Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area

In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socio-economic, and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.

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“…Finally, FCD resulted also useful in estimating the origin-destination matrix [347][348][349] and understanding travel choice behavior [350].…”

Section: Advanced Traffic Management System (Atms)mentioning

confidence: 99%

Sustainable Mobility: A Review of Possible Actions and Policies

Gallo

Marinelli

2020

Sustainability

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“…Set up two mixed data sets V i and V k in two different subspaces M i and M k and use D(i, k) to represent the Euclidean distance of two different subspaces and d(i, k) to represent the Euclidean distance of two mixed data sets [48], [49]. The high-dimensional clustering formula of two mixed data sets of two different subspaces:…”

Section: ) Clustering Analysis Of Multidimensional Subspace Data Setmentioning

confidence: 99%

High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

2020

IEEE Access

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In order to address the problem that high energy consumption, high memory usage and low clustering effect in traditional data set high-dimensional clustering algorithms, we propose the highdimensional clustering algorithm of incomplete mixed data set based on artificial intelligence. First, we construct the phase space reconstruction to ensure the invariance of features of incomplete mixed data set by analyzing the incomplete mixed data set and introduce the correlation dimension to obtain the feature correlation value. Second, we introduce the standard deviation and realize the extraction of features of incomplete mixed data set through calculating the sparsity of sample features. Third, we conduct repeated clustering for the mixed data set in the subspace according to the degree of correlation between incomplete mixed data sets in the multidimensional subspace. Last, we realize the design of high-dimensional clustering method for incomplete mixed data set in accordance with the stronger relevance in the mixed data sets. Experimental results show that the proposed algorithm has good correlation dimension processing effects, lower memory usage, time-consuming, lower and concentrated ensemble energy consumption (within 300J), good clustering effects, as high as 92%, which has some advantages and practical application value.

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“…As these above four log-likelihood formulations cannot take a closed form, they are approximated by simulation method. Then (9-12) are rewritten by (13)(14)(15)(16), respectively…”

Section: Simulation-based Estimationmentioning

confidence: 99%

Non‐linear fixed and multi‐level random effects of origin–destination specific attributes on route choice behaviour

Miwa

et al. 2018

IET Intelligent Transport Systems

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Experimental analyses and clustering of travel choice behaviours by floating car big data in a large urban area

Cited by 26 publications

References 23 publications

Sustainable Mobility: A Review of Possible Actions and Policies

Sustainable Mobility: A Review of Possible Actions and Policies

High-Dimensional Clustering for Incomplete Mixed Dataset Using Artificial Intelligence

Non‐linear fixed and multi‐level random effects of origin–destination specific attributes on route choice behaviour

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