scholarly journals THE USE OF BAYESIAN NETWORK IN ANALYSIS OF URBAN INTERSECTION CRASHES IN CHINA

Transport ◽  
2013 ◽  
Vol 30 (4) ◽  
pp. 411-420 ◽  
Author(s):  
Jinbao Zhao ◽  
Wei Deng
Keyword(s):  
Risk Factors ◽  
Bayesian Network ◽  
Traffic Safety ◽  
Collision Probability ◽  
Parameter Learning ◽  
Heavy Vehicles ◽  
Crash Data ◽  
Frontal Collision ◽  
Good Potential ◽  
Side Collision

Traffic fatalities and injuries on urban roads especially at urban intersections constitute a growing problem in China. This study aims at researching urban intersection crashes in China and drawing conclusions by using hierarchical structured data with reference to Bayesian network (BN). On the basis of 3584 recorded crashes collected from the urban intersections of Changshu, China, a BN topological structure is developed to reflect the hierarchical characteristic of crash variables. The parameter learning process is completed with Dirichlet prior distribution. Junction tree engine is used to make inference on crash types at urban intersections with two respective given evidences, i.e. human factor and vehicle type. Parameter learning results suggest the efficacy of BN approach in the prediction accuracy. The average learned probability of illegal driving is 40.83%, which is much higher than other learned probabilities of human factors. The inferred probabilities of frontal collision at urban intersection crashes involving bicycles and electric bikes are 43.16% and 40.44% respectively, which is higher than the probabilities involving small cars and heavy vehicles. However, heavy vehicles have a higher inferred probability in side collision than light vehicles, whose inferred side collision probability is 41.02%. This study has a good potential in traffic safety discipline to reveal the correlation exists in traffic risk factors. By means of BN, researchers can make an intensive study on the hierarchical traffic crash data, determine the key risk factors and then propose corresponding and appropriate improvement measures.

scholarly journals Investigating Rural Single-Vehicle Crash Severity by Vehicle Types Using Full Bayesian Spatial Random Parameters Logit Model

2021 ◽  
Vol 11 (17) ◽  
pp. 7819
Author(s):  
Fulu Wei ◽  
Zhenggan Cai ◽  
Zhenyu Wang ◽  
Yongqing Guo ◽  
Xin Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spatial Correlation ◽  
Traffic Safety ◽  
Logit Model ◽  
Unobserved Heterogeneity ◽  
Crash Severity ◽  
Random Parameters ◽  
Crash Data ◽  
Random Parameters Logit ◽  
Single Vehicle

The effect of risk factors on crash severity varies across vehicle types. The objective of this study was to explore the risk factors associated with the severity of rural single-vehicle (SV) crashes. Four vehicle types including passenger car, motorcycle, pickup, and truck were considered. To synthetically accommodate unobserved heterogeneity and spatial correlation in crash data, a novel Bayesian spatial random parameters logit (SRP-logit) model is proposed. Rural SV crash data in Shandong Province were extracted to calibrate the model. Three traditional logit approaches—multinomial logit model, random parameter logit model, and random intercept logit model—were also established and compared with the proposed model. The results indicated that the SRP-logit model exhibits the best fit performance compared with other models, highlighting that simultaneously accommodating unobserved heterogeneity and spatial correlation is a promising modeling approach. Further, there is a significant positive correlation between weekend, dark (without street lighting) conditions, and collision with fixed object and severe crashes and a significant negative correlation between collision with pedestrians and severe crashes. The findings can provide valuable information for policy makers to improve traffic safety performance in rural areas.

scholarly journals Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

2019 ◽  
Vol 16 (7) ◽  
pp. 1166 ◽  
Author(s):  
Tianpei Tang ◽  
Senlai Zhu ◽  
Yuntao Guo ◽  
Xizhao Zhou ◽  
Yang Cao
Keyword(s):  
Bayesian Network ◽  
Low Cost ◽  
Road Networks ◽  
Access Point ◽  
Safety Risk ◽  
Crash Data ◽  
Risk Levels ◽  
The Road ◽  
Run Off ◽  
Point Density

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

Predicting Traffic Safety Risk Factors Using an Ensemble Classifier

2018 ◽  
pp. 201-216
Author(s):  
Nasim Arbabzadeh ◽  
Mohammad Jalayer ◽  
Mohsen Jafari
Keyword(s):  
Risk Factors ◽  
Traffic Safety ◽  
Ensemble Classifier ◽  
Safety Risk

scholarly journals USING GENERALIZED STOCHASTIC METHOD TO EVALUATE PROBABILITY OF CONFLICT IN CONTROLLED AIR TRAFFIC

Aviation ◽  
2007 ◽  
Vol 11 (2) ◽  
pp. 31-36 ◽  
Author(s):  
Vitaly Babak ◽  
Volodymyr Kharchenko ◽  
Volodymyr Vasylyev
Keyword(s):  
Differential Equation ◽  
Partial Differential Equation ◽  
Traffic Safety ◽  
Evaluation Method ◽  
Air Traffic ◽  
Collision Probability ◽  
Parabolic Partial Differential Equation ◽  
Partial Differential ◽  
Potential Conflict ◽  
Infinitesimal Operator

The introduction of the new concepts of air traffic management (ATM) and transition from centralized to decentralized air traffic control (ATC) with the change of traditional ATM to Cooperative ATM sets new tasks and opens new capabilities for air traffic safety systems. This paper is devoted to the problem of evaluating the probability of aircraft collision under the condition of Cooperative ATM, when the necessary information is available to the subjects involved in the decision‐making process. The generalized stochastic conflict probability evaluation method is developed. This method is based on the generalized conflict probability equation for evaluation of potential conflict probability and aircraft collision probability that is derived by taking into account stochastic nature and time correlation of deviation from planned flight trajectory in controlled air traffic. This equation is described as a multi‐dimensional parabolic partial differential equation using a differential (infinitesimal) operator of the multi‐dimensional stochastic process of relative aircraft movement. The common procedure for the prediction of conflict probability is given, and the practical application of the generalized method presented is shown. All equational coefficients of a differential operator for a practical solution of a parabolic partial differential equation are derived. For some conditions, the numerical solution of the conflict probability equation is obtained and illustrated graphically.

scholarly journals Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data

2021 ◽  
Vol 2021 (1) ◽  
pp. 1054-1064
Author(s):  
Salwa Rizqina Putri ◽  
Thosan Girisona Suganda ◽  
Setia Pramana
Keyword(s):  
Big Data ◽  
Bayesian Network ◽  
Data Model ◽  
Spanning Tree ◽  
Cross Validation ◽  
Parameter Learning ◽  
Maximum Spanning Tree

Untuk mendukung pertumbuhan ekonomi hijau Indonesia, diperlukan analisis lebih lanjut terkait aktivitas ekonomi di masa pandemi dan keterkaitannya dengan kondisi lingkungan. Penelitian ini bertujuan untuk menerapkan pendekatan Bayesian Network dalam memodelkan kondisi ekonomi hijau Indonesia di masa pandemi berdasarkan variabel-variabel yang disinyalir dapat berpengaruh seperti aktivitas ekonomi, kualitas udara, tingkat mobilitas penduduk, dan kasus positif COVID-19 yang diperoleh melalui big data. Model Bayesian Network yang dikonstruksi secara manual dengan algoritma Maximum Spanning Tree dipilih sebagai model terbaik dengan rata-rata akurasi 5-cross validation dalam memprediksi empat kelas PDRB sebesar 0,83. Model terbaik yang dipilih menunjukkan bahwa kondisi ekonomi Indonesia di era pandemi secara langsung dipengaruhi oleh intensitas cahaya malam (NTL) yang menunjukkan aktivitas ekonomi, kualitas udara (AQI), dan kasus positif COVID-19. Analisis parameter learning menunjukkan bahwa pertumbuhan ekonomi provinsi-provinsi Indonesia masih cenderung belum sejalan dengan terpeliharanya kualitas udara sehingga usaha untuk mencapai kondisi ekonomi hijau masih harus ditingkatkan.

scholarly journals Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies

2021 ◽  
pp. 036119812110444
Author(s):  
Jonathan Stiles ◽  
Armita Kar ◽  
Jinhyung Lee ◽  
Harvey J. Miller
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Travel Demand ◽  
Multinomial Logistic Regression ◽  
High Volume ◽  
Time Of Day ◽  
Bayesian Hierarchical ◽  
Crash Data ◽  
The Road ◽  
At Home

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

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