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      The impact of rainfall on the temporal and spatial distribution of taxi passengers

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          Abstract

          This paper focuses on the impact of rainfall on the temporal and spatial distribution of taxi passengers. The main objective is to provide guidance for taxi scheduling on rainy days. To this end, we take the occupied and empty states of taxis as units of analysis. By matching a taxi's GPS data to its taximeter data, we can obtain the taxi's operational time and the taxi driver's income from every unit of analysis. The ratio of taxi operation time to taxi drivers' income is used to measure the quality of taxi passengers. The research results show that the spatio-temporal evolution of urban taxi service demand differs based on rainfall conditions and hours of operation. During non-rush hours, taxi demand in peripheral areas is significantly reduced under increasing precipitation conditions, whereas during rush hours, the demand for highly profitable taxi services steadily increases. Thus, as an intelligent response for taxi operations and dispatching, taxi services should guide cruising taxis to high-demand regions to increase their service time and ride opportunities.

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          Most cited references28

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          Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

          This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.
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            Labor Supply of New York City Cabdrivers: One Day at a Time

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              The impact of climate change and weather on transport: An overview of empirical findings

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                Author and article information

                Contributors
                Role: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Project administration
                Role: Formal analysisRole: Visualization
                Role: Software
                Role: Software
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                5 September 2017
                2017
                : 12
                : 9
                : e0183574
                Affiliations
                [001]School of Transportation, Southeast University, Nanjing, Jiangsu Province, China
                Beihang University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-6606-4275
                Article
                PONE-D-16-47889
                10.1371/journal.pone.0183574
                5584943
                28873430
                708600cb-2eae-4cb0-a3e8-2871bdbd5eaa
                © 2017 Chen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 December 2016
                : 7 August 2017
                Page count
                Figures: 7, Tables: 4, Pages: 16
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: No.71372198
                Award Recipient :
                Funded by: Jiangsu Science and Technology Department
                Award ID: BY2015070-25
                Award Recipient :
                Funded by: Key Technology of Charging Station Network Construction and Operation in Nanjing
                Award ID: Ks1513
                Award Recipient :
                This study was funded by the National Natural Science Foundation of China (No. 71372198), the Jiangsu Science and Technology Department (BY2015070-25), and the Key Technology of Charging Station Network Construction and Operation in Nanjing (Ks1513) to YZ. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Earth Sciences
                Atmospheric Science
                Meteorology
                Rain
                Engineering and Technology
                Transportation
                People and Places
                Geographical Locations
                Asia
                China
                Engineering and Technology
                Navigation
                Global Positioning System
                Computer and Information Sciences
                Information Technology
                Data Processing
                Earth Sciences
                Atmospheric Science
                Meteorology
                Weather
                Physical Sciences
                Mathematics
                Numerical Analysis
                Interpolation
                Earth Sciences
                Geography
                Cartography
                Longitude
                Custom metadata
                Data available from the Dryad Digital Respository: http://dx.doi.org/10.5061/dryad.kk6bs.

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                Uncategorized

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