Intelligent evacuation management systems: A review

Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios

機械学習技術の視点による通勤交通手段選択と自動車所有に関する研究

過去数十年の間に，世界は急速な都市化プロセスを経験し，人々の生活には自動車が急速に普及した。モータリゼーションは我々に経済発展の機会を与えると同時に，生活の質に影響を与える地球環境に負荷をかけている。都市の自己増殖は，自家用車の所有と使用の増加を引き起こす主要な理由である。旅行モードの選択，車両所有パターン，およびそれらの決定要因に対して影響力のあるメカニズムを理解することは，土地利用と交通計画上の政策決定に大いに役立つ。この課題は，グローバリゼーションの時代に持続可能な交通の発展を目指す途上国において，大いに注目されている。本研究では，多項ロジットモデル，ニューラルネットワーク，ランダムフォレストを用いて，プノンペン市における将来のインパクトレベルと車両所有パターンの予測を行った。交通手段選択に関して，本研究では，勾配ブースティングマシン，機械学習アルゴリズム，およびLIMEを適用して，インドネシアのジャカルタ市の大都市圏における複数交通手段によるトリップパターンとその決定要因を推定した。両方の分析は，国際協力機構（JICA）から提供された世帯インタビュー調査データを使用した。分析結果は，家計収入がプノンペンのモータリゼーションに影響を与える最も強力な変数であることを示した。合計旅行回数などの個々の旅行特性の補足，通勤目的で行われた移動回数と全体の移動距離は全て，分類子として効果的に作用した。ジャカルタ市におけるケーススタディにおいては，単一交通機関の旅行に影響を与える要因として旅行費用や移動時間といった限られた変数が選ばれる一方で，複数交通機関の旅行については幅広い変数の影響を受けていることが示された。さらに，機械学習アプローチによる予測においては，精度を予測するという点だけでなく，統計的アプローチと比較して不均衡なカテゴリを処理するという点でも優れていたことが認められた。特に，グラディエントブースティングマシンは，ビッグデータで課題を解決する際，優れた潜在能力があることが示された。これら二つの結果は，旅行行動分析の分野に関して機械学習技術を適用する優位性を示し，他の分析に関しても，機械学習技術が応用できる可能性を示唆している。In the last decades, the world has seen the rapid urbanization process with the boom of motorized vehicles. The motorization, on one hand, gives opportunities for economic development and on the other hand, it puts pressure on the environment that affects the quality of life. The self-proliferating of the city is identified as a major that causes the rise of private vehicle ownership and usage. Understanding the influential mechanism of the travel mode choice, vehicle ownership patterns, and their determinants will greatly help policymaking for land use and transportation. This issue has been paid even greater attention in developing countries that aspire to reach sustainable transportation development goals in the era of globalization.In this study, the Multinomial logit model, Neural Networks and Random Forest were applied to examine the features’ impact level and to also predict vehicle ownership patterns in Phnom Penh city. Regarding travel mode choice, this study introduces the application of Gradient Boosting Machine, a Machine Learning algorithm, and Local Interpretable Model-agnostic Explanations technique to investigate the multi-mode trip pattern and its determinants in the metropolitan area of Jakarta city, Indonesia. Both analyses used the household interview survey data provided by the Japan International Cooperation Agency (JICA). The results indicate that household income is the most powerful variable affecting motorization in Phnom Penh. Supplementation of individual trip characteristics such as total number of trips made, number of trips made for work purposes and overall travel distance all make effective contributions as classifiers. The results from the case study of Jakarta city show that there was a limit of features (travel cost, time, etc.) that affected the single-mode trip while the multi-mode travel was influenced by the wide range of variables. Furthermore, it is acknowledged that the machine-learning approach outperformed not only in terms of predicting accuracy but also in dealing with unbalanced categories when compared with the statistical approach. Especially, the Gradient Boosting Machine indicated the impressive potentiality in solving the subject with big data. This detection supplies the advantages of applying machine learning techniques in terms of, but not limited to, the field of travel behavior.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Development of a Dynamical Egress Behavioural Model under Building Fire Emergency

Building fire accidents, as a continuing menace to the society, not only incur enormous property damage but also pose significant threats to human lives. More recently, driven by the rapid population growth, an increasing number of large-capacity buildings are being built to meet the growing residence demands in many major cities globally, such as Sydney, Hong Kong, London, etc. These modern buildings usually have complex architectural layouts, high-density occupancy settings, which are often filled with a variety of flammable materials and items (i.e., electrical devices, flammable cladding panels etc.). For such reasons, in case of fire accidents, occupants of these buildings are likely to suffer from an extended evacuation time. Moreover, in some extreme cases, occupants may have to escape through a smoke-filled environment. Thus, having well-planned evacuation strategies and fire safety systems in place is critical for upholding life safety. Over the last few decades, due to the rapid development in computing power and modelling techniques, various numerical simulation models have been developed and applied to investigate the building evacuation dynamics under fire emergencies. Most of these numerical models can provide a series of estimations regarding building evacuation performance, such as predicting building evacuation time, visualising evacuation dynamics, identifying high-density areas within the building etc. Nevertheless, the behavioural variations of evacuees are usually overlooked in a significant proportion of such simulations. Noticeably, evacuees frequently adjust their egress behaviours based on their internal psychological state (i.e., the variation of stress) and external stimulus from their surrounding environments (i.e., dynamical fire effluents, such as high-temperature smoke). Evidence suggests that evacuees are likely to shift from a low-stress state to a high-stress state and increase their moving speed when escaping from a high-temperature and smoke-filled environment. Besides, competitive behaviours can even be triggered under certain extremely stressful conditions, which can cause clogging at exits or even stampede accidents. Without considering such behavioural aspects of evacuees, the predicted evacuation performance might be misinterpreted based on unreliable results; thereby, misleading building fire safety designs and emergency precautions. Therefore, to achieve a more realistic simulation of building fire evacuation processes, this research aims to advance in modelling of human dynamical behaviour responses of each evacuee and integrating it into building fire evacuation analysis. A dynamical egress behaviour-based evacuation model that considering the evacuee’s competitive/cooperative egress movements and their psychological stress variation is developed. Furthermore, a fire hazard-integrated evacuation simulation framework is established by coupling with the fire dynamics simulator (i.e., FDS). By means of tracking dynamical interactions between evacuees and the evolutionary fire dynamics within the building space, evacuees’ local fire risks and stress levels under the impacts of locally encountered fire hazards (i.e., radiation, temperature, toxic gas, and visibility) can be effectively quantified. In this study, the developed simulation tool can provide a further in-depth building fire safety assessment. Thus, it contributes to performance-based fire safety engineering in designs and real applications, including reducing budgets and risks of participating in evacuation drills, supporting emergency evacuation strategy planning, mitigating fire risks by identifying risk-prone areas associated with building fire circumstances (e.g., putting preventative measures in place beforehand to intervene or mitigate safety risks, such as mass panic, stampede, stress evoked behaviours)