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Neural Computing and Applications
Published in: Neural Computing and Applications 16/2020

09-01-2020 | Original Article

Deep neural network-based predictive modeling of road accidents

Authors: Gyanendra Singh, Mahesh Pal, Yogender Yadav, Tushar Singla

Published in: Neural Computing and Applications | Issue 16/2020

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Abstract

This work proposes to use deep neural networks (DNN) model for prediction of road accidents. DNN consists of two or more hidden layers with large number of nodes. Accident data of non-urban sections of eight highways were collected from official records, and dataset consists of a total of 2680 accidents. The data of 16 explanatory variables related to road geometry, traffic and road environment were collected from official records as well as through field studies. Out of a total of 222 data points of accident frequency, 148 were used for training and remaining 74 to test the models. To compare the performance of DNN-based modeling approach, gene expression programming (GEP) and random effect negative binomial (RENB) models were used. A correlation coefficient value of 0.945 (root mean square error = 5.908) was achieved by DNN in comparison with 0.914 (RMSE = 7.474) by GEP, and 0.891 (RMSE = 8.862) by RENB with the test dataset, indicating an improved performance by DNN in prediction of road accidents. In comparison with DNN, though lower value of correlation coefficient was achieved by GEP model, it quantified the effects of various variables on accident frequency and provided a ranked list of variables based upon their importance.
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Literature
1.
MORTH (2017) Basic road statistics, 2017. Ministry of Road Transport and Highways, Government of India, India
2.
NIC (2017) State-wise distribution of accidental deaths by unnatural causes during 2017. National Informatics Centre Department of Electronics and Information Technology, Ministry of Communications and Information Technology, Government of India
3.
MORTH (2018) Road accidents in India, 2018. Ministry of Road Transport and Highways, Government of India, India
4.
Shaheem S, Mohammed KMS, Rajeevan (2006) Evaluation of cost effectiveness of improvements of accident prone locations on NH-47 in Kerala state. Indian Highw 34(2006):35–46
5.
Elvik R (2011) Assessing causality in multivariate accident models. Accid Anal Prev 43:253–264CrossRef
6.
Savolainen PT, Mannering FL, Lord D, Quddus MA (2011) The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accid Anal Prev 43(5):1666–1676CrossRef
7.
8.
Lord D, Mannering F (2010) The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives. Transp Res Part A 44:291–305
9.
Mohan D, Tsimhoni O, Sivak M, Flannagan, MJ (2009) Road safety in India: challenges and opportunities. UMTRI, 1
10.
Mohan D (2002) Traffic safety and health in Indian Cities. J Transp Infrastruct 9(1):79–94
11.
MORTH (2007) Sundar committee report, February, 2007. Ministry of Road Transport and Highways
12.
Sharma AK, Landge VS, Deshpande NV (2013) Modeling motorcycle accidents on rural highway. Int J Chem Environ Biol Sci 1(2):313–317
13.
Jacob A, Anjaneyulu MVLR (2013) Development of crash prediction models for two-lane rural highways using regression analysis. Highw Res J 6(1):59–70
14.
Fletcher JP, Baguley CJ, Sexton B, Done S (2006) Road accident modeling for highway development and management in developing countries. Main Report: Trials in India and Tanzania. Project Report No: PPR095, DFID
15.
16.
Cafiso S, Graziano AD, Silvestro GD, Cava GL, Persaud B (2010) Development of comprehensive accident models for two lane rural highways using exposure, geometry, consistency and context variables. Accid Anal Prev 42:1072–1079CrossRef
17.
Hosseinpour M, Yahaya AS, Sadullah AF (2014) Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian Federal. Accid Anal Prev 62:209–222CrossRef
18.
Caliendo C, Guglielmo MLD, Guida M (2015) Comparison and analysis of road tunnel traffic accident frequencies and rates using random-parameter models. J Transp Saf Secur 8(2):177–195CrossRef
19.
20.
21.
22.
Xie Y, Lord D, Zhang Y (2007) Predicting motor vehicle collisions using Bayesian Neural Network models: an empirical analysis. Accid Anal Prev 39:922–933CrossRef
23.
24.
Singh G, Sachdeva SN, Pal M (2018) Comparison of three parametric and machine learning approaches for modeling accident severity on non-urban sections of Indian highways. Adv Transp Stud Int J Sect B 45:123–140
25.
Chang LY, Wang HW (2006) Analysis of traffic injury severity: an application of non-parametric classification tree techniques. Accid Anal Prev 38(5):1019–1027CrossRef
26.
Singh G, Sachdeva SN, Pal M (2016) M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India. Accid Anal Prev 96:108–117CrossRef
27.
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117CrossRef
28.
Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129MathSciNetMATH
29.
Zhang Z, Wang Y, Chen P, Yu G (2018) Application of long short-term memory neural network for multi-step travel time forecasting on urban expressways. In: CICTP 2017: transportation reform and change—equity, inclusiveness, sharing, and innovation. American Society of Civil Engineers, Reston, pp 444–454
30.
Zhou L, Chen X (2018) Short-term forecasting of traffic flow and speed: a deep learning approach. In: CICTP 2018: intelligence, connectivity, and mobility. American Society of Civil Engineers, Reston, VA, pp 2186–2196
31.
Deng F, He Y, Zhou S, Yu Y, Cheng H, Wu X (2018) Compressive strength prediction of recycled concrete based on deep learning. Constr Build Mater 175:562–569CrossRef
32.
Kumar SS, Abraham DM (2019) A deep learning based automated structural defect detection system for sewer pipelines. In: Computing in civil engineering 2019: smart cities, sustainability, and resilience. American Society of Civil Engineers, Reston, VA, pp 226–233
33.
Dick K, Russell L, SouleyDosso Y, Kwamena F, Green JR (2019) Deep learning for critical infrastructure resilience. J Infrastruct Syst 25(2):05019003CrossRef
34.
Ding F, Zhang Z, Zhou Y, Chen X, Ran B (2019) Large-scale full-coverage traffic speed estimation under extreme traffic conditions using a big data and deep learning approach: case study in China. J Transp Eng Part A Syst 145(5):05019001CrossRef
35.
Nguyen T, Kashani A, Ngo T, Bordas S (2019) Deep neural network with high-order neuron for the prediction of foamed concrete strength. Comput Aided Civil Infrastruct Eng 34(4):316–332CrossRef
36.
37.
38.
39.
40.
41.
Malik H, Mishra S (2016) Application of gene expression programming (GEP) in power transformers fault diagnosis using DGA. IEEE Trans Ind Appl 52(6):4556–4565CrossRef
42.
Dindarloo SR, Pollard JP, Siami-Irdemoosa E (2016) Off-road truck-related accidents in US mines. J Saf Res 58:79–87CrossRef
43.
Yaacob W, Lazim M, Wah Y (2010) Evaluating spatial and temporal effects of accidents likelihood using random effects panel count model. In: Proceedings of the 2010 international conference on science and social research, Kuala Lumpur, Malaysia
44.
45.
IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. IBM Corp., Armonk, NY
46.
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, USAMATH
47.
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), Haifa, Israel, June 21–24, pp 807–814
48.
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256
49.
50.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958MathSciNetMATH
51.
Agresti A, Booth JG, Caffo B (2000) Random-effects modeling of categorical response data. Sociol Methodol 30:27–80CrossRef
52.
Dinu RR, Veeraragavan A (2011) Random parameter models for accident prediction on two-lane undivided highways in India. J Saf Res 42(1):39–42CrossRef
53.
Quimby A, Maycock G, Palmer C, Buttress S (1999) The factors the influence a driver’s choice of speed: a questionnaire study. Transport Research Laboratory, Crowthorne, UK
54.
Taylor MC, Baruya A, Kennedy JV (2002) The relationship between speed and accidents on rural single-carriageway roads, vol 511. TRL, Crowthorne
55.
Elvik R, Vaa T (2004) The handbook of road safety measures. Elsevier, Amsterdam
56.
57.
Ackaah W, Salifu M (2011) Crash prediction model for two-lane rural highways in the Ashanti region of Ghana. IATSS Res 35(1):34–40CrossRef
58.
Harwood DW, Council FM, Hauer E, Hughes WE, Vogt A (2000) Prediction of the expected safety performance of rural two-lane highways (No. FHWA-RD-99-207, MRI 4584-09, Technical Report). Federal Highway Administration, United States
59.
Fitzpatrick K, Lord D, Park BJ (2010) Horizontal curve accident modification factor with consideration of driveway density on rural four-lane highways in Texas. J Transp Eng 136(9):827–835CrossRef
Metadata
Title
Deep neural network-based predictive modeling of road accidents
Authors
Gyanendra Singh
Mahesh Pal
Yogender Yadav
Tushar Singla
Publication date
09-01-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 16/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04695-8

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