Cost-effectiveness analysis of Origin-Destination matrices estimation using Floating Car Data. Experimental results from two real cases

Transportation Research Procedia(2022)

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摘要
The aim of this paper is to estimate static Origin-Destination (OD) matrices combining traditional data sources and Floating Car Data (FCD), testing several scenarios with different penetration rates and representativeness of probe vehicles. For each scenario, the accuracy of results is related to the cost of the data acquisition. To the best of the authors’ knowledge, this paper represents the first attempt to define a relationship among FCD penetration rate and representativeness, traditional data sources, OD matrices accuracy and cost of estimation, thus contributing to the literature on the subject and providing useful considerations for practical applications. The procedure was applied to two real cases for which ground-truth matrices were available, in order to calculate goodness of fit indicators for the estimated matrices. In this way, it was possible to define optimal solutions, which can maximize the reliability of results and minimize their cost. Results highlighted that the representativeness of probe vehicles is the main factor affecting the difference between estimated and real matrices. Moreover, when heterogeneity of penetration rates among OD pairs occurs, estimation errors can be reduced by the availability of traffic counts from on-site observations, although increasing costs. For this reason, optimal solutions were identified among scenarios balancing economic effort and estimation performance. In addition, the use of FCD was found to be competitive with procedures exclusively based on traditional on-field activities, both in terms of obtained OD matrix accuracy and costs.
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关键词
Floating Car Data,OD matrix estimation,big data,cost-effectiveness analysis,multi-objective optimization
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