A comparison and evaluation of map construction algorithms using vehicle tracking data

Abstract

Map construction construction methods automatically produce and/or update street map datasets using vehicle tracking data. Enabled by the ubiquitous generation of geo-referenced tracking data, there has been a recent surge in map construction algorithms coming from different computer science domains. A crosscomparison of the various algorithms is still very rare, since (i) algorithms and constructed maps are generally not publicly available and (ii) there is no standard approach to assess the result quality, given the lack of benchmark data and quantitative evaluation methods. This work represents a first comprehensive attempt to benchmark such map construction algorithms. We provide an evaluation and comparison of seven algorithms using four datasets and four different evaluation measures. In addition to this comprehensive comparison, we make our datasets, source code of map construction algorithms and evaluation measures publicly available on mapconstruction.org. This site has been established as a repository for map construction data and algorithms and we invite other researchers to contribute by uploading code and benchmark data supporting their contributions to map construction algorithms.

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

“…2017, 6, 400 3 of 15 for generating road networks have been proposed in recent years [16,17]. In general, these methods can be organized into three categories [18]: (1) point clustering [19][20][21], which assumes that the input raw data consist of a set of points that are then clustered in various ways (such as by the k-means algorithm) to obtain street segments that are finally connected to form a road network; (2) incremental track insertion [10,[22][23][24][25][26], which constructs a road network by incrementally inserting trajectory data into an initially empty graph; and (3) intersection linking [27][28][29], in which the intersection vertices of the road network are first detected and then linked together by recognizing suitable road segments. Some of the representative algorithms of each category are listed in Table 1.…”

“…The algorithm developed by Davies et al [20] was found to significantly outperform other algorithms under a variety of conditions. In recent years, directed Hausdorff, path-based, shortest-path-based, and graph-sampling-based distance measures have been adopted to evaluate the quality of the topological data and other features of constructed road networks [18,21]. [29] In this paper, a digital map construction method using vehicle GPS trajectories is proposed.…”

Extracting Lane Geometry and Topology Information From Vehicle Fleet Trajectories in Complex Urban Scenarios Using a Reversible Jump McMc Method

“…To overcome the great diversity of approaches Ahmed et al (2015) introduced a categorization in three classes: A) intersection linking, B) incremental track insertion and C) point clustering. Algorithms of category A firstly transfer detected curves and intersections into nodes in the graph and, secondly, analyze the trajectories to identify the connections between nodes.…”

Geosocial recommender systems

“…For each streetname in OSM, we geocoded 'all' house numbers. 1 We used these addresses with their respective coordinate pair as additional points while creating the Voronoi diagram, and then discarded their POLOIs. The results of this approach are illustrated in Figure 4.5e.…”

“…Results have 71 led, for example, to improved telematics services using live traffic assessment by means of vehicle tracking and, more recently, map construction algorithms resulting into automatic road network generation and updates (e.g. [1]). In this chapter, we focus on how we can extract user behavior from trajectories, to use this information for UGC quality and relevance assessment.…”