37 10 2011 10 Vol 37 No 10 JOURNAL OF BEIJING UNIVERSITY OF TECHNOLOGY Oct 2011 100124 30 U 491 3 A 0254-0037 2011 10-1511 - 06 1-3 Papadelis 4 Riemersma 5 RR Saroj 6 Akerstedt 7 4 δ 0 5 ~ 4 Hz θ 4 ~ 8 Hz α 8 ~ 14 Hz β 14 ~ 30 Hz 1 1 1 MCCARTT 8-10 11 30 30 35 2 a 72 h 3 h / 8h 2009-11-30 JJ004011200803 1972
1512 2011 Pack 10 12 30 14 00 1 2 1 Autosim VGA 60 40 1 2 KF2 1 3 Neuroscan 32 Fig 1 Driving Simulator DC ICA /PCA Neuroscan 32 64 128 256 32 1 3 40 km 3% 2% 3 5 m 1 5 m 1 50 km /h 20 ~ 23 5 Lx 1 4 1 h 2 5 min SOFI-C swedish occupational fatigue inventory-25 20 min 30 min 30 min 12 2 SOFI-C Fig 2 The flowchart of experiment 10 s 13 1 5 KF2 R h R r RR S r RR M r
10 1513 S r = 1 槡 T N N ri i = 1 M r = 1 N T r i - T rr 2 - T rr 1 2 S r RR M r RR N T ri i RR T rr RR Neuroscan 10 ~ 20 32 A2 EEG 14 δ 0 5 ~ 4 Hz θ 4 ~ 8 Hz α 8 ~ 14 Hz β 14 ~ 30 Hz 2 2 1 SOFI-25 swedish occupational fatigue inventory-25 0 10 5 3 5 3 Fig 3 Fatigue of subjective feeling 3 2 2 30 4 S r M r R h R r 2 min 15 8 min 8 min 30 min 4 S r M r S r Fig 4 4 The variations of ECG time-domain with driving time
1514 2011 15 R h t 8 min 30 min S r M r R h 1 Table 1 t Results of paired-t test t P P < 0 05 R r S r - 7 841 0 000 P > 0 05 1 M r - 3 772 0 001 2 3 R r R h 4 385 0 000 R r 0 686 0 06 T4 T5 T6 TP 7 TP 8 FT 7 FT 8 5 6 EEG 4 δ θ α 24 min β 3 r αθ α + θ /β θ /β r α α /β Fig 5 5 Temporal activity plotted over time during driving for basic index 4 min 4 min t 4 3 2 δ P < 0 05 3 P > 0 05 r αθ P < 0 05 P < 0 01 r α P < 0 01 δ r αθ r α 4 4 r αθ r α Table 2 α 1 43 0 166 6 r αθ - 5 255 0 000 Fig 6 Temporal activity plotted over time during driving for ratio index r θ r α - 3 416-6 439 0 006 0 000 2 t Results of paired-t test t P δ - 2 513 0 023 β - 1 503 0 149 θ - 0 521 0 608
10 1515 2 4 2 3 p < 0 05 Table 3 3 The analysis of Pearson correlation coefficient S r M r R h r αθ r θ r α σ S r 1 0 919 /0 000-0 957 /0 000 0 928 /0 000 0 803 /0 003 0 936 /0 000 0 841 /0 001 M r 0 919 /0 000 1-0 969 /0 000 0 889 /0 000 0 794 /0 004 0 877 /0 000 0 700 /0 016 R h - 0 957 /0 000-0 969 /0 000 1-0 910 /0 000-0 805 /0 003-0 899 /0 000-0 807 /0 003 r αθ 0 928 /0 000 0 889 /0 000-0 910 /0 000 1 0 946 /0 000 0 888 /0 000 0 756 /0 007 r θ 0 803 /0 003 0 794 /0 004-0 805 /0 003 0 946 /0 000 1 0 693 /0 018 0 687 /0 019 r α 0 936 /0 000 0 877 /0 000-0 899 /0 000 0 888 /0 000 0 693 /0 018 1 0 700 /0 016 σ 0 841 /0 001 0 700 /0 016-0 807 /0 003 0 756 /0 007 0 687 /0 019 0 700 /0 016 1 S r M r δ r αθ r α SPSS S r M r δ r αθ r β r α 1 85% 1 S r = 0 98 M r = 0 936 r αθ = 0 976 = 0 888 r α = 0 922 σ = 0 84 y = 0 98S r + 0 936M r + 0 976r αθ + 0 888r θ + 0 922r α + 0 84δ 2 5 4 1 7 7 y y 30 min y 2 min y t 4 y 4 Table 4 t Paired-t test results of before and after Fig 7 7 Variations of overall index with driving time t p 1-8 236 0 000 2-7 256 0 000 3-6 567 0 000 4-7 498 0 000
1516 2011 3 30 min M r R h R r 4 δ β θ α δ S r M r δ r αθ r α S r 1 LAL S K L CRAIG A Electroence phalography activity associated with driver fatigue implications for a fatigue countermeasure device J Journal of Psychophysiology 2001 15 3 1151-1156 2 PHILIPA H G NATHNIEL S M IAN J et al Investigating driver fatigue in truck crashes trial of a systematic methodology J Transportation Research Part F 2006 9 1 65-76 3 J 2000 10 43-44 CHEN Wei Driving fatigue prevention of french strafety J Social Developmeut 2000 10 43-44 in Chinese 4 PAPADELIS C CHEN Z PAPADELI C K et al Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents J Clinical Neurophysiology 2007 118 9 1906-1922 5 RIEMERSMA A R SANDERS C Performance determent during prolonged night driving M New York Flenum Press 1977 41-58 6 SAROJ K L LAL A C A critical review of the psychophysiology of driver fatigue J Biological Psychology 2001 55 173-194 7 AKERSTEDT T KECKLUND G KNUTSSON A Manifest sleepiness and the spectral content of the EEG during shift work J Sleep 1991 14 3 221-225 8 MCCARTT A T RIBNER S A PACK A I et al The scope and nature of the drowsy driving problem in New York State J Accident Analysis and Prevention 1996 28 4 511-517 9 SAGBERG F Road accidents caused by drivers falling asleep J Accident Anal Prevent 1999 31 6 639-649 10 PACK A I PACK A M RODGMAN E et al Characteristics of crashes attributed to the driver having fallen asleep J Accident Anal Prevent 1995 27 6 769-775 11 BROWN I D Methodological issues in driver fatigue research M London Taylor & Francis 1995 155-166 12 GILLBERG M KECKLUND G AKERSTEDT T Sleepiness and performance of professional drivers in a truck simulator - Comparisons between day and night driving J Journal of Sleep Research 1996 5 1 12-15 13 THIFFAULT P BERGERON J Monotony of road environment and driver fatigue a simulator study J Accident Analysis and Prevention 2003 35 3 381-391 14 JAP B T LAL S FISCHER P et al Using EEG spectral components to assess algorithms for detecting fatigue J Expert Systems With Applications 2009 36 2 2352-2359 15 J 2002 5 94-95 YANG Yu-shu YAO Zhen-qiang LI Zeng-yong et al Investigation on correlation between ECG indexes and driving fatigue J Machine Design & Manufacture 2002 10 5 94-95 in Chinese 1523
10 1523 Research on Travel Time of Basic Road Based on Cellular Automata QI Hong-sheng SONG Xian-min WANG Dian-hai GUO Wei-wei Transportation College Jilin University Changchun 130022 China Abstract The focus of the paper is the research of travel time of urban basic roads with multi-lane and channelized sections as well as traffic signal control A cellular automata model CAUB is established based on NaSch model through the description of basic move rules lane changing features and responses to signals At last CAUB is used to simulate travel time of basic roads Results show that the travel time on basic roads displays break nature and is influenced by flow and signal setting which relates to flow structure Key words traffic engineering travel time cellular automata lane changing behavior 1516 Experiment Study on Comprehensive Evaluation Method of Driving Fatigue Based on Physiological Signals ZHAO Xiao-hua FANG Rui-xue RONG Jian MAO Ke-jun Beijing Key Lab of Transportation Engineering Beijing University of Technology Beijing 100124 China Abstract A dynamic Driving simulation experiment of thirty subjects is carried out using the virtual driving simulator to analyze the variation of ECG and EEG indicators with driving time and the experiment validates the effectiveness of the ECG and EEG indicators as the evaluation of driving fatigue There is a significant correlation between EEG and ECG according to the Pearson Correlation Finally the relationship is established between EEG and ECG and the overall index of driving fatigue is defined by Principal component analysis which can exclude disturbed factors and lower data volatility therefore to improve accuracy of driving fatigue evaluation Key words driving fatigue driving simulation experiment electracardiogram electroencephalogram