バスロケーションデータを用いたバスバンチングの予測と路線バス利用者の需要推定に関する研究

京都大学0048新制・課程博士博士(工学)甲第22653号工博第4737号新制||工||1740(附属図書館)京都大学大学院工学研究科都市社会工学専攻(主査)教授 山田 忠史, 教授 藤井 聡, 准教授 SCHMOECKER Jan-Dirk学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Restrictive and stimulative impacts of COVID-19 policies on activity trends: A case study of Kyoto

This paper employs regression with ARIMA errors (RegARIMA) to quantify the impacts of multiple non-pharmaceutical interventions, daily new cases, seasonal and calendar effects, and other factors on activity trends across the timeline of the ongoing COVID-19 pandemic in Japan. The discussion focuses on two controversial policy sets imposed by the Japanese government that aim to contain the pandemic and to stimulate the recovery of the economy. The containing effect was achieved by stay-at-home requests and declaring a “State of Emergency” in the combat against the first waves of infectious cases. After observing reduced cases, Go-to-travel and Go-to-eat campaigns were launched in July 2020 to encourage recreational travel and to revive the economy. To better understand the impact of the policies we utilize “Google trends” which measure how much these policies are looked up online. We suggest this reflects how much they are part of the public discussion. A case study is conducted in Kyoto, a city famous for tourism. The proposed RegARIMA model is compared with linear regression and time series models. The outperformances in measuring the magnitude of intervention impacts and forecasting the future trends are confirmed by using a total of twelve activity and mobility indices as the dependent variable. Nine indices are released by Google and Apple and three are obtained from local Wi-Fi packet sensors. The effect of the State of Emergency declaration is found to erode at the second implementation, and the second stage of the Go-to-travel campaign successfully stimulated travel demand in the autumn sighting season of 2020

DGMem: Learning Visual Navigation Policy without Any Labels by Dynamic Graph Memory

In recent years, learning-based approaches have demonstrated significant promise in addressing intricate navigation tasks. Traditional methods for training deep neural network navigation policies rely on meticulously designed reward functions or extensive teleoperation datasets as navigation demonstrations. However, the former is often confined to simulated environments, and the latter demands substantial human labor, making it a time-consuming process. Our vision is for robots to autonomously learn navigation skills and adapt their behaviors to environmental changes without any human intervention. In this work, we discuss the self-supervised navigation problem and present Dynamic Graph Memory (DGMem), which facilitates training only with on-board observations. With the help of DGMem, agents can actively explore their surroundings, autonomously acquiring a comprehensive navigation policy in a data-efficient manner without external feedback. Our method is evaluated in photorealistic 3D indoor scenes, and empirical studies demonstrate the effectiveness of DGMem.Comment: 8 pages, 6 figure

Robust Navigation with Cross-Modal Fusion and Knowledge Transfer

Recently, learning-based approaches show promising results in navigation tasks. However, the poor generalization capability and the simulation-reality gap prevent a wide range of applications. We consider the problem of improving the generalization of mobile robots and achieving sim-to-real transfer for navigation skills. To that end, we propose a cross-modal fusion method and a knowledge transfer framework for better generalization. This is realized by a teacher-student distillation architecture. The teacher learns a discriminative representation and the near-perfect policy in an ideal environment. By imitating the behavior and representation of the teacher, the student is able to align the features from noisy multi-modal input and reduce the influence of variations on navigation policy. We evaluate our method in simulated and real-world environments. Experiments show that our method outperforms the baselines by a large margin and achieves robust navigation performance with varying working conditions.Comment: Accepted by ICRA 202

Synchronous MDADT-Based Fuzzy Adaptive Tracking Control for Switched Multiagent Systems via Modified Self-Triggered Mechanism

In this paper, a self-triggered fuzzy adaptive switched control strategy is proposed to address the synchronous tracking issue in switched stochastic multiagent systems (MASs) based on mode-dependent average dwell-time (MDADT) method. Firstly, a synchronous slow switching mechanism is considered in switched stochastic MASs and realized through a class of designed switching signals under MDADT property. By utilizing the information of both specific agents under switching dynamics and observers with switching features, the synchronous switching signals are designed, which reduces the design complexity. Then, a switched state observer via a switching-related output mask is proposed. The information of agents and their preserved neighbors is utilized to construct the observer and the observation performance of states is improved. Moreover, a modified self- triggered mechanism is designed to improve control performance via proposing auxiliary function. Finally, by analysing the re- lationship between the synchronous switching problem and the different switching features of the followers, the synchronous slow switching mechanism based on MDADT is obtained. Meanwhile, the designed self-triggered controller can guarantee that all signals of the closed-loop system are ultimately bounded under the switching signals. The effectiveness of the designed control method can be verified by some simulation results

Frozen city: Analysing the disruption and resilience of urban activities during a heavy snowfall event using Google Popular Times

Understanding the impact of climate change on cities is fundamental to address the increasing occurrence of extreme weather events. This research aims to raise awareness and emphasise the need and potential of proactive measures to mitigate the adverse effects of climate change. To do so, this article conducts a case study for a huge snowfall that occurred in the city of Madrid (Spain) in January 2021, blocking the city for several days. The analysis is based on geolocated big data sourced from Google Popular Times (GPT), which captures the occupancy of establishments throughout the city over the entire study period. An exploratory spatial-temporal analysis has been conducted to examine the impact of the snowfall on the daily activities of the city, taking into consideration the demographic characteristics. The findings reveal a distinction in the impact of the snowfall on activities. Essential activities experience less impact compared to leisure activities. Furthermore, at the socio-economic level, the impact on low-income neighbourhoods is observed to be less affected than on high-income neighbourhoods. The implications of this research contribute to the body of knowledge on climate change resilience and adaptation, providing valuable insights for urban management strategies and informing future research in this field

Bus bunching along a corridor served by two lines

Headway fluctuations and “bus bunching” are well known phenomena on many bus routes where an initial delay to one service can disturb the whole schedule due to resulting differences in dwell times of subsequent buses at stops. This paper deals with the influence of a frequent but so far largely neglected characteristic of bus networks on bus bunching, that is the presence of overtaking and common lines. A set of discrete state equations is implemented to obtain the departure times of a group of buses following the occurrence of an exogenous delay to one bus at a bus stop. Two models are distinguished depending on whether overtaking at stops is possible or not. If two buses board simultaneously and overtaking is not possible, passengers will board the front bus. If overtaking is possible, passengers form equilibrium queues in order to minimise their waiting times. Conditions for equilibrium queues among passengers with different choice sets are formulated. With a case study we then illustrate that, if overtaking is not allowed, the presence of common lines worsens the service regularity along the corridor. Conversely, common lines have positive effects when overtaking is possible. We suggest hence that appropriate network design is important to reduce the negative effects of delay-prone lines on the overall network performance

Explaining a century of Swiss regional development by deep learning and SHAP values

We use a graph convolutional neural network (GCN) for regional development prediction with population, railway network density, and road network density of each municipality as development indicators. By structuring the long-term time series data from 2833 municipalities in Switzerland during the years 1910–2000 as graphs over time, the GCN model interprets the indicators as node features and produces an acceptable prediction accuracy on their future values. Moreover, SHapley Additive exPlanations (SHAPs) are used to make the results of this approach explainable. We develop an algorithm to obtain SHAP values for the GCN and a sensitivity indicator to quantify the marginal contributions of the node features. This explainable GCN with SHAP decomposes the indicator into the contribution by the previous status of the municipality itself and the influence from other municipalities. We show that this provides valuable insights into understanding the history of regional development. Specifically, the results demonstrate that the impacts of geographical and economic constraints and urban sprawl on regional development vary significantly between municipalities and that the constraints are more important in the early 20th century. The model is able to include more information and can be applied to other regions and countries

IFN-γ Attenuates Eosinophilic Inflammation but Is Not Essential for Protection against RSV-Enhanced Asthmatic Comorbidity in Adult Mice

The susceptibility to respiratory syncytial virus (RSV) infection in early life has been associated with a deficient T-helper cell type 1 (Th1) response. Conversely, healthy adults generally do not exhibit severe illness from RSV infection. In the current study, we investigated whether Th1 cytokine IFN-γ is essential for protection against RSV and RSV-associated comorbidities in adult mice. We found that, distinct from influenza virus, prior RSV infection does not induce significant IFN-γ production and susceptibility to secondary Streptococcus pneumoniae infection in adult wild-type (WT) mice. In ovalbumin (OVA)-induced asthmatic mice, RSV super-infection increases airway neutrophil recruitment and inflammatory lung damage but has no significant effect on OVA-induced eosinophilia. Compared with WT controls, RSV infection of asthmatic Ifng-/- mice results in increased airway eosinophil accumulation. However, a comparable increase in eosinophilia was detected in house dust mite (HDM)-induced asthmatic Ifng-/- mice in the absence of RSV infection. Furthermore, neither WT nor Ifng-/- mice exhibit apparent eosinophil infiltration during RSV infection alone. Together, these findings indicate that, despite its critical role in limiting eosinophilic inflammation during asthma, IFN-γ is not essential for protection against RSV-induced exacerbation of asthmatic inflammation in adult mice