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Frontiers of Computer Science in China : Volume 11

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Urban computing: enabling urban intelligence with big data

A survey of network update in SDN

Image categorization with resource constraints: introduction, challenges and advances

An identification model of urban critical links with macroscopic fundamental diagram theory

Understanding bike trip patterns leveraging bike sharing system open data

Real-time and generic queue time estimation based on mobile crowdsensing

ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints

HWM: a hybrid workload migration mechanism of metadata server cluster in data center

An efficient and highly available framework of data recency enhancement for eventually consistent data stores

Allocating workload to minimize the power consumption of data centers

Fast counting the cardinality of flows for big traffic over sliding windows

Understanding co-run performance on CPU-GPU integrated processors: observations, insights, directions

A genetic algorithm based entity resolution approach with active learning

Extracting optimal actionable plans from additive tree models

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Understanding bike trip patterns leveraging bike sharing system open data

Content Provider	Springer Nature Link
Author	Chen, Longbiao Ma, Xiaojuan Nguyen, Thi Mai Trang Pan, Gang Jakubowicz, Jérémie
Copyright Year	2017
Abstract	Bike sharing systems are booming globally as a green and flexible transportationmode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and station management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips (i.e., origin-destination station pairs) is much larger than the number of stations, we define the trip inference as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data fromWashington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.
Starting Page	38
Ending Page	48
Page Count	11
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	11
Issue Number	1
e-ISSN	20952236
Language	English
Publisher	Higher Education Press
Publisher Date	2017-04-07
Publisher Institution	Chinese Universities
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	bike sharing system open data ill-posed inverse problems urban computing Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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Inferring bike trip patterns from bike sharing system open data

Bike sharing station placement leveraging heterogeneous urban open data

Applications of Haar basis method for solving some ill-posed inverse problems

Recovering the Initial Distribution for a Time-Fractional Diffusion Equation

Quadratic optimization in ill-posed problems

Generalized sampling: extension to frames and inverse and ill-posed problems

Solving ill-posed Image Processing problems using Data Assimilation

Data based regularization for discrete deconvolution problems

Morozov-type discrepancy principle for nonlinear ill-posed problems under η-condition

Understanding bike trip patterns leveraging bike sharing system open data

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