Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

Xie, Yifei; Danaf, Mazen; Azevedo, Carlos Lima; Akkinepally, Arun; Atasoy, Bilge; Jeong, Kyungsoo; Seshadri, Ravi; Ben‐Akiva, Moshe

doi:10.1007/s11116-019-10011-z

Cited by 39 publications

(29 citation statements)

References 24 publications

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“…Millennials (16-34 years) were more likely to use smartphones for trip planning and perceived increase in travel outcomes compared to other age groups. Xie et al [18] proposed a modeling framework which was essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multimodal dynamic simulators.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model

Yang

Xie

et al. 2020

Journal of Advanced Transportation

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With the concept of sharing economic entering into our lives, many parking Apps are designed for connecting the drivers and vacated parking spaces. However, there are not many drivers who use the mobile Apps to reserve and find available parking spaces, which is largely due to the insufficient information provided by the parking App. In order to better explain, predict, and improve drivers’ acceptance of parking App, the conceptual framework based on technology acceptance model was developed to establish the relationships between the drivers’ intention to accept parking App, trust in parking App, perceived usefulness of parking App, and perceived ease of its use. Then structural equation model was established to analyze the relationship between various variables. The results show that the trust in parking App, perceived usefulness, perceived ease of use, and parking App attributes are the main factors that determine the intention to use parking App. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on acceptance intention, with a standardized coefficient of 0.984, followed by parking App attribute (0.743), perceived ease of use (0.384), and trust in parking App (0.381).

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Section: Literature Reviewmentioning

confidence: 99%

Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model

Yang

Xie

et al. 2020

Journal of Advanced Transportation

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“…Furthermore, once ride-hailing is more common in our study area, we will reconduct an RP survey with a redesign of the scenarios. This approach would better reflect the actual experience of RH users, similar to the study done by (14). The new survey will also be distributed differently to cover the entire population more homogeneously.…”

Section: Resultsmentioning

confidence: 99%

Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

Shoman

Moreno

2021

Transportation Research Record

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The growth of ride-hailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters. Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for RH. The model results indicate RH services’ popularity among those aged 18–39, larger households and households with fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.

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“…The total body of this literature has been exclusively published from 2011 onwards, with more papers being issued every year. These studies cover many MaaS angles, including user experience [52][53][54], attitudes [55,56], integration with public transport [57][58][59], business models [60,61], travel behaviour [62,63], governance and policy [64,65], urban futures [66,67] and even Blockchain adaption [68].…”

Section: Mobility-as-a-servicementioning

confidence: 99%

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

et al. 2020

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Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

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Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

Cited by 39 publications

References 24 publications

Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model

Analyzing Drivers’ Intention to Accept Parking App by Structural Equation Model

Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

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