Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships

Despite the growing body of research on ride-hailing travel behaviors in Western countries, empirical evidence for changes in travel patterns resulting from the use of app-based services in developing countries remains rare. This study explores factors affecting an Iranian on-demand ride service called Snapp Taxi by using a comprehensive dataset collected from 22 municipality zones in metropolitan Tehran (N = 582). Our conceptual framework emphasizes the transportation mode choice effects of technology adoption, travel mode, ride-sourcing attributes, individual attitudes, land use measures, residential attributes, and socio-economic characteristics of the respondents. Results from Structural Equation Models (SEM) show that factors such as cost effectiveness, trip security, anti-shared mobility, and technology-oriented riders have a significant impact on travel mode choice and the frequency of ride-hailing trips. This study suggests that individuals who prefer driving and semi-public transit also have higher numbers of Snapp trips than other demographics. According to our findings, on-demand ride services could complement or compete with other modes of transport, especially in areas with limited access to public transit. However, the presence of ride-hailing services does not necessarily result in fewer car trips if the service operates as a private (single-party occupancy) vehicle and not as a shared mobility option.

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Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

Journal of Transport Geography ◽

10.1016/j.jtrangeo.2021.103134 ◽

2021 ◽

Vol 95 ◽

pp. 103134

Author(s):

Jinjun Tang ◽

Fan Gao ◽

Chunyang Han ◽

Xuekai Cen ◽

Zhitao Li

Keyword(s):

Public Transit ◽

Heterogeneous Effects ◽

Shared Mobility

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Partnerships with Technology-Enabled Mobility Companies

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2649-12 ◽

2017 ◽

Vol 2649 (1) ◽

pp. 106-112 ◽

Cited By ~ 6

Author(s):

Marla Westervelt ◽

Joshua Schank ◽

Emma Huang

Keyword(s):

Service Delivery ◽

Public Transit ◽

Transportation Network ◽

Public Agencies ◽

On Demand ◽

Transportation Network Companies

The rise and the proliferation of the on-demand economy are creating a new mobility marketplace. This research explored how these new options could be synergistic with public transit models and detailed the experiences of two transit operators that entered into service delivery partnerships with a transportation network company and a micro-transit operator. Based on a series of interviews and the experiences of these two public agencies, this research provides a set of key takeaways and recommendations for transit operators exploring the potential of partnering with new mobility services such as transportation network companies (e.g., Uber or Lyft) and microtransit (e.g., Bridj or Via).

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Who uses Transport Network Companies?: Characterization of Demand and its Relationship with Public Transit in Medellín

10.18235/0003621 ◽

2021 ◽

Author(s):

Felipe Bedoya-Maya ◽

Lynn Scholl ◽

Orlando Sabogal-Cardona ◽

Daniel Oviedo

Keyword(s):

Latin American ◽

Public Transit ◽

Descriptive Analysis ◽

Discrete Choice Models ◽

Transport Network ◽

Gender Effects ◽

Transport Systems ◽

Factors Affecting ◽

On Demand ◽

Latin American Region

Transport Network Companies (TNCs) have become a popular alternative for mobility due to their ability to provide on-demand flexible mobility services. By offering smartphone-based, ride-hailing services capable of satisfying specific travel needs, these modes have transformed urban mobility worldwide. However, to-date, few studies have examined the impacts in the Latin American context. This analysis is a critical first step in developing policies to promote efficient and sustainable transport systems in the Latin-American region. This research examines the factors affecting the adoption of on-demand ride services in Medellín, Colombia. It also explores whether these are substituting or competing with public transit. First, it provides a descriptive analysis in which we relate the usage of platform-based services with neighborhood characteristics, socioeconomic information of individuals and families, and trip-level details. Next, factors contributing to the election of platform-based services modeled using discrete choice models. The results show that wealthy and highly educated families with low vehicle availability are more likely to use TNCs compared to other groups in Medellín. Evidence also points at gender effects, with being female significantly increasing the probability of using a TNC service. Finally, we observe both transit complementary and substitution patterns of use, depending on the context and by whom the service is requested.

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On-Demand Public Transit: A Markovian Continuous Approximation Model

Transportation Science ◽

10.1287/trsc.2021.1063 ◽

2021 ◽

Author(s):

Daniel F. Silva ◽

Alexander Vinel ◽

Bekircan Kirkici

Keyword(s):

System Performance ◽

Performance Metrics ◽

Public Transit ◽

Continuous Approximation ◽

Efficient Operation ◽

Fleet Sizing ◽

On Demand ◽

Single Vehicle ◽

Transit Agencies ◽

Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

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