scholarly journals Railway Traffic Accident Forecast Based on an Optimized Deep Auto-encoder

2018 ◽  
Vol 30 (4) ◽  
pp. 379-394 ◽  
Author(s):  
Fenling Feng ◽  
Wan Li ◽  
Qiwei Jiang
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Gray Relational Analysis ◽  
Railway Transportation ◽  
Accident Prediction ◽  
Railway Traffic ◽  
Complex Relationships ◽  
Hidden Layer ◽  
Searching Ability

Safety is the key point of railway transportation, and railway traffic accident prediction is the main content of safety management. There are complex nonlinear relationships between an accident and its relevant indexes. For this reason, triangular gray relational analysis (TGRA) is used for obtaining the indexes related to the accident and the deep auto-encoder (DAE) for finding out the complex relationships between them and then predicting the accident. In addition, a nonlinear weight changing particle swarm optimization algorithm, which has better convergence and global searching ability, is proposed to obtain better DAE structure and parameters, including the number of hidden layers, the number of neurons at each hidden layer and learning rates. The model was used to forecast railway traffic accidents at Shenyang Railway Bureau, Guangzhou Railway Corporation, and Nanchang Railway Bureau. The results of the experiments show that the proposed model achieves the best performance for predicting railway traffic accidents.

Highway Traffic Accident Prediction Based on SVR Trained by Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 5886-5889 ◽  
Author(s):  
Zhen Qi Yang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Regression Function ◽  
Support Vector ◽  
Highway Traffic ◽  
Prediction Ability ◽  
Accident Prediction ◽  
Better Than

The multi researches and experiments show that the future highway traffic accident situation is shown by the highway traffic accident prediction. In the paper, support vector regression trained by genetic algorithm is presented in highway traffic accident prediction. In the method, genetic algorithm is used to train the parameters of support vector regression. Firstly, the regression function of support vector regression algorithm is introduced, and the parameters of support vector regression are optimized by genetic algorithm. The computation results between G-SVR and SVR can indicate that the prediction ability for highway traffic accidents of G-SVR is better than that of SVR absolutely.

scholarly journals Contribution to Accident Prediction Models Development for Rural Two-Lane Roads in Serbia

2016 ◽  
Vol 28 (4) ◽  
pp. 415-424 ◽  
Author(s):  
Draženko Glavić ◽  
Miloš Mladenović ◽  
Aleksandar Stevanovic ◽  
Vladan Tubić ◽  
Marina Milenković ◽  
...  
Keyword(s):  
Multivariate Regression ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Future Research ◽  
Practical Application ◽  
Accident Prediction ◽  
Accident Data ◽  
Road Characteristics ◽  
The Relationship

Over the last three decades numerous research efforts have been conducted worldwide to determine the relationship between traffic accidents and traffic and road characteristics. So far, the mentioned studies have not been carried out in Serbia and in the region. This paper represents one of the first attempts to develop accident prediction models in Serbia. The paper provides a comprehensive literature review, describes procedures for collection and analysis of the traffic accident data, as well as the methodology used to develop the accident prediction models. The paper presents models obtained by both univariate and multivariate regression analyses. The obtained results are compared to the results of other studies and comparisons are discussed. Finally, the paper presents conclusions and important points for future research. The results of this research can find theoretical as well as practical application.

scholarly journals Traffic accident analysis based on C4.5 algorithm in WEKA

2019 ◽  
Vol 272 ◽  
pp. 01035 ◽  
Author(s):  
Jiajia Li ◽  
Jie He ◽  
Ziyang Liu ◽  
Hao Zhang ◽  
Chen Zhang
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Depth Information ◽  
Highway Traffic ◽  
Safety Issues ◽  
C4.5 Algorithm ◽  
Accident Data ◽  
The Impact

At present, China is in a period of steady development of highways. At the same time, traffic safety issues are becoming increasingly serious. Data mining technology is an effective method for analysing traffic accidents. In-depth information mining of traffic accident data is conducive to accident prevention and traffic safety management. Based on the data of Wenli highway traffic accidents from 2006 to 2013, this study selected factors including time factor, linear factor and driver characteristics as research indicators, and established the decision tree using C4.5 algorithm in WEKA to explore the impact of various factors on the accident. According to the degree of contribution of each variable to the classification effect of the model, various modes affecting the type of the accident are obtained and the overall prediction accuracy is about 80%.

Analysis and Countermeasure of Traffic Accidents in a Dalians Transportation Group

2014 ◽  
Vol 501-504 ◽  
pp. 2436-2440
Author(s):  
Jin Yao Li
Keyword(s):  
Traffic Accident ◽  
Traffic Accidents ◽  
Regional Difference ◽  
Safety Management ◽  
Weather Conditions ◽  
Accident Analysis ◽  
Prevention Measures ◽  
Safety Education ◽  
Climatic Characteristics ◽  
And Training

Nowadays, traffic accidents have become one of the ten major threats to human health, and reducing the damage to life and property has been the people's demands. Traffic accident analysis has its inherently regional difference. The main objectives of this paper is to conduct investigation and statistical analysis on the weather conditions, month, week, hour, road environment, the drivers age and driving-age of the accidents, based on the traffic accidents records of a Dalians transportation group from 2008 to 2012. According to the acquired statistics and countering the geographical and climatic characteristics of Dalian area, this paper put forward traffic accident prevention measures proposed for Dalian area:Timely safety education and training for the drivers, establishing enterprise safety culture and establishing and improving the safety management system of modern traffic.

scholarly journals Traffic Accident Prediction Based on Multivariable Grey Model

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 184
Author(s):  
Wei Li ◽  
Xujian Zhao ◽  
Shiyu Liu
Keyword(s):  
Correlation Analysis ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
General Purpose ◽  
Grey Model ◽  
Accident Prediction ◽  
Traffic System ◽  
The Road ◽  
Grey Correlation Analysis

Owing to frequent traffic accidents and casualties nowadays, the ability to predict the number of traffic accidents in a period is significant for the transportation department to make decisions scientifically. However, owing to many variables affecting traffic accidents in the road traffic system, there are two critical challenges in traffic accident prediction. The first issue is how to evaluate the weight of each variable’s impact on the accident. The second issue is how to model the prediction process for multiple interrelated variables. Aiming to solve these two problems, we propose effective solutions to deal with traffic accident prediction. Firstly, for the first issue, we exploit the grey correlation analysis to measure the correlation of factors to accident occurrence. Then, for the second issue, we select the main factors by correlation analysis to establish a multivariable grey model—MGM(1,N) for prediction process modeling. Further, we explore the collinearity between variables and better optimize the predictive model. The experimental results show that our approach achieves best performance than four general-purpose comparative algorithms in traffic accident prediction task.

scholarly journals The Development of Data Warehouse to Support Data Mining Technique for Traffic Accident Prediction

2018 ◽  
Vol 73 ◽  
pp. 12007
Author(s):  
Budiawan Wiwik ◽  
Singgih Saptadi ◽  
Ary Arvianto
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Data Access ◽  
Design Data ◽  
Accident Prediction ◽  
The World

Traffic accidents are one of the major health problems that cause serious death in the world and ranks 9th in the world. Traffic accidents in Indonesia ranks 5th in the world. One effort to improve traffic safety is to design traffic accident prediction models. Prediction models will utilize accident-related data in traffic through data mining processing. The data warehouse offers benefits as a basis for data mining. Building an effective data warehouse requires knowledge and attention to key issues in database design, data acquisition and processing, as well as data access and security. This study is the first step in the development of data mining accidents based prediction system. The output of this initial stage is the design of data warehouses that can provide periodic and incidental data to the data mining process, especially in the prediction of accidents. The method used to design data warehouse is Entity Relationship Diagram (ERD).

scholarly journals Countermeasures and Prevention of Railway Traffic Accidents in the Early Days of the Founding of the People's Republic of China

2019 ◽  
Vol 3 (5) ◽  
Author(s):  
Qinghua Lu ◽  
Zhaoguo Zhang ◽  
Wei Zheng
Keyword(s):  
Large Scale ◽  
Traffic Accidents ◽  
People’S Republic Of China ◽  
Transportation Industry ◽  
People's Republic Of China ◽  
Railway Transportation ◽  
Republic Of China ◽  
Railway Traffic ◽  
The Face ◽  
The Right

In the early days of the founding of the People's Republic of China, in order to restore the national economy as soon as possible, the Communist Party of China carried out maintenance and repairs on the railways that caused serious damage to transportation tasks through war-torn smoke, and carried out large-scale railway construction. However, in the course of operation, railway safety accidents occur frequently due to outdated equipment, low quality of staff, lack of rules and regulations, vandalism, and various natural disasters. In the face of severe conditions, the railway authorities summed up the accidents and learned the safety of the railways, so that the railway transportation industry, which started after the founding of the country, was gradually on the right track, and laid a solid foundation for the safe development of the railway industry.

scholarly journals Development of road infrastructure safety management system according to updates of EU Directive 96/2008/EC

2021 ◽  
Vol 1 (1) ◽  
pp. 35-41
Author(s):  
Fadila Kiso ◽  
Ajdin Džananović ◽  
Samira Šabanović-Karičić
Keyword(s):  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Traditional Approach ◽  
Preventive Action ◽  
New Approach ◽  
Road Infrastructure ◽  
The Road ◽  
Eu Directive

The traditional approach to the analysis of traffic accidents has mostly involved identifying omissions in vehicles and drivers, which led to the occurrence of a traffic accident. However, more recent EU directives dealing with this area emphasize infrastructure failures that may be the real cause of the accident. This approach refers to preventive action, ie the design of such infrastructure that will, in case of failure of the driver, "forgive" the driver his mistake and prevent the occurrence of a traffic accident or reduce the consequences of a traffic accident. To achieve this, a completely new approach to the problem is needed, ie to build, reconstruct and regenerate the road infrastructure according to its real function from the aspect of traffic safety. The realization of these concepts in our area implies primarily the education of all entities that have contact with road infrastructure (designers, managers, auditors, etc.), with emphasis on the fact that savings on the material are significantly less than the savings achieved by reducing the number of accidents, with injured faces and fatalities.

scholarly journals Traffic Accident Prediction Based on LSTM-GBRT Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Zhihao Zhang ◽  
Wenzhong Yang ◽  
Silamu Wushour
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Short Term Memory ◽  
Road Traffic ◽  
Boosted Regression Trees ◽  
Safety Level ◽  
Accident Prediction ◽  
Road Traffic Safety

Road traffic accidents are a concrete manifestation of road traffic safety levels. The current traffic accident prediction has a problem of low accuracy. In order to provide traffic management departments with more accurate forecast data, it can be applied in the traffic management system to help make scientific decisions. This paper establishes a traffic accident prediction model based on LSTM-GBRT (long short-term memory, gradient boosted regression trees) and predicts traffic accident safety level indicators by training traffic accident-related data. Compared with various regression models and neural network models, the experimental results show that the LSTM-GBRT model has a good fitting effect and robustness. The LSTM-GBRT model can accurately predict the safety level of traffic accidents, so that the traffic management department can better grasp the situation of traffic safety levels.

Identification of Urban Traffic Accident Hotspot Zones Using GIS: A Case Study of Dhaka Metropolitan Area

2020 ◽  
Vol 3 (1) ◽  
pp. 36-42
Author(s):  
Arif Ahmad ◽  
Khandaker Hossain ◽  
Mallik Hossain
Keyword(s):  
Spatial Patterns ◽  
Metropolitan Area ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Safety Management ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Information ◽  
Gis Technology

The issue of traffic safety becomes increasingly prominent and has attracted widespread attention from researchers and policy makers. Dhaka, the capital of Bangladesh, is the most vulnerable city both in terms of total number of accidents and accident rates. GIS technology has been widely applied to urban traffic information and safety management. This paper presents a geospatial analysis to identify the road traffic accident (RTA) hotspot zones in Dhaka Metropolitan Area (DMA). ‘Spatial analysis’ and ‘spatial statistics tools’ are used to examine spatial patterns of accident data. A systematic comparison of identified hotspot zones using Local Moran’s-I Statistic, Getis-Ord Gi* statistic and Kernel Density Estimation (KDE) carried out to examine spatial patterns of high cluster of traffic accidents. These analyses revealed a total 22 hotspot zones in DMA during the years 2010-2012. This kind of research would help generating new parameters for reducing road traffic accidents in Dhaka Metropolitan Area.

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