A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing
نویسندگان
چکیده
منابع مشابه
Prediction of Bike Sharing Demand
Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with z...
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Recent network analysis research has made remarkable progress on studying complex networks in a social context with machine learning techniques. One example of such a complex network is the system data of the New York Citibike bike sharing program, which consists of the data from 49 million bike trips in New York City since July 2013. By inspecting the dataset, we find out that significantly mo...
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We study the problem of future bike availability prediction of a bike station through the moment analysis of a PCTMC model with time-dependent rates. Given a target station for prediction, the moments of the number of available bikes in the station at a future time can be derived by a set of moment equations with an initial set-up given by the snapshot of the current state of all stations in th...
متن کاملService Network Design of Bike Sharing Systems
Bike sharing has recently enabled sustainable means of shared mobility through automated rental stations in metropolitan areas. Spatio-temporal variation of bike rentals leads to imbalances in the distribution of bikes causing full or empty stations in the course of a day. Ensuring the reliable provision of bikes and bike racks is crucial for the viability of these systems. This paper presents ...
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In this project, two different approaches to predict Bike Sharing Demand are studied. The first approach tries to predict the exact number of bikes that will be rented using Support Vector Machines (SVM). The second approach tries to classify the demand into 5 different levels from 1 (lowest) to 5 (highest) using Softmax Regression and Support Vector Machines. Index Terms –regression, classific...
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ژورنال
عنوان ژورنال: Complexity
سال: 2019
ISSN: 1076-2787,1099-0526
DOI: 10.1155/2019/7643905