Generating lane-based intersection maps from crowdsourcing big trace data |
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| Affiliation: | 1. State Key Lab for Information Engineering in Surveying, Mapping and Remote Sensing (Wuhan University), LuoYu Road 129, 430079 Wuhan, China;2. School of Urban Design (Wuhan University), LuoYu Road 129, 430079 Wuhan, China;3. Shenzhen Key Laboratory of Spatial Smart Sensing and Services, College of Civil Engineering (Shenzhen University), Shenzhen 518060, China;1. EURECOM, Communication System Department, Sophia Antipolis, France;2. Université Côte d’Azur, Inria, CNRS, LJAD, France;1. Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Berkeley, 107 McLaughlin Hall, Berkeley, CA 94720, USA;2. Department of Civil and Environmental Engineering, Institute of Transportation Studies, University of California, Berkeley, 114 McLaughlin Hall, Berkeley, CA 94720, USA;1. Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Information Science and Engineering, Xiamen University, Xiamen 361005, China;2. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China;3. Departments of Geography and Environmental Management/Systems Design Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada;4. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA;1. The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China;2. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong |
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| Abstract: | Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data. |
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| Keywords: | Road network Lane-based intersection map Multi-level strategies method Crowdsourcing trace Big data |
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