Mobility Market Forecasting + AI

  • Paper:
    Journal of Choice Modelling, 2020 (PDF)
Choice Data Generation using Usage Scenarios and Discounted Cash Flow Analysis

Discrete choice analysis with hierarchical Bayesian (HB) modeling is a popular methodology to estimate heterogeneous customer preferences. Despite the increased model accuracy gained from more choice data, this option is untenable due to the increased cost and time required to acquire substantial choice data of target customers. We thus propose a method for choice data generation for commercial products whose expected money value is a key factor in consumer choice (e.g., commercial vehicles and financial product). Using an individual usage scenario, we generate a discounted cash flow (DCF) model instead of a utility model to estimate the discount rates—than partworths—of individual consumers. The DCF model helps spawn numerous synthetic choice data with almost full factorial design. Using these data, we employ an HB-based discrete choice analysis. We conclude the study with a case study regarding preference estimation of hybrid courier truck conversion. The results reveal that the usage scenario-based HB estimation outperforms the traditional HB estimation.

  • Paper:
    Journal of Cleaner Production, 2019. (PDF)
Feasibility Study on the Korean Government’s Hybrid Conversion Project of Small Diesel Trucks for Parcel Delivery Services

The Korean government is planning an R&D project to convert existing small diesel trucks for parcel delivery services to diesel-electric hybrid trucks as part of its effort to reduce emissions of greenhouse gases and particulate matter (PM). This project started from the realization that parcel delivery trucks have become one of the main sources of PM emissions especially as the parcel delivery service industry has rapidly grown in Korea amid inefficient driving environment for delivery trucks. To evaluate the marketability and feasibility of the project, this study analyzed diesel truck owners’ preferences for converting to hybrid vehicles. Hierarchical Bayesian choice-based conjoint analysis was employed to forecast conversion probabilities and willingness to pay for various vehicle attributes, and the influencing demographic factors were then analyzed. The demand forecasting results are expected to enable decision makers to set a reasonable budget for hybrid conversion. For policy implications, the findings suggest that truck owners should be allowed to pay for the conversion in installments, not in a lump sum, and it would be strategically effective to target the consumer segment that prioritizes fuel cost savings relative to other vehicle attributes to promote hybrid conversion.

  • Paper:
    J. Korean Soc. Transp, 2019. (PDF)
A Study on the Alternative Selection of Eco-friendly Modification Techniques for Small Diesel Trucks

Light-duty trucks are a major means of transporting domestic short-haul road freight, and the importance of light-duty trucks is expected to increase with the expansion of electronic commerce. However, light-duty trucks are perceived as the main cause of fine dust in the city center due to vehicle deterioration, frequent driving and idling. This study aims to derive the technical factors and alternatives (electric, hybrid and DPF-equipped trucks) to convert light-duty trucks into eco-friendly trucks through an expert survey. To this end, the relative importance of the retrofit technology was evaluated using the analytic hierarchy process method. Results confirmed that the retrofit and maintenance costs should be considered as the first priority when evaluating alternative truck technology. In addition, the government needs to reduce the economic burden of truck owners through various subsidies to convert light-duty trucks into eco-friendly trucks. We hope that the results of this study can be used as a basic evaluation index in the development of eco-friendly truck conversion technology and design of policy for converting old trucks.

  • Paper:
    Design Science, 2016. (PDF)
Public Investment and Electric Vehicle Design: A Model-based Market Analysis Framework with Application to a USA-China Comparison Study

Governments encourage use of electric vehicles (EV) via regulation and investment to minimize greenhouse gas (GHG) emissions. Manufacturers produce vehicles to maximize profit, given available public infrastructure and government incentives. EV public adoption depends not only on price and vehicle attributes, but also on EV market size and infrastructure available for refueling, such as charging station proximity and recharging length and cost. Earlier studies have shown that government investment can create EV market growth, and that manufacturers and charging station operators must cooperate to achieve overall profitability. This article describes a framework that connects decisions by the three stakeholders (government, EV manufacturer, charging station operator) with preferences of the driving public. The goal is to develop a framework that allows the effect of government investment on the EV market to be quantified. This is illustrated in three scenarios in which we compare optimal public investment for a city in USA (Ann Arbor, Michigan) and one in China (Beijing) to minimize emissions, accounting for customer preferences elicited from surveys conducted in the two countries. Under the modeling assumptions of the framework, we find that high customer sensitivity to prices, combined with manufacturer and charging station operator profit maximization strategies, can render government investment in EV subsidies ineffective, while a collaboration among stakeholders can achieve both emission reduction and profitability. When EV and station designs improve beyond a certain threshold, government investment influence on EV adoption is attenuated apparently due to diminishing customer willingness to buy. Furthermore, our analysis suggests that a diversified government investment portfolio could be especially effective for the Chinese market, with charging costs and price cuts on license plate fees being as important as EV subsidies.

  • Paper:
    Design Science, 2019. (PDF)
Designing Profitable Joint Product-Service Channels

Consumer choices of services and the product platform that delivers them, such as apps and mobile devices or eBooks and eReaders, are becoming inextricably interrelated. Market viability demands product-service combinations be compatible across multiple producers and service channels, and producers profitability must include both service and product design. Some services may be delivered contractually or physically through a wider range of products than others, and so optimizing producers’ contingent product, service, and channel decisions becomes a combined decision problem. This article examines three common product-service design scenarios: exclusive, non-exclusive asymmetric, and non-exclusive symmetric. An enterprise-wide decision framework is proposed to optimize integrated services and products for each scenario. Optimization results can provide guidelines for strategies that are mutually profitable for partnercompetitor firms. An e-book service and tablet example, using market-level information from four firms (Amazon, Apple, B&N, GooglePlay) and conjoint-based product-service choice data, illustrates the approach using a scalable sequential optimization algorithm. The example results suggest that firms in a market equilibrium can differ markedly on the services they should seek to provide via other firms’ products, and demonstrate the interrelationship among marketing, services, and product design.