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Real-vehicle\n          tests were conducted in two typical extra-long tunnels and the speed of skilled and\n          unskilled drivers were collected simultaneously. The quantified model of drivers\u2019 behavior\n          risk was proposed based on the safety speed difference. The variation characteristics of\n          behavior risk both inside the tunnel and ordinary highway were analysed. Further, the NARX\n          neural network was used to predict real-time speed with the heart rate regarded as the\n          input variable. Results showed that skilled drivers showed the highest behavior risk in\n          the internal zone, while the highest value of unskilled drivers was at the exit zone in\n          the tunnel section. Both two types of drivers presented the highest and the lowest\n          behavior risk on the ordinary highway and the tunnel entrance zone respectively. The\n          proposed NARX model could predict synchronous speed with high accuracy. These results of\n          the present study concern the driver\u2019s risk characteristics in Internet of Vehicles and\n          how to establish the automated driver model in the simulation driving environment.<\/jats:p>","DOI":"10.3233\/jifs-179395","type":"journal-article","created":{"date-parts":[[2019,9,24]],"date-time":"2019-09-24T14:54:56Z","timestamp":1569336896000},"page":"207-217","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["An innovative method to measure and predict drivers\u2019 behaviour in highway          extra-long tunnels using time-series modelling"],"prefix":"10.1177","volume":"38","author":[{"given":"Ling","family":"Wu","sequence":"first","affiliation":[{"name":"School of Vehicle Engineering, Xi\u2019an Aeronautical University, Xi\u2019an, 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