雷霞;周宝定;

• 1:珠海市规划设计研究院
• 2:深圳大学土木与交通工程学院
• 3:深圳大学城市智慧交通与安全运维研究院
• 4:深圳大学广东省城市空间信息工程重点实验室

摘要(Abstract)：

由于现有行人路网的基础数据中缺失属性信息，造成行人导航无法满足个性化需求，影响了行人出行的舒适度与安全性。将路网分为平路与坡路两类，提出了一种基于智能手机的行人路网坡度属性检测方法。首先，基于智能手机传感器数据，采用机器学习算法实现对坡度属性的检测。其次，通过数据融合与地图匹配，得到带有属性信息的行人路网数据。最后，对该数据投票与修正并进行可视化。方法最终达到了97.3%的属性检测精度，表明了本文方法的有效性。所得路网数据可为个性化导航提供数据基础。

关键词(KeyWords)： 行人路网;属性检测;机器学习;智能手机

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（41701519）;; 深圳市科技计划项目（JCYJ20180305125058727）;; 广东省自然科学基金面上项目（2019A1515011910）

作者(Authors): 雷霞;周宝定;

DOI: 10.14188/j.2095-6045.2020214

参考文献(References)：

• [1] Gawad S M A,Mougy A E,El-Meligy M A. Dynamic Mapping of Road Conditions Using Smartphone Sensors and Machine Learning Techniques[C]. 2016IEEE 84th Vehicular Technology Conference(VTCFall),Montreal,QC,2016
• [2] Congwei D,Masayuki I,Yoshito T B,et al. A Framework for Pedestrian Comfort Navigation Using Multimodal Environmental Sensors[J]. Pervasive and Mobile Computing,2013,9(3):421-436
• [3] Thorsten V,Weber G. Route Checkr:Personalized Multicriteria Routing for Mobility Impaired Pedestrians[C]. The 10th International ACM Sigaccess Conference on Computers and Accessibility,Halifax,Nova Scotia,Canada,2008
• [4] Ding D,Parmanto B,Karimi H A,et al. Design Considerations for a Personalized Wheelchair Navigation System[C]. The 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,Lyon,France,2007
• [5] John S,Hahmann S,Rousell A,et al. Deriving Incline Values for Street Networks from Voluntarily Collected GPS Traces[J]. Cartography and Geographic Information Science,2017,44(2):152-169
• [6] Menkens C,Sussmann J,Breitsameter E,et al. Easy Wheel—A Mobile Social Navigation and Support System for Wheelchair Users[C]. 2011 Eighth International Conference on Information Technology:New Generations,Las Vegas,NV,USA,2011
• [7]周宝定，李清泉，毛庆洲，等.用户行为感知辅助的室内行人定位[J].武汉大学学报·信息科学版，2014,39(6):719-723
• [8]李清泉，周宝定，马威，等. GIS辅助的室内定位技术研究进展[J].测绘学报，2019,48(12):1 498-1 506
• [9] Mohan P,Padmanabhan V N,Ramjee R. Nericell:Rich Monitoring of Road and Traffic Conditions Using Mobile Smartphones[C]. The 6th International Conference on Embedded Networked Sensor Systems(SenSys2008),ACM,Releigh,NC,USA,2008
• [10] Eriksson J,Girod L,Hull B,et al. The Pothole Patrol:Using a Mobile Sensor Network for Road Surface Monitoring[C]. The 6th International Conference on Mobile Systems,Applications,and Services(MobiSys2008),ACM,Breckenridge,Colorado,USA,2008
• [11]杨超.基于众包模式的城市级路面危害坑洞检测技术[D].上海：上海交通大学，2015
• [12]沈毅仁.基于Android手机传感器的车道检测设计与实现[D].南京：南京邮电大学，2018
• [13] Qin Z,Yu C,Li Q Q,et al. CrackTree:Automatic Crack Detection from Pavement Images[J]. Pattern Recognition Letters,2012,33(3):227-238
• [14]李荣，卢毅，宋萍.多功能路面状况检测技术的发展[J].测绘地理信息，2013,38(4):78-81
• [15]张德津，李清泉.公路路面快速检测技术发展综述[J].测绘地理信息，2015,40(1):1-8
• [16] Zhou B D,Yang J,LI Q Q. Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network[J]. Sensors(Basel,Switzerland),2019,19(3):621-638
• [17] Cheng H T,Sun F T,Griss M,et al. NuActiv:Recognizing Unseen New Activities Using Semantic Attribute-Based Learning[C]. The 11th International Conference on Mobile Systems(MobiSys 2013),ACM,Taibei,China,2013
• [18] Karimi H A,Kasemsuppakorn P. Pedestrian Network Map Generation Approaches and Recommendation[J].International Journal of Geographical Information Science,2013,27(5):947-962