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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Generating lane-based intersection maps from crowdsourcing big trace data
ترجمه فارسی عنوان مقاله:
ایجاد نقشه تقاطع مبتنی بر خط از crowdsourcing داده های ردیابی بزرگ
منبع:
Sciencedirect - Elsevier - Transportation Research Part C, 89 (2018) 168-187: doi:10:1016/j:trc:2018:02:007
نویسنده:
Xue Yanga, Luliang Tanga,⁎, Le Niua, Xia Zhangb, Qingquan Lic
چکیده انگلیسی:
Lane-based road information plays a critical role in transportation systems, a lane-based inter
section map is the most important component in a detailed road map of the transportation in
frastructure. Researchers have developed various algorithms to detect the spatial layout of in
tersections 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 al
gorithms 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 facil
itates LIM refinement from crowdsourced traces more efficiently than methods based on other
types of sensor data.
Keywords: Road network ، Lane-based intersection map ، Multi-level strategies method ، Crowdsourcing trace ، Big data
قیمت: رایگان
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