The development of the Internet of Things (IoT) has enhanced smart city services for traffic monitoring, leading to numerous schemes for accurate flow prediction based on traffic sensors. However, existing approaches primarily capture spatio-temporal (ST) dependencies from traffic graphs and train their models using randomly ordered data. This overlooks the fact that the modeling difficulty of each sensor/node in the ST traffic graph can vary significantly due to its spatial dependencies and temporal trends, resulting in unreliable and unstable predictions in IoT scenarios. In this context, we argue that a well-designed curriculum with an easy-to-difficult order can improve the training of ST models. Therefore, this paper introduces an ST difficulty measurer to score the node-level difficulty of traffic graph from both spatial and temporal aspects, and then implements a curriculum in the ST model training process. More specifically, based on the tiered ST difficulty score, the ST model training begins with a subgraph consisting of “easy” nodes characterized by relatively consistent spatial relationships and regular temporal patterns. Gradually, more difficult nodes are incorporated into the subgraph and participate in subsequent training stages. Comprehensive experiments and analysis on two real-world traffic flow datasets confirm the effectiveness of our proposed approach.

Maritime traffic analysis plays a vital role in port operations and logistics coordination. Although Automatic Identification System (AIS) provides rich vessel movement data, analyzing port-to-port traffic remains challenging due to data heterogeneity and missing trajectory segments. We present PortVIS, a web-based interactive system that supports the comprehensive analysis and visualization of maritime traffic. PortVIS integrates multi-sourced datasets—including AIS records, port and anchorage metadata, and wind field data—to enable trajectory segmentation, regional analysis, and data imputation. Users can import raw AIS records and segment them into port-to-port trips using our system. These segmented trajectories can then be filtered and queried based on vessel information and trip attributes. Users can also define custom zones (e.g., anchorages or transit areas) and explore traffic patterns through maps and charts. Missing trajectory segments are reconstructed using our recent imputation approach to improve data quality. By integrating trajectory processing and various visual analytics, PortVIS provides a unified tool for maritime mobility analysis.

Analyzing the causal impact of various government-related policies on the epidemic spread is of critical importance. This article aims to investigate the problem of assessing the causal effects of different COVID-19-related policies on the USA epidemic spread in different counties at any given time period, while eliminating biased interference from unobserved confounders (e.g., the vigilance of residents). However, the infection outcome of each region is influenced not only by its own confounding factors but also by policy interventions implemented in neighboring regions. Furthermore, the government policy index may exhibit a time-delay influence on outbreak dynamics. To this end, we implement observational data about different COVID-19-related policies (treatment) and outbreak dynamics (outcome) across different U.S. counties over time and develop a causal framework that learns the representations of time-varying confounders to tackle the aforementioned issues. More specifically, we employ one recurrent structure to capture the accumulative effects stemming from the policy history and then utilize hypergraph neural network to model the interactions among spatial regions. Our experimental results demonstrate the effectiveness of the proposed framework in quantifying the causal impact of different policy types on epidemics. Compared with baseline methods, our assessment provides valuable insights for future policy-making endeavors.

Maritime transportation, vital for nearly 90% of global trade, necessitates precise vessel trajectory prediction for safety and efficiency. Although the Automatic Identification System (AIS) provides a comprehensive data source, how to model these multi-modal and heterogeneous time-varying sequences (such as vessels’ kinetic information and ocean weather factors) poses a formidable challenge. Moreover, most existing approaches are limited by the confined scope of vessel trajectory modeling, making it impossible to consider the unique characteristics of maritime transportation system. To tackle these challenges, we propose a novel framework called AISFuser to i) encode unique maritime traffic network into graphical representations, and ii) introduce the heterogeneity into multi-modal temporal embeddings through Self-Supervised Learning (SSL). Specifically, our AISFuser is constructed by combining an attention-based graph block with a transformer network to encode information across space and time, respectively. In terms of temporal dimension, one SSL auxiliary task is also designed to enhance the heterogeneity of temporal representations and supplement the main vessel prediction task. We validate the effectiveness of the proposed AISFuser on a real-world AIS dataset. Extensive experimental results demonstrate that our method can forecast multiple attributes of vessel trajectory for over 10 hours into the future, outperforming competitive baselines.

Maritime traffic management increasingly relies on vessel position information provided by terrestrial and satellite networks of the Automatic Identification System (AIS). Unfortunately, the problem of missing AIS data can lead to long-term gaps in vessel trajectory, raising corresponding security concerns regarding collision risks and illicit activities. Existing imputation approaches are often constrained by vehicle-based low-sampling trajectories, hindering their ability to address unique characteristics of maritime transportation systems and long-term missing scenarios. To tackle these challenges, we propose a novel generative framework for long-term vessel trajectory imputation. Our framework considers irregular tracks of vessels, which differ from those of cars due to the absence of a structured road network, and ensures the continuity of multi-point imputed trajectories. Specifically, we first utilize a pre-trained trajectory embedding block to capture patterns of vessel movements. Subsequently, we introduce a diffusion-based model for generating missing trajectories, where observed trajectory modeling with transformer encoding architecture and embeddings of both historical vessel trajectory and external factors serve as conditional information. In particular, we design a physics-guided discriminator in the training stage, which imposes kinematic constraints between locations and angles to improve the continuity of the imputed trajectories. Comprehensive experiments and analysis on a real-world AIS dataset confirm the effectiveness of our proposed approach.

To understand human mobility following the typhoon, analyzing the causal impact of extreme typhoon weather on human mobility is important for disaster emergency management. However, the unobserved confounders (e.g., the characteristic of each region) correlate with the strength of typhoon weather and also affect human mobility during typhoon, which may generate biased influences on the causal analysis process. Besides, these confounders may be time-varying following the dynamic movements of typhoon. In this work, we develop a neural network-based continuous causal effect estimation framework to mitigate the interference from (unobserved) confounders and assess the continuous causal responses of typhoon-related weather (treatment) on several types of human mobility (outcome) across different counties at any given period. To this end, we integrate the big data from two huge typhoons in Japan (i.e., Typhoon Faxai and Hagibis) and leverage multiple sources of covariates (i.e., residents' vigilance and basic mobility patterns) from different counties to learn the representations of time-varying confounders. The experimental results indicate the effectiveness of our proposed framework in capturing the confounders for quantifying the causal impact of extreme weather during the typhoon process, compared with several existing causal studies.

How to generate road conditions from urban GPS trajectory is an important problem in transportation systems. However, this computation process usually suffers from serious missing value problem due to the observation uncertainty or limited reports from crowdsourcing systems. Conventional tensor factorization approaches learn the spatio-temporal dependencies in a collaborative filtering way, which ignores the complex road network structure information and temporal heterogeneity. In this study, we propose a multi-view model with multiple aspects of prior knowledge to impute traffic state computed from a real-world trajectory dataset. More specifically, in the spatial view, rather than focusing on a specific type of road segment, we take the heterogeneity of road network into consideration and model the multiple relations of adjacent road segments. Meanwhile, the temporal pattern is also viewed as a heterogeneous graphical structure that discriminates the weekly/hourly adjacency in the temporal view. Finally, we fuse the above spatio-temporal features to provide a robust estimation under different sparse conditions. Intensive experiments on two types of missing scenarios (i.e., random and non-random) demonstrate that the proposed imputation method outperforms all the other state-of-the-art approaches. In addition, our model represents interpretable patterns for spatio-temporal graph analysis.

Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances. As we all know, the flow at a citywide level is in a mixed state with several basic patterns (e.g., commuting, working, and commercial) caused by the city area functional distributions (e.g., developed commercial areas, educational areas and parks). However, existing technologies have been criticized for their lack of considering the differences in the flow patterns among regions since they want to build only one comprehensive model to learn the mixed flow tensors. Recognizing this limitation, we present a new perspective on flow prediction and propose an explainable framework named ST-ExpertNet, which can adopt every spatial-temporal model and train a set of functional experts devoted to specific flow patterns. Technically, we train a bunch of experts based on the Mixture of Experts (MoE), which guides each expert to specialize in different kinds of flow patterns in sample spaces by using the gating network. We define several criteria, including comprehensiveness, sparsity, and preciseness, to construct the experts for better interpretability and performances. We conduct experiments on a wide range of real-world taxi and bike datasets in Beijing and NYC. The visualizations of the expert's intermediate results demonstrate that our ST-ExpertNet successfully disentangles the city's mixed flow tensors along with the city layout, e.g., the urban ring road structure. Different network architectures, such as ST-ResNet, ConvLSTM, and CNN, have been adopted into our ST-ExpertNet framework for experiments and the results demonstrates the superiority of our framework in both interpretability and performances.

Travel time estimation from GPS trips is of great importance to order duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is not always available due to the limitation of data privacy and acquisition, while the origin-destination (OD) type of data, such as NYC taxi data, NYC bike data, and Capital Bikeshare data, is more accessible. To address this issue, this paper starts to estimate the OD trips travel time combined with the road network. Subsequently, aMulti-taskWeaklySupervisedLearning Framework forTravelTimeEstimation (MWSL-TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously. Technically, given an OD pair, the transition probability intends to recover the most possible route. And then, the output of travel time is equal to the summation of all segments’ and intersections’ travel time in this route. A novel route recovery function has been proposed to iteratively maximize the current routes’ co-occurrence probability, and minimize the discrepancy between routes’ probability distribution and the inverse distribution of routes’ estimation loss. Moreover, the expected log-likelihood function based on a weakly-supervised framework has been deployed in optimizing the travel time from road segments and intersections concurrently. We conduct experiments on a wide range of real-world taxi datasets in Xi’an and Chengdu and demonstrate our method's effectiveness on route recovery and travel time estimation.

The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission. Medical experts and policymakers have expressed the urgency to effectively evaluate the outcome of human restriction policies with the aid of big data and information technology. Thus, based on big human mobility data and city POI data, an interactive visual analytics system called Epidemic Mobility (EpiMob) was designed in this study. The system interactively simulates the changes in human mobility and infection status in response to the implementation of a certain restriction policy or a combination of policies (e.g., regional lockdown, telecommuting, screening). Users can conveniently designate the spatial and temporal ranges for different mobility restriction policies. Then, the results reflecting the infection situation under different policies are dynamically displayed and can be flexibly compared and analyzed in depth. Multiple case studies consisting of interviews with domain experts were conducted in the largest metropolitan area of Japan (i.e., Greater Tokyo Area) to demonstrate that the system can provide insight into the effects of different human mobility restriction policies for epidemic control, through measurements and comparisons.

Estimating the travel time of a given path is an important topic for the intelligent transportation system and serves as the foundation for various real-world applications. However, building an estimation model for such a data-driven task requires a large amount of mobile users' trajectory data which directly relates to their privacy and thus is less likely to be shared. Therefore, we propose GPF-TTE, Generative Personalized Federated Learning Framework for Travel Time Estimation (poster version of our previous work [1]) based on the issue of privacy protection for the mobile user group, in which 1) utilizes the federated learning approach, allowing private data to be kept on client devices while training, 2) apart from sharing a base model, we also adapt a fine-tuned personalized model for each client to study their personal driving habits, making up for the residual error caused by the prediction of the localized global model (the base model in local device), and 3) the cloud server aggregates localized models into the global model as a generative model to infer the future road traffic state.

Estimating the travel time of a path is an essential topic for the intelligent transportation system. It serves as the foundation for real-world applications, such as traffic monitoring, route planning, and taxi dispatching. However, building a model for such a data-driven task requires a large amount of users’ travel information, which closely relates to their privacy and, thus, is less likely to be shared. The not independent and identically distributed (Non-IID) trajectory data across data owners also make a predictive model extremely challenging to be personalized if we directly apply federated learning. Finally, previous work on travel time estimation (TTE) does not consider the real-time traffic state of roads, which we argue, can significantly influence the prediction. To address the above challenges, we introduce GOF-TTE for the mobile user group, generative online federated learning framework for TTE, which 1) utilizes the federated learning approach, allowing private data to be kept on client devices while training, and designs the global model as an online generative model shared by all clients to infer the real-time road traffic state and 2) apart from sharing a base model at the server, adapts a fine-tuned personalized model for every client to study their personal driving habits, making up for the residual error made by localized global model prediction. We also employ a simple privacy attack to our framework and implement the differential privacy mechanism to guarantee privacy safety further. Finally, we conduct experiments on two real-world public taxi data sets of DiDi Chengdu and Xi’an. The experimental results demonstrate the effectiveness of our proposed framework.