Mobility in the Era of Digitalization: Thinking Mobility as a Service (MaaS)

Barreto, Luís; Amaral, António; Baltazar, Sara

doi:10.1007/978-3-030-38704-4_12

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…For instance, ride-hailing is found to substitute for public transit in the city center or the service area of the subway and complement public transit in suburban areas (Kong et al, 2020b). Thus, different services must be collaborated and cooperated, customized to the specific city context (Barreto et al, 2020). Otherwise, MaaS operators may compel occasional public transit users toward the use of less sustainable but more expensive transport modes, particularly under a private pushed MaaS development (Hasselwander et al, 2022), which violates the original intention of MaaS.…”

Section: Transport-flow Infrastructure For Maasmentioning

confidence: 99%

“…Although some of them are intended from the point of collection, others opportunistically draw on relevant populations and attributes to enhance the overall available information (Cottrill, 2020). This case requires guaranteeing their protection, confidentiality, and privacy without conditioning the operational efficiency of the system once they are pooled (Barreto et al, 2018b;Barreto et al, 2020).…”

Section: Information-flow Infrastructure For Maasmentioning

confidence: 99%

“…It has been implemented on many map navigation apps, such as Google Maps and Baidu map, and successfully integrated into the MaaS platform, such as Citymapper, Moovit, Whim, and SMILE. Arrival time estimation is an “en-route” service that provides the arrival time information for the user to reduce their uncertainty and anxiety in waiting for the consequential services, particularly bus and ride-hailing (Yu et al, 2011), which may give confidence to the user in intermodal mobility (Barreto et al, 2020). It can be decomposed into the estimation of the arrival time of each component mode based on the location information collected by the GPS terminal and AI algorithm (Chen, 2018; Wang et al, 2018; Yu et al, 2011).…”

Section: Overview Of the Infrastructurementioning

confidence: 99%

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Mobility as a Service and urban infrastructure: From concept to practice

Qiao

Huang

Yeh

2022

Transactions in Urban Data, Science, and Technology

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The debate about how Mobility as a Service (MaaS) will revolutionize individual and collective mobility is gaining increasing attention from researchers, industries, and public sectors. MaaS is expected to create a techno-utopia with a new organization and operation of transport systems where residents have equal access to instant and ubiquitous mobility services. However, transport service is an artifact that is highly dependent on the construction of urban infrastructure. The success of MaaS requires the support of urban infrastructure that we categorize as (1) transport-flow infrastructure, (2) information-flow infrastructure, and (3) computing-flow infrastructure. The connotation of urban infrastructure here includes not only conventional concepts, such as transportation infrastructure, but also intelligent transportation concepts, such as high-speed communication networks and autonomous fleets. Moreover, travel behavior data collected by city sensors, communication networks, and intelligent vehicles require appropriate infrastructures to dynamically compute for fleet dispatch and demand-supply match. Based on the degree of integration of these infrastructures, MaaS projects will have different results in varying cities. From concept to practice, given that a vast disparity in infrastructure exists between cities, we need an inclusive, mobile, and global understanding of the MaaS concept to make it successful in different parts of the world.

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Section: Transport-flow Infrastructure For Maasmentioning

confidence: 99%

Section: Information-flow Infrastructure For Maasmentioning

confidence: 99%

Section: Overview Of the Infrastructurementioning

confidence: 99%

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Mobility as a Service and urban infrastructure: From concept to practice

Qiao

Huang

Yeh

2022

Transactions in Urban Data, Science, and Technology

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“…Several challenges and barriers to providing inclusive and equitable mobility services amongst vulnerable user groups, such as people with disabilities and impairments, arise. These commonly include poverty [18] budget constraints, lack of accessibility, inadequate service integration, lack of correspondence between user needs and service provision, preliminary safety measures or unsafe infrastructure, and lack of access to or understanding of technology [19,20]. Another common problem is improper parking in places reserved for people with disabilities or even in public spaces in such a way that interfere with the mobility of people with reduced mobility.…”

Section: Urban Mobility Challenges Trends and Inclusivenessmentioning

confidence: 99%

Image Recognition-Based Architecture to Enhance Inclusive Mobility of Visually Impaired People in Smart and Urban Environments

et al. 2022

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The demographic growth that we have witnessed in recent years, which is expected to increase in the years to come, raises emerging challenges worldwide regarding urban mobility, both in transport and pedestrian movement. The sustainable development of cities is also intrinsically linked to urban planning and mobility strategies. The tasks of navigation and orientation in cities are something that we resort to today with great frequency, especially in unknown cities and places. Current navigation solutions refer to the precision aspect as a big challenge, especially between buildings in city centers. In this paper, we focus on the segment of visually impaired people and how they can obtain information about where they are when, for some reason, they have lost their orientation. Of course, the challenges are different and much more challenging in this situation and with this population segment. GPS, a technique widely used for navigation in outdoor environments, does not have the precision we need or the most beneficial type of content because the information that a visually impaired person needs when lost is not the name of the street or the coordinates but a reference point. Therefore, this paper includes the proposal of a conceptual architecture for outdoor positioning of visually impaired people using the Landmark Positioning approach.

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ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning

Sharif

Heendeniya

Lückemeyer

2022

iCity. Transformative Research for the Livable, Intelligent, and Sustainable City

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Electromobility has profound economic and ecological impacts on human society. Much of the mobility sector’s transformation is catalyzed by digitalization, enabling many stakeholders, such as vehicle users and infrastructure owners, to interact with each other in real time. This article presents a new concept based on deep reinforcement learning to optimize agent interactions and decision-making in a smart mobility ecosystem. The algorithm performs context-aware, constrained optimization that fulfills on-demand requests from each agent. The algorithm can learn from the surrounding environment until the agent interactions reach an optimal equilibrium point in a given context. The methodology implements an automatic template-based approach via a continuous integration and delivery (CI/CD) framework using a GitLab runner and transfers highly computationally intensive tasks over a high-performance computing cluster automatically without manual intervention.

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Mobility in the Era of Digitalization: Thinking Mobility as a Service (MaaS)

Cited by 4 publications

References 34 publications

Mobility as a Service and urban infrastructure: From concept to practice

Mobility as a Service and urban infrastructure: From concept to practice

Image Recognition-Based Architecture to Enhance Inclusive Mobility of Visually Impaired People in Smart and Urban Environments

ARaaS: Context-Aware Optimal Charging Distribution Using Deep Reinforcement Learning

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