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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (5): 32-41    DOI: 10.11925/infotech.2096-3467.2017.05.05
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Studying User Distractions with GPS Based Pedestrian Navigation System
Wu Dan(), Yuan Fang
School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] The purpose of this paper is to reduce user distractions effectively by analyzing their reactions to various positioning accuracy of a pedestrian navigation system. [Methods] First, we collected users’ behavior data from the simulation and controlled experiments. Then, we compared the user distraction frequencies and durations with descriptive statistics and significance tests. [Results] We found that users paid more attention to the orientation of the GPS, which increased the number of stopovers and reduced interactions with the APP. [Limitations] We could not exclude the influence of the individual factors on the results and few previous studies discussed the theoretical foundation of our study. [Conclusions] To reduce distractions, pedestrians should decrease their reliance on the navigation system, while the latter needs to provide more specific and comprehensive information.

Key wordsMobile Map      Pedestrian Navigation      User Information Behavior      Human-Computer Interaction      Distraction     
Received: 07 February 2017      Published: 06 June 2017
ZTFLH:  G250  

Cite this article:

Wu Dan,Yuan Fang. Studying User Distractions with GPS Based Pedestrian Navigation System. Data Analysis and Knowledge Discovery, 2017, 1(5): 32-41.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.05.05     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I5/32

数据项 GPS组 非GPS组
受客观环境影响
的分心
最大次数 4 4
最小次数 0 0
平均次数 0.5 0.75
受客观环境影响
的分心类型
外人干扰 3 5
周边环境吸引实
验者注意力
7 10
中途停留分心 最大次数 3 3
最小次数 0 0
平均次数 1.2 0.6
中途停留分心
的行为类型
过马路 8 2
辨别方向 12 6
道路不顺而停留 0 3
地图故障 2 1
手机充电 2 0
地图操作分心 最大次数 97 135
最小次数 2 5
平均次数 33.2 45.4
地图操作分心
的行为类型
滑动 357 483
点击 201 160
缩放 106 265
时长 GPS组
平均次数(标准差)
非GPS组
平均次数(标准差)
P值
<5 sec 0.2(0.68) 0.3(0.9) 0.799
5-20 sec 0.25(0.43) 0.45(0.74) 0.640
>20 sec 0.15(0.48) 0.1(0.3) 0.989
时长 GPS组
平均次数(标准差)
非GPS组
平均次数(标准差)
P值
<5 sec 0(0) 0(0) 1.00
5-20 sec 0.4(0.58) 0.1(0.3) 0.174
>20 sec 0.7(0.71) 0.45(0.67) 0.289
交互操作 GPS组 非GPS组 P值
滑动 平均次数(标准差) 17.85(13.27) 24.15(25.44) 0.718
平均时长(标准差) 40.75(37.91) 60(59.33) 0.414
点击 平均次数(标准差) 10.05(1) 8(6.66) 0.512
缩放 平均次数(标准差) 5.3(11.23) 13.25(11.65) 0.03*
平均时长(标准差) 12.6(20.87) 25.4(23.10) 0.072
地图
操作
平均次数(标准差) 33.2(23.14) 45.4(30.96) 0.165
平均时长(标准差) 53.35(55.39) 85.4(76.97) 0.289
时长 交互操作 GPS组
平均次数
(标准差)
非GPS组
平均次数
(标准差)
P值
<5 sec 滑动行为 8.8(5.89) 15.6(10.65) 0.035*
缩放行为 2.55(2.85) 10.2(9.71) 0.005*
地图操作行为 11.35(7.03) 25.8(19.73) 0.02*
5-20 sec 滑动行为 2.7(2.55) 3.2(3.63) 1.00
缩放行为 0.9(1.67) 0.95(1.20) 0.398
地图操作行为 3.6(3.72) 4.15(3.95) 0.799
>20 sec 滑动行为 0.1(0.44) 0.2(0.4) 0.461
缩放行为 0.05(0.22) 0(0) 0.799
地图操作行为 0.15(0.65) 0.2(0.4) 0.461
验证项 假设验证结果
H1.1 受客观环境影响的分心次数 不成立
H1.2 中途停留分心次数 成立
H1.3 地图滑动分心次数 不成立
地图缩放分心次数 成立
地图点击分心次数 不成立
验证项 假设验证结果
H2.1 受客观环境影响的分心时长 不成立
H2.2 中途停留分心时长 不成立
H2.3 地图操作分心时长 不成立
[1] Brooks C A, Rakotonirainy A.In-vehicle Technologies, Advanced Driver Assistance Systems and Driver Distraction: Research Challenges[C]// Proceeding of the 2005 International Conference on the Distraction in Driving, Sydney, New South Wales, Australia. 2007: 599-621.
[2] BBC News. One in Three ‘Distracted’ as Cross Road [EB/OL]. (2012-12-13). [2017-04-10]. .
[3] 新华网. 玩手机分心?美国行人死亡人数创纪录[EB/OL]. (2017-04-01). [2017-04-10]. .
[3] (Distracted by Mobile Phone? The Number of Pedestrian Deaths in the United States Increased [EB/OL]. (2017-04-01). [2017-04-10].
[4] Brown I D.Driver Fatigue[J]. Human Factors, 1994, 36(2): 298-314.
[5] Jensen B S, Skov M B, Thiruravichandran N.Studying Driver Attention and Behaviour for Three Configurations of GPS Navigation in Real Traffic Driving[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2010: 1271-1280.
[6] Morris A, Reed S, Welsh R, et al.Distraction Effects of Navigation and Green-driving Systems - Results from Field Operational Tests (FOTs) in the UK[J]. European Transport Research Review, 2015, 7(3): 1-9.
doi: 10.1007/s12544-015-0175-3
[7] Green P. Customer Needs, New Technology, Human Factors,Driver Science Research for Future Automobiles[J]. Journal of the Japan Society of Mechanical Engineers, 1996, 99: 15-18.
[8] Liberty E, Mazzae E, Garrott R, et al.NHTSA Driver Distraction Research: Past Present and Future[C]// Proceedings of the International Technical Conference on the Enhanced Safety of Vehicles.2001.
[9] Green P. Driver Distraction, Telematics Design, and Workload Managers: Safety Issues and Solutions[J/OL]. SAE International, 2004. .
[10] Lee B, Lee Y J N, Park S, et al. Driver’s Distraction and Understandability (EOU) Change due to the Level of Abstractness and Modality of GPS Navigation Information During Driving[J]. Procedia Computer Science, 2014, 39: 115-122.
doi: 10.1016/j.procs.2014.11.017
[11] Green P.Driver Interface/HMI Standards to Minimize Driver Distraction/Overload [J]. SAE International, 2008. .
[12] Liang Y, Reyes M L, Lee J D.Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines[J]. IEEE Transactions on Intelligent Transportation Systems, 2007, 8(2): 340-350.
doi: 10.1109/TITS.2007.895298
[13] Wang S, Zhang Y, Wu C, et al.Online Prediction of Driver Distraction Based on Brain Activity Patterns[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(1): 136-150.
doi: 10.1109/TITS.2014.2330979
[14] Larsson P, Niemand M.Using Sound to Reduce Visual Distraction from In-vehicle Human-Machine Interfaces[J]. Traffic Injury Prevention, 2015, 16(S1): 25-30.
doi: 10.1080/15389588.2015.1020111 pmid: 26027972
[15] Hamann C, Dulf D, Baraganandrada E, et al.Contributors to Pedestrian Distraction and Risky Behaviours During Road Crossings in Romania[J]. Injury Prevention, 2017. DOI: 10. 1136/injuryprev-2016-042219.
pmid: 28193714
[16] Thompson L L, Rivara F P, Ayyagari R C, et al.Impact of Social and Technological Distraction on Pedestrian Crossing Behaviour: An Observational Study[J]. Injury Prevention, 2013, 19(4): 232-237.
doi: 10.1136/injuryprev-2012-040601 pmid: 3717764
[17] Kautz J A.Pedestrian Distraction : Pedestrian Behaviors at Midblock Crossings Considering Geometric and Environmental Conditions [D]. Corvallis: Oregon State University, 2015.
[18] 应用月度Top榜[EB/OL]. [2017-04-10].
[18] (Monthly Top Application [EB/OL]. [2017-04-10].
[1] Wu Dan,Cheng Lei. Route Planning in Pedestrian-Map APP Interactions[J]. 数据分析与知识发现, 2017, 1(5): 12-22.
[2] Wu Dan,Li Yi,Dong Jing. Impacts of Time Constraint on Information Behaviors in Pedestrian Navigation[J]. 数据分析与知识发现, 2017, 1(5): 2-11.
[3] Wu Dan,Lu Liuxing. Navigation Awareness of Pedestrian Using Think-Aloud Method[J]. 数据分析与知识发现, 2017, 1(5): 23-31.
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