GPS data in urban online ride-hailing: A simulation method to evaluate impact of user scale on emission performance of system

JOURNAL OF CLEANER PRODUCTION

卷:287

Article

Chen, Jinyu¹  Li, Wenjing¹  Zhang, Haoran¹  Cai, Zekun¹  Sui, Yi^2,3  Long, Yin⁴  Song, Xuan⁵  Shibasaki, Ryosuke¹ 

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

[2] Qingdao Univ, Coll Comp Sci & Technol, Ningxia Rd 308, Qingdao 266071, Peoples R China

[3] Inst Smart City & Big Data Technol, Ningxia Rd 308, Qingdao 266071, Peoples R China

[4] Univ Tokyo, Grad Sch Frontier Sci, Dept Environm Syst, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778563, Japan

[5] Southern Univ Sci & Technol SUSTech, Dept Comp Sci & Engn, SUSTech UTokyo Joint Res Ctr Super Smart City, Shenzhen, Peoples R China

关键词: GPS Data;    Ride-hailing;    User scale;    Emission performance;   

DOI  :  10.1016/j.jclepro.2020.125567

来源: Elsevier

【 摘 要 】

With the spread of the ride-hailing service over the world, many scholars still argue whether ride-hailing is an effective travel mode for emission reduction. Since rider-hailing is a crowdsourcing system, the user scale can have a great impact on the emission performance of the system. A clearer pattern of the impact of user scale on emission performance of the ride-hailing system should be provided for better local market development and policymaking. In this study, based on massive Didi GPS records, we proposed a cross simulation model to evaluate the impact of user scale on the emission performance of the ride hailing system and adapted the Gibbs sampling for a comprehensive computation. The result shows a strong impact of user scale on the emission performance. The mean of void cruising distance proportion varies from 2.12% to 44.58% under all situation simulation. Moreover, according to the simulation results under different day conditions, the relationship between the user scale and emission performance is not concerned with the day condition but the local regular travel pattern. Based on this relationship, we provided approximate user scales under expected thresholds of the emission and efficiency performance of ride-hailing. This work can be a foundation and guideline for future decision making on ride-hailing. (c) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free