MaaS in Bike-Sharing: Smart Phone GPS Data Based Layout Optimization and Emission Reduction Potential Analysis

被引：15

作者：

Zhang, Haoran ^{[1
,2
]}

Song, Xuan ^{[1
,3
]}

Xi, Tianqi ^{[1
]}

Zheng, Jianqin ^{[2
]}

Haung, Dou ^{[1
]}

Shibasaki, Ryosuke ^{[1
]}

Yan, Yamin ^{[2
]}

Liang, Yongtu ^{[2
]}

机构：

[1] Univ Tokyo, Ctr Spatial Informat Sci, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778568, Japan

[2] China Univ Petr, Beijing Key Lab Urban Oil & Gas Distribut Technol, Fuxue Rd 18, Changping, Peoples R China

[3] Natl Inst Adv Ind Sci & Technol, Artificial Intelligence Res Ctr, Koto Ku, 2-3-26 Aomi, Tokyo 1350064, Japan

来源：

CLEANER ENERGY FOR CLEANER CITIES | 2018年 / 152卷

关键词：

Bike-sharing; Mobility as a Service; Layout optimization; Emission reduction potential; PARTICLE SWARM; BEHAVIOR;

D O I：

10.1016/j.egypro.2018.09.225

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

As a representation of smart and green city development, bike-sharing system is one of the hottest topic in the fields of transportation, public health, urban planning, and so on. With the development of Mobility as a Service (MaaS), emerging technologies such as mobile data mining give some new solutions for optimizing bike-sharing system and predicting the emission reduction. Here, we propose a bike-sharing layout optimization and emission reduction potential analysis structure under the concept of MaaS. A human travel mode detection method and a geometry-based probability model are proposed to support the particle swarm optimization process. We implement a comparison study to analyze the computational efficiency. Taking Setagaya ward, Tokyo as the study case with about 3 million GPS trajectories, the result shows that with the increase of station number from 30 to 90, the adoption of bike-sharing system can reduce about 3.1-3.8 thousand tonnes of CO2 emission. Copyright (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：649 / 654

页数：6

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