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Allocating mobility resources (e.g., shared bikes/e-scooters, ridesharing vehicles) is crucial for rebalancing the mobility demand and supply in the urban environments. We propose in this work a novel multi-agent reinforcement learning named Hierarchical Adaptive Grouping-based Parameter Sharing (HAG-PS) for dynamic mobility resource allocation. HAG-PS aims to address two important research challenges regarding multi-agent reinforcement learning for mobility resource allocation: (1) how to dynamically and adaptively share the mobility resource allocation policy (i.e., how to distribute mobility resources) across agents (i.e., representing the regional coordinators of mobility resources); and (2) how to achieve memory-efficient parameter sharing in an urban-scale setting. To address the above challenges, we have provided following novel designs within HAG-PS. To enable dynamic and adaptive parameter sharing, we have designed a hierarchical approach that consists of global and local information of the mobility resource states (e.g., distribution of mobility resources). We have developed an adaptive agent grouping approach in order to split or merge the groups of agents based on their relative closeness of encoded trajectories (i.e., states, actions, and rewards). We have designed a learnable identity (ID) embeddings to enable agent specialization beyond simple parameter copy. We have performed extensive experimental studies based on real-world NYC bike sharing data (a total of more than 1.2 million trips), and demonstrated the superior performance (e.g., improved bike availability) of HAG-PS compared with other baseline approaches. 

Accurate prediction of citywide crowd activity levels (CALs),i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service applications, and the entrepreneurs in commercial strategic planning. Existing studies have not thoroughly taken into account the complex spatial and temporal interactions among different categories of CALs and their extreme occurrences, leading to lowered adaptivity and accuracy of their models. To address above concerns, we have proposedIE-CALP, a novel spatio-temporalInteractive attention-based andExtreme-aware model forCrowdActivityLevelPrediction. The tasks ofIE-CALPconsist of(a)forecasting the spatial distributions of various CALs at different city regions (spatial CALs), and(b)predicting the number of participants per category of the CALs (categorical CALs). To realize above, we have designed a novel spatial CAL-POI interaction-attentive learning component inIE-CALPto model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition,IE-CALPincorporate the multi-level trends (e.g., daily and weekly levels of temporal granularity) of CALs through a multi-level temporal feature learning component. Furthermore, to enhance the model adaptivity to extreme CALs (e.g., during extreme urban events or weather conditions), we further take into account theextreme value theoryand model the impacts of historical CALs upon the occurrences of extreme CALs. Extensive experiments upon a total of 738,715 CAL records and 246,660 POIs in New York City (NYC), Los Angeles (LA), and Tokyo have further validated the accuracy, adaptivity, and effectiveness ofIE-CALP’s interaction-attentive and extreme-aware CAL predictions.