Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential

Zhang，Haoran1,2; Chen，Jinyu2; Li，Wenjing2; Song，Xuan1,2; Shibasaki，Ryosuke2

2020-07-01

10.1016/j.apenergy.2020.115038

APPLIED ENERGY   影响因子和分区

0306-2619

1872-9118

Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing of car journeys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO is estimated to be reduced by 84.52%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56% of CO emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers.

Deep learning Potential emission reduction Ride-sharing Users matching

SCI ; EI ; SSCI

英语

第一 ; 通讯

Japan Ministry of Education, Culture, Sports, Science, and Technology (MEXT)[17H01784]

Energy & Fuels ; Engineering

Energy & Fuels ; Engineering, Chemical

WOS:000537619800030

ELSEVIER SCI LTD

20201808591278

Decision making ; Behavioral research ; Air quality ; Carbon dioxide ; Learning systems ; Deep learning ; Digital storage ; Urban transportation ; Emission control

Highway Transportation:432 ; Railroad Transportation:433 ; Air Pollution Control:451.2 ; Ergonomics and Human Factors Engineering:461.4 ; Data Storage, Equipment and Techniques:722.1 ; Inorganic Compounds:804.2 ; Management:912.2 ; Social Sciences:971

ENGINEERING

2-s2.0-85083862149

Scopus

1.SUSTech-UTokyo Joint Research Center on Super Smart City,Department of Computer Science and Engineering,Southern University of Science and Technology (SUSTech),Shenzhen,China
2.University of Tokyo,Center for Spatial Information Science,Kashiwa,Japan

Zhang，Haoran,Chen，Jinyu,Li，Wenjing,et al. Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential[J]. APPLIED ENERGY,2020,269.

Zhang，Haoran,Chen，Jinyu,Li，Wenjing,Song，Xuan,&Shibasaki，Ryosuke.(2020).Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential.APPLIED ENERGY,269.

Zhang，Haoran,et al."Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential".APPLIED ENERGY 269(2020).