Map inference in the face of noise and disparity

Abstract

This paper describes a process for automatically inferring maps from large collections of opportunistically collected GPS traces. In this type of dataset, there is often a great disparity in terms of coverage. For example, a freeway may be represented by thousands of trips, whereas a residential road may only have a handful of observations. Additionally, while modern GPS receivers typically produce high-quality location estimates, errors over 100 meters are not uncommon, especially near tall buildings or under dense tree coverage. Combined, GPS trace disparity and error present a formidable challenge for the current state of the art in map inference. By tuning the parameters of existing algorithms, a user may choose to remove spurious roads created by GPS noise, or admit less-frequently traveled roads, but not both.In this paper, we present an extensible map inference pipeline, designed to mitigate GPS error, admit less-frequently traveled roads, and scale to large datasets. We demonstrate and compare the performance of our proposed pipeline against existing methods, both qualitatively and quantitatively, using a real-world dataset that includes both high disparity and noise. Our results show significant improvements over the current state of the art.

A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

“…Different from these clustering approaches, density-based methods [9][10][11][12] provide another way to derive road maps from vehicle traces, also taking all track points as input. In these methods, the collection of track points in a study area is first converted to a raster image.…”

“…A vast body of literature addressing the problem of road map construction from vehicle tracking data currently exists [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], which can be divided into two categories in terms of the form of data input, offline and online algorithms. These methods take a full collection or a continuous stream of data as input, respectively, which will be detailed in the following section.…”

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.…”

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

“…(1) The Chicago Campus Bus Dataset contains 118,364 GPS points and 889 trajectories within a region of 3.8 km × 2.4 km at the University of Illinois at Chicago [40], as shown in Figure 9a. Since the campus buses travel on relatively fixed routes, the GPS tracks are distributed over fixed roads and relatively clean.…”