Formulating a GIS-based geometric design quality assessment model for Mountain highways

Zhang, Hong; Zhang, Min; Zhang, Chi; Hou, Lei

doi:10.1016/j.aap.2021.106172

Cited by 11 publications

(11 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the modeling results can be used to make predictions and interpretations. The same thing as modeling accident-prone locations with a fuzzy expert system approach, this research does not only look at the geometric aspect of accident risk and does not relate it to ITS or expert systems [22].…”

Section: Discussion Of the Accident Risk Modelmentioning

confidence: 99%

“…Modeling the relationship between road geometry and accident risk has been carried out by several previous researchers. Various points of view have been considered, both regarding the geometric design criteria, as well as evaluating the geometric conditions that have existed before [22]. There was a relationship between the frequency of accidents and the geometric aspects of the road.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Accident risk analysis of road geometric components using functional worthiness approach

Suraji

Mulyono

2022

EEJET

View full text Add to dashboard Cite

The geometric design of the road should be based on the road design criteria and requirements contained in the design guidelines. However, the limitations of natural conditions and considerations during the design make the road design not ideal according to the guidelines. Deviations from the guidelines may indicate that the road is not standard and has potential accident risk. This study intends to analyze the risk of accidents from the geometric aspect of the road. The purpose of this study is to find out how the level of accident risk is due to the geometric conditions of the road. The approach used in this analysis is functional worthiness where each road component is compared with field data and design guidelines. The data collection method was carried out by measuring the geometric components of the road, which included horizontal and vertical alignments. The location of data collection was national roads in the Jambi Province area with a total road length of 1.095 km. For analysis purposes, the road data is divided into 82 segments. The analytical method used is multiple regression with various statistical parameters related to these results. The modeling results show that the regression coefficients for each variable horizontal alignment, vertical alignment, and combination of horizontal and vertical alignment are 0.248, 0.349, and 1.170, respectively. While the constant in this regression equation model is 3.366. The greatest value of the accident risk contribution is in the horizontal and vertical alignment variables with a coefficient of 1.170. The results of statistical tests show that the relationship between accident risk and the geometric aspect of the road has a strong linear relationship

show abstract

Section: Discussion Of the Accident Risk Modelmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Accident risk analysis of road geometric components using functional worthiness approach

Suraji

Mulyono

2022

EEJET

View full text Add to dashboard Cite

show abstract

“…Mountain highways are prone to frequent traffic accidents due to their complex terrain and special geological conditions, a susceptibility which has a substantial negative impact on economic development and people's health [1,2]. In severe cases, delayed rescue may increase the probability of a secondary accident [3,4]. Therefore, accurately quantifying the radiation range of mountain highway accidents can provide auxiliary decision support for rapid emergency rescue measures, accelerate road congestion evacuation and reduce the impact of accidents.…”

Section: Introductionmentioning

confidence: 99%

Ellipse-Like Radiation Range Grading Method of Traffic Accident Influence on Mountain Highways

Wang

Long

et al. 2022

Sustainability

View full text Add to dashboard Cite

To improve the efficiency of accident treatment on mountain highways and reduce the degree of disruption from traffic accidents, the grading method of the ellipse-like radiation range of traffic accident impact is proposed. First, according to the propagation law of traffic accidents, the general function of mountain highways affected by traffic accidents was constructed based on the Gaussian plume model. Then, based on the gravity field theory, the influence of the accident source point on the accident road was analyzed in the aftermath of a supposed accident. Additionally, considering the cascading failure of the road network, the influence of the accident-intersecting roads was demarcated by the cascading failure load propagation function. Based on this analysis, the ellipse-like radiation range models of traffic accidents on the accident road and the intersecting roads were proposed, respectively. Next, the adjustment parameter was further introduced to incorporate the different levels of influence of traffic accidents on the surrounding road network into the model, and the grading impacts of the accident on the potentially utilized opposite lane were discussed. Finally, according to the queuing theory model, simulation design, and portability analysis, the accuracy of the ellipse-like radiation range grading model was verified. The research results show that, compared with queuing theory and simulation results, the error of the grading model of the ellipse-like radiation range affected by traffic accidents was within a reasonable range; that is, the model can reasonably quantify the difference of traffic accident propagation on the accident road and the intersecting roads. Moreover, the heterogeneity of traffic accident propagation was verified by taking the non-occupied opposite lanes as an example. The grading method of influence radiation range utilized for traffic accidents on mountain highways can quickly provide corresponding auxiliary decision support for accident rescue within varying influence ranges.

show abstract

“…According to relevant statistics for China, the annual death toll per 1000 km of expressways is 43.68, which is 4 times that of ordinary roads, and the direct property damage per traffic crash on expressways is US$6500, which is 10 times that of ordinary roads [ 1 ]. In particular, the safety level of highways in mountainous areas is worse due to poor alignment conditions [ 2 ], high proportion of tunnels [ 3 ], complex weather [ 4 ], etc. In 2018 alone, there were more than 70,000 crashes in the mountainous areas in China, resulting in more than 100,000 casualties and millions of dollars in losses [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction

Wang

Zhang

Wong

et al. 2022

IJERPH

View full text Add to dashboard Cite

Road infrastructure has significant effects on road traffic safety and needs further examination. In terms of traffic crash prediction, recent studies have started to develop deep learning classification algorithms. However, given the uncertainty of traffic crashes, predicting the traffic risk potential of different road sections remains a challenge. To bridge this knowledge gap, this study investigated a real-world expressway and collected its traffic crash data between 2013 and 2020. Then, according to the time-spatial density ratio (Pts), road sections were assigned into three classes corresponding to low, medium, and high risk levels of traffic. Next, different classifiers were compared that were trained using the transformed and resampled feature data to construct a traffic crash risk prediction model. Last, but not least, partial dependence plots (PDPs) were employed to interpret the results and analyze the importance of individual features describing the geometry, pavement, structure, and weather conditions. The results showed that a variety of data balancing algorithms improved the performance of the classifiers, the ensemble classifier superseded the others in terms of the performance metrics, and the combined SMOTEENN and random forest algorithms improved the classification accuracy the most. In the future, the proposed traffic crash risk prediction method will be tested in more road maintenance and design safety assessment scenarios.

show abstract

Formulating a GIS-based geometric design quality assessment model for Mountain highways

Cited by 11 publications

References 28 publications

Accident risk analysis of road geometric components using functional worthiness approach

Accident risk analysis of road geometric components using functional worthiness approach

Ellipse-Like Radiation Range Grading Method of Traffic Accident Influence on Mountain Highways

Comparing Resampling Algorithms and Classifiers for Modeling Traffic Risk Prediction

Contact Info

Product

Resources

About