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Meroux, Dominique
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Towards Design of Sustainable Smart Mobility Services through a Cloud Platform

Ford Motor Company-Dominique Meroux, Cassandra Telenko, Zhen Jiang, Yan Fu
  • Technical Paper
  • 2020-01-1048
To be published on 2020-04-14 by SAE International in United States
People and their communities are looking for transportation solutions that reduce travel time, improve well-being and accessibility, and reduce emissions and traffic congestion. Although new mobility services like ridesharing advertise improvements in these areas, closer inspection has revealed a discrepancy between industry claims and reality. Mobility service providers have the opportunity to leverage connected vehicle and connected device data through cloud-based APIs. We propose a CO2 data analytics framework that functions on top of a cloud platform to provide unique system-level perspectives on operating transportation services, from procuring the most environmentally and people friendly vehicles to scheduling and designing the services based on data insights. The motivation behind such an approach is two-fold: first, quantification enables transparency to build trust between the mobility service provider and their constituent communities; and second, identifying and acting to improve sustainability improves profitability. Using a benchmark problem with real-world vehicle and mobile device data, we demonstrate the functionality of our CO2 analytics framework.
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Machine Learning with Decision Trees and Multi-Armed Bandits: An Interactive Vehicle Recommender System

Carnegie Mellon University-Tong Yu, Ole Mengshoel
Ford Motor Co., Ltd.-Dominique Meroux, Zhen Jiang
Published 2019-04-02 by SAE International in United States
Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences. With these preferences as prior information, the multi-armed bandit algorithm, using Thompson sampling, efficiently leverages the user’s feedback to improve the recommendations in an online fashion. The empirical results show that…
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Policies to Maximize Fuel Economy of Plug-In Hybrids in a Rental Fleet

Ford Motor Company-Dominique Meroux
University of California - Davis-Gil Tal
Published 2018-04-03 by SAE International in United States
Plug-in hybrid (PHEV) technology offers the ability to achieve zero tailpipe emissions coupled with convenient refueling. Fleet adoption of PHEVs, often motivated by organizational and regulatory sustainability targets, may not always align with optimal use cases. In a car rental application, barriers to improving fuel economy over a conventional hybrid include: diminished benefits of additional battery capacity on long-distance trips, sparse electric charging infrastructure at the fleet location, lack of renter understanding of electric charging options, and a principle-agent problem where the driver accrues fewer benefits than costs for actions that improve fuel economy, like charging and eco-driving. This study uses high-resolution driving data collected from twelve Ford Fusion Energi sedans owned by University of California, Davis (UC Davis), where the vehicles are rented out for university-related activities. The data is analyzed to understand the degree to which the electric battery is taken advantage of by fleet management and end users to reduce fuel costs and emissions. Specifically, characteristics of trips assigned to those vehicles, driver behavior, locations of charging events and missed charging opportunities,…
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