Application of the Point-Descriptor-Precedence representation for micro-scale traffic analysis at a non-signalized T-junction
作者机构：Department of GeographyGhent UniversityGhentBelgium Department of Data Analysis and Mathematical ModellingGhent UniversityGhentBelgium Department of Telecommunications and Information ProcessingGhent UniversityGhentBelgium
出 版 物：《Geo-Spatial Information Science》 (地球空间信息科学学报（英文）)
年 卷 期：2023年第26卷第3期
页 面：406-430页
核心收录：
学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统]
基 金：supported by the Higher Education Commission(HEC),Pakistan[grant number 50040696] Bernard De Baets and Guy De Tréreceived funding from the Flemish Government under the“Onderzoeksprogramma Artificiële Intelligentie(AI)Vlaanderen”program
主 题：Vehicle interaction traffic research pattern identification traffic safety spatiotemporal representation movement pattern
摘 要：An intersection of two or more roads poses a risk for potential conflicts among *** the reasons triggering such conflicts are not clear,as they might be too subtle for the human *** environment also plays a part in understanding where,when,and why a particular vehicle interaction has occurred in a certain ***,it is of paramount importance to dive deeper into the vehicle interaction at a micro-scale within the embedded geographical environment,particularly at the *** would in turn assist in evaluating the association of vehicle interactions with conflict risks and near-miss ***,detection of such micro traffic interactions could also be used to improvise the complexity of the already established transport ***,traffic at intersections has been explored mainly for flow estimation,capacity and width measurements,and traffic congestion,etc.,whereas the detection of micro-scale traffic interactions at intersections remains relatively *** this paper,we present a novel approach to retrieve and represent micro-scale traffic movement interactions at a non-signalized T-junction by extending a recently introduced qualitative spatiotemporal Point-Descriptor-Precedence(PDP)*** study how the PDP representation offers a fine solution to study the interaction of traffic flows at *** permits tracking the micro-movement of vehicles in much finer detail,which is used later to retrieve movement patterns from a motion *** conventional approaches,we start our approach with the actual movements before modeling the static intersection ***,with the aid of illustrative examples,we discuss how the length,width,and speed of the vehicles can be exploited in our approach to detect specific patterns more ***,we address the potential benefits of our approach for traffic safety assessment and how it can be extended to a network of
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