Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks.

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale floating vehicle dataset of per-street segment traffic information, Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities (MeTS-10), available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019–2021 and covering more than 1500 square kilometers per metropolitan area. MeTS-10 features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne, and Moscow. The dataset leverages the industrial-scale floating vehicle Traffic4cast data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. We detail the efficient matching approach mapping the data to the OpenStreetMap (OSM) road graph. We evaluate the dataset by comparing it with publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). The comparison highlights the differences across datasets in spatio-temporal coverage and variations in the reported traffic caused by the binning method. MeTS-10 enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data. The large spatial and temporal coverage offers an opportunity for joining the MeTS-10 with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings.

Artificial intelligence (AI) is revolutionizing many areas of our lives, leading a new era of technological advancement. Particularly, the transportation sector would benefit from the progress in AI and advance the development of intelligent transportation systems. Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. However, such deep neural networks are difficult to interpret and lack robustness, which slows the deployment of these AI-powered algorithms in practice. To improve their usability, increasing research efforts have been devoted to developing interpretable and robust machine learning methods, among which the causal inference approach recently gained traction as it provides interpretable and actionable information. Moreover, most of these methods are developed for image or sequential data which do not satisfy specific requirements of mobility data analysis. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness, summarizes recent developments in using causal inference for improving the interpretability and robustness of machine learning methods, and highlights opportunities in developing causally-enabled machine learning models tailored for mobility analysis. This research direction will make AI in the transportation sector more interpretable and reliable, thus contributing to safer, more efficient, and more sustainable future transportation systems.

Volunteered Geographic Information (VGI), defined as geographic information contributed voluntarily by individuals, has grown exponentially with the aid of ubiquitous GPS-enabled technologies. VGI projects have generated a large amount of geographic data, providing a new data source for scientific research. However, many scientists are concerned about the quality of VGI data for research, given the lack of rigorous and systematic quality control procedures. This study contributes to the improvement of quality control procedures by proposing a Cross-Volunteer Referencing Anomaly Detection (CVRAD) method to filter anomalous data, using the crowdsourced Safecast radiation data as a case study. The anomaly detection method is validated using two data sets: (1) an official radiation survey data set collected by the KURAMA car-borne system, (2) a set of anomalous Safecast measurements filtered by Safecast moderators. The validation results show that the proposed CVRAD method outperformed the 1.5 IQR benchmark method in minimizing the overall measurement error and detecting abnormal imports of Safecast measurements, thus demonstrating the effectiveness of the proposed method in improving the overall accuracy of crowdsourced radiation measurements.

Characterizing event attendees’ travel patterns is key to understanding the dynamics of social events in cities. However, the scientific investigation of event travel patterns has been hindered by the difficulty in gathering travel diaries of participants. Geotagged microblogs provide new opportunities for studying event travel patterns by offering rich locational and semantic information of attendees. Here, we develop, implement, and apply a workflow to characterize travel behaviors of event attendees with geotagged Twitter data, using college football events as a case study. The workflow includes five steps: 1) filtering event attendees using real-time geotagged tweets, 2) identifying origins of the event attendees using historical timeline tweets, 3) identifying past sports-related activities at travel destinations using topic modeling, 4) computing user movement features using origin-destination travel flows, and 5) identifying atypical travel patterns to characterize event attendees. The travel patterns uncovered in the study offer insights into user interests and travel behaviors related to sporting event attendance. The findings demonstrate that our method holds promise in revealing long-term event travel patterns (not limited to sporting events) through the use of geotagged microblogs.