Road accident cost prediction model using systems dynamics approach

被引：21
|
作者：
Partheeban, Pachaivannan [1 ]
Arunbabu, Elangovan [1 ]
Hemamalini, Ranganathan Rani [2 ]
机构：
[1] St Peters Engn Coll, Dept Civil Engn, Madras 600054, Tamil Nadu, India
[2] St Peters Engn Coll, Dept Elect & Instrumentat Engn, Madras 600054, Tamil Nadu, India
来源：
TRANSPORT | 2008年 / 23卷 / 01期
关键词：
accident; modelling; systems dynamics; safety; accident cost;
D O I：
10.3846/1648-4142.2008.23.59-66
中图分类号：
U [交通运输];
学科分类号：
08 ; 0823 ;
摘要：
Accident costs are an important component of external costs of traffic, a substantial part is related to fatal accidents. The evaluation of fatal accident costs crucially depends on the availability of an estimate for the economic value of a statistical life. This paper aims to develop a model for road accident through systems dynamics approach. To build an accident model, various factors causing the road accident and cost were identified. This model is capable of calculating the accident rate and its costs for the future. in this study the accident caused by bus alone is considered. The cost model is dealt more in this study as it requires more complex assessment. The accident model is built on the year 2000 data and predicted the accidents up to 2020 for every 5-year interval. The accident model is valuated by comparing the predicted and actual accident data for the year 2005. Three scenarios were studied by changing the income growth rate and discount rate. Finally, best scenario is suggested for implementation. The outcome of the study is highly useful for the planners, administrators and police to make their decisions effectively for road safety investment projects.
引用
收藏
页码：59 / 66
页数：8
相关论文
共 50 条
  • [21] On accident prediction functions for urban road intersections
    Costescu, Dorinela
    Roşca, Mircea
    Burciu, Ştefan
    RuscǍ, Florin
    UPB Scientific Bulletin, Series D: Mechanical Engineering, 2016, 78 (03): : 55 - 64
  • [22] The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach
    Koo, ChoongWan
    Hong, TaeHoon
    Hyun, ChangTaek
    EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (07) : 8597 - 8606
  • [23] Enhancing road safety in internet of vehicles using deep learning approach for real-time accident prediction and prevention
    Wei X.
    International Journal of Intelligent Networks, 2024, 5 : 212 - 223
  • [24] Development of an accident duration prediction model on the Korean Freeway Systems
    Chung, Younshik
    ACCIDENT ANALYSIS AND PREVENTION, 2010, 42 (01): : 282 - 289
  • [25] ACTUARIAL MODEL FOR THE VALUATION OF SOCIAL SECURITY COST FOR PERMANENT DISABILITY DUE TO A ROAD ACCIDENT
    Ochotorena, Ainhoa Agueero
    Esteban, J. Inaki De La Pena
    Pena-Cerezo, Miguel Angel
    ANALES DEL INSTITUTO DE ACTUARIOS ESPANOLES, 2023, (29): : 23 - 35
  • [26] A Novel Multi-Model Machine Learning Approach to Real-Time Road Accident Prediction and Driving Behavior Analysis
    Dinesh, Diya
    2021 INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND INTELLIGENT CONTROLS (ISCSIC 2021), 2021, : 67 - 72
  • [27] Run-off-Road Accident Prediction Model for Two-Lane Highway
    Li Changcheng
    Zhang Gaoqiang
    Li Wei
    PROGRESS IN SAFETY SCIENCE AND TECHNOLOGY, VOL VII, PTS A AND B, 2008, 7 : 2016 - 2020
  • [28] The Prediction Model of Macro-road Traffic Accident Basing on Radial Basis Function
    Song, Chengju
    Li, Quanyan
    ADVANCED TRANSPORTATION, PTS 1 AND 2, 2011, 97-98 : 981 - +
  • [29] Macro prediction model of road traffic accident based on neural network and genetic algorithm
    Qin Liyan
    Shao Chunfu
    ICICTA: 2009 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL I, PROCEEDINGS, 2009, : 354 - 357
  • [30] An Artificial Neural Network Model for Road Accident Prediction: A Case Study of a Developing Country
    Ogwueleka, Francisca Nonyelum
    Misra, Sanjay
    Ogwueleka, Toochukwu Chibueze
    Fernandez-Sanz, L.
    ACTA POLYTECHNICA HUNGARICA, 2014, 11 (05) : 177 - 197
12345