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      City-scale car traffic and parking density maps from Uber Movement travel time data

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          Abstract

          Car parking is of central importance to congestion on roads and the urban planning process of optimizing road networks, pricing parking lots and planning land use. The efficient placement, sizing and grid connection of charging stations for electric cars makes it even more important to know the spatio-temporal distribution of car parking densities on the scale of entire cities. Here, we generate car parking density maps using travel time measurements only. We formulate a Hidden Markov Model that contains non-linear functional relationships between the changing average travel times among the zones of a city and both the traffic activity and flow direction probabilities of cars. We then sample the traffic flow for 1,000 cars per city zone for each city from these probability distributions and normalize the resulting spatial parking distribution of cars in each time step. Our results cover the years 2015–2018 for 34 cities worldwide. We validate the model for Melbourne and reach about 90% accuracy for parking densities and over 93% for circadian rhythms of traffic activity.

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          Most cited references19

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          A cellular automaton model for freeway traffic

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            Experimental Features of Self-Organization in Traffic Flow

            B. Kerner (1998)
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              Cruising for parking

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                Author and article information

                Contributors
                arsam.aryandoust@usys.ethz.ch
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                21 August 2019
                21 August 2019
                2019
                : 6
                : 158
                Affiliations
                ISNI 0000 0001 2156 2780, GRID grid.5801.c, Climate Policy Research Group, Environmental Systems Science Department, , Swiss Federal Institute of Technology (ETH Zurich), ; Zurich, Switzerland
                Author information
                http://orcid.org/0000-0002-3964-428X
                Article
                159
                10.1038/s41597-019-0159-6
                6704179
                31434904
                5197c28f-26bd-4fbc-906b-d9cc2a8d4932
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 March 2019
                : 24 July 2019
                Categories
                Analysis
                Custom metadata
                © The Author(s) 2019

                civil engineering,environmental impact,energy modelling

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