2015 Informs Annual Meeting

TC69

INFORMS Philadelphia – 2015

TC69 69-Room 201C, CC Multimodal Traffic Signal Control in a Connected Vehicle Environment Sponsor: TSL/Intelligent Transportation Systems (ITS) Sponsored Session Chair: K. Larry Head, University of Arizona, Tucson, AZ, United States of America, larry@sie.arizona.edu 1 - The Multi Modal Intelligent Traffic Signal Control System (MMITSS) K. Larry Head, University of Arizona, Tucson, AZ, United States of America, larry@sie.arizona.edu, Yiheng Feng, Mehdi Zamanipour, Shayan Khoshmagham, Byunho Beak, Sara Khosravi The Multi Modal Intelligent Traffic Signal Control System (MMITSS) is a Dynamic Mobility Application for connected vehicles in signalized networks. MMITSS provides intelligent signal control, priority control for emergency vehicles, transit, trucks, and pedestrians, and performance observation. MMITSS has been implemented in the Arizona Connected Vehicle Testbed in Anthem, AZ. 2 - Personalized Signaling for Connected Travelers in a Multi Modal Traffic Signal System Sara Khosravi, University of Arizona, Tucson, AZ, United States of America, sarakhosravi@email.arizona.edu, Sriharsha Mucheli, K. Larry Head Smartphones have become standard equipment for almost all travelers. The smartphone can be used to provide personalized signaling information for multi modal travelers including pedestrians and bicycles at signalized intersections, transit riders, and automobile drivers using navigation applications. This talk will explore how smartphone applications can impact the transportation system. 3 - Multi-Modal Intelligent Traffic Signal System, Optimal Priority Control Mehdi Zamanipour, University of Arizona, Yiheng Feng, K. Larry Head, Shayan Khoshmagham A priority control algorithm is presented that simultaneously considers the needs of different modal users in a Connected Vehicle environment. A mathematical programming framework that allows multiple priority requests to be considered simultaneously based on a hierarchical control policy at the intersection level will be presented. 4 - Real-Time Performance Observation under Connected Vehicle Technology Shayan Khoshmagham, University of Arizona, K. Larry Head, Yiheng Feng, Mehdi Zamanipour This paper introduces an approach to observe the performance measures of a multi-modal transportation system in a connected vehicle environment. Different types of metrics including traffic-based, CV-based and signal-based measures are observed and estimated by mode by movement. Challenges regarding low market penetration rate and privacy of the road users are addressed respectively TC70 70-Room 202A, CC Predictive Analytics in Railway – Practice Sponsor: Railway Applications Sponsored Session Chair: Dharma Acharya, President, KOSU Services LLC, 241 Auburndale Dr., Ponte Vedra, FL, 32081, United States of America, acharya.dharma@gmail.com 1 - State of Railway Analytics Dharma Acharya, President, KOSU Services LLC, 241 Auburndale Dr., Ponte Vedra, FL, 32081, United States of America, acharya.dharma@gmail.com A brief overview of how the new emerging technology “Analytics” has been leveraged by railroads will be presented. Potential new areas where railways might be able to further utilize this new techniques to bring bottom line value to the company will also be discussed. 2 - Big Data Analytics for Optimized Track Maintenance and Renewal Management Luca Ebreo, MERMEC Inc., 110 Queen Parkway, Columbia, NY, United States of America, Luca.ebreo@mermecgroup.com, Pietro Pace Nowadays, track inspection technology allows railways to collect more and more data on track’s condition. These data are comparable to “big data” and require proper analysis in order to extract information for properly managing Track

Maintenance and Renewals. Since railways need to make use as much as possible of the available data for optimizing their maintenance programs, the required analytics to support key decisions in an efficient and effective manner will be illustrated and discussed. 3 - Using Data Visualization to Assess Performance Risk Eric Pachman, Director, Network Modeling & Analytics, CSX, 500 Water Street, Jacksonville, FL, 32202, United States of America, Eric_Pachman@csx.com At CSX, the way we think about “capacity” is changing. By adding data visualization to traditional industry modeling tools, discussions on capacity are shifting to discussions on risk and reliability. Our evolution in capacity analysis is helping CSX better prioritize infrastructure projects to improve network fluidity. In addition, through data visualization, we can start to “see” how various operating and commercial requirements and initiatives impact line of road capacity and risk. 4 - Deploying Predictive Analytics Solutions in the Rail Industry and Seeing a Return on the Investment Robert Morris, Chief Science Officer, Predikto, Inc., 1320 Ellsworth Industrial Blvd, Suite A1600, Atlanta, GA, 30318, United States of America, Robert@predikto.com, Mario Montag In this panel, Predikto will provide an overview of automated dynamic predictive analytics solutions specific to the rail industry. Use cases currently in deployment across the globe specific to predicting and reducing downtime in freight and commuter locomotives are discussed alongside the challenges that organizations face during the process of deploying such technology. Also considered are strategies to assist in expediting monetary return on investments in predictive maintenance. TC71 71-Room 202B, CC Transportation Planning I Contributed Session Chair: Antonio Antunes, Professor, University of Coimbra, Dept. of Civil Engineering, Coimbra, Portugal, antunes@dec.uc.pt 1 - Plug-in Electric Vehicle Charging Infrastructure Planning using Cellular Network Data Jing Dong, Assistant Professor, Iowa State University, 350 Town Engineering Building, Ames, IA, 50011, United States of America, jingdong@iastate.edu, Luning Zhang This paper presents a method to identify activity-travel patterns, in terms of timing and duration at home, work, and other major destinations, using multiday cell phone records. A Hidden Markov Model (HMM) is built to link traveler’s activity transitions to the observed cell tower locations. The probabilistic parameters of HMM are estimated using the Baum–Welch algorithm. The derived travel distances and dwell times are key inputs for plug-in electric vehicle charging infrastructure planning. 2 - The Barriers of Electric Vehicles Spread Adoption in China Faping Wang, Ph.d Candidate, Tsinghua University, Shenzhen Graduate School of Tsinghua, Shenzhen, GD, 518100, China, wfp13@mails.tsinghua.edu.cn This paper present a survey research about barriers of electric vehicle spread adoption in China, 1000 questionnaire was designed and send to participants which come from Beijing, Shanghai, Guangzhou and Shenzhen, all of which are large city in China. Majority of participants have EVs driving experience or owner of EV or PHEV. The demographic data was analyzed by statistic methods which reveal that more different choice behavior exit between western consumer and Chinese in EVs consumption. 3 - Strategic Infrastructure Development for Alternative Fuel Vehicles with Routing Considerations Seong Wook Hwang, PhD Student, The Pennsylvania State University, 232 Leonhard Building, The Pennsylvania State University, University Park, PA, 16802, United States of America, soh5223@psu.edu, Sang Jin Kweon, Jose A. Ventura This research considers decisions on the siting of alternative fuel (AF) refueling stations and on the routing of AF vehicles when drivers take a detour to refuel. Supposing that a driver takes any path whose distance is less than or equals to a detour distance, we provide an algorithm that finds feasible paths between origins and destinations. Then, a mathematical model is proposed to determine the locations of AF refueling stations with the objective of maximizing the covered flows.

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