Understanding how cities are perceived from on-site visitors' perspectives can provide valuable insights for urban planning and development applications. However, existing studies estimated people's perceptions by having them view photographed landscape images; the scores derived by these methods were thus merely quantified impressions of specific viewpoints that do not necessarily represent perceptions people would have were they at the site. To address this issue, we developed a framework, named Omni-CityMood, for quantifying people's on-site perceptions of urban atmospheres. Based on the idea that the viewpoint influences the perception of an urban landscape, the proposed framework identifies critical viewpoints of a location by using both visual-based features of landscape images and geographical characteristics of the site. In particular, Omni-CityMood enables the mood of a location to be evaluated from viewpoints over a range of 360 degrees by leveraging the techniques of neural recommendation systems. We evaluated Omni-CityMood on a dataset we built that includes perceived atmosphere experiences in various cities. Experiments and extensive analyses demonstrate the promising capability of modeling landscape viewpoints to quantify urban on-site atmospheres.

When visiting universities, you might notice the distinctive atmospheres of each university, such as a calm and serious environment or a lively enthusiasm for sports. Capturing these atmospheres could help in promoting universities and fostering development in the communities around the universities. To explore the atmospheres of universities, we analyze the thoughts and interests of university community members, such as students and faculty members. Specifically, we use a large-scale dataset derived from search queries and GPS logs to quantify visitors’ interests. Additionally, to extract the meaningful atmospheres of universities, we apply topic modeling to the dataset.

Forecasting rail congestion is crucial for efficient mobility in transport systems. We present rail congestion forecasting using reports from passengers collected through a transit application. Although reports from passengers have received attention from researchers, ensuring a sufficient volume of reports is challenging due to passenger's reluctance. The limited number of reports results in the sparsity of the congestion label, which can be an issue in building a stable prediction model. To address this issue, we propose a semi-supervised method for congestion forecasting for trains, or SURCONFORT. Our key idea is twofold: firstly, we adopt semi-supervised learning to leverage sparsely labeled data and many unlabeled data. Secondly, in order to complement the unlabeled data from nearby stations, we design a railway network-oriented graph and apply the graph to semi-supervised graph regularization. Empirical experiments with actual reporting data show that SURCONFORT improved the forecasting performance by 14.9% over state-of-the-art methods under the label sparsity.

Forecasting the number of visitors at a public event, termed event crowd forecasting (ECF), has recently garnered attention due to its social significance. Although existing ECF methods have pioneered successful feature design by considering event contents with contexts (e.g., weather, type of day, time), their scalability across different event types is limited due to the necessity of costly feature engineering. To address this issue, we propose a novel ECF framework, named EventOutlook. Based on our observation of various events, online event announcements indicate the factors that induce crowded events. Thus, we incorporate event announcements into ECF methods. To handle such unstructured data, which have no unified format among events, we leverage large language models (LLM) to extract crowding factors and embed them into an LLM-driven crowding-indicator feature (LCIF). Empirical experiments with real-world event data show that EventOutlook significantly improved ECF performance compared to state-of-the-art methods.

Early crowd dynamics forecasting, such as one week in advance, plays an important role in risk-aware decision-making in urban regions such as congestion mitigation or crowd control for public safety. Although previous approaches have addressed crowd dynamics prediction, they have failed to deal with the scarcity of anomalous events, which results in a large model bias and could not quantify the number of visitors in anomalous crowd gathering. To provide an elaborate early forecast, we focus on the successive properties of importance weighting (IW) to penalize the anomalous data in terms of model bias; however, leveraging the concept of IW is challenging because dividing dataset into normal and abnormal sets is difficult. Motivated by these challenges, we propose CityOutlook, a novel forecasting model based on unbiased regression with importance-based reweighting. To make IW applicable to our approach, we design an anomaly-aware data annotation scheme by utilizing the heterogeneous property of mobility data to determine the data anomaly. We evaluate CityOutlook using the datasets of large-scale mobility and transit search logs. The experimental results show that CityOutlook outperforms the state-of-the-art models on crowd anomaly forecast, providing the same level accuracy in forecasting normal dynamics.

Forecasting anomalies in urban areas is of great importance for the safety of people. In this paper, we propose Supervised-CityProphet (SCP), an anomaly score matching-based method towards accurate prediction of anomalous crowds. We re-formulate CityProphet as a regression model via data source association with mobility logs and transit search logs to leverage user's schedules and the actual number of visitors. We evaluate Supervised-CityProphet using the datasets of real mobility and transit search logs. Experimental results show that Supervised-CityProphet can predict anomalous crowds 1 week in advance more accurately than baselines.