Abstract
Road crashes are a human tragedy, which involve immense human suffering. Road accidents have huge socioeconomic impact, especially in developing nations like India. Indian cities are expanding rapidly, causing rapid increment in the vehicle population leading to enhanced risk of fatalities. There is an urgent need to reduce number of road crashes by identifying the parameters affecting crashes in a road network. This paper describes a multiple regression model approach that can be applied to crash data to predict vehicle crashes. In this paper, crash prediction models were developed on the basis of accident data observed during a 5-year monitoring period extending between 2011 and 2015 in Bhopal which is a medium size city and capital of the state of Madhya Pradesh, India. The model developed in this paper appears to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and geometric improvement, and prediction of accident counts when comparing different design options.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
WHO: Global status report on road safety 2015. http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/
Dhamaniya, A.: Development of accident prediction model under mixed traffic conditions: a case study. In: Urban Public Transportation System, pp. 124–135 (2003)
Ramadan, T.M., Obaidat, M.T.: Traffic accidents at hazardous locations of urban roads. Jordan J. Civil Eng. 6(4), 436–444 (2012)
Koornstra, M., Oppe, S.: Predictions of road safety in industrialized countries and Eastern Europe. In: International Conference Road Safety in Europe, pp. 1–22 (1992)
Golias, I., Karlaftis, G.: Effects of road geometry and traffic volumes on rural roadway accident rates. Accid. Anal. Prev. 34, 357–365 (2002)
Elvik, R., Sorensen, S.: State-of-the-art Approaches to Road Accident Black Spot Management and Safety Analysis Of Road Networks, p. 883. Transportation institute, Oslo (2007)
Sawalha, Z., Sayed, T.: Statistical issues in traffic accident modeling. Can. J. Civ. Eng. 33(9), 1115–1124 (2003)
Shirazi, M., Lord, D., Geedipally, S.R.: Sample-size guidelines for recalibrating crash prediction models: recommendations for the highway safety manual. Accid. Anal. Prev. 93, 160–168 (2006)
Joshi, H.: Multicollinearity diagnostics in statistical modelling and remedies to deal with it using SAS [Lecture] Pune, India (2015)
Frost, J.: Regression analysis: how do I interprete R-squared and assess the goodness-of-fit. The Minitab Blog, 30 (2013)
Ratkowsky, D.A.: A statistical examination of five models for preferred orientation in carbon materials. Carbon 24(2), 211–215 (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rokade, S., Kumar, R. (2019). Road Crash Prediction Model for Medium Size Indian Cities. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_61
Download citation
DOI: https://doi.org/10.1007/978-981-13-0589-4_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0588-7
Online ISBN: 978-981-13-0589-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)