Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Literature Review
Methodology
Data Pre-Processing
Conclusions
References
All Topics
Engineering
Civil Engineering

A hybrid optimization framework for road traffic accident data

Profile image of Forster OwusuForster Owusu

2019, International Journal of Crashworthiness

https://doi.org/10.1080/13588265.2019.1701905
visibility

…

description

13 pages

link

1 file

Abstract

With the exponential growth of the number of vehicles in the traffic, road traffic accidents have become a fast-growing and wide-spreading menace, causing the loss of precious human life and economic assets. In addition to the fast growth of population and motorization, roads are highly occupied by cars, buses, trucks, motorcycles, minibikes, taxis, pedestrians, animals, and other travelers. The main challenges in the road traffic accident data prediction and analysis is the small size of the dataset that can be used for training. While road traffic accident causes millions of deaths and injuries every year, their density in time and space is-fortunately-low. This makes machine learning very challenging at the local level. The main target of this work is to minimize the Causality Severity of road traffic accidents. Mathematical analysis for the attributes' effect on Causality Severity was guided to select effective twenty decision variables. An optimization search requires a reliable simulated model that depicts the necessary characteristics of the system under study. The good formulation of the simulated model was reduced the non-linearity and interaction between the variables. The simulated model has been verified by successive linear technique with aid of the Successive Linear Programming (SLP). Mixed Integer nonlinear Program (MINLP) has proven a reliable optimization search with the present model. Reasonable agreements have found when compared the simulated solutions with the experimental data. From the output results, we observed that some of the attributes have a positive effect on Causality-Severity, while other attributes have a negative effect on it. In addition to the implementation output of both MINLP and SLP, we can state that the Age of Driver, Speed Limit, and Age of Causality are necessary and sensitive variables for objective Causality Severity. Optimal results would guide for selecting the best conditions.

Related papers

A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling
Amir Mehdizadeh

Sensors

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) ...

View PDFchevron_right
Cause, effect and regression in road safety: A case study
Ezra Hauer

Accident Analysis & Prevention, 2010

Researchers use various ways to determine what change in safety is caused by some treatment. One way is to fit regression equations to cross-section data. Can this work? Another way is to do a before-after study. Is this better? I examine these questions in the setting of a case study. The treated units are railhighway grade crossings, the treatment is the replacement of 'crossbucks' by 'flashers', and as evidence serve published papers and reports.

View PDFchevron_right
Analysis of the Effect of the Speed Factor on Highway Safety Using the Machine Learning Method
MILAD SASHURPOUR

Slovak Journal of Civil Engineering, 2021

Speed is one of the most important factors that can significantly change the severity of accidents. Providing a model with predictive factors leads to designing traffic plans to promote safety. This study aims to create statistical models for accidents occurred on Firuzkuh highway, Iran. Moreover, the probability of each type of accident was determined using the logit model. Various modeling methods, such as backward, forward, and entering methods, were evaluated to find the best method. Finally, since the backward method had the best performance in terms of R2 and goodness of fit, the logit model of accidents was created. According to the model, the independent variables of the 12-24 hours, rainy weather, a speed of 81-95 and 96-110 km/h, the lack of attention ahead and the Pride brand of vehicle increased the severity of accidents, while the variables with negative coefficients of Tuesdays, the summer and spring seasons, sunny weather, a male driver, and daylight, reduced the seve...

View PDFchevron_right
Research Paper: Providing an Appropriate Prediction Model for Traffic Accidents: A Case Study on Accidents in Golestan, Mazandaran, Guilan, and Ardebil Provinces
Health in Emergencies and Disasters Quarterly (USWR)
View PDFchevron_right
Factors affecting road crash modeling
Malaya Mohanty

Keywords: factors affecting accidents accidental study models logistic regression statistics Road accident fatalities have been on an increasing trend for the last decade or so in India. Hence traffic safety management has emerged as a topic of discussion for researchers all over the world. Hence accident modelling on different factors causing them has to be conducted. Accident modelling helps us to know the real causative agents behind an accident to occur. The effect of one cause can be greater than the other. And those causes can only be known from accident modelling. In this paper we have tried to divide this accident modelling techniques into two different categories based on the location of road i.e. accidents on urban roads and on rural roads. In both urban and rural road accident studies it was seen that mainly regression techniques like linear, multi-linear, logit and poisons regression have been used for modelling the road crashes. It was also marked that mostly authors have tried to research on one cause and go deep into it rather considering all factors at a time. From the studies it was found that speed and age along with gender has been the area of study for accident causes in urban areas whereas in rural roads mostly all authors have limited their studies to speed on roads and has been noted as the major cause of accidents in rural areas. This paper has tried to review as much papers as possible and various gaps in research along with future scope of study in this area has been indicated. Starting from the basic models like negative binomial/Poisson's model to the logistic and linear regressions to the new modeling techniques involving genetic mining and fuzzy logics have been discussed explicitly in the paper.

View PDFchevron_right
Accident Prediction Models for Freeways
Bhagwant Persaud

Transportation Research Record, 1993

View PDFchevron_right
Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis
Venky Shankar

Analytic Methods in Accident Research, 2020

The analysis of highway accident data is largely dominated by traditional statistical methods (standard regression-based approaches), advanced statistical methods (such as models that account for unobserved heterogeneity), and data-driven methods (artificial intelligence, neural networks, machine learning, and so on). These methods have been applied mostly using data from observed crashes, but this can create a problem in uncovering causality since individuals that are inherently riskier than the population as a whole may be over-represented in the data. In addition, when and where individuals choose to drive could affect data analyses that use real-time data since the population of observed drivers could change over time. This issue, the nature of the data, and the implementation target of the analysis imply that analysts must often tradeoff the predictive capability of the resulting analysis and its ability to uncover the underlying causal nature of crashcontributing factors. The selection of the data-analysis method is often made without full consideration of this tradeoff, even though there are potentially important implications for the development of safety countermeasures and policies. This paper provides a discussion of the issues involved in this tradeoff with regard to specific methodological alternatives and presents researchers with a better understanding of the trade-offs often being inherently made in their analysis.

View PDFchevron_right
Evaluation of Contributing Factors Affecting Number of Vehicles Involved in Crashes Using Machine Learning Techniques in Rural Roads of Cosenza, Italy
Sina Shaffiee Haghshenas

Safety, 2022

The evaluation of road safety is a critical issue having to be conducted for successful safety management in road transport systems, whereas safety management is considered in road transportation systems as a challenging task according to the dynamic of this issue and the presence of a large number of effective parameters on road safety. Therefore, the evaluation and analysis of important contributing factors affecting the number of vehicles involved in crashes play a key role in increasing the efficiency of road safety. For this purpose, in this research work, two machine learning algorithms, including the group method of data handling (GMDH)-type neural network and a combination of support vector machine (SVM) and the grasshopper optimization algorithm (GOA), are employed. Hence, the number of vehicles involved in an accident is considered to be the output, and the seven factors affecting transport safety, including Daylight (DL), Weekday (W), Type of accident (TA), Location (L), Speed limit (SL), Average speed (AS), and Annual average daily traffic (AADT) of rural roads in Cosenza, southern Italy, are selected as the inputs. In this study, 564 data sets from rural areas were investigated, and the relevant, effective parameters were measured. In the next stage, several models were developed to investigate the parameters affecting the safety management of road transportation in rural areas. The results obtained demonstrated that the "Type of accident" has the highest level and "Location" has the lowest importance in the investigated rural area. Finally, although the results of both algorithms were the same, the GOA-SVM model showed a better degree of accuracy and robustness than the GMDH model.

View PDFchevron_right
An Artificial Intelligent Approach to Traffic Accident Estimation: Model Development and Application
Ali Akgüngör

Transport, 2009

This study proposes an Artificial Neural Network (ANN) model and a Genetic Algorithm (GA) model to estimate the number of accidents (A), fatalities (F) and injuries (I) in Ankara, Turkey, utilizing the data obtained between 1986 and 2005. For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. In the GA approach, two forms of genetic algorithm models including a linear and an exponential form of mathematical expressions were developed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for predicting the number of injuries. The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively. In the second scenario, the average number of vehicles per capita is assumed to reach 0.60, which represents approximately two and a half-fold increase in fifteen years. The results obtained from both scenarios reveal the suitability of the current methods for road safety applications.

View PDFchevron_right
Analysis of intrinsic factors contributing to urban road crashes
Dorinela Costescu

International Journal of Safety and Security Engineering, 2017

The impact of risk associated with traffic is rarely included in the evaluation of projects for increasing urban traffic fluency, although the social costs of traffic crashes are estimated as very high. Lately, more frequently, the risk associated with urban road traffic is included, as a supplementary criterion, in the selection of the best urban planning scenarios, in order to a-priory minimize the number of crashes. Therefore, we aim to develop tools to enable the analysis of different intrinsic factors (characteristics of urban area and of road network) on traffic safety performances. The paper presents an analysis of the traffic crashes registered in Bucharest in the period 2008-2014. Following the analysis, the highest values of the average number of crashes were identified for signalized intersections that include tram infrastructure. Hence, the study is continued for this category of network features for which a model to estimate traffic crashes is proposed.

View PDFchevron_right

References (56)

  1. World Health Organization, et al. Road traffic injuries. World Health Organization; 2018. Available from: http://www.who.int/ mediacentre/factsheets/fs358/en/
  2. Road traffic injuries among vulnerable road users [Internet]. [cited 2018. Nov 11]. Available from: http://www.euro.who.int/ data/assets/pdf_file/0004/98779/polbrief_road_injuries.pdf
  3. World Health Organization. Global health expenditure Database [Internet]. 2018. http://apps.who.int/nha/database
  4. World Health Organization. Global health estimates [Internet].
  5. Global status report on road safety 2018: summary. Geneva: World Health Organization; 2018. (WHO/NMH/NVI/18.20) License: CC BY-NC-SA 3.0 IGO).
  6. Wang SC. Treat the patient, not just their disease.In: Proceedings of the 2017 International IRCOBI Conference on the Biomechanics of Injury. September 13-15, 2017; Antwerpm, Belgium.
  7. Nantulya VM, Reich MR. The neglected epidemic: road traffic injuries in developing countries. BMJ. 2002;324(7346): 1139-1141.
  8. World Health Organization. Disease, injury, and causes of death country estimates, 2000-2015 [Internet]. World Health Organization; 2017. [cited 2018 Oct 29]. Available from: http:// www.who. int/healthinfo/global_burden_disease/estimates_coun- try_2000_2015/en/
  9. United Nations General Assembly. 64/255. Improving global road safety [Internet]. 2010. [cited 2018 May 16]. Available from: http://www.who.int/violence_injury_prevention/publica- tions/road_traffic/
  10. Li JL, Sun WW. Cause analysis of traffic accidents on express highway and study on their countermeasures [J]. China Saf Sci J. 2005;15(1):59-62.
  11. Liu Q, Lu H, Zhang Y, et al. Characteristic analysis and coun- termeasure study on road traffic accidents in China. China Saf Sci J. 2006;16(6):123-128.
  12. Lin L, Wang Q, Sadek AW. 2014. Data mining and complex network algorithms for traffic accident analysis. Transportation Research Board 93 rd Annual Meeting (No. 14-4172).
  13. Milton JC, Shankar VN, Mannering FL. Highway accident severities and the mixed logit model: an exploratory empirical analysis. Acc Anal Prev. 2008;40(1):260-266.
  14. Zhan C, Gan A, Hadi M. Prediction of lane clearance time of freeway incidents using the m5p tree algorithm. IEEE Trans Intell Transport Syst. 2011;12(4):1549-1557.
  15. Zhao H, Yu H, Li D, et al. Vehicle accident risk prediction based on AdaBoost-so in vanets. IEEE Access. 2019;7: 14-549-14-557.
  16. Xin C, Na C, Yeshuai B. Analysis on key technologies of traffic prediction and path guidance in intelligent transportation. 2016 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). IEEE; 2016. p. 5-8.
  17. Chen Q, Song X, Yamada H, et al. Learning deep representa- tion from big and heterogeneous data for traffic accident infer- ence. 13th AAAI Conference on Artificial Intelligence; February 12 -17, 2016, Phoenix, Arizona, USA.
  18. Yasaswini L, Mahesh G, Siva Shankar R, et al. Identifying road accidents severity using convolutional neural networks. IJCSE. 2018;6(7):354.
  19. Huang H, Zeng Q, Pei X, et al. Predicting crash frequency using an optimized radial basis function neural network model. Transportmetrica A: Transp Sci. 2016;12(4):330-345.
  20. Hashmienejad SH-A, Hasheminejad SMH. Traffic accident severity prediction using a novel multi-objective genetic algo- rithm. Int J Crashworthiness. 2017;22(4):425-440.
  21. Hasheminejad SH-A, Zahedi M, Hasheminejad SMH. A hybrid clustering and classification approach for predicting crash injury severity on rural roads. Int J Injury Control Saf Promotion. 2018;25(1):85-101.
  22. Wahab L, Jiang H. A comparative study on machine learning- based algorithms for prediction of motorcycle crash severity. PloS One. 2019;14(4):e0214966.
  23. Jeong H, Jang Y, Bowman PJ, et al. Classification of motor vehicle crash injury severity: a hybrid approach for imbalanced data. Acc Anal Prev. 2018;120:250-261.
  24. Cigdem A, Ozden C. Predicting the severity of motor vehicle accident injuries in Adana-turkey using machine learning methods and detailed meteorological data. Int J Intell Syst Appl Eng. 2018;6(1):72-79.
  25. Wahab L, Jiang H. Severity prediction of a motorcycle crash with machine learning methods. Int J Crashworthiness. Vol. 24, 2019:1-8.
  26. Chung Y, Song T-J, Yoon B-J. Injury severity in delivery- motorcycle to vehicle crashes in the Seoul metropolitan area. Acc Anal Prev. 2014;62:79-86.
  27. Hussain S, Muhsion ZF, Salal YK, et al. Prediction model on student performance based on internal assessment using deep learning. Int J Emerg Technol Learn. 2019;14(8):4-22.
  28. Hussain S, Muhammad L, Ishaq F, et al. Performance evalu- ation of various data mining algorithms on road traffic accident dataset. In: Information and communication technology for intelligent systems.Smart Innovation, Systems and Technologies, Springer, vol 106. 2019, p. 67-78, Singapore.
  29. Kunt MM, Aghayan I, Noii N. Prediction for traffic accident severity: comparing the artificial neural network, genetic algo- rithm, combined genetic algorithm and pattern search methods. Transport. 2012;26(4):353-366.
  30. Singh J, Singh G, Singh P, et al. Evaluation and classification of road accidents using machine learning techniques. In: Emerging research in computing, information, communication, and applications. Advances in Intelligent Systems and Computing, vol 882. 2019, p 193-204, Springer, Singapore.
  31. Garc ıa Cuenca L, Puertas Sanz E, Aliane N, et al. Traffic acci- dents classification and injury severity prediction. In 2018 3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE) (pp. 52-57). IEEE.
  32. Iranitalab A, Khattak A. Comparison of four statistical and machine learning methods for crash severity prediction. Acc Anal Prev. 2017;108:27-36.
  33. Ahmadi A, Jahangiri A, Berardi V, et al. Crash severity analysis of rear-end crashes in California using statistical and machine learning classification methods. J Transp Saf Secur. Vol. 11, 2018:1-25.
  34. Munyazikwiye BB, Karimi HR, Robbersmyr KG. Optimization of vehicle-to-vehicle frontal crash model based on measured data using a genetic algorithm. IEEE Access. 2017;5:3131-3138.
  35. Qiu C, Wang C, Fang B, et al. A multiobjective particle swarm optimization-based partial classification for accident severity analysis. Appl Artif Intell. 2014;28(6):555-576.
  36. Gharehchopogh FS, Dizaji ZA. A new chaos agent-based approach in prediction of the road accidents with a hybrid of pso optimization and chaos optimization algorithms: a case study. IJAR. 2014;6(2):108.
  37. Gu X, Li T, Wang Y, et al. Traffic fatalities prediction using support vector machine with hybrid particle swarm optimiza- tion. J Algorithms Comput Technol. 2018;12(1):20-29.
  38. Alkheder S, Taamneh M, Taamneh S. Severity prediction of traffic accident using an artificial neural network. J Forecast. 2017;36(1):100-108.
  39. Park S-h, Kim S-M, Ha Y-G. Highway traffic accident predic- tion using VDS big data analysis. J Supercomput. 2016;72(7): 2815-2831.
  40. LeCun Y, Bengio Y, Hinton G. Deep learning. Nat Int J Sci. 2015;521(7553):436-444.
  41. Sameen MI, Pradhan B, Shafri H, et al. Applications of deep learning in severity prediction of traffic accidents. Global Civil Engineering Conference. Springer 2017, vol 9. p, 793-808, Singapore.
  42. Ren H, Song Y, Wang J, et al. A deep learning approach to the citywide traffic accident risk prediction. 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE; 2018. p. 3346-3351, Maui, United States.
  43. Sameen M, Pradhan B. Severity prediction of traffic accidents with recurrent neural networks. Appl Sci. 2017;7(6):476.
  44. Zheng M, Li T, Zhu R, et al. Traffic accident's severity predic- tion: a deep-learning approach-based CNN network. IEEE Access. 2019;7:39-897-39 910.
  45. An J, Fu L, Hu M, et al. A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. IEEE Access. 2019;7:20708-20722.
  46. Zhang Z, He Q, Gao J, et al. A deep learning approach for detecting traffic accidents from social media data. Transp Res Part C: Emerging Technol. 2018;86:580-596.
  47. Huang W, Song G, Hong H, et al. Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transport Syst. 2014;15(5):2191-2201.
  48. Lv Y, Duan Y, Kang W, et al. Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst. 2014;16(2):865-873.
  49. Polson NG, Sokolov VO. Deep learning for short-term traffic flow prediction. Transp Res Part C: Emerging Technol. 2017; 79:1-17.
  50. Dong C, Shao C, Li J, et al. An improved deep learning model for traffic crash prediction. J Adv Transp. 2018;2018:1.
  51. Belsnes MM, Wolfgang O, Follestad T, et al. Applying succes- sive linear programming for stochastic short-term hydropower optimization. Electr Power Syst Res. 2016;130:167-180.
  52. Kılınc ¸MR, Sahinidis NV. Exploiting integrality in the global optimization of mixed-integer nonlinear programming prob- lems with BARON. Optim Methods Software. 2018;33(3): 540-562.
  53. Rangaiah GP. Multi-objective optimization: techniques and applications in chemical engineering. Vol. 1. World Scientific, 2009.
  54. Parkinson AR, Balling R, Hedengren JD. Optimization methods for engineering design. Brigham Young University 2013;5:11.
  55. Eren Y, K€U¸C€Ukdemiral IB, And_ I_U. Introduction to optimiza- tion. In: Optimization in renewable energy systems.recent per- spectives. ButterworthHeinemann, Elsevier 6th March 2017, p. 27-74.
  56. Huang G-B, Ding X, Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing. 2010;74(1-3):155-163.

Related papers

International Journal of Crashworthiness A hybrid optimization framework for road traffic accident data A hybrid optimization framework for road traffic accident data
Ghanim Alwan

With the exponential growth of the number of vehicles in the traffic, road traffic accidents have become a fast-growing and wide-spreading menace, causing the loss of precious human life and economic assets. In addition to the fast growth of population and motorization, roads are highly occupied by cars, buses, trucks, motorcycles, minibikes, taxis, pedestrians, animals, and other travelers. The main challenges in the road traffic accident data prediction and analysis is the small size of the dataset that can be used for training. While road traffic accident causes millions of deaths and injuries every year, their density in time and space is-fortunately-low. This makes machine learning very challenging at the local level. The main target of this work is to minimize the Causality Severity of road traffic accidents. Mathematical analysis for the attributes' effect on Causality Severity was guided to select effective twenty decision variables. An optimization search requires a reliable simulated model that depicts the necessary characteristics of the system under study. The good formulation of the simulated model was reduced the non-linearity and interaction between the variables. The simulated model has been verified by successive linear technique with aid of the Successive Linear Programming (SLP). Mixed Integer nonlinear Program (MINLP) has proven a reliable optimization search with the present model. Reasonable agreements have found when compared the simulated solutions with the experimental data. From the output results, we observed that some of the attributes have a positive effect on Causality-Severity, while other attributes have a negative effect on it. In addition to the implementation output of both MINLP and SLP, we can state that the Age of Driver, Speed Limit, and Age of Causality are necessary and sensitive variables for objective Causality Severity. Optimal results would guide for selecting the best conditions. ARTICLE HISTORY

View PDFchevron_right
Abojaradeh, M., (2015). "Development of Traffic Accident Prediction Models to Improve Traffic Safety and to Reduce Traffic Accident Severity and Rate in Jordan". Journal of Biology, Agriculture and Healthcare issued from IISTE USA. Volume 5, No. 2, 2015. pp 42-54.
Mohammad Abu-Jaradeh

In the past few years traffic accidents have become an epidemic. They cause the death of over one million people per year worldwide including those walking and riding bikes. Recent studies have shown that Jordan suffers massive human and economical losses as well as social and emotional effects from traffic accidents every year. Despite the efforts of the public and private sectors, traffic accidents are still increasing and exhaust Jordan's resources at the price of other areas of development and construction.The main objectives of this study are: to analyze traffic accidents in Jordan and their main causes; to reduce the number of traffic accidents and their severity. Also, to study the effect of driver behavior mistakes on traffic accidents and their severity. In addition, to determine and build prediction statistical regression models, which relates the number of accidents (dependent variable), with drivers behavior mistakes (independent variable) by using the Statistical Package for Social Sciences (SPSS) computer software.The study was conducted based on accident data provided by the Jordan Traffic Institute from the year 2000 to year 2010. The study investigates 394188 total accidents during the period of the study with five independent variables (close following, lane violation, speeding or violation of speed limit, wrong passing and red light violation).Regression techniques were used to analyze the collected data and to create four models .The models were developed by SPSS statistical package computer program. The first developed predicted model was for the total accident, the result indicated that the close following and lane violation are the most causes of accidents .The second developed model was for the fatal accidents, and the results indicated that the violation of speed limit and the lane violation are the most causes of the fatalities. The third and fourth models were developed for the slight and sever injuries; the result showed that the same independent variables causes of fatalities are applicable for injuries.The accident prediction model can be used to develop warrants and standards for law enforcement, geometric design, and traffic operation and to improve the required countermeasures in order to reduce the traffic accidents especially fatal and injury accidents.

View PDFchevron_right
Optimization of Highway Safety and Operation by Using Crash Prediction Models with Accident Modification Factors
Mohamadreza Banihashemi

Transportation Research Record: Journal of the Transportation Research Board, 2007

The linear programming model that is presented optimizes the crash and delay costs of highway networks by selecting safety and operational improvements with implementation costs within a constrained budget. The costs that are minimized in this study are those of crashes plus the costs of delay times. This optimization model works with crash prediction models that estimate the expected number of crashes by using base models and accident modification factors (AMFs). In these models the base model predicts the expected number of crashes for a base highway or intersection. The AMFs modify the prediction on the basis of the specific characteristics of each highway segment or intersection. Several improvement alternatives for different highways and intersections within a network would mean many improvement combinations, each with a different cost for implementation, which would cause certain crashes and delay. The proposed model uses linear optimization to find the combination of improvem...

View PDFchevron_right
Development of a Traffic Accident Prediction Model for Urban Roads
Dongmin Lee

Journal of Transport Research, 2011

The current procedure of estimating accident reduction benefit applies fixed accident rates for each road level. So that estimating accident reduction benefit is not considering the various characteristics of roadway geometries and traffics. In order to solve the problems mentioned in the above, models were developed considering the characteristics of roadway alignments and traffic characteristics. The developed models can be used to estimate the accident rates on new or improved roads. First of all, factors influence accident rates were selected. Those factors such as traffic volumes, intersections, connecting roads, pedestrian traffic signals, existence of median barrier, lanes are also selected based upon the obtain ability at the planning stage of roads. In this study, roads were classified into 4 groups based on lanes, roads level and the existence of median barriers. The regression analysis had been performed for each group with actual data associated with traffic, roads and accidents.

View PDFchevron_right
Analysis of Traffic Safety Factors and Their Impact Using Machine Learning Algorithms
Civil Engineering Journal (civilejournal.org)

Civil Engineering Journal, 2024

The safety of road traffic is facing increasing challenges from a range of factors, and this study aims to address this issue. The paper describes the development of a model that assesses both the quantitative and qualitative aspects of the current traffic situation and can also predict future trends based on monthly data on traffic accidents over a period of years. The dataset is composed of the number of accidents that occurred in the Pristina region over a 10-year period, and these are categorized based on the type of accident and safety factors, including human, vehicle, and road factors. By using machine learning algorithms, a model has been developed that determines the factor with the greatest impact on traffic safety. To create the model, the algorithms Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Random Trees (RT) were used. The model evaluates the contribution of human, road, and vehicle factors to traffic accidents, using machine learning algorithms and 36 types of traffic accidents to analyze the relevant statistics. The results indicate a very good fit of the model according to the MLR algorithm, and this model also identifies the road factor as the main influencer of the traffic safety level.

View PDFchevron_right

Related topics

  • Civil Engineering
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved