Assessing the welfare impacts of shared mobility and Mobility as a Service (MaaS)

Mobility as a Service (MaaS) aims to allow less biased mode choice decisions by overcoming market segmentation. To this end, all available modes are offered at their respective marginal cost for each trip. Such a setting favors shared modes, where fixed costs can be apportioned among a large number of users. In turn, car-sharing, bike-sharing or ridehailing may themselves become an efficient alternative of public transport. Although early field studies confirm the expected changes in behaviour, impacts have not been studied for larger transport systems yet. This research conducts a first joint simulation of car-sharing, bike-sharing and ride-hailing for a city-scale transport system using MATSim. Results show that in Zurich, through less biased mode choice decisions, transportrelated energy consumption can be reduced by 25 %. In addition, introduction of shared modes may increase transport system efficiency by up to 7 %. Efficiency gains may reach 11 % if shared modes were used as a substitute for public transport in lower-density areas. Hence, a MaaS scheme with shared mobility allows to increase system efficiency (travel times & cost), while substantially reducing energy consumption.

[1]  Qing Li,et al.  Incorporating free-floating car-sharing into an activity-based dynamic user equilibrium model: A demand-side model , 2018 .

[2]  Jana L. Sochor,et al.  Mobility as a Service: Development scenarios and implications for public transport , 2018, Research in Transportation Economics.

[3]  Hani S. Mahmassani,et al.  Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach , 2017 .

[4]  Sebastian Hörl,et al.  Agent-based simulation of autonomous taxi services with dynamic demand responses , 2017, ANT/SEIT.

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

[6]  Francesco Ciari,et al.  Modeling the impact of parking price policy on free-floating carsharing: Case study for Zurich, Switzerland , 2017 .

[7]  Robert Cervero,et al.  Mobility Niches: Jitneys to Robo-Taxis , 2017 .

[8]  Melinda Matyas,et al.  Stated Preference Design for Exploring Demand for “Mobility as a Service” Plans , 2017 .

[9]  J. Nelson,et al.  Flexible Transport Services: A New Market Opportunity for Public Transport , 2009 .

[10]  Ruimin Li,et al.  Dynamic Pricing in Shared Mobility on Demand Service , 2018, 1802.03559.

[11]  Gonçalo Homem de Almeida Correia,et al.  Carsharing systems demand estimation and defined operations: a literature review , 2013, European Journal of Transport and Infrastructure Research.

[12]  Francesco Ciari,et al.  How Disruptive Can Shared Mobility Be? A Scenario-Based Evaluation of Shared Mobility Systems Implemented at Large Scale , 2016 .

[13]  Elliot K. Fishman,et al.  Bikeshare: A Review of Recent Literature , 2016 .

[14]  Paul Schonfeld,et al.  Maximizing net benefits for conventional and flexible bus services , 2015 .

[15]  Kay W. Axhausen,et al.  Modeling free-floating car-sharing use in Switzerland: A spatial regression and conditional logit approach , 2017 .

[16]  Kay W. Axhausen,et al.  Carsharing Demand Estimation , 2015 .

[17]  C. Mulley Mobility as a Services (MaaS) – does it have critical mass? , 2017 .

[18]  Helena Strömberg,et al.  Trying Out Mobility as a Service: Experiences from a Field Trial and Implications for Understanding Demand , 2016 .

[19]  Kay W. Axhausen,et al.  The Multi-Agent Transport Simulation , 2016 .

[20]  Frances Sprei,et al.  Comparison of free-floating car-sharing services in cities , 2017 .

[21]  Yu-Hsin Tsai,et al.  City CarShare in San Francisco, California: Second-Year Travel Demand and Car Ownership Impacts , 2004 .

[22]  Joseph Y. J. Chow,et al.  An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market , 2017 .

[23]  Hai Yang,et al.  Economic Analysis of Ride-sourcing Markets , 2016 .

[24]  Monica Menendez,et al.  Empirics of multi-modal traffic networks – Using the 3D macroscopic fundamental diagram , 2017 .

[25]  David A. Hensher,et al.  Future bus transport contracts under a mobility as a service (MaaS) regime in the digital age: Are they likely to change? , 2017 .

[26]  Tal Raviv,et al.  Static repositioning in a bike-sharing system: models and solution approaches , 2013, EURO J. Transp. Logist..

[27]  Kay W. Axhausen,et al.  Changes in Swiss Accessibility since 1850 , 2005 .

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

[29]  W. Deng,et al.  Ridership and effectiveness of bikesharing: The effects of urban features and system characteristics on daily use and turnover rate of public bikes in China , 2014 .

[30]  Kay W. Axhausen,et al.  A first look at bridging discrete choice modeling and agent-based microsimulation in MATSim , 2018, ANT/SEIT.

[31]  Kay W. Axhausen,et al.  Cost-based analysis of autonomous mobility services , 2017 .

[32]  Kay W. Axhausen,et al.  Modeling car-sharing membership as a mobility tool: A multivariate Probit approach with latent variables , 2017 .

[33]  Kay W. Axhausen,et al.  Comparing Car-Sharing Schemes in Switzerland: User Groups and Usage Patterns , 2016 .

[34]  Xiugang Li,et al.  Feeder transit services: Choosing between fixed and demand responsive policy , 2010 .