An innovative method to measure and predict drivers’ behaviour in highway extra-long tunnels using time-series modelling

Abstract

Previous studies lack a comprehensive evaluation model that combined the subjective perception of the driver and the objective driving environment. This work investigates the characteristics of drivers’ behavior risk in highway extra-long tunnels. Real-vehicle tests were conducted in two typical extra-long tunnels and the speed of skilled and unskilled drivers were collected simultaneously. The quantified model of drivers’ behavior risk was proposed based on the safety speed difference. The variation characteristics of behavior risk both inside the tunnel and ordinary highway were analysed. Further, the NARX neural network was used to predict real-time speed with the heart rate regarded as the input variable. Results showed that skilled drivers showed the highest behavior risk in the internal zone, while the highest value of unskilled drivers was at the exit zone in the tunnel section. Both two types of drivers presented the highest and the lowest behavior risk on the ordinary highway and the tunnel entrance zone respectively. The proposed NARX model could predict synchronous speed with high accuracy. These results of the present study concern the driver’s risk characteristics in Internet of Vehicles and how to establish the automated driver model in the simulation driving environment.

Keywords

Time-series modelling NARX neural network driver behavior risk characteristics safety speed difference

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References

J.X.

Lai ,

Zhang ,

Zhou , et al., Study of rules of traffic accidents in expressway tunnels [J], Tunnel Construction 37(1) (2017), 37–42.

Calvi ,

M.R.

De Blasiis and

Guattari , An empirical study of the effects of road tunnel on driving performance[J], Procedia-Social and Behavioral Sciences 53(53) (2012), 1098–1108.

Domenichini ,

La Torre ,

Vangi , et al., Influence of the lighting system on the driver’s behavior in road tunnels: A driving simulator study[J], Journal of Transportation Safety & Security 2016 (2016), 1–23.

S.R.

Zhang ,

Z.L.

Ma and

Shi , Distribution characteristics and countermeasures of traffic accidents in expressway tunnel group[J], Journal of Chang’an University: Natural Science Edition 27(1) (2007), 63–66.

Lin and

Yang , Bayesian prediction of traffic accident based on vehicle speed[J], Chinese Journal of Safety Science 13(2) (2003), 34–36.

W.H.

Zhao ,

H.X.

Liu ,

Liu , et al., Visual features of driver in exit and entrance of tunnel group[J], Journal of Transportation Science and Engineering 27(3) (2011), 75–81.

Nijil Raj and Dr.

Mahalekshmi , Human membrane protein classification using KNN multi label classifier (KNNMLC), International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(12) (2017).

Z.G.

Du ,

X.D.

Pan ,

Yang , et al., Research on visual turbulence and driving safety of freeway tunnel entrance and exit[J], China Journal of Highway and Transport 20(5) (2007), 101–105.

Fang ,

S.J.

Wang ,

Z.D.

Zhu , et al., Operating speed models for trucks at expressway tunnel sections[J], Journal of Traffic and Transportation Engineering 10(3) (2010), 90–94.

10.

Guo ,

X.D.

Pan and

S.H.

Jiang , Relative difference function-based traffic safety assessment of tunnel groups[J], Journal of Tongji University, Natural Science Edition (1) (2013), 101–105.

11.

H.Z.

Tu ,

Mou ,

Y.F.

Bi , et al., Incorporating safety speed analysis into highway safety assessment[J], Inner Mongolia Highway and Transport (6) (2015), 1–7.

12.

Yang and

Z.Y.

Guo , Research on the change of speed of tunnel entrance and exit[J], Shanghai Highway (1) (2006), 48–51.

13.

T.R.

Jing , Relation between traffic accidents and highway conditions[J], Journal of Tongji University, Natural Science Edition 19(3) (1991), 287–294.

14.

Liang ,

Z.Y.

Guo and

S.E.

Fang , Statistic analyses of relations between road alignment and road safety [J], Journal of Tongji University, Natural Science Edition 30(2) (2002), 203–206.

15.

Jaisinghani , Artificial intelligence using forward chaining and backward chaining, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 6(1) (2018).

16.

Y.L.

Ye ,

J.L.

Xu and

H.Z.

Yang , Intelligent evaluation for highway safety with uncertainty based on driving behavior[J], Journal of Chang’an University, Natural Science Edition 30(6) (2010), 39–43.

17.

Yan ,

Chen and

J.Q.

Wei , Influence of drivers’ perceptual features on traffic safety in tunnel group[J], China Safety Science Journal 21(11) (2011), 16–21.

18.

Aarts and

I.V.

Schagen , Driving speed and the risk of road crashes, A review[J], Accident Analysis & Prevention 38(2) (2006), 215–224.

19.

Rao and

Konnur , Street light automation system using arduino uno, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(11) (2017).

20.

Lu ,

Lai and

Feng , The modeling and simulation of collision protection system between the driver of non-motor vehicle and car door[J], EURASIP Journal on Wireless Communications and Networking 2018(1) (2018), 99.

21.

M.H.

Eiza ,

Ni ,

Thomas and

G.Y.

Min , Investigation of routing reliability of vehicularadHocnetworks[J], Eurasip Journal on Wireless Communications & Networking 2013(1) (2013), 179.

22.

J.P.

Gao and

Z.Y.

Guo , Evaluation of highway alignment design quality based on operating speed [J], Journal of Tongji University, Natural Science Edition 32(7) (2004), 906–911.

23.

Iacobucci ,

S.S.

Posavac ,

F.R.

Kardes ,

Schneider and

Popovich , Toward a more nuanced understanding of the statistical properties of a median split[J], Journal of Consumer Psychology 25(4) (2015), 652–665.

24.

Paul ,

Ahmad ,

M.M.

Rathore and

Jabbar , Smartbuddy: Defining human behaviors using big data analytics in social internet of things, IEEE Wireless Communications 23(5) (2016), 68–74.

25.

A.S.

Nissinen ,

H.N.

Koivo and

Koivisto , Optimization of neural network topologies using genetic algorithm, Intelligent Automation & Soft Computing 5(3) (1999), 211–223.