Kota Tsubouchi

Yahoo! JAPAN Research

Individual mobility prediction holds significant importance in urban computing, supporting various applications such as place recommendations. Current studies primarily focus on frequent mobility patterns including commuting trips to residential and workplaces. However, such studies do not accurately forecast irregular trips, which incorporate jour...

Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, w...

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications including transportation modeling, disaster management, and urban planning. The recent availability of large-scale human movement data collected from mobile devices has enabled the development of complex human mobility prediction models....

Forecasting crowd congestion is crucial for ensuring comfortable mobility and public safety. Existing methods forecast crowding by capturing the increase in planned visits, which facilitates the methods in estimating the start of crowding. However, forecasting the change in the degree of crowding until the end is challenging owing to the lack of vi...

This article studies crowd dynamics forecast one week in advance to detect irregular urban events, which plays an important role in infection prevention and crowd control. Previous approaches have failed to deal with the scarcity of anomalous events, resulting in a large model bias, and could not quantify the number of visitors in anomalous crowdin...

Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collected from mobile devices have enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested o...

Wi-Fi fingerprint-based localization is known to be prominent for indoor positioning technology; however, it is still challenging on sustainability of its performance for long-term use due to distribution drifts of the signal strength across time. Therefore, the laborious continual surveys on fingerprint and periodic model recalibration are inevita...

The indoor crowd density monitoring system using BLE beacons is one of the effective ways to prevent overcrowded indoor situations. The indoor crowd density monitoring system consists of a mobile application at the user's side and the beacon sensor network as the infrastructure. Since the performance of crowd density monitoring highly depends on ho...

Perceiving and modeling urban crowd movements are of great importance to smart city-related fields. Governments and public service operators can benefit from such efforts as they can be applied to crowd management, resource scheduling, and early emergency warning. However, most prior research on urban crowd modeling has failed to describe the dynam...

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 relucta...

Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has...

In recent geospatial research, the importance of modeling large-scale human mobility data via self-supervised learning is rising, in parallel with progress in natural language processing driven by self-supervised approaches using large-scale corpora. Whereas there are already plenty of feasible approaches applicable to geospatial sequence modeling...

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical...

In recent years, the use of smartphone Global Positioning System (GPS) logs has accelerated the analysis of urban dynamics. Predicting the population of a city is important for understanding the land use patterns of specific areas of interest. The current state-of-the-art predictive model is a variant of bilinear Poisson regression models. It is in...

Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, which can be widely applied to emergency management, traffic regulation, and urban planning. In particular, by meshing a large urb...

COVID-19 has disrupted the global economy and well-being of people at an unprecedented scale and magnitude. To contain the disease, an effective early warning system that predicts the locations of outbreaks is of crucial importance. Studies have shown the effectiveness of using large-scale mobility data to monitor the impacts of non-pharmaceutical...

Large-scale mobility data collected from mobile phones provide us with an opportunity to monitor and understand the impacts of non-pharmaceutical interventions during the COVID-19 outbreak with an unprecedented spatio-temporal granularity and scale. While such data has been used in various countries, the changes in mobility patterns in Japan where...

Every day, people are using search engines for different purposes such as research, shopping, or entertainment. Among the behaviors of search engine users, we are particularly interested in search-and-go behavior, which intuitively corresponds to a simple but challenging question, i.e., will users go where they search? Accurately estimating such be...

Understanding individual and crowd dynamics in urban environments is critical for numerous applications, such as urban planning, traffic forecasting, and location-based services. However, researchers have developed travel demand models to accomplish this task with survey data that are expensive and acquired at low frequencies. In contrast, emerging...

Extracting identifiable information from human trajectories is a fundamental task in many location-based services (LBS), such as personalized POI recommendation system, irregular human movement detection and privacy protection. Existing studies assume that we have collected sufficient trajectory data for each user, and therefore a classifier could...

Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phon...

This report provides a summary of the 2019 edition of the International Workshop on Prediction of Human Mobility (PredictGIS 2019), which was held in conjunction with ACM SIGSPATIAL 2019, in Chicago, IL on November 5th, 2019.

Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places. Place embeddings with high spatial resolution are desirable for many applications, however, downscaling the spatial resolution deteriorates the quality of embeddings due to data sparsity, especially in less populated a...

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places...

Nowadays, massive urban human mobility data are being generated from mobile phones, car navigation systems, and traffic sensors. Predicting the density and flow of the crowd or traffic at a citywide level becomes possible by using the big data and cutting-edge AI technologies. It has been a very significant research topic with high social impact, w...

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places...

Extracting identifiable information from human trajectories is a fundamental task in many location-based services (LBS). However, various mobility patterns underlain in human trajectories are difficult to model by existing models. Moreover, we could hardly define a clear user set for user identification because the set of users are dynamic and chan...

This paper describes a method that simultaneously predicts the next visiting location and time of mobile users, i.e., spatio-temporal prediction (STP) from global positioning system (GPS) log dataset acquired from users' smartphones.

This paper presents a novel virtual reality demonstration program that provides people immersive urban experience, i.e. helps people understand characteristics of the city atmosphere from big data analysis on GPS location logs and search query logs. In contrast to the other demonstration systems that show the characteristics of areas of interests b...

Conventional car navigation systems that require manual input of a user's destination are not frequently used for familiar routes such as daily commutes and regular shopping. In this paper, we propose and realize proactive car navigation, which integrates daily destination prediction system to car navigation system in order to eliminate input by us...

The limited attentional resource of users is a bottleneck to delivery of push notifications in today's mobile and ubiquitous computing environments. Adaptive mobile notification scheduling, which detects opportune timings based on mobile sensing and machine learning, has been proposed as a way of alleviating this problem. However, it is still not c...

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real ti...

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and und...

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real ti...

Despite the rising importance of enhancing community resilience to disasters, our understanding on how communities recover from catastrophic events is limited. Here we study the population recovery dynamics of disaster affected regions by observing the movements of over 2.5 million mobile phone users across three countries before, during and after...

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate t...

Predicting behaviors of a population from location-oriented log data from smartphones, i.e., urban population dynamics, has become more common in mobile and pervasive computing. A bilinear representation approach has been proposed to improve the prediction accuracy of urban population dynamics by adding contexts such as geographical information and...

This paper is the first work to replicate and simulate urban dynamics by learning individuals' decision-making processes and creating human-like agents from GPS data. We develop a novel agent model by learning from historical data via reinforcement learning techniques. We test our methodology in different scenarios at the citywide level using real...

Predicting user behavior makes it possible to provide personalized services. Destination prediction (e.g. predicting a future location) can be applied to various practical applications. An example of destination prediction is personalized GIS services, which are expected to provide alternate routes to enable users to avoid congested roads. However,...

We propose a nonparametric Bayesian mixture model that simultaneously optimizes the topic extraction and group clustering while allowing all topics to be shared by all clusters for grouped data. In addition, in order to enhance the computational efficiency on par with today’s large-scale data, we formulate our model so that it can use a closed-form...

Thanks to the recent popularity of GPS-enabled mobile phones, modeling people flow or population dynamics is attracting a great deal of attention. Advances in methods where regular population patterns with respect to factors such as holidays or weekdays are extracted have provided successful results in irregularity detection. With large-scale crowd...

Understanding social relationships plays an important role in smooth information sharing and project management. Recently, extracting social relationships from activity sensor data has gained popularity, and many researchers have tried to detect close relationship pairs based on the similarities between activity sensor data, namely, unsupervised ap...

Understanding people flow in a city (urban dynamics) is of great importance in urban planning, emergency management, and commercial activity. With the spread of smart devices, many studies on urban dynamics modeling with mobility logs have been conducted. It is predictive analysis, not analysis of the past, that enables various applications contrib...

This paper describes a pedestrian population trend estimation method using location data of smartphone users. This technique is intended to be an alternative to traffic censuses using tally counters. Traffic censuses using tally counters are still commonly used to survey the number of pedestrians despite their cost and limitations in area and time....

Existing car-sharing systems have difficulty meeting the demands of one-way trips and connecting to other sharing systems. Therefore, in this study, a multi-mobility sharing service management system that was able to meet the demands of the one-way and round-way trips and shared diverse transportation modes such as cars (electric car/gasoline car),...

This paper proposes an innovative way to detect working relationships by using only the step tracking data acquired from pedometers like Fitbit. The idea makes the cost of working-relationship detection much lower than that of previous approaches. We can find out if people have a working relationship and spend their daily lives together by making t...

An on-demand bus is like a shared taxi that operates only when riders want to travel between the origin and destination locations. It offers many advantages over fixed-route buses, but the riders are bothered by the need to tediously enter such data as origins, destinations, and deadlines. A location recommendation system that predicts such data wo...

The introduction of the On-demand Bus is promoted in various places such as the rural town, the city, the hospital and so on. The On-demand Bus system developed by University of Tokyo, calculates efficient route plan in a few micro second and confirms reservation with user. This system has been introduced at more than 30 cities. Through field exper...