Incremental Road Network Generation Based on Vehicle Trajectories

Zhongyi, Ni,; Xie, Lei; Xie, Tian; Shi, Binhua; Zheng, Yao

doi:10.3390/ijgi7100382

Cited by 8 publications

(3 citation statements)

References 25 publications

(38 reference statements)

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“…As technological advances have permitted to get more and more data in real time, map databases are constantly extended and updated with images of the road and estimation of the local traffic level apart from other vehicle sensors [2]. [3] goes a step forward and addresses the map-matching problem to build the road network from scratch thanks to all representation points that are connected by a Delaunay triangulation network and applying a shortest path searching approach between the connected representation point pairs. Authors in [34] evaluate the accuracy of map-matching algorithms by using FCD to identify trajectories and extract traffic patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

A Long-Distance Smart Driving Service Based on Floating Car Data and Open Data

et al. 2022

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The purpose of this manuscript is to present the Smart Driving Service (SDS), a customized mobile application and a complex microservices framework that is intended for not only professional drivers but also for novel people that need help during the driving time in their long-distance journeys. The European regulation about driving times, breaks and rest periods for drivers engaged in the carriage of freight is implemented into the system. Additionally, it is necessary to have a feedback report to detect the behavior of the drivers and what to do differently to drive better. This issue is addressed by implementing a Route Performance Index (RPI) to measure the drivers' compliances. The proposed service has been running in a production stage for 6 months with a reduction of the consumption of 2 liters/100km. Having into account that the company runs more than 100M km per year, the savings in fuel is very relevant apart from the environment impact reduction.

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Section: Literature Reviewmentioning

confidence: 99%

A Long-Distance Smart Driving Service Based on Floating Car Data and Open Data

et al. 2022

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“…It is not trivial to integrate the usage of the spatial information from trajectory points and their order within the trajectory. This fact leads to approaches that ignore the order of points in the trajectory [7], use available topology information [8,9], or apply regression [10]. The Medoid approach [11] focuses on the selection of one of the input trajectories without combining the information from several trajectories.…”

Section: Related Workmentioning

confidence: 99%

Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories

Leichter

Werner

2019

Applied Sciences

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This work proposes a fast and straightforward method, called natural point correspondences (NaPoCo), for the extraction of road segment shapes from trajectories of vehicles. The algorithm can be expressed with 20 lines of code in Python and can be used as a baseline for further extensions or as a heuristic initialization for more complex algorithms. In this paper, we evaluate the performance of the proposed method. We show that (1) the order of the points in a trajectory can be used to cluster points among the trajectories for road segment shape extraction and (2) that preprocessing using polygonal approximation improves the results of the approach. Furthermore, we show based on “averaging GPS segments” competition results, that the algorithm despite its simplicity and low computational complexity achieves state-of-the-art performance on the challenge dataset, which is composed of data from several cities and countries.

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“…Map update refer both to the road network itself and the various features that come on top of the latter. Dozens of studies have been focused on the automatic generation of the road network from GPS tracks [31][32][33][34][35] and on subtopics that referred to the latter as main topic, such as intersection detection [36][37][38][39]. However, that interest is not uniform for the map feature categories that also need to be automatically updated.…”

Section: Introductionmentioning

confidence: 99%

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Zourlidou¹,

Sester²

2019

IJGI

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Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

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Incremental Road Network Generation Based on Vehicle Trajectories

Cited by 8 publications

References 25 publications

A Long-Distance Smart Driving Service Based on Floating Car Data and Open Data

A Long-Distance Smart Driving Service Based on Floating Car Data and Open Data

Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

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