):  
Logistic Regression ◽  
Likelihood Estimation ◽  
Crash Severity ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Contributory Factors ◽  
Severity Prediction ◽  
Traffic Environment ◽  
Road Crash ◽  
Road Segments

With the study of traffic crashes on curved road segments as the focus of research, a logistic regression based curve road crash severity prediction model was established based on a sample crash database of 20000 entries collected from 4 regions of China and 15 evaluation indicators involving driver, driving environment, and traffic environment factors. Maximum Likelihood Estimation and step-back technique were deployed for data analysis, the conclusion of which is that the three main contributory factors on curve road crash severity are weather, roadside protection facility, and pavement structure. Hosmer and Lemeshow tests were used to verify the reliability of the model, and the model variables were discussed to a certain degree as well.

Identifying Patterns of Key Factors in Sun Glare-Related Traffic Crashes

2021 ◽  
pp. 036119812110378
Author(s):  
Subasish Das ◽  
Xiaoduan Sun ◽  
Bahar Dadashova ◽  
M. Ashifur Rahman ◽  
Ming Sun
Keyword(s):  
Environmental Issues ◽  
The United States ◽  
Traffic Information ◽  
Key Factors ◽  
Contributing Factors ◽  
Additional Insight ◽  
Traffic Crashes ◽  
Traffic Crash ◽  
Single Vehicle ◽  
Open Country

Sun glare is one of the major environmental issues contributing to traffic crashes. Every year, many traffic crashes in the United States are attributed to sun glare. However, quantitative analysis of the influence of sun glare on traffic crashes has not been widely undertaken. This study used traffic crash narrative data for 7 years (2010–2016) from Louisiana to identify crash reports that provided evidence of drivers indicating sun glare as the primary contributing factor of the crashes. Additional geometry and traffic information was collected to identify the list of key crash-contributing factors. This study used cluster correspondence analysis to perform the data analysis. After performing several iterations, six clusters were identified that provided additional insight in relation to sun glare-related crashes. The six clusters are associated with mixed (business and residential) localities, intersection-related crashes on U.S. roadways, single-vehicle crashes on residential two-lane undivided roadways, curve-related crashes on parish roadways in residential localities, interstate-related crashes in open country localities, and curve-related crashes in open country localities. The findings of the current study can add insights to the ongoing safety analysis on sun glare-related crashes.

scholarly journals School-Aged Pedestrian–Vehicle Crash Vulnerability

2019 ◽  
Vol 11 (4) ◽  
pp. 1214 ◽  
Author(s):  
Kinga Ivan ◽  
József Benedek ◽  
Silviu Ciobanu
Keyword(s):  
Traffic Safety ◽  
Local Level ◽  
Urban Traffic ◽  
Vehicle Speed ◽  
Vehicle Crashes ◽  
Time Intervals ◽  
Local Authorities ◽  
Traffic Crashes ◽  
Triggering Factors ◽  
Pedestrian Crashes

The analysis of pedestrian–vehicle crashes makes a significant contribution to sustainable pedestrian safety. Existing research is based mainly on the statistical analysis of traffic crashes involving pedestrians and their causes, without the identification of areas vulnerable to traffic crashes that involve pedestrians. The main aim of this paper is to identify areas vulnerable to school-aged pedestrian–vehicle crashes at a local level to support the local authorities in implementing new urban traffic safety measures. The vulnerable areas were determined by computing the severity index (SI) based on the number of fatal, serious, and slight casualties throughout the 2011–2016 period in a large urban agglomeration (Bucharest). As well as the vulnerable areas, the triggering factors and the time intervals related to school-aged pedestrian–vehicle crashes were identified. The outcomes of the study showed that the vulnerable areas were concentrated only in districts 2 and 4 of Bucharest, and they were associated with high vehicle speed and pedestrians’ unsafe crossing behavior. The findings revealed that speed and age are triggering factors in generating school-aged pedestrian–vehicle crashes. The identified time peaks with a high number of traffic crashes correspond to the afternoon time intervals, when scholars go home from school. The identification of the areas vulnerable to school-aged pedestrian crashes may help local authorities in identifying and implementing measures to improve traffic safety in large urban agglomerations.

scholarly journals The Microsimulation Model for Assessing the Impact of Inbound Traffic Flows for Container Terminals Located near City Centers

2020 ◽  
Vol 12 (22) ◽  
pp. 9478
Author(s):  
Neven Grubisic ◽  
Tomislav Krljan ◽  
Livia Maglić ◽  
Siniša Vilke
Keyword(s):  
Fuel Consumption ◽  
Container Terminal ◽  
Urban Traffic ◽  
Container Terminals ◽  
Critical Parameters ◽  
Traffic Flows ◽  
Traffic Demand ◽  
Gate Time ◽  
The Impact ◽  
The City

The growth of container transport places increasing demand on traffic, especially in situations where container terminals are located near the city centers. The main problem is traffic congestion on networks caused by the integration of Heavy-Duty Vehicles and urban traffic flows. The main objective is to identify the critical traffic parameters which cause negative organizational and environmental impacts on the existing and future traffic demand. A micro-level traffic simulation model was implemented for the testing of the proposed framework-based supply, demand, and control layers. The model was generated and calibrated based on the example of a mid-size Container Terminal “Brajdica” and the City of Rijeka, Croatia. The results indicate that the critical parameters are Queue Length on the approach road to the Container Terminal and the Stop Delay on the main city corridor. High values of these parameters cause negative effects on the environment because of increased fuel consumption and the generation of extra pollution. Due to this problem, a sensitivity analysis of the traffic system performance has been conducted, with a decrement of Terminal Gate Time distribution by 10%. After re-running simulations, the results indicate the impact of subsequent variation in Terminal Gate Time on the decrease of critical parameters, fuel consumption, and vehicle pollution.

scholarly journals Influence of External Environmental Factors on Range Estimation of Autonomous Hybrid Vehicles

2019 ◽  
Vol 1 (1) ◽  
pp. 472-480 ◽  
Author(s):  
Máté Zöldy ◽  
Imre Zsombók
Keyword(s):  
Fuel Consumption ◽  
Autonomous Vehicles ◽  
Combustion Engine ◽  
Hybrid Vehicles ◽  
Urban Traffic ◽  
Environmentally Conscious ◽  
Electric Cars ◽  
Electric Driving ◽  
Starting Point ◽  
The Matrix

AbstractDuring our research, we focus on a less researched area in the development of autonomous vehicles. Automotive industry is turning more and more from conventional, internal combustion engine equipped vehicles to the electric cars. Today, electric driving is mostly limited to urban traffic, this is the area where range and refueling limits can be a real alternative. However, it is important to think of those who intend to use vehicle in longer distances, and hybrid technology can provide them a modern, environmentally conscious way of transport.In this article, we describe the method of creating the fuel consumption influencing factors matrix, which is the starting point of our research. We studied relevant researches and based on refueling studies we created the matrix. Based on results of real tests, we determined the factor mix that are the basis of our fuel consumption prediction model. These results will be inputs of planning routes of autonomous vehicles with optimized refueling and fuel consumption.

scholarly journals Analyzing Traffic Crash Severity With Combination of Information Entropy and Bayesian Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 63288-63302 ◽  
Author(s):  
Fang Zong ◽  
Xiangru Chen ◽  
Jinjun Tang ◽  
Ping Yu ◽  
Ting Wu
Keyword(s):  
Bayesian Network ◽  
Information Entropy ◽  
Crash Severity ◽  
Traffic Crash
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