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Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model
Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a sub...
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| Format: | Default Article |
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2023
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| Online Access: | https://hdl.handle.net/2134/23616735.v1 |
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| _version_ | 1818165317983010816 |
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| author | Yasir Ali Md Mazharul Haque |
| author_facet | Yasir Ali Md Mazharul Haque |
| author_sort | Yasir Ali (13768642) |
| collection | Figshare |
| description | Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated. |
| format | Default Article |
| id | rr-article-23616735 |
| institution | Loughborough University |
| publishDate | 2023 |
| record_format | Figshare |
| spelling | rr-article-236167352023-04-26T00:00:00Z Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model Yasir Ali (13768642) Md Mazharul Haque (13455534) Response time Random parameters Parametric duration model Machine learning Young drivers Driving simulator Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated. 2023-04-26T00:00:00Z Text Journal contribution 2134/23616735.v1 https://figshare.com/articles/journal_contribution/Modelling_the_response_times_of_mobile_phone_distracted_young_drivers_A_hybrid_approach_of_decision_tree_and_random_parameters_duration_model/23616735 CC BY-NC-ND 4.0 |
| spellingShingle | Response time Random parameters Parametric duration model Machine learning Young drivers Driving simulator Yasir Ali Md Mazharul Haque Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model |
| title | Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model |
| title_full | Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model |
| title_fullStr | Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model |
| title_full_unstemmed | Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model |
| title_short | Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model |
| title_sort | modelling the response times of mobile phone distracted young drivers: a hybrid approach of decision tree and random parameters duration model |
| topic | Response time Random parameters Parametric duration model Machine learning Young drivers Driving simulator |
| url | https://hdl.handle.net/2134/23616735.v1 |