Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis. (15th May 2019)

• Record Type: Journal Article
• Authors: Zhang, Haoran; Song, Xuan; Long, Yin; Xia, Tianqi; Fang, Kai; Zheng, Jianqin; Huang, Dou; Shibasaki, Ryosuke; Liang, Yongtu
• Is Part Of: Applied energy. Volume 242(2019)
• Page Start: 138
• Page End: 147
• Publication Date: 2019-05-15
• Subjects: Bicycle-sharing -- Geometry-based probability model -- Particle swarm optimization -- Rebalancing optimization -- Potential emission reduction; MD Medium truck -- ILP Integer linear programming -- LGT Light truck -- MILP Mixed-integer linear programming -- MINLP Mixed-integer nonlinear programming -- OD Origin–destination -- PoI Points of interest -- PSO Particle swarm optimization -- VCR Velocity change rate -- VRP Vehicle routing problem; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.03.119
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: 3.7 million trajectories data are adopted to develop an integrated method. A geometry-based probability model is proposed for uncertainties of the issue. A multi-scenario programming model is proposed for rebalancing operations. A multi-sided sensitivity analysis is made for potential emissions reduction. Abstract: As a representation of smart and sustainable city development, bicycle-sharing system is one of the hottest topics in the domains of transportation, public health, urban planning, and so on. In this paper, a model is proposed for analyzing the potential reduction in emissions associated with the adoption of a bicycle-sharing system. Methods are proposed for extracting human travel modes from mobile phone GPS trajectories, together with a geometry-based probability model, to support particle swarm optimization. A comparison study is implemented to analyze the model's computational efficiency. Based on the resulting optimal layout for the network of bicycle docking stations, and considering demand uncertainty, a multi-scenario integer linear programming model is proposed to optimize rebalancing procedures (i.e., moving bicycles between docking stations according to demand), to determine the detailed design-scale information required. Mobile phone GPS trajectories from approximately 3.7 million local mobilities are used to construct a case study for Setagaya Ward, Tokyo. The results show that, compared with the previous methods, the optimal layout solved by the proposed method could reduce emissions by a further 6.4% and 4.4%. With an increase from 30 to 90 bicycle stations, the adoption of bicycle-sharing can reduce CO2 emissions by approximately 3.1–3.8 thousand tonnes. However, emission reduction will maximally decrease by 21.26% after offset by bicycles production and rebalancing-generated emission.