KEYWORDS: Data fusion, Sensors, Roads, Data processing, Algorithm development, Reliability, Information technology, Data integration, Prototyping, Detection and tracking algorithms
In our present work we introduce the use of data fusion in the field of Transportation and more
precisely for motorway travel time estimation. We present an Ad-hoc approach as the operational
foundation for the development of a novel travel time estimation algorithm, called Modified
Cumulative Traffic Counts Method (MCTC). Based on a data fusion paradigm, we combine in real
time multiple evidence derived from two complementary sources to feed our MCTC inference
engine and attempt to best estimate prevailing travel time. Our approach has as its main advantages
the modeling power of Theory of Evidence in expressing beliefs in some hypotheses, the ability to
add the notions of uncertainty in terms of confidence interval. We evaluate our travel estimation
algorithm prototype through a set of experiments that were conducted with real network traffic. We
conclude that data fusion is a promising approach as it increases the estimation and prediction
capability of our MCTC algorithm and increase the robustness of the estimation process.
This paper addresses the road travel time estimation on an urban axis by classification method based on evidence theory. The travel time (TT) indicator can be used either for traffic management or for drivers' information. The information used to estimate the travel time (induction loop sensor, cameras, probe vehicle,...) is complementary and redundant. It is then necessary to implement strategies of multi-sensors data fusion. The selected framework is the evidence theory. This theory takes more into account the imprecision and uncertainty of multisource information. Two strategies were implemented. The first one is classifier fusion where each information source, was considered as a classifier. The second approach is a distance-based classification for belief functions modelling. Results of these approaches, on data collected on an urban axis in the South of France, show the outperformance of fusion strategies within this application.
The principal motivation for combining estimators has been to avoid the a priori choice of which estimation
method to use, by attempting to aggregate all the information which each estimation model embodies.
In selecting the "best" model, one is often discarding useful independent evidence in those models which are
rejected. This paper deals with estimation fusion; that is, data fusion for the purpose of estimation. More
specifically, estimation fusion is studied under heterogeneous data source configurations.
Two estimation fusion schemes could be considered: projective and aggregative. An unified linear model and
general framework for later schemes are established. Explicit optimal fusion strategies in the sense of the best
linear estimation and weighted least squares are presented. The evaluation of the effectiveness of the proposed
schemes was conducted on the traffic application, namely, travel time estimation in a given path of a road
network.
In this problem, data comes from sensors and other sources of information geographically distributed where
communication limitations and other considerations often eliminate the possibility of transmitting the observations
into a central node processing where computation is performed.
The objective of this paper is to present an analysis of recent applications of data fusion (DF) in road traffic engineering.
First, we report the most significant applications of data fusion techniques in road traffic engineering area: traffic
monitoring, signal control, Automatic Incident Detection, traffic forecasting, Intelligent Transportation Systems ..., as
well as the extent and direction of DF interest in the field. Second, a classification including applications, fusion goals
and mathematical tools is proposed.
Conference Committee Involvement (4)
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2008
19 March 2008 | Orlando, Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007
11 April 2007 | Orlando, Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006
19 April 2006 | Orlando (Kissimmee), Florida, United States
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005
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