Number of place names of freeway guide signs based on data envelopment analysis
(2.College of Humanities and Social Sciences, Fuzhou University, Fuzhou, Fujian Province, China 350108)
【Abstract】As road networks are getting increasingly complicated, there are more and more traffic accidents caused by overloaded information of the guide signs at present. To comprehensively determine the reasonable number of place names for guide signs on freeway interchange and propose corresponding suggestions to improve the setting of these signs, we studied the setting of guide signs on the driver’s response efficiency based on the data envelopment analysis (DEA) method, selected representative indexes reflecting the driver’s cognitive response to guide signs, and constructed a macro model for the study of the number of place names on freeway guide signs. We used the eye tracker and electroencephalogram (EEG) while performing the driving simulation tests in relevant scenarios and obtained eye movement parameters, brain wave data, and driving behavior of the subjects in six different scenarios. When the model was used for data analysis, the mean overall efficiency values of the driver’s cognitive response to the guide signs in six scenarios (the numbers of place names were 4, 5, 6, 7, 8, and 9) were observed to be 0.983, 0.956, 0.902, 0.796, 0.699, and 0.617, respectively. Then, according to the different index sets, we performed the index sensitivity analysis to obtain the overall efficiency values of the drivers in the six scenarios. The results reveal that as the number of place names rises, the mean overall efficiency of the driver’s cognitive response to guide signs exhibits a downward trend. When information volume exceeds the drivers’ cognitive threshold, the system’s overall efficiency value decreases rapidly. Drivers have different levels of cognitive response efficiency in different scenarios, so we should make targeted modifications to response efficiency by improving the layout design on the guide signs. From the perspective of a driver’s cognitive response, the results show that the threshold of place names of freeway guide signs is suggested at 6. This study used the DEA method to construct a comprehensive index system to analyze the number of place names of guide signs from the perspective of a driver’s cognitive response to the guide signs and provided a practical reference for the layout of the guide signs on freeway interchange in China.
【Keywords】 traffic engineering; guide sign; data envelopment analysis; number of place names; cognitive efficiency;
 LIU Wei-ming, DENG Ru-feng, ZHANG Yang, et al. Setting Model of Safe Distance of Advance Guide Signs at Highway Exits [J]. Journal of South China University of Technology (Natural Science Edition), 2013, 41 (2): 37–43 (in Chinese).
 PALINKO O, KUN A L, SHYROKOV A, et al. Estimating Cognitive Load Using Remote Eye Tracking in a Driving Simulator [C]// Symposium on Eye-tracking Research & Applications. 2010.
 AL-MADANI H, AL-JANAHI A R. Role of Drivers’ Personal Characteristics in Understanding Traffic Sign Symbols [J]. Accident Analysis & Prevention, 2002, 34 (2): 185–196.
 VICTOR T W, HARBLUK J L, ENGSTR M J A. Sensitivity of Eye-movement Measures to In-vehicle Task Difficulty [J]. Transportation Research Part F Psychology & Behaviour, 2005, 8 (2): 167–190.
 Ng A W Y, CHAN A H S. The Guessability of Traffic Signs: Effects of Prospective-user Factors and Sign Design Features [J]. Accident Analysis & Prevention, 2007, 39 (6): 1245–1257.
 YUAN L, MA Y F, LEI Z Y, et al. Driver’s Comprehension and Improvement of Warning Signs [J]. Advances in Mechanical Engineering, 2014, 2014: 1–9.
 GUO Z, WEI Z, WANG H. The Expressway Traffic Sign Information Volume Threshold and AGS Position Based on Driving Behaviour [J]. Transportation Research Procedia, 2016, 14: 3801–3810.
 SHAO Hai-peng, MU Wei, YE Yi-xiang. A Quantitative Model for the Validity of Guide Sign Information at Intersection [J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17 (4): 70–75 (in Chinese).
 WANG Jian-jun, WANG Juan, WU Hai-gang, et al. A Study on Threshold Value of Traffic Sign Information Overload [J]. Journal of Chang'an University (Natural Science Edition), 2009 (4): 174–180 (in Chinese).
 GUO Jing-fu, YANG De-li. Overview of Data Envelopement Analysis Method [J]. Journal of Dalian University of Technology, 1998, 38 (2): 236–341 (in Chinese).
 HUANG Hai-xia, ZHANG Zhi-he. Research on Science and Technology Resource Allocation Efficiency in Chinese Emerging Strategic Industries Based on DEA Model [J]. China Soft Science, 2015 (1): 150–159 (in Chinese).
 FENG Shu-min, SHEN Xiang-hao. Evaluation of Coordination between Bus Lines Resource Allocation and Peak Passenger Flow [J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15 (4): 129–133 (in Chinese).
 HOU Ya-bing, LIANG Xuan, ZHANG Yao, et al. Assessment and Prediction on Technical Efficiency for Medical Institutions in China Based on Network Data Envelopment Analysis and Mixed Effect Model [J]. Chinese Journal of Public Health, 2019, 35 (2): 226–230 (in Chinese).
 SONG Miao-huan. Study on Evaluation of Beijing Rail Transit Operation Efficiency Based on DEA Model [D]. Beijing Jiaotong University, 2017 (in Chinese).
 LIU Li-fen, ZHAO Wei-zhong, LIU Xing-xing, et al. Evaluation on Underground Road Transportation Engineering Design Project Based on Data Envelopment Analysis [J]. Journal of Chang’an University (Natural Science Edition), 2017, 37 (3): 106–112 (in Chinese).
 ZHOU Yang, ZHANG Bing-qi, LI Qiang, et al. Assessing Efficiency of Public Bicycle System by DEA [J]. Journal of Beijing Normal University (Natural Science), 2017, 53 (3): 351–357 (in Chinese).
 WEI Quan-ling. Data Envelopment Analysis (DEA). Chinese Science Bulletin, 2000, 45 (17): 1793–1808 (in Chinese).
 MU Xin, CHENG Xueqing, ZHANG Xi, et al. Efficiency Evaluation of Railway Heavy Haul Freight Car Based on DEA Overlapping Efficiency Model [J]. China Railway Science, 2014, 35 (1): 130–134 (in Chinese).
 KABER D, ZHANG Y, JIN S, et al. Effects of Hazard Exposure and Roadway Complexity on Young and Older Driver Situation Awareness and Performance [J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2012, 15 (5): 600–611.
 PEI Yu-long, JIN Ying-qun, CHEN He-fei. Fatigue Characteristics in Drivers of Different Ages Based on Analysis of brain wave [J]. China Journal of Highway and Transport, 2018, 31 (4): 59–65+77 (in Chinese).
 LEE S,OLSEN E,SIMONS-MORTON B. Eyeglance Behavior of Novice Teen and Experienced Adult Drivers [J]. Transportation Research Record Journal of the Transportation Research Board, 2006, 1980 (1): 57–64．
 YUAN Wei. Experimental Study on Dynamic Visual Characteristics of Automobile Drivers in Urban Road Environment [D]. Chang’an University, 2008 (in Chinese).
 LIU S, YAO S, SPENCE A. Comparison of Caffeine and Music as Fatigue Countermeasures in Simulated Driving Tasks [J]. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2014, 58 (1): 2373–2377.
 WALKER G H, STANTON N A, KAZI T A, et al. Does Advanced Driver Training Improve Situational Awareness? [J]. Applied Ergonomics, 2009, 40 (4): 678–687.
 FENG Zhong-xiang, YUAN Hua-zhi, LIU Jing, et al. Influence of Driver Personal Characteristics on Vehicle Velocity [J]. Journal of Traffic and Transportation Engineering, 2012, 12 (6): 89–96 (in Chinese).
 ROBERT J K J, KEITH S K. Commentary on Section 4. Eye Tracking in Human-computer Interaction and Usability Research: Ready to Deliver the Promises [J]. Minds Eye, 2003, 2 (3): 573–605.
 YUAN Wei, FU Rui, MA Yong, et al. Effects of vehicle speed and traffic sign text height on drivers’ visual search patterns [J]. Journal of Traffic and Transportation Engineering, 2011, 11 (01): 119–126 (in Chinese).
 MA Yong. Study on Drivers’ Visual Search Pattern Based on Analysis of Eye Movements [D]. Xi’an: Chang’an University, 2006 (in Chinese).
 YANG M,WU L,TANG C. Study of Influence of Foreign Characters in Guide Signs on Legibility [J]. Journal of Highway & Transportation Research & Development, 2012, 6 (2): 91–95.
 MASAKI H, OHIRA M, UWANO H, et al. A Quantitative Evaluation on the Software Use Experience with Electroencephalogram [C]// International Conference. DBLP, 2012.
 LYU Neng-chao, CAO Yue, QIN Ling, et al. Research on the Effectiveness of Driving Workload Based on Traffic Sign Information Volume [J]. China Journal of Highway and Transport, 2018, 31 (8): 165–172 (in Chinese).
 ZHAO Ni-na, ZHAO Xiao-hua, LIN Zhan-zhou, et al. Exit Gore Sign Forms for Freeway Based on Driving Behavior [J]. China Journal of Highway and Transport, 2020, 33 (4): 137–145 (in Chinese).
 LI Ping-fan. Research on Driving Behavior Representation Index and Analysis Method [D]. Jilin University, 2010 (in Chinese).
 LIU Guang-hui, XIA Guo-dong, PAN Xiao-dong. Evaluation Model of Information in Guide Signs [J]. Highway Engineering, 2013, 38 (1): 169–173 (in Chinese).
 LIU Guang-hui, XIA Guo-dong, PAN Xiao-dong. Evaluation Model of Information in Guide Signs [J]. Highway Engineering, 2013, 38 (1): 169–173.
 LYU N C,XIE L,WU C Z, et al. Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case, Study of the Highways in China [J]. International Journal of Environmental Research and Public Health, 2017, 14 (2): 203–227.
 JUAN Zhi-cai, CAO Peng, WU Wen-jing. Study on Driver Traffic Signs Comprehension Based on Cognitive Psychology [J]. China Safety Science Journal, 2005, 15 (8): 8–11 (in Chinese).
 ZHANG Y,HARRIS E,ROGERS M, et al. Driver Distraction and Performance Effects of Highway Logo Sign Design [J]. Applied Ergonomics, 2013, 44 (3): 472–479.
 RAYNER K, LI X, JUHASZ B J, et al. The Effect of Word Predictability on the Eye Movements of Chinese Readers [J]. Psychonomic Bulletin & Review, 2005, 12 (6): 1089–1093.