Trajectory prediction models that can infer both future trajectories and their associated uncertainties of the target vehicles is crucial for safe and robust navigation and path planning of autonomous vehicles. However, the majority of existing trajectory prediction models have neither considered reducing the uncertainty as one objective during the training stage nor provided reliable uncertainty quantification during inference stage, especially under potential distribution shift. Therefore, in this paper, we propose the Conformal Uncertainty Quantification under Distribution Shift framework, CUQDS, to quantify the uncertainty of the predicted trajectories of existing trajectory prediction models under potential data distribution shift, while improving the prediction accuracy of the models and reducing the estimated uncertainty during the training stage. Specifically, CUQDS includes 1) a learning-based Gaussian process regression module that models the output distribution of the base model (any existing trajectory prediction neural networks) and reduces the estimated uncertainty by an additional loss term, and 2) a statistical-based Conformal P control module to calibrate the estimated uncertainty from the Gaussian process regression module in an online setting under potential distribution shift between training and testing data. Experimental results on various state-of-the-art methods using benchmark motion forecasting datasets demonstrate the effectiveness of our proposed design.

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.

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.

After years of development, bike sharing has been one of the major choices of transportation for urban residents worldwide. However, efficient use of bike sharing resources is challenging due to the unbalanced station-level demands and supplies, which causes the maintenance of the bike sharing systems painstaking. To achieve system efficiency, efforts have been made on accurate prediction of bike traffic (demands/pick-ups and returns/drop-offs). Nonetheless, bike station traffic prediction is difficult due to the spatio-temporal complexity of bike sharing systems. Moreover, such level of prediction over the entire bike sharing systems is also challenging due to the large number of bike stations.To fill this gap, we propose BikeGAAN, a graph adjacency attention neural network to predict station-level bike traffic for entire bike sharing systems. The proposed prediction system consists of a graph convolutional network with an attention mechanism differentiating the spatial correlations between features of bike stations in the system and a long short-term memory network capturing temporal correlations. We have conducted extensive data analysis upon bike usage, weather, points of interest and event data, and derived the graph representation of the bike sharing networks. Through experimental study on over 27 millions trips of bike sharing systems of four metropolitan cities in the U.S., New York City, Chicago, Washington D.C. and Los Angeles, our network design has shown high accuracy in predicting the bike station traffic in the cities, outperforming other baselines and state-of-art models.