We propose an equity-awareGRAph-fusion differentiablePooling neural network to accurately predict the spatio-temporal urban mobility (e.g., station-level bike usage in terms of departures and arrivals) withEquity (GRAPE).GRAPEconsists of two independent hierarchical graph neural networks for two mobility systems—one as a target graph (i.e., a bike sharing system) and the other as an auxiliary graph (e.g., a taxi system). We have designed a convolutional fusion mechanism to jointly fuse the target and auxiliary graph embeddings and extract the shared spatial and temporal mobility patterns within the embeddings to enhance prediction accuracy. To further improve the equity of bike sharing systems for diverse communities, we focus on the bike resource allocation and model prediction performance, and propose to regularize the predicted bike resource as well as the accuracy across advantaged and disadvantaged communities, and thus mitigate the potential unfairness in the predicted bike sharing usage. Our evaluation of over 23 million bike rides and 100 million taxi trips in New York City and Chicago has demonstratedGRAPEto outperform all of the baseline approaches in terms of prediction accuracy (by 15.80% for NYC and 50.55% for Chicago on average) and social equity awareness (by 32.44% and 24.43% in terms of resource fairness for NYC and Chicago, and 13.36% and 16.52% in terms of performance fairness).

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 proposed IE-CALP , a novel spatio-temporal I nteractive attention-based and E xtreme-aware model for C rowd A ctivity L evel P rediction. The tasks of IE-CALP consist 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 in IE-CALP to model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition, IE-CALP incorporate 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 the extreme value theory and 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 of IE-CALP ’s interaction-attentive and extreme-aware CAL predictions.

Thanks to advances in the urban big data, the bike sharing, especially station-based bike sharing, has emerged as the important first-/last-mile connectivities in many smart cities. Bike station network (BSN) expansion recommendation, i.e., recommending placement locations of new stations, is essential for satisfying local mobility demands, enhancing the BSN service quality, and may significantly affect the resource fairness and accessibility of different communities in the neighborhood. Furthermore, the dynamic and complex urban mobility environments make the station placement highly challenging to satisfy the mobility needs.

The occurrence of special contexts or events (e.g., extreme weather conditions, festival events, other urban anomalies) can significantly influence the movement patterns of urban mobility (e.g., human crowds, transportation systems). Accurate mobility modeling and prediction under the occurrences of such anomaly events is therefore imperative for city management and urban resource allocation. In this study, we propose EALGAP, a novel Extreme-Aware Local-Global Attention urban mobility Prediction model. Specifically, EALGAP models the spatio-temporal global and local impacts of mobility in different regions and time steps for mobility prediction at various city regions. EALGAP takes into account the global impacts by extracting the overall or regular spatial dependencies and temporal patterns of mobility systems for different regions. We have designed a temporally-varying normalization and data-driven technique to quantify the extreme degrees, i.e., how significantly the extreme events have impacted the local mobility trend, of the patterns within different regions and time steps. We have conducted ex-tensive experimental studies upon four different mobility datasets (over 13 million trips in total) harvested from two metropolitan cities in U.S. with anomalous natural or social events (e.g., hurricane events, other extreme weather conditions, and the Federal holidays). Our results have demonstrated the accuracy, effectiveness, and extreme-awareness of our proposed EALGAP with more than 44.12% error reduction on average compared with other state-of-the-art approaches.

Understanding and learning the actor-to-X inter-actions (AXIs), such as those between the focal vehicles (actor) and other traffic participants (e.g., other vehicles, pedestrians) as well as traffic environments (e.g., city/road map), is essential for the development of a decision-making model and simulation of autonomous driving (AD). Existing practices on imitation learning (IL) for AD simulation, despite the advances in the model learnability, have not accounted for fusing and differentiating the heterogeneous AXIs in complex road environments. Furthermore, how to further explain the hierarchical structures within the complex AXIs remains largely under-explored. To overcome these challenges, we propose HGIL, an interaction- aware and hierarchically-explainable Heterogeneous _Graph- based Imitation Learning approach for AD simulation. We have designed a novel heterogeneous interaction graph (HIG) to provide local and global representation as well as awareness of the AXIs. Integrating the HIG as the state embeddings, we have designed a hierarchically-explainable generative adversarial imitation learning approach, with local sub-graph and global cross-graph attention, to capture the interaction behaviors and driving decision-making processes. Our data-driven simulation and explanation studies have corroborated the accuracy and explainability of HGIL in learning and capturing the complex AXIs.

Taking into account the availability of the historical GPS trajectories of drivers, given a new GPS trajectory, Driver mobility fingerprint (DMF) identification aims at (i) determining whether a generated trajectory belongs to a potential driver, and (ii) detecting if a trajectory is likely anomalous based on a driver's historical data. Prior studies often consider hand-crafted feature engineering techniques to extract DMFs while contextual factors like weather and points-of-interest (POIs) are hardly accounted for, which might not achieve satisfactory identification results. To address above, we propose RM-Drive, a novel framework based on reinforced feature extraction and multi-resolution learning. Specifically, we first employ spatio-temporal inverse reinforcement learning (ST-IRL) to extract DMFs from historical trajectories. Then, we generate trajectory embeddings by fusing the extracted DMFs and the contextual factors using the multi-resolution trajectory embedding network (MTE-Net). Our proposed MTE-Net consists of multi-resolution convolutional neural network (MR-CNN), which enables the model to learn the multi-resolution features of the DMFs. Finally, we leverage the trajectory embeddings for the driver classification and anomaly detection. We have conducted extensive evaluation studies upon RM-Drive with two real-world datasets, and our results demonstrate the performance improvements from the state-of-the-art of driver classification and anomaly detection respectively by 21% and 11% on average based on several evaluation metrics, including accuracy, precision, and recall, etc.