Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture
| dc.contributor.author | Deng, Chao | |
| dc.contributor.author | Cao, Shi | |
| dc.contributor.author | Wu, Chaozhong | |
| dc.contributor.author | Lyu, Nengchao | |
| dc.date.accessioned | 2020-02-24T14:31:36Z | |
| dc.date.available | 2020-02-24T14:31:36Z | |
| dc.date.issued | 2019-04-04 | |
| dc.description | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
| dc.description.abstract | Drivers' reaction time of reading signs on expressways is a fundamental component of sight distance design requirements, and reaction time is affected by many factors such as information volume and concurrent tasks. We built cognitive simulation models to predict drivers' direction sign reading reaction time. Models were built using the queueing network-adaptive control of thought rational (QN-ACTR) cognitive architecture. Drivers' task-specific knowledge and skills were programmed as production rules. Two assumptions about drivers' strategies were proposed and tested. The models were connected to a driving simulator program to produce prediction of reaction time. Model results were compared to human results in sign reading single-task and reading while driving dual-task conditions. The models were built using existing modelling methods without adjusting any parameter to fit the human data. The models' prediction was similar to the human data and could capture the different reaction time in different task conditions with different numbers of road names on the direction signs. Root mean square error (RMSE) was 0.3 s, and mean absolute percentage error (MAPE) was 12%. The results demonstrated the models' predictive power. The models provide a useful tool for the prediction of driver performance and the evaluation of direction sign design. | en |
| dc.description.sponsorship | The research was supported by National Natural Science Foundation of China (51678460, U1664262); Open Project of Key Laboratory of Ministry of Public Security for Road Traffic Safety (2017ZDSYSKFKT02); Natural Science Foundation of Hubei Province, China (ZRMS2017001571); Wuhan Youth Science and Technology Plan (2017050304010268); Fundamental Research Funds for the Central Universities (2017-JL-003). This work was supported in part by Natural Sciences and Engineering Research Council of Canada Discovery Grant RGPIN-2015-04134 (to SC). | en |
| dc.identifier.uri | https://doi.org/10.1049/iet-its.2018.5160 | |
| dc.identifier.uri | http://hdl.handle.net/10012/15669 | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.subject | driving safety | en |
| dc.subject | direction sign design | en |
| dc.subject | reaction time | en |
| dc.subject | QN-ACTR | en |
| dc.subject | concurrent tasks | en |
| dc.title | Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture | en |
| dc.type | Article | en |
| dcterms.bibliographicCitation | C. Deng, S. Cao, C. Wu and N. Lyu, "Predicting drivers' direction sign reading reaction time using an integrated cognitive architecture," in IET Intelligent Transport Systems, vol. 13, no. 4, pp. 622-627, 4 2019. | en |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.contributor.affiliation2 | Systems Design Engineering | en |
| uws.peerReviewStatus | Reviewed | en |
| uws.scholarLevel | Faculty | en |
| uws.typeOfResource | Text | en |