Abstract
SummaryTraffic data fusion has much to do with combining available or considered data sources in the best possible way. In this, it is very similar to optimizing a portfolio of financial assets in regard of return and risk. This article draws the analogy between these two mostly different scientific worlds, i.e. finance and engineering. Similarities and differences in context of weighted‐mean data fusion based on numerical traffic flow measurements such as travel times or speeds are discussed. This, in particular, includes guessing the potential benefit of negative weights. Optimal weights are derived following a strict mathematical theory based on assumptions (parameters) about systematic bias and correlations of the considered data sources. Moreover, a specific way of reducing the systematic bias of the fusion results is proposed and compared to common methods. The whole approach is demonstrated based on position data from two independent vehicle fleets in Athens, Greece. In this context, the problem of parameter calibration is solved by applying an advanced tool for such floating car data systems, called “self‐evaluation”. The experiments show that the proposed methods reliably reduce the systematic bias and variance of the fusion results with regard to the original data as well as in comparison to the naïve fusion approach that uses equal weights for all data sources. Copyright © 2015 John Wiley & Sons, Ltd.
Highlights
Data fusion has become “an inevitable tool” [1] in connection with intelligent transportation systems (ITS) over the last decades
The authors distinguish between statistical methods, probabilistic approaches (e.g. Bayesian inference, Kalman filtering) and techniques based on artificial intelligence
The relevant literature mentions several layers of data fusion ranging from basic refinements of measurement signals to higherlevel aggregation of information in order to fully describe the current state of a larger system, e.g. the overall efficiency of the transport network of a city
Summary
Data fusion has become “an inevitable tool” [1] in connection with intelligent transportation systems (ITS) over the last decades. The relevant literature (cf [4, 5]) mentions several layers of data fusion ranging from basic refinements of measurement signals to higherlevel aggregation of information in order to fully describe the current state of a larger system, e.g. the overall efficiency of the transport network of a city Within this extensive framework, the present article focuses on the integration of pre-processed traffic data of one type, e.g. mean travel times or mean speeds, at the level of single road sections, thereby. The section starts with a very short review of the principles of portfolio optimization (see Section 2.1) and draws the analogy between finance and traffic data fusion (see Section 2.2) As a result, this formal correspondence generates a new level of understanding of what happens in context of weighted-mean data fusion as in (1). The article ends up with some conclusions (see Section 5) including a short discussion of the fundamental difference of combining biased or unbiased input data in terms of the achievable accuracy (i.e. variance) of the fusion result
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