ORCID Profile
0000-0001-7499-8799
Current Organisation
University of Newcastle Australia
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Publisher: Elsevier BV
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Hindawi Limited
Date: 24-07-2020
DOI: 10.1155/2020/1360491
Abstract: Path planning for the multiagent, which is generally based on the artificial potential energy field, reflects the decision-making process of pedestrian walking and has great importance on the field multiagent system. In this paper, after setting the spatial-temporal simulation environment with large cells and small time segments based on the disaggregation decision theory of the multiagent, we establish a generalized dynamic potential energy model (DPEM) for the multiagent through four steps: (1) construct the space energy field with the improved Dijkstra algorithm, and obtain the fitting functions to reflect the relationship between speed decline rate and space occupancy of the agent through empirical cross experiments. (2) Construct the delay potential energy field based on the judgement and psychological changes of the multiagent in the situations where the other pedestrians have occupied the bottleneck cell. (3) Construct the waiting potential energy field based on the characteristics of the multiagent, such as dissipation and enhancement. (4) Obtain the generalized dynamic potential energy field by superposing the space potential energy field, delay potential energy field, and waiting potential energy field all together. Moreover, a case study is conducted to verify the feasibility and effectiveness of the dynamic potential energy model. The results also indicate that each agent’s path planning decision such as forward, waiting, and detour in the multiagent system is related to their in idual characters and environmental factors. Overall, this study could help improve the efficiency of pedestrian traffic, optimize the walking space, and improve the performance of pedestrians in the multiagent system.
Publisher: Wiley
Date: 27-12-2022
DOI: 10.1111/MICE.12958
Abstract: Mobility‐as‐a‐Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the on‐demand 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 two‐class 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.
Publisher: Elsevier
Date: 2022
Publisher: MDPI AG
Date: 06-01-2021
DOI: 10.20944/PREPRINTS202101.0109.V1
Abstract: Mobility as a Service (MaaS) is an innovative transport concept, anticipated to provide travelers with different kinds of travel services, more sustainable than a private car, in a simpler, packaged way. It combines different transport modes to offer a tailored mobility package, like a monthly mobile phone contract. The rapid development of intelligent transportation system and the shared economy has speeded up the development of MaaS in these years. In this paper, we aim at classifying the existing research on MaaS and the characteristics of MaaS into different categories, in order to answer the following questions after reviewing the existing literature: What is MaaS? Who are the main actors in MaaS? How can MaaS be implemented? Why should it be implemented? Where will MaaS end up in this wave of disruption? When we talk about MaaS, what are we focusing on? What is the future leading frequency of MaaS? Finally, based on the existing literature, we envision the leading future of MaaS.
Publisher: American Society of Civil Engineers
Date: 02-07-2018
Publisher: American Society of Civil Engineers
Date: 02-07-2018
Publisher: Elsevier BV
Date: 06-2022
Publisher: American Society of Civil Engineers
Date: 02-07-2019
Publisher: Informa UK Limited
Date: 05-05-2022
Publisher: Public Library of Science (PLoS)
Date: 30-07-2020
Publisher: Elsevier BV
Date: 2022
DOI: 10.2139/SSRN.4195506
Location: United States of America
No related grants have been discovered for Haoning Xi.