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Jiang, Zhen
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A New Approach of Generating Travel Demands for Smart Transportation Systems Modeling

Ford Motor Company-Zhen Jiang, Chen Liang, Cassandra Telenko, Bo Wang, Yan Fu
Purdue University-Ruoxi Wen, Hua Cai
  • Technical Paper
  • 2020-01-1047
To be published on 2020-04-14 by SAE International in United States
The transportation sector is facing three revolutions: shared mobility, electrification, and autonomous driving. To inform decision making and guide smart transportation system development at the city-level, it is critical to model and evaluate how travelers will behave in these systems. Two key components in such models are (1) individual travel demands with high spatial and temporal resolutions, and (2) travelers’ sociodemographic information and trip purposes. These components impact one’s acceptance of autonomous vehicles, adoption of electric vehicles, and participation in shared mobility. Existing methods of travel demand generation either lack travelers’ demographic information and trip purposes, or only generate trips at a zonal level. Higher resolution demand and sociodemographic data can enable analysis of trips’ shareability for car sharing and ride pooling and evaluation of electric vehicles’ charging needs. To address this data gap, we propose a new approach of travel demand generation based on households. Census data provide the demographic information for each household (e.g., the number of adults and kids, income and education level, vehicle ownership etc.). The travel demands of each individual…
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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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Reliability-Based Design Optimization with Model Bias and Data Uncertainty

SAE International Journal of Materials and Manufacturing

Ford Motor Company-Yan Fu, Ren-Jye Yang
Northwestern University-Zhen Jiang, Wei Chen
  • Journal Article
  • 2013-01-1384
Published 2013-04-08 by SAE International in United States
Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties. The proposed technique is demonstrated through a vehicle design problem aiming at minimizing the vehicle weight through gauge optimization while satisfying reliability constraints. The RBDO result considering model uncertainty is compared with the one from conventional RBDO to demonstrate the benefits of the…
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