Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (5): 12-22    DOI: 10.11925/infotech.2096-3467.2017.05.03
Orginal Article Current Issue | Archive | Adv Search |
Route Planning in Pedestrian-Map APP Interactions
Wu Dan(), Cheng Lei
School of Information Management, Wuhan University, Wuhan 430072, China
Download: PDF (1542 KB)   HTML ( 1
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper constructs a model for route planning based on the impacts of different contexts on pedestrian’s walking behaviors. [Methods] First, we collected data from 30 participants of an outdoor pedestrian navigation experiment. Then, we analyzed the ties between contexts and users’ behaviors with correlation and regression tests. [Results] At the initial planning stage, more destinations chosen by the pedestrians meant longer searching time, while more users’ attention to the estimated time led to longer browsing time. User’s subjective time pressure, system location and destination choice also affected their attention to estimated time. In the re-planning stage, different genders and ages had different subjective time pressures on the users. The more difficult tasks generated fewer operations. [Limitations] There were some subjective issues with the data processing. The changing of user’s psychology and behaviors may also influence the results. [Conclusions] The proposed model focuses on the user factors and reveals the relationship among the contexts of the initial plannings and the re-routings, which provide valuable information to the mobile map developers.

Key wordsRoute Planning      User Information Behavior      Mobile Map      Context      Human-Computer Interaction     
Received: 07 February 2017      Published: 06 June 2017
ZTFLH:  G250  

Cite this article:

Wu Dan,Cheng Lei. Route Planning in Pedestrian-Map APP Interactions. Data Analysis and Knowledge Discovery, 2017, 1(5): 12-22.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.05.03     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I5/12

分类 主要因素 解释
个人因素 性别 用户的性别
年龄 用户的年龄
主观时间压力 用户感受到的任务完成的紧急程度
估计时间的
在意度
用户对地图系统路线估计时间的关注度
行为因素 目的地选择次数 用户在搜索目的地时选择目的地的次数
搜索时长 用户搜索路线的时长
浏览时长 用户确定出行路线到真正出发之前的一段时间
操作次数 在步行路线模式下的放大、缩小、左滑、右滑等的操作次数
查看路线详情次数 查看路线详情的次数
任务因素 客观时间限制 任务完成的客观时间要求
路线估计时长 系统对出行路线的估计时长
步行距离 系统显示的路线步行距离
任务时长 用户完成任务所需的时间
环境因素 移动设备 屏幕尺寸、手机型号
系统定位 地图系统定位准确度
天气因素 晴、雨、多云等
室外温度 任务当天的平均气温
因变量 自变量 相关系数
搜索时长 目的地选择次数 .547**
操作次数 .435*
浏览时长 客观时间限制 .532**
系统定位 -.458*
估计时间的在意度 -.413*
查看路线详情次数 操作次数 -.371*
任务时长 路线估计时长 .634**
步行距离 .563**
估计时间的在意度 主观时间压力 .542**
系统定位 -.486**
目的地选择次数 .465**
因变量 自变量 非标准
化系数
显著性 VIF 调整R2
搜索时长 目的地选择次数 57.393 .007 1.001 0.357
操作次数 -6.449 .008 1.001
浏览时长 估计时间的
在意度
-13.836 .095 1.274 0.087
客观时间限制 -9.135 .655 1.184
系统定位 -4.423 .451 1.425
查看路线
详情次数
操作次数 -.084 .121 1.000 0.051
非标准化
系数
标准化
系数
显著性 VIF
常数 92.432 .000
估计时间的在意度 -16.022 -.347 .028 1.000
调整R2 0.131
因变量 自变量 非标准化
系数
显著性 VIF 调整R2
任务时长 路线估计时长 43.887 .095 9.372 0.436
步行距离 -.109 .798 9.372
估计时间的在意度 主观时间压力 .728 .000 1.029 0.564
系统定位 .323 .001 1.003
目的地选择次数 -.377 .033 1.029
非标准化系数 标准化系数 显著性 VIF
常数 590.223 .000
路线估计时长 38.100 .688 .000 1.000
调整R2 0.454
原因
位置
个人因素 系统因素 合计
误操作 查找(确认)
目的地
定位
不准
导航方
式错误
起点附近 2 2
前进中 4 3 5 2 14
目的地附近 11 11
合计 6 14 5 2 27
分类 主要因素 解释
个人因素 任务困难度 用户对任务难易度的感受
行为因素 地点输入方式 用户输入目的地的方式
地点输入范围的
变化
用户重新规划输入的目的地与
初始规划输入的目的地的变化
因变量 自变量 相关系数
主观时间压力 性别 -.467*
年龄 -.572**
搜索时长 地点输入方式 .689**
移动设备 .443*
浏览时长 性别 -.419*
任务困难度 -.410*
操作次数 任务困难度 -.432*
步行距离 地点输入方式 .485*
任务困难度 -.539**
路线估计时长 地点输入方式 .540**
步行距离 .963**
任务困难度 -.533**
因变量 自变量 非标准化系数 显著性 VIF 调整R2
主观时间
压力
性别 -.863 .007 1.001 0.373
年龄 -.658 .008 1.001
搜索时长 地点输入方式 14.367 .000 1.174 0.517
移动设备 5.736 .675 1.174
浏览时长 性别 -8.236 .261 1.204 0.133
任务困难度 -4.273 .146 1.204
操作次数 任务困难度 -.694 .042 1.000 0.122
因变量 自变量 非标准化系数 显著性 VIF 调整R2
步行
距离
地点输入方式 185.531 .039 1.121 0.430
任务困难度 -185.317 .004 1.121
路线估
计时长
地点输入方式 1.076 .060 1.344 0.912
步行距离 .013 .000 1.902
任务困难度 -.224 .588 1.588
重新规
划行为
(因变量)
初始规
划行为
(自变量)
Spearman相关系数 非标准化系数 显著性 VIF 调整R2
地点输
入方式
步行距离 .447* .001 .375 13.226 0.411
查看路线
详情次数
.624** 1.497 .002 1.102
路线估计时长 .398* -.027 .649 13.034
搜索时长 搜索时长 .593** .023 .895 1.101 .284
步行距离 .555** -.450 .453 13.226
查看路线
详情次数
.471* 18.697 .046 1.084
路线估计时长 .568** .887 .020 1.084
浏览时长 搜索次数 .415* 2.468 .322 1.000 .001
[1] 比达网. 2016年第3季度中国手机地图(导航)市场研究报告[R/OL]. (2016-11-06). [2016-12-25]. .
[1] (Bigdata-Research. Research Report of the 3rd Quarter of 2016 China Mobile Map (Navigation) Market [R/OL]. (2016-11-06). [2016-12-25].
[2] 迪莉娅. 西方信息行为认知方法研究[J]. 中国图书馆学报, 2011, 37(2): 97-104.
[2] (Di Liya.Studies on Cognitive Approach to Information Behavior in Western Countries[J]. Journal of Library Science in China, 2011, 37(2): 97-104.)
[3] Nivala A M, Sarjakoski L T.Need for Context-Aware Topographic Maps in Mobile Devices[C]//Proceedings of the 9th Scandinavian Research Conference on Geographical Information Science, Espoo, Finland. 2003: 15-29.
[4] Cai G, Xue Y.Activity-oriented Context-aware Adaptation Assisting Mobile Geo-spatial Activities[C]// Proceedings of the 11th International Conference on Intelligent User Interfaces, Sydney, Australia.New York, USA: ACM, 2006: 354-356.
[5] 齐晓飞, 王光霞, 薛志伟,等. 位置地图情境分类分级与切换研究[J]. 地理信息世界, 2013,20(6): 13-18.
[5] (Qi Xiaofei, Wang Guangxia, Xue Zhiwei, et al.Research on Location Map Context Classification, Grading and Switching[J]. Geomatics World, 2013, 20(6): 13-18.)
[6] Hölscher C, Tenbrink T, Wiener J M.Would You Follow Your Own Route Description? Cognitive Strategies in Urban Route Planning[J]. Cognition, 2011, 121(2): 228-247.
doi: 10.1016/j.cognition.2011.06.005 pmid: 21794850
[7] Agrawal A W, Schlossberg M, Irvin K. How Far, By Which Route,Why? A Spatial Analysis of Pedestrian Preference[J]. Journal of Urban Design, 2007, 13(1): 81-98.
[8] Ishikawa T, Fujiwara H, Imai O, et al.Wayfinding with a GPS-based Mobile Navigation System: A Comparison with Maps and Direct Experience[J]. Journal of Environmental Psychology, 2008, 28(1): 74-82.
doi: 10.1016/j.jenvp.2007.09.002
[9] Lu X, Wang C, Yang J M, et al.Photo2Trip: Generating Travel Routes from Geo-Tagged Photos for Trip Planning[C]// Proceedings of the 18th International Conference on Multimedia 2010, Firenze, Italy. 2010:143-152.
[10] Borst H C, De Vries S I, Graham J. Influence of Environmental Street Characteristics on Walking Route Choice of Elderly People[J]. Journal of Environmental Psychology, 2009, 29(4): 477-484.
doi: 10.1016/j.jenvp.2009.08.002
[11] Yuan W, Schneider M.Supporting 3D Route Planning in Indoor Space Based on the LEGO Representation[C]// Proceedings of the 2nd International Workshop on Indoor Spatial Awareness. 2010:16-23.
[12] Kruger A, Butz A, Muller C, et al.The Connected User Interface: Realizing a Personal Situated Navigation Service[C]// Proceedings of the 9th International Conference on Intelligent User Interfaces. 2004:161-168.
[13] Abowd G D, Dey A K, Brown P J, et al.Towards a Better Understanding of Context and Context-awareness[C]// Proceedings of the 1st International Symposium on Handheld and Ubiquitous Computing.1999.
[14] Schmidt A, Boigl M, Gollcrson H W.There is More to Context than Location[J]. Compliers and Graphics, 1999, 23(6): 893-902.
doi: 10.1016/S0097-8493(99)00120-X
[15] 郭顺利. 基于情境感知的移动图书馆用户模型研究[D]. 曲阜: 曲阜师范大学, 2015.
[15] (Guo Shunli.A User Model for Mobile Library Based on Context-awareness[D]. Qufu: Qufu Normal University, 2015.)
[16] 刘芳静. 基于情境体验的移动地图设计研究[D]. 长沙: 湖南大学, 2014.
[16] (Liu Fangjing.The Research of Mobile Map Design Based on Situational Experience[D]. Changsha: Hunan University, 2011. )
[17] Schwarz S.A Context Model for Personal Knowledge Management Applications[C]// Proceedings of the 2nd International Workshop on Modeling and Retrieval of Context. 2005.
[18] 王芳, 郭丽杰. 基于情境模型的手机图书馆个性化服务研究[J]. 图书馆学研究, 2011(7): 93-96.
[18] (Wang Fang, Guo Lijie.Research of Mobile Phone Library Personality Service Based on Context Model[J]. Researches in Library Science, 2011(7): 93-96.)
[19] 申园园, 余文. 一种基于位置服务信息的移动推荐模型[J]. 计算机应用与软件, 2016,33(12): 202-206.
[19] (Shen Yuanyuan, Yu Wen.A Mobile Recommendation Model Based on Location-Based Service[J]. Computer Applications and Software, 2016, 33(12): 202-206. )
[20] Pirkka A, Lassi A L.Interacting with Context Factors in Music Recommendation and Discovery[J]. International Journal of Human-Computer Interaction, 2017, 33(3): 165-179.
doi: 10.1080/10447318.2016.1225881
[1] Tan Ying, Tang Yifei. Extracting Citation Contents with Coreference Resolution[J]. 数据分析与知识发现, 2021, 5(8): 25-33.
[2] Yi Huifang,Liu Xiwen. Analyzing Patent Technology Topics with IPC Context-Enhanced Context-LDA Model[J]. 数据分析与知识发现, 2021, 5(4): 25-36.
[3] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[4] Hyonil Kim,Ou Shiyan. Identifying Citation Texts with Unsupervised Method[J]. 数据分析与知识发现, 2021, 5(1): 66-77.
[5] Zheng Songyin,Tan Guoxin,Shi Zhongchao. Recommending Tourism Attractions Based on Segmented User Groups and Time Contexts[J]. 数据分析与知识发现, 2020, 4(5): 92-104.
[6] Bi Datian,Wang Fu. Multidimensional Information Acceptance Contexts of Mobile Library[J]. 数据分析与知识发现, 2018, 2(7): 101-111.
[7] Hou Jun,Liu Kui,Li Qianmu. Classification Recommendation Based on ESSVM[J]. 数据分析与知识发现, 2018, 2(3): 9-21.
[8] Yin Cong,Zhang Liyi. Recommendation Algorithm for Post-Context Filtering Based on TF-IDF: Case Study of Catering O2O[J]. 数据分析与知识发现, 2018, 2(11): 28-36.
[9] 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.
[10] Wu Dan,Yuan Fang. Studying User Distractions with GPS Based Pedestrian Navigation System[J]. 数据分析与知识发现, 2017, 1(5): 32-41.
[11] Xia Lixin,Yang Jinqing,Cheng Xiufeng. Collecting Mobile Data Based on Content Awareness——An Empirical Study[J]. 数据分析与知识发现, 2017, 1(5): 82-93.
[12] Xu Jian,Li Gang,Mao Jin,Ye Guanghui. Recognizing and Analyzing Cited Spans in Literature[J]. 数据分析与知识发现, 2017, 1(11): 37-45.
[13] Hong Liang,Qian Chen,Fan Xing. Context-aware Recommendation System for Mobile Digital Libraries[J]. 现代图书情报技术, 2016, 32(7-8): 110-119.
[14] Duan Yufeng, Zhu Wenjing, Chen Qiao, Liu Wei, Liu Fenghong. A Domain Concepts Triple-layer Filter Method[J]. 现代图书情报技术, 2015, 31(4): 26-33.
[15] Lu Xiaoming. Research on a Lightweight Academic Library Context-aware Recommendation Service Platform Based on GimbalTM[J]. 现代图书情报技术, 2015, 31(3): 101-107.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn