35,026 research outputs found
A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data
The growing use of probe vehicles generates a huge number of GNSS data.
Limited by the satellite positioning technology, further improving the accuracy
of map-matching is challenging work, especially for low-frequency trajectories.
When matching a trajectory, the ego vehicle's spatial-temporal information of
the present trip is the most useful with the least amount of data. In addition,
there are a large amount of other data, e.g., other vehicles' state and past
prediction results, but it is hard to extract useful information for matching
maps and inferring paths. Most map-matching studies only used the ego vehicle's
data and ignored other vehicles' data. Based on it, this paper designs a new
map-matching method to make full use of "Big data". We first sort all data into
four groups according to their spatial and temporal distance from the present
matching probe which allows us to sort for their usefulness. Then we design
three different methods to extract valuable information (scores) from them: a
score for speed and bearing, a score for historical usage, and a score for
traffic state using the spectral graph Markov neutral network. Finally, we use
a modified top-K shortest-path method to search the candidate paths within an
ellipse region and then use the fused score to infer the path (projected
location). We test the proposed method against baseline algorithms using a
real-world dataset in China. The results show that all scoring methods can
enhance map-matching accuracy. Furthermore, our method outperforms the others,
especially when GNSS probing frequency is less than 0.01 Hz.Comment: 10 pages, 11 figures, 4 table
Performance of a New Enhanced Topological Decision-Rule Map-Matching Algorithm for Transportation Applications
Indexación: Web of Science; ScieloMap-matching problems arise in numerous transportation-related applications when spatial data is collected using inaccurate GPS technology and integrated with a flawed digital roadway map in a GIS environment. This paper presents a new enhanced post-processing topological decision-rule map-matching algorithm in order to address relevant special cases that occur in the spatial mismatch resolution. The proposed map-matching algorithm includes simple algorithmic improvements: dynamic buffer that varies its size to snap GPS data points to at least one roadway centerline; a comparison between vehicle heading measurements and associated roadway centerline direction; and a new design of the sequence of steps in the algorithm architecture. The original and new versions of the algorithm were tested on different spatial data qualities collected in Canada and United States. Although both versions satisfactorily resolve complex spatial ambiguities, the comparative and statistical analysis indicates that the new algorithm with the simple algorithmic improvements outperformed the original version of the map-matching algorithm.El problema de la ambigüedad espacial ocurre en varias aplicaciones relacionadas con transporte, especÃficamente cuando existe inexactitud en los datos espaciales capturados con tecnologÃa GPS o cuando son integrados con un mapa digital que posee errores en un ambiente SIG. Este artÃculo presenta un algoritmo nuevo y mejorado basado en reglas de decisión que es capaz de resolver casos especiales relevantes en modo post-proceso. El algoritmo propuesto incluye las siguientes mejoras algorÃtmicas: un área de búsqueda dinámica que varÃa su tamaño para asociar puntos GPS a al menos un eje de calzada, una comparación entre el rumbo del vehÃculo y la dirección del eje de calzada asignada, y un nuevo diseño de la secuencia de pasos del algoritmo. Tanto el algoritmo original como el propuesto fueron examinados con datos espaciales de diferentes calidades capturados en Canadá y Estados Unidos. Aunque ambas versiones resuelven satisfactoriamente el problema de ambigüedad espacial, el análisis comparativo y estadÃstico indica que la nueva versión del algoritmo con las mejoras algorÃtmicas entrega resultados superiores a la versión original del algoritmo.http://ref.scielo.org/9mt55
Investigating the mobility habits of electric bike owners through GPS data
This paper investigates the mobility habits of electric bike owners as well as their preferred routes. Through a GPS tracking campaign conducted in the city of Ghent (Belgium) we analyze the mobility habits (travel distance, time spent, speed) during the week of some e-bike users. Moreover, we propose the results of our map matching, based on the Hausdorff criterion, and preliminary results on the route choice of our sample. We strongly believe that investigating the behavior of electric bikes’ owners can help us in better understanding how to incentivize the use of this mode of transport. First results show that the trips with a higher travel distance are performed during the working days. It could be easily correlated with the daily commuting trips (home-work). Moreover, the results of our map-matching highlight how 61% of the trips are performed using the shortest path
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
Recurrence networks - A novel paradigm for nonlinear time series analysis
This paper presents a new approach for analysing structural properties of
time series from complex systems. Starting from the concept of recurrences in
phase space, the recurrence matrix of a time series is interpreted as the
adjacency matrix of an associated complex network which links different points
in time if the evolution of the considered states is very similar. A critical
comparison of these recurrence networks with similar existing techniques is
presented, revealing strong conceptual benefits of the new approach which can
be considered as a unifying framework for transforming time series into complex
networks that also includes other methods as special cases.
It is demonstrated that there are fundamental relationships between the
topological properties of recurrence networks and the statistical properties of
the phase space density of the underlying dynamical system. Hence, the network
description yields new quantitative characteristics of the dynamical complexity
of a time series, which substantially complement existing measures of
recurrence quantification analysis
- …