Investigation of Traffic-Information Quantity Measurement Based on Information Theory
-
摘要: 针对语音信息缺乏量化和指路标志信息度量未反应实际出行条件下路网规模的影响作用问题，引入信息理论，构建语音信息量度量模型和图像信息量度量模型. 设计包含23组不同信息量的实验路网，测定不同信息量下的驾驶员反应时间. 研究结果显示：交通信息总量为52.22、57.90 bit时，出现反应时间的区域峰值；信息总量在50.74～57.38 bit区间段时，利于驾驶员对交通信息的认知且不产生信息量过载的现象；标志信息量为45.1、49.7 bit时，语音信息量位于5.12～10.24 bit区间利于降低驾驶员反应时间；语音信息量为5.12 bit时，标志信息位于49.70～54.30 bit区间利于提高驾驶员对交通状况认知.Abstract: In view of the lack of voice-information quantification and that road-sign information measures have no effect on the road network size under actual travel conditions, information theory was introduced to construct a voice-information volume measurement model and an image-information volume measurement model. An experimental road network, including 23 sets of different amounts of information, was designed to measure the driver reaction time under different information volumes. The results of the study demonstrate that when the total amount of traffic information is 52.22 bits and 57.90 bits, there is a regional response-time peak. When the total amount of information is in the 50.74 bit to 57.38 bit interval, it can help drivers recognize traffic information without generating information overload. When the information volume is 45.10 bits and 49.70 bits, the voice-information volume should be in the range of 5.12 bits to 10.24 bits to reduce the driver reaction time. When the voice-information volume is 5.12 bits, the mark information is in the range of 49.70 bits to 54.30 bits, and it can improve the driver awareness of traffic conditions.
-
图 2 交通信息总量下的反应时间
Figure 2. Reaction time for total traffic information quantity information
图 4 交通标志信息与反应时间统计
Figure 4. Reaction time for various traffic-sign information quantities
表 1 完整交通语音信息关键词结构特征
Table 1. Complete traffic speech information, including keywords and structural features
名称 地点 道路 方向 事件 交通状况 建议 案例描述 二环至三环 交大路 由南向北 三车相撞 交通拥堵 变更驾驶路径 
下载: 导出CSV
表 2 各交叉口交通信息量统计
Table 2. Traffic information quantities at various intersections
交叉口名称 语音信息量 交通标志信息量 交通信息总量 交叉口名称 语音信息量 交通标志信息量 交通信息总量 O 0 0 0 M 7.68 45.10 52.78 A 2.56 40.50 43.06 N 7.68 40.50 48.18 B 2.56 45.10 47.66 P 7.68 35.90 43.58 C 2.56 49.70 52.26 R 10.24 54.30 64.54 E 2.56 54.30 56.86 S 10.24 49.70 59.94 F 5.12 54.30 59.42 T 10.24 45.10 55.34 G 5.12 49.70 54.82 U 10.24 40.50 50.74 H 5.12 45.10 50.22 V 10.24 35.90 46.14 I 5.12 40.50 45.62 W 12.80 35.90 48.70 J 2.56 35.90 38.46 X 12.80 40.50 53.30 K 7.68 54.30 61.98 Y 12.80 45.10 57.90 L 7.68 49.70 57.38 Z 12.80 49.70 62.50 D 0 0 0
下载: 导出CSV
表 3 不同信息量下驾驶员反应时间统计
Table 3. Reaction time for different information quantities at various intersections
交叉口名称 语音信息量/bit 交通标志信息量/bit 交通信息总量/bit 反应时间均值/ms 反应时间标准差/ms 交叉口名称 语音信息量/bit 交通标志信息量/bit 交通信息总量/bit 反应时间均值/ms 反应时间标准差/ms A 2.56 40.50 43.06 2 587.63 432.54 M 7.68 45.10 52.78 3 856.95 746.04 B 2.56 45.10 47.66 3 772.18 463.78 N 7.68 40.50 48.18 3 964.71 702.43 C 2.56 49.70 52.26 3 923.54 501.36 P 7.68 35.90 43.58 2 856.38 632.54 E 2.56 54.30 56.86 3 861.44 603.41 R 10.24 54.30 64.54 4 367.93 890.17 F 5.12 54.30 59.42 3 973.75 687.09 S 10.24 49.70 59.94 4 104.04 1 402.45 G 5.12 49.70 54.82 3 985.84 765.37 T 10.24 45.10 55.34 3 940.62 1 032.31 H 5.12 45.10 50.22 4 395.76 699.03 U 10.24 40.50 50.74 3 899.95 934.26 I 5.12 40.50 45.62 3 167.84 590.31 V 10.24 35.90 46.14 3 878.58 902.35 J 2.56 35.90 38.46 2 033.11 571.34 W 12.80 35.90 48.70 4 089.36 993.78 K 7.68 54.30 61.98 4 256.88 945.23 X 12.80 40.50 53.30 3 891.01 1 043.56 L 7.68 49.70 57.38 4 144.73 810.21 Y 12.80 45.10 57.90 4 406.03 1 203.67 Z 12.80 49.70 62.50 4 590.33 1 675.13
下载: 导出CSV
-
MA J Q, BRIAN L S, MICHEAL D F. Comparison of in-vehicle auditory public traffic information with roadside dynamic message signs[J]. Journal of Intelligent Transportation Systems, 2016, 20(3): 244-254 JAMSON H A, MERAT N, CARSTEN O. Behavioral changes in drivers experiencing highly-automated vehicle control in varying traffic conditions[J]. Transportation Research Part C, 2013, 30(3): 116-125 GREEDIPALLY S R, LORD D, DHAVALA S S. The negative binomial-Lindley generalized linear model: Characteristics and application using crash data[J]. Accident Analysis and Prevention, 2012, 45: 258-265 SHINAR D, VOGELZANG M. Comprehension of traffic signs with symbolic versus text displays[J]. Transportation Research Part F, 2013, 18(1): 72-82 CHAURADND N, BOSSART F, DELHOMME P. Impact of framed messages on highway drivers’ speed[J]. Transportation Research Part F, 2015, 35(3): 37-44 RONCHI E, NILSSON D. Traffic Information Signs, colour scheme of emergency exit portals and acoustic systems for road tunnel emergency evacuations[R]. Sweden: Department of Fire Safety Engineering, Lund University, 2014 GUO Z, WEI Z, WANG H. The expressway traffic sign information volume threshold and AGS position based on driving behavior[J]. Transportation Research Procedia, 2016, 31(14): 3801-3810 左淑霞,席建锋,肖殿良. " 特色交通标志” 设计及其信息量度量方法研究[J]. 中国安全科学学报,2010,20(11): 139-144ZUO Shuxia, XI Jianfeng, XIAO Dianliang. Design of " feature traffic sign” and its information measurement[J]. China Safety Science Journal, 2010, 20(11): 139-144 刘小明,王蔚,姜明. 组合交通标志信息量阈值研究[J]. 交通运输工程学报,2016,16(1): 141-148LIU Xiaoming, WANG Wei, JIANG Ming. Investigation of information quantity threshold on combined traffic signs[J]. Journal of Traffic and Transportation Engineering, 2016, 16(1): 141-148 GAO S, FREIJINER E, BEN A M. Cognitive cost in route choice with real-time information: An exploratory analysis[J]. Transportation Research Part A, 2011, 45(9): 916-926 CRISTEA M, DELHOMME P. Comprehension and acceptability of on-board traffic information: Beliefs and driving behavior[J]. Accident Analysis & Prevention, 2014, 65(3): 123-130 ZHAO J, GUO J, ZHOU F, et al. Time course of Chinese monosyllabic spoken word recognition: evidence from ERP analyses[J]. Neuropsychologia, 2011, 49(7): 1761-1770 SAEED A B, MADJID T, DEBORA D C, et al. A multi-user decision support system for online city bus tour planning[J]. Journal of Modern Transportation, 2017, 25(2): 59-73 交通部公路科学研究院. 交通标志视认性关键特征参数及数学模型[R]. 北京: 交通部公路科学研究院, 2008 刘澜,骆晨,尹俊淞,等. 多源信息环境下的路径决策模型[J]. 西南交通大学学报:自然科学版,2015,50(5): 891-897LIU Lan, LUO Chen, YIN Junsong, et al. Route decision model under the environment of multi-source information[J]. Journal of Southwest Jiaotong University, 2015, 50(5): 891-897 WANG J, NIU H. Graded-information feedback strategy in two-route systems under ATIS[J]. Journal of Traffic and Transportation Engineering: English Edition, 2014, 1(2): 138-145 -
下载:
点击查看大图
图(4) / 表(3)
计量
- 文章访问数: 420
- HTML全文浏览量: 157
- PDF下载量: 47
- 被引次数: 0