Forecasting Lifespan of Crowded Events With Acoustic Synthesis-Inspired Segmental Long Short-Term Memory

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 visitors’ return plans and the deviation of visitor movements from preannounced event schedules. To address this issue, this study developed a novel framework for forecasting the start of crowding and its change over time (termed the lifespan of crowded events (LCE)). Based on the concept that event purposes influence the crowding patterns, our framework models these patterns according to the event purposes. Inspired by the acoustic synthesis that can successfully model the change in the sound volume for each instrument, we extended a canonical long short-term memory (LSTM) model with the concept of ADSR envelope, wherein the sound (crowd) volume changes can be represented within simple state transitions. The proposed versatile acoustic tri-state envelope for segmental LSTM, namely VATES, is evaluated on two datasets: synthetic and real-world mobility datasets. The results demonstrate that VATES can forecast crowding patterns with a 24.3% performance improvement, and precisely predict the start and end times of crowding, thereby improving by 6.6% and 26.1% respectively. We believe that our method enhances urban safety and mobility in crowded events, contributing to smarter city management

Workingrelationship detection from fitbit sensor data

Abstract This paper proposes an innovative way to detect working relationships by using only the step tracking data acquired from pedometers like Fitbit [1]. 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 them wear a pedometer. Results of an experiment in Japan showed that this approach is very effective and practical. An organizations profile can be written automatically by analyzing the data

Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

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 understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas

Fast Inverse Reinforcement Learning with Interval Consistent Graph for Driving Behavior Prediction

Maximum entropy inverse reinforcement learning (MaxEnt IRL) is an effective approach for learning the underlying rewards of demonstrated human behavior, while it is intractable in high-dimensional state space due to the exponential growth of calculation cost. In recent years, a few works on approximating MaxEnt IRL in large state spaces by graphs provide successful results, however, types of state space models are quite limited. In this work, we extend them to more generic large state space models with graphs where time interval consistency of Markov decision processes are guaranteed. We validate our proposed method in the context of driving behavior prediction. Experimental results using actual driving data confirm the superiority of our algorithm in both prediction performance and computational cost over other existing IRL frameworks