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

Abstract 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.

[1]  Xuan Song,et al.  GPS data in urban online ride-hailing: A comparative analysis on fuel consumption and emissions , 2019, Journal of Cleaner Production.

[2]  Jianlei Lang,et al.  Air pollutant emissions from on-road vehicles in China, 1999-2011. , 2014, The Science of the total environment.

[3]  Yi-Ming Wei,et al.  How app-based ride-hailing services influence travel behavior: An empirical study from China , 2020, International Journal of Sustainable Transportation.

[4]  Xusen Cheng,et al.  Does Subsidy Work? An Investigation of Post-Adoption Switching on Car-Hailing Apps , 2016 .

[5]  Ryosuke Shibasaki,et al.  Mobile phone GPS data in urban bicycle-sharing: Layout optimization and emissions reduction analysis , 2019, Applied Energy.

[6]  Rui Xie,et al.  The effects of transportation infrastructure on urban carbon emissions , 2017 .

[7]  Zhaoyuan Yu,et al.  Carbon emission flow from self-driving tours and its spatial relationship with scenic spots - a traffic-related big data method. , 2017 .

[8]  Judd N. L. Cramer,et al.  Disruptive Change in the Taxi Business: The Case of Uber , 2016 .

[9]  Michal Maciejewski,et al.  Simulation-based optimization of service areas for pooled ride-hailing operators , 2018, ANT/SEIT.

[10]  Irene Celino,et al.  City data dating: Emerging affinities between diverse urban datasets , 2016, Inf. Syst..

[11]  Susan Shaheen,et al.  Carsharing and Personal Vehicle Services: Worldwide Market Developments and Emerging Trends , 2013 .

[12]  Shaojun Zhang,et al.  Mapping dynamic road emissions for a megacity by using open-access traffic congestion index data , 2020 .

[13]  Leo G. Kroon,et al.  Crowdsourced Delivery - A Dynamic Pickup and Delivery Problem with Ad Hoc Drivers , 2016, Transp. Sci..

[14]  R. Shibasaki,et al.  Battery electric vehicles in Japan: Human mobile behavior based adoption potential analysis and policy target response , 2018, Applied Energy.

[15]  Evangelos Mitsakis,et al.  Agent based modeling for simulation of taxi services , 2013 .

[16]  D. Sun,et al.  Analyzing spatiotemporal traffic line source emissions based on massive didi online car-hailing service data , 2018, Transportation Research Part D: Transport and Environment.

[17]  Jingzheng Ren,et al.  Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China , 2017 .

[18]  Tian Wu,et al.  Development and application of an energy use and CO2 emissions reduction evaluation model for China's online car hailing services , 2018, Energy.

[19]  R. Cervero,et al.  Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco , 2016 .

[20]  Yung-Hsiang Cheng,et al.  Urban transportation energy and carbon dioxide emission reduction strategies☆ , 2015, Applied Energy.

[21]  Michal Maciejewski,et al.  Large-scale Microscopic Simulation of Taxi Services , 2015, ANT/SEIT.

[22]  Elliot W. Martin,et al.  Impact of Carsharing on Household Vehicle Holdings , 2010 .

[23]  Stuart J. Barnes,et al.  Opportunities or threats: The rise of Online Collaborative Consumption (OCC) and its impact on new car sales , 2018, Electron. Commer. Res. Appl..

[24]  Chandra R. Bhat,et al.  Investigating objective and subjective factors influencing the adoption, frequency, and characteristics of ride-hailing trips , 2019, Transportation Research Part C: Emerging Technologies.

[25]  P. Santi,et al.  Addressing the minimum fleet problem in on-demand urban mobility , 2018, Nature.

[26]  Elliot W. Martin,et al.  Greenhouse Gas Emission Impacts of Carsharing in North America , 2011, IEEE Transactions on Intelligent Transportation Systems.

[27]  S. Xie,et al.  Estimation of vehicular emission inventories in China from 1980 to 2005 , 2007 .

[28]  Denis Marcotte,et al.  Gibbs sampling on large lattice with GMRF , 2018, Comput. Geosci..

[29]  Karla Hoffman,et al.  Decision diagrams for solving traveling salesman problems with pickup and delivery in real time , 2019, Oper. Res. Lett..

[30]  Alejandro Henao,et al.  The impact of ride-hailing on vehicle miles traveled , 2018, Transportation.