Single-leader multi-follower games for the regulation of two-sided mobility-as-a-service markets

Xi, Haoning; Aussel, Didier; Liu, Wei; Waller, S. Travis; Rey, David

doi:10.1016/j.ejor.2022.06.041

Cited by 12 publications

(4 citation statements)

References 39 publications

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“…According to the unified MaaS pricing framework proposed in Xi, Aussel et al. (2022), the fare of a mobility/delivery service is determined by the traveling distance and the transport time of the selected MaaS bundle between an OD pair. Let d denote the average distance between an OD pair and

t_{i}

denote the transport time of MaaS bundle i , then the corresponding price of mobility and delivery service are denoted as

p_{i}^{M}(t_{i})

and

p_{i}^{D}(t_{i})

, respectively.…”

Section: Two‐class Bue With Fixed Demandmentioning

confidence: 99%

“…In the proposed MaaS ecosystem, we assume that each MaaS bundle has a specific transport time. According to the unified MaaS pricing framework proposed in Xi, Aussel et al (2022), the fare of a mobility/delivery service…”

Section: Generalized Costs For Mobility and Delivery Usersmentioning

confidence: 99%

“…While several studies focused on developing general algorithms for optimal solutions, for example, Bard and Falk (1982), Patriksson (2008), and Xi et al. (2022), this study aims to interpret the interactions and impacts of the BSRS on the MaaS ecosystem for decision making, computer‐aided assessment, and implementations. To this end, we prove the model properties and develop a solution algorithm to find the optimal solutions to the proposed bilevel programming problem by analyzing the feasibility and uniqueness of the optimal solutions under BSRS.…”

Section: Introductionmentioning

confidence: 99%

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Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems

Tang

Waller

et al. 2022

Computer aided Civil Eng

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Mobility-as-a-Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the ondemand mobility services, instant delivery services have increased rapidly and particularly boomed during the coronavirus (COVID-19) pandemic, requiring online orders to be delivered timely. In this study, to deal with the redundant mobility resources and high costs of instant delivery services, we model an MaaS ecosystem that provides mobility and instant delivery services by sharing the same multimodal transport system. We derive a two-class bundle choice user equilibrium (BUE) for mobility and delivery users in the MaaS ecosystems. We propose a bilateral surcharge-reward scheme (BSRS) to manage the integrated mobility and delivery demand in different incentive scenarios. We further formulate a bilevel programming problem to optimize the proposed BSRS, where the upper level problem aims to minimize the total system equilibrium costs of mobility and delivery users, and the lower level problem is the derived twoclass BUE with BSRS. We analyze the optimal operational strategies of the BSRS and develop a solution algorithm for the proposed bilevel programming problem based on the system performance under BSRS. Numerical studies conducted with real-world data validate the theoretical analysis, highlight the computational efficiency of the proposed algorithm, and indicate the benefits of the BSRS in managing the integrated mobility and delivery demand and reducing total system equilibrium costs of the MaaS ecosystems. INTRODUCTIONThe coronavirus (COVID-19) outbreak has significantly impacted people's daily lives worldwide. To save lives and prevent the spread of COVID-19, more and more people have to stay home because of social distance guidelines, directives to stay home, and working from home. More than 3.9 billion people in over 90 countries or territories © 2022 Computer-Aided Civil and Infrastructure Engineering.have been asked or ordered to remain at home by their governments by April 2020. Meanwhile, those at home remain in need of essentials, such as medicine, meal, and food, therefore inducing a significant increase in the demand for online shopping and instant delivery services. 11 We use "instant delivery" in this study for the emphasis of the time emergency of the delivery, which is widely used in literature.

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t_{i}

denote the transport time of MaaS bundle i , then the corresponding price of mobility and delivery service are denoted as

p_{i}^{M}(t_{i})

and

p_{i}^{D}(t_{i})

, respectively.…”

Section: Two‐class Bue With Fixed Demandmentioning

confidence: 99%

Section: Generalized Costs For Mobility and Delivery Usersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems

Tang

Waller

et al. 2022

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…As more and more intelligent devices step into our daily life, ubiquitous computing environments are gradually coming into reality (Xi et al 2022a). However, most existing Human-Computer Interaction (HCI) works are designed to understand and handle the explicit instructions of users, while ignoring their internal psychological and emotional states (Zeng et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

Luo

Zhu

Yang

et al. 2023

AAAI

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Existing methods on facial expression recognition (FER) are mainly trained in the setting when multi-class data is available. However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Class Facial Expression Recognition Network (HS-OCFER) which can construct the decision boundary of a given expression class (called normal class) by training on only one-class data. Specifically, HS-OCFER consists of three novel components. First, hierarchical bottleneck modules are proposed to enrich the representation power of the model and extract detailed feature hierarchy from different levels. Second, multi-scale spatial regularization with facial geometric information is employed to guide the feature extraction towards emotional facial representations and prevent the model from overfitting extraneous disturbing factors. Third, compact intra-class variation is adopted to separate the normal class from alien classes in the decision space. Extensive evaluations on 4 typical FER datasets from both laboratory and wild scenarios show that our method consistently outperforms state-of-the-art One-Class Classification (OCC) approaches.

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Online Continual Learning with Contrastive Vision Transformer

Wang

Liu

Kong

et al. 2022

Lecture Notes in Computer Science

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Single-leader multi-follower games for the regulation of two-sided mobility-as-a-service markets

Cited by 12 publications

References 39 publications

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems

Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

Online Continual Learning with Contrastive Vision Transformer

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