A Predict-Then-Optimize Couriers Allocation Framework for Emergency Last-mile Logistics

Xia, Kaiwen; Li, Lin; Wang, Shuai; Wang, Haotian; Zhang, Desheng; He, Tian

doi:10.1145/3580305.3599766

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…Lastly, in terms of future academic research, fusing large language models with computational, interactive agents seems to be a path to realistic simulations of human behavior (Xu et al, 2023). Based on emerging urban sensing technologies (e.g., drones and crowdsensing (Chen et al, 2019;Guo et al, 2021;Xia et al, 2023), diverse and heterogeneous human behavioral data are continuously mined. Integrating travel behavior and large models to construct urban individual profiles for simulating travel behaviors appears to be a promising direction.…”

Section: Discussionmentioning

confidence: 99%

Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

Zhao,

Wang,

Zhang

et al. 2024

Humanit Soc Sci Commun

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Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

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Section: Discussionmentioning

confidence: 99%