Development of Machine Learning based approach to predict fuel consumption and maintenance cost of Heavy-Duty Vehicles using diesel and alternative fuels

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and effective methods to predict fuel consumption, maintenance costs, and total cost of ownership for heavy-duty vehicles. Every improvement so achieved in this direction is a direct contributor to driving the reduction in the total cost of ownership for a fleet owner, thereby bringing economic prosperity and reducing oil imports for the economy. Motivated by these crucial goals, the present research considers integrating data-driven techniques using machine learning algorithms on the historical data collected from medium- and heavy-duty vehicles. The primary motivation for this research is to address the challenges faced by the medium- and heavy-duty transportation industry in reducing emissions and operating costs. The development of a machine learning-based approach can provide a more accurate and reliable prediction of fuel consumption and maintenance costs for medium- and heavy-duty vehicles. This, in turn, can help fleet owners and operators to make informed decisions related to fuel type, route planning, and vehicle maintenance, leading to reduced emissions and lower operating costs. Artificial Intelligence (AI) in the automotive industry has witnessed massive growth in the last few years. Heavy-duty transportation research and commercial fleets are adopting machine learning (ML) techniques for applications such as autonomous driving, fuel economy/emissions, predictive maintenance, etc. However, to perform well, modern AI methods require a large amount of high-quality, diverse, and well-balanced data, something which is still not widely available in the automotive industry, especially in the division of medium- and heavy-duty trucks. The research methodology involves the collection of data at the West Virginia University (WVU) Center for Alternative Fuels, Engines, and Emissions (CAFEE) lab in collaboration with fleet management companies operating medium- and heavy-duty vehicles on diesel and alternative fuels, including compressed natural gas, liquefied propane gas, hydrogen fuel cells, and electric vehicles. The data collected is used to develop machine learning models that can accurately predict fuel consumption and maintenance costs based on various parameters such as vehicle weight, speed, route, fuel type, and engine type. The expected outcomes of this research include 1) the development of a neural network model 3 that can accurately predict the fuel consumed by a vehicle per trip given the parameters such as vehicle speed, engine speed, and engine load, and 2) the development of machine learning models for estimating the average cost-per-mile based on the historical maintenance data of goods movement trucks, delivery trucks, school buses, transit buses, refuse trucks, and vocational trucks using fuels such as diesel, natural gas, and propane. Due to large variations in maintenance data for vehicles performing various activities and using different fuel types, the regular machine learning or ensemble models do not generalize well. Hence, a mixed-effect random forest (MERF) is developed to capture the fixed and random effects that occur due to varying duty-cycle of vocational heavy-duty trucks that perform different tasks. The developed model helps in predicting the average maintenance cost given the vocation, fuel type, and region of operation, making it easy for fleet companies to make procurement decisions based on their requirement and total cost of ownership. Both the models can provide insights into the impact of various parameters and route planning on the total cost of ownership affected by the fuel cost and the maintenance and repairs cost. In conclusion, the development of a machine learning-based approach can provide a reliable and efficient solution to predict fuel consumption and maintenance costs impacting the total cost of ownership for heavy-duty vehicles. This, in turn, can help the transportation industry reduce emissions and operating costs, contributing to a more sustainable and efficient transportation system. These models can be optimized with more training data and deployed in a real-time environment such as cloud service or an onboard vehicle system as per the requirement of companies

A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC's development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method

Motor Vehicle Usage Patterns in Australia: A Comparative Analysis of Driver, Vehicle & Purpose Characteristics for Household & Freight Travel

An ordered probit model is used to predict motor vehicle usage in Australia on the basis of the unit record files underlying the Australian Bureau of Statistics’ Survey of Motor Vehicle Use. Both household and freight transport are analysed. The paper examines the statistical significance of a number of driver, vehicle and travel purpose variables on the level of motor vehicle usage. Factors analysed include driver age and gender, vehicle and fuel type, age of the vehicle, purpose of trip, place of registration, type of freight and number of drivers. The results indicate that the cut-off points between very low, low, medium, high and very high vehicle usages are significant and that the factors associated with differences in usage include driver age, engine size and age of vehicle for household vehicles and the type of freight, type of vehicle, gender and number of drivers for freight usage.Motor vehicle usage, driver, vehicle and purpose characteristics, ordered probit.

CHOICE AND TEMPORAL WELFARE IMPACTS: DYNAMIC GEV DISCRETE CHOICE MODELS

Welfare economics is often employed to measure the impact of economic policies or externalities. When demand is characterized by discrete choices, static models of consumer demand are employed for this type of analysis because of the difficulty in estimating dynamic discrete choice models. In this paper we provide a tractable approach to estimating dynamic discrete choice models of the Generalized Extreme Value (GEV) family that addresses many of the problems identified in the literature and provides a rich set of parameters describing dynamic choice. We apply this model to the case of recreational fishing site choice, comparing dynamic to static versions. In natural resource damage assessment cases, static discrete choice models of recreational site choice are often employed to calculate welfare measures, which will be biased if the underlying preferences are actually dynamic in nature. In our empirical case study we find that the dynamic model provides a richer behavioral model of site choice, and reflects the actual choices very well. We also find significant differences between static and dynamic welfare measures. However, we find that the dynamic model raises several concerns about the specification of the policy impact and the subsequent welfare measurement that are not raised in static cases.Demand and Price Analysis,

Assessing Importance and Satisfaction Judgments of Intermodal Work Commuters with Electronic Survey Methodology, MTI Report WP 12-01

Recent advances in multivariate methodology provide an opportunity to further the assessment of service offerings in public transportation for work commuting. We offer methodologies that are alternative to direct rating scale and have advantages in the quality and precision of measurement. The alternative of methodology for adaptive conjoint analysis for the measurement of the importance of attributes in service offering is implemented. Rasch scaling methodology is used for the measurement of satisfaction with these attributes. Advantages that these methodologies introduce for assessment of the respective constructs and use of the assessment are discussed. In a first study, the conjoint derived weights were shown to have predictive capabilities in applications to respondent distributions of a fixed total budget to improve overall service offerings. Results with the Rasch model indicate that the attribute measures are reliable and can adequately constitute a composite measure of satisfaction. The Rasch items were also shown to provide a basis to discriminate between privately owned vehicles (POVs) and public transport commuters. Dissatisfaction with uncertainty in travel time and income level of respondents were the best predictors of POV commuting

The Demand for Passenger Transport

Series: Discussion Papers of the Institute for Economic Geography and GIScienc

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

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