5 research outputs found
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Digital maps have become a part of our daily life with a number of commercial
and free map services. These services have still a huge potential for
enhancement with rich semantic information to support a large class of mapping
applications. In this paper, we present Map++, a system that leverages standard
cell-phone sensors in a crowdsensing approach to automatically enrich digital
maps with different road semantics like tunnels, bumps, bridges, footbridges,
crosswalks, road capacity, among others. Our analysis shows that cell-phones
sensors with humans in vehicles or walking get affected by the different road
features, which can be mined to extend the features of both free and commercial
mapping services. We present the design and implementation of Map++ and
evaluate it in a large city. Our evaluation shows that we can detect the
different semantics accurately with at most 3% false positive rate and 6% false
negative rate for both vehicle and pedestrian-based features. Moreover, we show
that Map++ has a small energy footprint on the cell-phones, highlighting its
promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on
Sensing, Communication, and Networking (IEEE SECON 2014
A Force-Directed Approach for Offline GPS Trajectory Map Matching
We present a novel algorithm to match GPS trajectories onto maps offline (in
batch mode) using techniques borrowed from the field of force-directed graph
drawing. We consider a simulated physical system where each GPS trajectory is
attracted or repelled by the underlying road network via electrical-like
forces. We let the system evolve under the action of these physical forces such
that individual trajectories are attracted towards candidate roads to obtain a
map matching path. Our approach has several advantages compared to traditional,
routing-based, algorithms for map matching, including the ability to account
for noise and to avoid large detours due to outliers in the data whilst taking
into account the underlying topological restrictions (such as one-way roads).
Our empirical evaluation using real GPS traces shows that our method produces
better map matching results compared to alternative offline map matching
algorithms on average, especially for routes in dense, urban areas.Comment: 10 pages, 12 figures, accepted version of article submitted to ACM
SIGSPATIAL 2018, Seattle, US
An efficient algorithm for mapping vehicle trajectories onto road networks
Modern mobile technology has enabled the collection of large scale vehicle trajectories using GPS devices. As GPS measurements may come with error, vehicle trajectories are often noisy. A common practice to alleviate this issue is to apply map-matching, i.e., to align vehicle trajectories with the road segments in a digitized road network. This paper presents an efficient solution for map-matching problem that won the SIGSPATIAL CUP 2012. Given a road network, our solution first constructs a gird index on the road segments. For each point p on a vehicle trajectory, we employ the index to identify a candidate set of road segments that are close to p, and then we refine the candidate set to select a segment that matches p with the highest probability. The selection of the best match is based on a metric that takes into account (i) the correlation between consecutive GPS measurements as well as (ii) the directions and shapes of the road segments. Experimental results on real vehicle trajectories and road networks demonstrate the effectiveness and efficiency of the proposed solution