Hourly Demand Prediction of Shared Mobility Ridership

This research focuses on predicting the hourly number of bikes needed using Citi bike data. Micro mobility is the new trend that serves the transportation sector in any city. With the development of technology and introduction of new modes, comes new challenges. Bike sharing is the most developed and standard micro mobility device with extensive data sources. In this research we introduce the rebalancing bike sharing problem, which is very recent and interesting problem. Bikes are being ridden from a station and returned to another, not necessarily the same one of departure, this procedure can cause some stations to be empty while others to be full, as a result, there is a need for a method by which distribution of bikes among stations are done. Using year-round historical trip data obtained from one of the famous bike operators in New York that is Citi bike. The study aims to find the factors affecting bike ridership and then by utilizing some predictive algorithms such as, regression models, k-means, decision trees and random forest a model will be created to estimate the number of bikes needed in an hourly basis regardless of any specific stations initially. Where accuracy will be eventually calculated. The testing will be initially evaluating the data of Citi bike in New York, however, the same can be utilized to evaluate data from other cities worldwide and operators, as well as other micro mobility modes such as e-scooters, mopeds, and others. Initially the Prediction problem will be evaluated against the current data available in the open-source Citi-Bike data, however, weather factors, bike infrastructure, and some other open-source data can be integrated for better results

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

Delays prediction using data mining techniques for supply chain risk management company

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceGlobalization makes competition in supply chain management more intense. Pressure on improving the efficiency, guarantee that goods arrive on time and reduce the cost of shipment became higher. Shipment passes through different continents and cultures, dispersed around the world and encounter different conditions and risks. These risks are unexpected events that might disrupt the flow of materials or the planned operations. It can be due to late delivery, inaccuracy in forecasting, natural disasters like hurricane and earthquake or sociocultural events like strike. An effective use of supply chain risk management methods which includes risk identification, risk assessment, risk mitigation, and risk control is important for the organization to survive. For that reason, I was part of a team in XXX organization who has a goal to develop a predictive model to predict shipment delays for company’s customers