TECHNICAL PROPOSAL COVER SHEET - utrc2.org I... · 2014. 9. 24. · TECHNICAL PROPOSAL COVER SHEET...

CONSORTIUM MEMBERS

City University of New York, Clarkson University, Columbia University, Cornell University, Hofstra University, Manhattan College, New Jersey Institute of Technology, New York Institute of Technology, New York University, Polytechnic Institute of NYU, Rochester Institute of Technology, Rowan University, Rensselaer Polytechnic Institute,

Rutgers University*, State University of New York, Stevens Institute of Technology, Syracuse University, The College of New Jersey, University of Puerto Rico *Member under SAFETEA-LU Legislation

REGION II

UNIVERSITY TRANSPORTATION RESEARCH CENTER

Marshak Hall, Room 910 The City College of NY New York, NY 10031

REGION II New York, New Jersey, Puerto Rico, Virgin Islands

Tel: 212-650-8050 Fax: 212-650-8374 Website: www.utrc2.org

TECHNICAL PROPOSAL COVER SHEET

PROPOSAL TITLE: Innovative Travel Data Collection - Planning for the Next Two Decades

PURSUANT TO: RFP Number: Z-14-04

PRINCIPAL INVESTIGATOR: Ricardo Daziano David Croll Assistant Professor 305 Hollister Hall, CEE, Cornell University, Ithaca, NY 14850 Tel: 607-255-2018; Fax: 607-255-9004; Email: [email protected]

CO-PRINCIPAL INVESTIGATORS: Huaizhu (Oliver) Gao Associate Professor 220 Hollister Hall, CEE, Cornell University, Ithaca, NY 14850 Tel: 607 254-8334; Fax: 607-255-9004; Email: [email protected]

Linda Nozick Professor 220 Hollister Hall, CEE, Cornell University, Ithaca, NY 14850 Tel: 607-254-8334; Fax: 607-255-9004; Email: [email protected]

Joan Walker Associate Professor 111 McLaughlin Hall, University of California, Berkeley Tel: 510-642-6897; Fax: 510-643-5264; Email: [email protected]

SPONSOR: NYMTC

RESEARCH PROJECT MANAGER: Ricardo Daziano

PROJECT DURATION: 3/1/2015 - 8/31/2015; 6 Months

DATE SUBMITTED: September 24, 2014

Part I: Technical and Management Submittal Innovative Travel Data Collection -‐ Planning for the Next Two Decades Proposer’s Name: Cornell University Address: Office of Sponsored Programs 373 Pine Tree Road Ithaca, NY 14850-‐2820 Phone: 607-‐255-‐5014 Contact: Columbia Warren, Grant and Contract Officer Phone: 607-‐255-‐0655 Team that prepared the proposal: • Ricardo Daziano (Cornell) • Oliver Gao (Cornell) • Linda Nozick (Cornell) • Joan Walker (UC Berkeley)

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2. Table of Contents 2. TABLE OF CONTENTS ....................................................................................................................... 2 3. EXECUTIVE SUMMARY ..................................................................................................................... 3 4. APPROACH AND SCOPE OF SERVICES ......................................................................................... 4 4.1. OBJECTIVES ........................................................................................................................................................ 4 4.2. APPROACH .......................................................................................................................................................... 4 4.3. WORK PLAN AND SCOPE OF SERVICES .......................................................................................................... 6

5. EXPERIENCE ..................................................................................................................................... 11 6. ORGANIZATION, STAFFING AND SCHEDULE ......................................................................... 18 6.1. PRINCIPAL INVESTIGATOR AND PROJECT MANAGER .............................................................................. 18 6.2. KEY PERSONNEL ............................................................................................................................................. 18 6.3. COORDINATION AND MANAGEMENT PLAN ............................................................................................... 19 6.4. SCHEDULE ........................................................................................................................................................ 20

CVS ........................................................................................................................................................... 22

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3. Executive Summary Technology is already changing the way people make travel decisions by offering access to real-‐time information (via the use of GPS and GPS-‐based smartphone applications, such as MTA travel time apps). But real-‐time information is also creating a path toward a revolution in how travel data for planning and policymaking can be collected and managed. For example, travelers are transitioning from relatively passive objects of study to active data providers through the use of mobile crowdsourcing. Completely passive, non-‐intrusive data provision is also becoming a reality (via the use of automated travel diaries, for example). In this project a socio-‐technical approach to the analysis of transportation systems will be adopted to identify and describe rapidly emerging new methods of personal travel data collection for NYMTC-‐planning in an era that will be characterized by connected vehicles, infrastructure, and travelers. Identifying a path for best practices in data collection requires deep understanding of not only the opportunities that novel technology, such as multiple types of sensors, offers in terms of generation of data but also the expected impacts on decision-‐making and travel behavior models, as well as the challenges and socio-‐technical barriers that will emerge. The key element of analysis is thus the interactions between big data (generated by new technology and new collection methods that make use of new technology) and behavior (in terms of impacts of real time information on decision making by an increasing share of connected users and also of how those users provide feedback to inform the system). Outputs of this project will be centered on how to transform potentially massive amounts of data into valuable information to support NYMTC planning and decision-‐making. In fact, specific recommendations for NYMTC will be developed for ensuring full preparation to face the rapidly evolving new generation of technologies that support travel behavior analysis and of users of the transportation system, and to adopt best practices in travel data collection and modeling. Guidelines for the design of travel surveys for mobile and connected devices will also be developed. Cornell University, teamed with the University of California at Berkeley, offers leadership in cutting-‐edge academic and applied research in transportation systems analysis, transportation economics, and travel behavior, with deep understanding of how behavioral models inform policymaking and the data needs that are involved in the process of planning transportation activities. The research team also offers expertise in novel data collection methods, including pushing the knowledge frontier in the use of long-‐panel travel surveys combined with tracking data, as well as expertise in NYMTC’s operation and modeling needs.

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4. Approach and Scope of Services

4.1. Objectives Megacities such as New York face mega transportation problems, from big inefficiencies (congestion, delays) to health hazards (emissions, accidents). Smart cities should take advantage of the data and information coming from new technology – such as static and mobile sensors – for improving overall efficiency of the transportation system. The main goal of this project is thus to identify and describe rapidly evolving new methods of personal travel data collection in an era of connected vehicles, infrastructure, and travelers. The specific objectives are:

1. To identify, analyze, and valuate the socio-‐technical opportunities and challenges associated with the emerging use of real time transportation data for monitoring, analyzing, and planning movements (dynamic vehicle, passenger, freight, and pedestrian flows) in the city, including post-‐processing of potentially massive amounts of data.

2. To identify the shifts in data collection and transportation modeling that must take place to assist in describing, evaluating, and forecasting travel behavior,

3. To describe expected characteristics of the new units of study (connected vehicles and travelers) of travel behavior analysis, and

4. To discuss the impacts of such operational and modeling shifts to provide NYMTC with the expected outcomes, benefit, cost, and efficacy impacts of incorporating these emerging tools into its planning models and practices.

4.2. Approach To examine the value of novel travel data collection methods, a socio-‐technical approach to the analysis of transportation systems will be adopted. Because technology cannot be analyzed without consideration of its behavioral impacts, the adopted approach will recognize the interactions between real-‐time information and the behavior of users of the transportation system. In effect, travel survey methods need to respond to the fact that society and its mobility patters are also evolving in terms of the access and use of technology and information. In addition, richer data will have an impact on how travel demand models are built, which means that the currently established techniques may need to be revisited for taking into account the new data sources that will become standard in the future. In this project, data collection methods will be reviewed according to the technology (mobile sensors such as GPS-‐enabled devices, static sensors, smart cards) and platforms (web surveys, travel diaries) being used. Distinctions between passive and active data collection, and comparisons with traditional methods (such as paper travel surveys and diaries) will be made.

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The review will also summarize the opportunities, challenges, and expected impacts for the following focus areas that are relevant for NYMTC-‐planning:

1. Travel behavior and its social context: data and information as well as social network influence on travel demand

2. Transportation and air quality: data and valuation of information regarding environmental impacts of transportation as well as exposure to emissions

3. Transportation energy: data and valuation of information regarding fuel economy and fuel costs

4. Transportation safety: beyond toll-‐collection, devices such as E-‐ZPass have the potential to be used for pro-‐active safety management. Use of technology for accident mitigation and prevention. Crowdsourced data for avoiding hazards (for example, the GPS-‐based “waze” app where drivers report incidents and congestion levels that inform upstream drivers, Fig. 1)

5. Transportation and health: benefits of emission reductions. Active transportation (cycling and walking demand)

6. Extreme weather events, and pre-‐ and post-‐event planning: data and valuation of information about awareness, preparedness, evacuation, and survival to extreme weather hazards.

The following figure summarizes the approach and scope of the proposed project. Details of the work plan and tasks are discussed in subsection 4.3.

Fig. 2 Approach and Scope

The key element of analysis is the interactions between big data (generated by new technology and new collection methods that make use of new technology) and

New Technology

Mobile Sensors

Static Sensors

Behavior

Existing Models

New Models

Predictions

Socio-technical Integration

Big DataReal Time Information

New Collection Methods

Web surveys

GPS-enabled surveys

Smartphone-enabled surveys

Non-traditional data (emotions)

Crowdsourcing

Passive data (mobile sensors)

Passive data (smart cards)

Real Time InformationSocial Networks Air Quality

Energy Safety

Health Extreme Events

Post-processing

Storage / Cloud Servers

Validation / Reliability

Completion / Imputation

Automatic Updating

FeasibilityCost Benefit Analysis

Fig. 1 Crowdsourced reports of accidents, traffic jams, speed and police controls

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behavior (in terms of impacts of real time information on decision making by an increasing share of connected users and also of how those users provide feedback to inform the system). A central part of the discussion will be how existing models (NYMTC’s existing tools in particular) can be adapted to represent the impacts of real-‐time information. Limitations of existing models will be identified in the context of the new generation of large-‐scale transportation planning models that will fully respond to the challenges created by the use of big data. These challenges include the needs for post-‐processing, data storage, validation and reliability, completion and imputation (data mining), as well as automatic updating. Whereas big data is associated with increased computing costs, there is potential for large costs reductions in actual data collection activities. Additional benefits appear when considering that planning decisions will be improved with the use of richer information. Benefit and cost metrics will be constructed to evaluate the economic gains of the transition to new travel data collection and modeling.

4.3. Work Plan and Scope of Services

TASK 1: REVIEW OF PRACTICE AND RESEARCH OF THE ROLE OF TECHNOLOGY IN TRAVEL SURVEYS

A first step is to produce a comprehensive review of the literature (technical reports, working papers, white papers, scholar articles, and books) and practice (interviews with MPOs and other agencies1) of the use of sensors and mobile devices for travel data collection and modeling, both nationally and internationally. The area of technological and behavioral changes for travel data collection is well known to the proposers. In fact, the proposing team is leading its own relevant research projects. For instance, Dr. Daziano and Nozick are working on forecasting evacuation behaviors of coastal communities in response to storm hazard information; Dr. Gao is working on constructing a network of fixed and mobile sensors to monitor environmental quality, along with communication and modeling tools to interact with the network and end users; and Dr. Walker has several projects on creating mobile laboratories for analyzing human behavior (details are provided in section 5.) In particular, advances in the following topics will be reviewed and summarized:

• Web travel and stated preference surveys • GPS-‐enabled travel surveys2 and GPS-‐enabled data validation • Smartphone-‐enabled travel surveys (Fig. 2)3 • Continuous mobility surveys4

1 The city of San Francisco is a clear target due to existing contacts and projects. Internationally, the city of Montreal is an interesting case study as there are several projects that include the use of inductive loops for flow 2 Y. Asakura and E. Hato, Tracking survey for individual travel behaviour using mobile communication instruments, Transportation Research Part C: Emerging Technologies, vol. 12, 22 no. 3, pp. 273–291, 2004 3 A. Carrel, P. S. Lau, R. G. Mishalani, R. Sengupta, and J. L. Walker, Quantifying transit travel experiences from the users’ perspective with high-resolution smartphone and vehicle location data: Methodologies, validation, and example analyses. In Review, 2014. 4 J.D. Ortuzar, J. Armoogum, J.-L. Madre, and F. Potier, Continuous mobility surveys: the state of practice, Transport Reviews, vol. 31, no. 3, pp. 293–312, 2011

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• Crowdsourced data • User satisfaction surveys • Passive travel data collection using mobile-‐sensors (GPS, Fig. 3) • Passive travel data collection using smart cards • Automated surveys and travel diaries • Automated data collection for freight operations • Demand metrics (such as OD matrices) using passive data • Merging passive and active travel data (travel surveys) • Information-‐based mobility management • Use of qualitative and nontraditional data (subjective and non-‐instrumental

information such as satisfaction, attitudes, and emotions; use of tweets and image processing)5

Fig. 3 “Commute Warrior” – a travel diary app developed by Georgia Tech. From the app description: “Travel monitoring is automatic, recording second-‐by-‐second position and satellite details without any interaction on the part of the participant. Commute Warrior monitors walking, bicycling, transit, and personal vehicle trips.” Within the topics listed above, it is crucial to review how to use the new data effectively. There is the problem not only of managing massive amounts of data (the “big data problem”), but also how to ensure validity, reliability, and completion of the data.6 For example, there are methodologies to infer the destination of trips tracked with smart cards (such as the “MetroCard”), where the trip is validated only at the point of entry (origin) to the motorized system.7 Another problem is how to infer the transportation mode being used from passively GPS-‐traced routes. The following processing and computing technological challenges will be discussed 5 Daziano, RA and D Bolduc. Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian Hybrid Choice Model, Transportmetrica A: Transport Science 9(1), 74-106, 2013. 6 S. Itsubo and E. Hato, Effectiveness of household travel survey using GPS-equipped cell phones and web diary: comparative study with paper-based travel survey, in Transportation Research Board 85th Annual Meeting, 2006. 7 Munizaga, M.A., Palma, C. Estimation of a disaggregate multimodal public transport Origin–Destination matrix from passive smartcard data from Santiago, Chile, Transportation Research Part C, 24, 9-18, 2012

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and summarized:

• Post-‐processing (and visualizing) real-‐time information • Use of cloud servers8 • Validation and reliability (measurement error) of real-‐time data • Big data completion and imputation/mining (for example, using

accelerometer-‐based classifiers); Spatio-‐temporal data classification / clustering

• Automatic updating of policy decisions (for example, dynamic congestion pricing as a function of real-‐time flows)

• Battery life of mobile devices In accordance to the socio-‐technical approach adopted, technological barriers to the access to data provision by sectors of the population will be analyzed. Concerns regarding privacy issues are also of interest. Finally, a comparative approach will be adopted to compare the new data, data sources, and collection methods with not only NYMTC’s current practice, but also practice of other cities and communities. For example, dynamic parking pricing informed by wireless parking sensors is already being implemented in San Francisco. Dr. Walker, one of the experts of this research team, has been working on a smartphone-‐based Travel Quality study in San Francisco.9 Data collection started in the fall of 2013 with an initial enrollment of 856 participants. Data collected includes real-‐time phone locations, mobile surveys, entry and exit surveys, transit vehicle locations, and satisfaction and subjective well-‐being metrics. The high resolution of the smartphone location data allows travel time to be dissected into its individual components, and statistical analyses have shown how these data can provide a quantitative understanding of the relationship between service quality, delays, and customer satisfaction. Deliverables • A discussion paper summarizing the state of practice and research, showcasing

the opportunities and conceptual/methodological challenges of alternative data sources (such as sensor-‐generated counts or crowdsourcing) and real time travel surveys. The paper will also identify the specific data and approaches to collecting the data that could replace or supplement NYMTC’s modeling needs.

• The discussion paper will include synthetic tables that will summarize the multiple dimensions of the reviewed technologies and methods

8 J. Jariyasunant, M. Abou-Zeid, A. Carrel, V. Ekambaram, D. Gaker, R. Sengupta, and J. L. Walker, T-quantified traveler: Travel feedback meets the cloud to change behavior, Journal of Intelligent Transportation Systems, 2014. 9 Carrel, A., Sengupta, R., Walker, J.L., The San Francisco Travel Quality Study: Tracking Trials and Tribulations of a Transit Taker. Paper submitted for possible presentation at the 94th Transportation Research Board Meeting, 2014

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TASK 2: ANTICIPATING THE IMPACT OF NEW TRAVEL DATA ON THE NEW GENERATION OF LARGE-‐SCALE TRANSPORTATION PLANNING MODELS On the one hand, improved information will necessarily imply better decisions that will reduce inefficiencies of the transportation system. However, NYMTC models should take into account the potential impacts of the information accessed by an increasing share of connected travelers. Whereas disaggregate activity-‐based travel demand models for NYC consider multi-‐attribute decision-‐making, network models (used for strategic and tactic transportation planning) usually reduce the problem to a generalized cost representation that considers travel time only. Currently, only few studies have considered fuel consumption in addition of travel time for route choice decisions.10 Taking advantage of real-‐time information, advanced traveler general information systems (one of the avenues of research of the proposing team) will inform users of expanded variables such as fuel consumption and health-‐related emission costs that should be incorporated into the generalized cost function of network models. For example, some studies have developed a multi-‐user (mixed behavior) equilibrium model with endogenous market penetration for an advanced traveler information system. 11 On the other hand, the validity and feasibility of existing models will be questioned due to the massive amounts of data that will be produced by mobile sensors. At the limit, data will not necessarily represent samples, making classical statistical inference not valid. In addition, the new models will face the challenge of producing quick updates and fast processing of big data. Data visualization will also become a key element for planning, as the dimensionality of the information will need to be reduced to support decision-‐making. This task will discuss the anticipated impact of big data on current NYMTC’s travel demand models, identifying avenues of research that will be required to leverage this new travel data and data sources for the new generation of large-‐scale transportation planning models. In addition, data needs for these new models will be described. Deliverables • An appendix to the paper generated in Task 1 summarizing the expected

changes that the new data and approaches to collecting the data (as well as new complex models) will bring to NYMTC’s modeling needs.

TASK 3: COST EFFECTIVENESS AND EFFICACY OF EMERGING TRAVEL SURVEY TECHNIQUES

Data is necessary for good, informed decision making. However, data collection has always been costly. New data collection methods offer the potential of reducing actual data collection costs, while improving sample sizes, response rates, 10 Qian, Z., Zhang, H.M., 2011. Modeling multi-modal morning commute in a one-to-one corridor network. Transportation Research Part C, 19(8), 254–269, 2011. 11 Yang, H., Multiple equilibrium behaviors and advanced traveller information systems with endogenous market penetration. Transportation Research Part B, 32(3), 205–218, 1998.

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population representativity and coverage, and actual information attached to the data. However, the problem of big data that comes from cheaper data collection is validation, processing, visualization and (super-‐) computing costs. Data storage costs are not negligible either. Additional costs appear in the use of static sensors to collect information on speed, traffic counts, and parking availability, for example. This task will analyze benefits and costs of using massive amounts of data from mobile and static sensor networks for policymaking. Cost models for each topic discussed in Tasks 1 and 2 will be developed to support economic decisions that will justify investment in the new data and data sources. In particular, benefit and cost models will valuate the tradeoffs that will emerge from processing and computing technological challenges (post-‐processing real-‐time information; use of cloud servers; validation and reliability of real-‐time data; big data completion and imputation; and automatic updating of policy decisions). In addition, Task 2 will review the new generation of planning models that will adapt to the new sources of data. Benefits in terms of improved decisions coming from a richer understanding of mobility will be quantified. At the same time, learning and calibration costs will be taken into consideration. In sum, a clear methodology for evaluating costs and benefits of the new data, information, and models from an engineering economy perspective will be constructed. The output of this evaluation methodology will be metrics to support decisions regarding transfer to and investment in new data collection. Deliverables • A technical memo on how to build methodical cost benefit analyses of

undertaking the alternative data collection methods identified. For each reviewed method, a set of metrics summarizing benefits and costs will be generated and incorporated into the memo.

• A technical memo describing the assumptions and the methodology of the cost models and their implications on NYMTC’s data collection and modeling to address long-‐range Transportation Planning and other required work products.

TASK 4: DEVELOPING RECOMMENDATIONS FOR NYMTC’s DATA COLLECTION ACTIVITIES

From the output of Tasks 1, 2, and 3 specific recommendations for NYMTC will be developed for being fully prepared to face the rapidly evolving new generation of technologies that support travel behavior analysis and of users of the transportation system, and to adopt best practices in travel data collection and modeling. In particular, a suggested path will be provided for transitioning to the new generation of multiple-‐platform, real time data collection methods. Guidelines for the design and implementation of travel surveys for mobile and connected devices will also be developed. In addition, Task 4 will discuss how to transform potentially massive amounts of data into valuable information to support NYMTC planning and decision-‐making.

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Deliverables • A technical memo describing recommendations for NYMTC’s data collection,

processing, modeling, and visualization activities for policy and decision making, while meeting federal mandates including new regulations of MAP 21. The memo will contain guidelines for implementation of novel survey platforms, as well as a thorough discussion about how to exploit existing resources (such as EZ-‐Pass, the MetroCard, and MTA Travel Time Apps, Fig. 4) for collecting travel data.

• A technical presentation summarizing the outcomes of Tasks 1, 2, 3, and 4. The project team will deliver the presentation to the NYMTC staff and members, and an MS Powerpoint electronic file with the presentation will be shared with NYMTC for future use.

• Draft and Final Report: Culmination of Tasks 1, 2, 3, and 4. 20 hardcopies will be generated with an attachment of the final report as an MS Word file.

Fig. 4 “The Weekender” – an award-‐winning mobile application by MTA

5. Experience Cornell University – the lead institution for this proposal – has teamed with the University of California at Berkeley for this proposed work. This arrangement offers several benefits to NYMTC. First, the team understands the multidimensional needs of the region. For example, combining EPA’s Motor Vehicle Emission Simulator (MOVES) with NYMTC’s Best Practice travel demand model, Cornell developed the nation’s first web-‐based emissions post-‐processing software, CU-‐PPS.12 Team members at Cornell have also been working on behavioral models to better understand the role of information on extreme-‐weather evacuation decisions in New York City. Second, the team brings in expertise from outside the immediate region. In particular, Berkeley adds to the Cornell team expertise in novel data collection methods – and processing requirements for the new data – being tested in San Francisco and the Bay Area. 12 Wang, X., Gao, H.O., 2012. PPS-‐AQ: Post Processor Software for Regional Conformity Analysis, User Documentation, Prepared For The New York Metropolitan Transportation Council.

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Details of the individual expertise of the team members are presented below.

Ricardo Daziano is a professor at Cornell University and recognized expert in the field of theoretical and applied econometrics of consumer behavior and discrete choice models applied to technological innovation in transportation and energy efficiency. Dr. Daziano is an elected member in the graduate fields of 1) Transportation Systems Engineering in Civil and Environmental Engineering, 2) Systems Engineering, 3) Engineering Management, and 4) Regional Planning. Successful funding to date for his work on sustainable transportation includes an NSF CAREER award, two UTRC projects, a New York Sea Grant project, and a research grant from the University of Rome 3. Dr. Daziano’s research focuses on better understanding the interplay of consumer behavior with engineering, investment, and policy choices for energy-‐efficient technologies. Understanding individual choice behavior is in fact critical for several disciplines that need to account for supply and demand dynamics. Discrete choice models represent the cognitive process of economic decisions based on a probabilistic representation of neoclassical consumer theory. Discrete choice analysis is common tool in transportation engineering, applied economics, marketing, and urban planning. Discrete choice is used to forecast demand under differing pricing and marketing strategies and to determine how much consumers are willing to pay for qualitative improvements. Conventional methods in discrete choice modeling treat forecasts as deterministic, but D. Daziano’s research aims to overcome this limitation by deriving robust, computationally efficient statistical inference methods for policy-‐oriented analysis. In fact, describing and predicting the behavior of agents is extremely challenging. Sophisticated mathematical models and complex microdata are required to better represent individuals’ decisions among mutually exclusive alternatives. Dr. Daziano combines technical contributions in the search for more flexible structures of error heterogeneity – such as the derivation and analysis of estimators of advanced statistical models with less stringent assumptions over taste shocks – with empirical applications that necessitate a more flexible approach for providing more accurate predictions. In terms of data collection, Dr. Daziano has experience in designing and carrying out discrete choice experiments using web surveys. Dr. Daziano is in conversations with researches at McGill University to implement in the US a smartphone application that allow cyclists to record their routes (with real-‐time statistics such as time, speed, distance, calories burned, and emission offset), answer trip surveys, and share that information with planning authorities. This app has been successfully launched in Canadian cities such as Montreal and Toronto. In Toronto, more than 4,000 cyclists have reported more than 40,000 trips. Dr. Daziano has also served as consultant in consumer choice modeling and demand analysis in areas such as transportation and sustainable tourism. In 2010 he worked in a project commissioned by the Inter-‐American Development Bank and the Government of Bolivia that aimed at negotiating a $20 million loan for developing a national community-‐based tourism program. The project resulted in successful negotiation of the loan.

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Relevant Projects of Dr. Daziano (with Clients): • Forecasting evacuation behaviors of coastal communities in response to storm hazard

information. Agency: New York Sea Grant (NYSG). Program: Coastal Storm Awareness Program (CSAP). Role: PI. Amount: $150,000 (Daziano’s portion: $132,218). Period: 01/01/2014-‐12/31/2015.

• Analyzing Willingness to Improve the Resiliency of New York City’s Transportation System. University Transportation Research Center (UTRC), Region II (New York, New Jersey, and Puerto Rico). Role: PI. Amount: $80,000. Period: 03/01/2014-‐02/31/2014.

• CAREER Advanced demand estimators for energy-‐efficiency in personal transportation. Agency: National Science Foundation (NSF). Program: Faculty Early Career Development (CAREER), Environmental Sustainability, Chemical, Bioengineering, Environmental, & Transport Systems Division (CBET). Role: PI. Amount: $409,565. Period: 02/01/2013-‐12/31/2018.

• Data collection and econometric analysis of the demand for nonmotorized transportation. Agency: University Transportation Research Center (UTRC), Region II (New York, New Jersey, and Puerto Rico). Role: Sole PI. Amount: $80,000. Period: 10/01/2012-‐12/31/2013.

• Electric Car Objective, Behavioural Choice Analysis for Transport (ECO BEST) – Agency: Roma Tre University, Trieste University. Role: Collaborator (PI: Edoardo Marcucci, Roma Tre University). Amount: €12,000. Period: 04/01/2012-‐04/01/2014.

• Preparation of $20 Million Loan for National Community-‐Based Tourism Programme. Client: Inter-‐American Development Bank. Period: 05/01/2010-‐11/01/2010.

H. Oliver Gao is an award-‐winning professor at Cornell University and a world-‐renowned expert on transportation and environment/energy systems. Dr. Gao is an elected member in the graduate fields of 1) Cornell Institute of Public Affairs (CIPA), 2) Systems Engineering, 3) Transportation Systems Engineering in Civil and Environmental Engineering, 4) Air Quality in Earth and Atmospheric Science, and 5) Computing and Information Science at Cornell University. He is Editor-‐in-‐Chief of the leading international academic journal, Transportation Research D: Transport and the Environment. His research focuses on engineering/economics modeling and systems management solutions for sustainable and intelligent infrastructure and lifeline systems, low carbon and low emission transportation systems, environment (especially air quality and climate change)-‐energy systems, and the closely related issues of infrastructure and environment finance such as game theory and mechanism design for public-‐private partnership (PPP). He also studies alternative transportation/energy technologies, systems innovation, and green supply chain and logistics (e.g., sustainable food systems, quantifying and mitigating green-‐house gas emissions from food supply chains). He was a former member of the Transportation Research Board Committee on Transportation and Air Quality (ADC20), an academic member on the Federal Advisory Committee of US EPA MOVES model development, a current member of Transportation Research Board Committee on Maintenance Equipment (AHD60), and a member of the Cornell Atkinson Center for a Sustainable Future (ACSF). Gao received his graduate degrees (Ph.D. in Civil and Environmental Engineering, M.S. in Statistics, and M.S. in Agriculture and Resource Economics) from the University of California at Davis in 2004, M.S. degree in Civil Engineering in 1999, and duel undergraduate degrees in Environmental Science and Civil Engineering in 1996 from Tsinghua University, China. Gao also enjoys close and frequent intellectual interactions with his networks in finance – before joining Cornell, Gao was a quantitative analyst (QUANT) in the mathematical and econometrical modeling division at a Wall Street hedge fund

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specializing in emerging markets such as the Brazil, Russian, India, and China (BRIC). Since 2005 he has contributed invited presentations to international conferences in France, the Netherlands, Belgium, China, and Korea as well as in the US. Dr. Gao was a visiting professor with the French Institute of Science and Technology of Transport, Development and Networks (the IFSTTAR) in the summer of 2011, working with the Département Aménagement, Mobilités et Environnement (AME) on GHG emissions from French Freight Transportation. Professor Gao’s research on urban transportation infrastructure and air pollution/health has resulted in the development of an international leading research program in transportation and air quality studies at Cornell University. Through both solo efforts and collaborations with others, he has secured significant and continued research funding sponsored by US and international organizations such as US Department of Transportation, US Department of Agriculture, the Lloyd’s Register Foundation (UK), US Environmental Protection Agency, etc. His research publications have appeared in highly regarded transportation, environment, and management journals including Environmental Science & Technology, Transportation Research, Energy Policy, and Atmospheric Environment, etc. The outcome of his research has significant implications for improved capability to model, predict, and control transportation emissions and to evaluate their impacts on air quality, with the ultimate effect of optimizing transportation and air quality management strategies and thus improving public health.

Fig. 5 Hourly emission estimates in NYC using BPM and MOVES (left: emissions by links; right: emissions by

TAZs)

By using EPA’s Motor Vehicle Emission Simulator (MOVES) in conjunction with the New York Metropolitan Transportation Council’s (NYMTC’s) Best Practice travel demand model, Gao developed the nation’s first web-‐based emissions post-‐processing software, CU-‐PPS. The CU-‐PPS integrates the US EPA’s state-‐of-‐the-‐art emission model and activity-‐based travel demand model for emissions inventory estimation at a finely resolved link-‐by-‐link scale. The software has gone through rigorous testing and evaluation procedures and has been approved by the inter-‐agency consulting groups for official use of transportation conformity assessment in the NYMTC region. Figure 5 shows the GIS maps of hourly link-‐based and TAZ-‐based transportation emissions inventory in the NYC Metro area.

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Relevant Projects of Dr. Gao (with Clients): • PI, Evaluating the Role of Private Investment in Life Cycle Management of New York State’s

Infrastructure Assets, 3/1/2014—8/31/2015, $68,901, Agency: UTRC. • PI, Supplemental Agreement: Upgrading NYMTC PPS-‐AQ to MOVES2014, $250,580

09/01/2013—08/31/2015, Agency: NYMTC. • PI: Diesel Retrofit Assessment and Development of a Decision Supporting System, $363k, May

2008-‐Aug. 2010, sponsored by New York State Department of Transportation (NYSDOT) and US DOT.

• PI: Modeling Air Quality and Energy Impacts of Highway ROW Management, $172k, May 2008-‐Sep. 2010, sponsored by New York State Department of Transportation (NYSDOT) and US DOT.

• PI, (CO-‐PI, Johannes Gehrke from ECS) Next Generation Grid-‐Based Transportation Emissions Inventory Estimation Using MOVES and Activity-‐Based Travel Demand Models. $520k (out of $695k), Mar. 2010-‐May 2012, sponsored by NYMTC through UTRC2 (supplemental agreement in contracting process).

• PI: Improving Microscopic Particulate Emission Inventories—Modeling Sources of Variability, High-‐Emitting Events, and Size Distributions of Vehicular PM Emissions. $40k, Sep. 2008-‐Sep. 2010, sponsored by New York State Energy Research and Development Authority (NYSERDA).

• Co-‐PI (with K Max Zhang in MAE): Modeling Microenvironment Air Quality in Rochester, NY, $40k (out of $150k), Jun. 2008-‐Mar. 2011, sponsored by New York State Energy Research and Development Authority (NYSERDA).

• PI (Co-‐PI: K. Max Zhang in MAE): Impacts of Clean Diesel Strategies/Technologies on Air Quality and Exposure in New York, $75k (out of $147k), Feb. 2008-‐Mar. 2011, sponsored by New York State Energy Research and Development Authority (NYSERDA).

• PI: A Comprehensive Study of the NYS Clean Air School Bus Program: Operations and Potential Improvement for Effective Diesel Emission Reduction, $15k, Feb. 2008-‐May 2009, sponsored by New York State Energy Research and Development Authority (NYSERDA).

• Co-‐PI (with K. Max Zhang in MAE): Hot-‐Spot Analysis of Fine Particles (PM2.5) for Environmental and Health Impacts Assessment of Transportation Emissions in South Bronx, $10k, Jan. 2008-‐Dec. 2008, sponsored by 2008 UTRC2 research initiative.

• Co-‐PI (PI, Jeff Tester from ChemE), Verizon / Cornell -‐ Business/Sustainability Initiatives: Fleet management information system, $40k, Jun. 2009-‐May 2010, sponsored by Verizon Foundation.

• Co-‐PI (PI, Jeff Tester from ChemE), Verizon / Cornell -‐ Business/Sustainability Initiatives: PICS Management & Purchasing, $40k, Jun. 2009-‐May 2010, sponsored by Verizon Foundation.

• PI: The Diesel Retrofit Puzzle Extended: Optimal Fleet Owner Behavior over Multiple Time Periods, $25k. Jun. 2008—May 2009, sponsored by 2008 UTRC2 mini-‐grant for working papers.

• Co-‐PI (with Gene Fitzgerald at MIT) Biofuels in the United States: An assessment of the Potential for Biomass-‐To-‐Liquids Fuel Production Using Existing Sustainable Forest Resources, Sep. 2007-‐May 2009, $20k (out of $100k), sponsored by GE-‐Cornell Business of Science and Technology Initiative (BSTI).

• PI: Modeling High-‐Emitting Events of Vehicular Ultrafine PM Number Emissions, $5,000. Jan. 2009—Dec. 2009, sponsored by 2009 UTRC2 mini-‐grant for working papers.

• PI: Investment Planning for Optimized Decisions in Cleaning Up the Legacy Diesel Fleet, $5,000. Jan. 2007—Dec. 2007, sponsored by 2007 UTRC2 mini-‐grant for working papers.

Linda Nozick is a professor of civil and environmental engineering at Cornell University. She also is Director of the College Program in Systems Engineering, a program that she co-‐founded. She has been on the Cornell faculty since 1992 and has been a Full Professor since 2003. From 1998 to 1999, Dr. Nozick was Visiting Associate Professor in the Operations Research Department at the U.S. Naval Postgraduate School in Monterey, California. In 1998, she was Visiting Professor in

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the Operations Research Department at General Motors Research & Development in Warren, Michigan. She has played a leading role in developing optimization models for planning and policy to support the National Security Enterprise and Homeland Security. Dr. Nozick has served on two National Academy committees to advise the U.S. Department of Energy on renewal of their infrastructure. She has authored more than 60 peer-‐reviewed publications, many focused on transportation, moving hazardous materials, and modeling critical infrastructure systems. She has been an associate editor for Naval Research Logistics and a member of the editorial board of Transportation Research Part A. She has received numerous awards, including a CAREER award from the National Science Foundation and a Presidential Early Career Award for Scientists and Engineers from President Bill Clinton for "the development of innovative solutions to problems associated with the transportation of hazardous waste." Dr. Nozick also received several recognition awards from Sandia National Laboratories and the National Nuclear Security Administration for the development of modeling tools for nuclear stockpile analysis, transportation of hazardous/sensitive materials, enterprise planning, and budget analysis. Relevant Projects of Dr. Nozick (with Clients): • “Modeling Natural Disaster Risk Management: A Stakeholder Perspective”, Sponsor: National

Institutes of Standards and Technology, Duration 1/2009-‐1/2013. • “Joint Optimization of Evacuation and Shelter Location for Hurricanes, PI: L. Nozick, Sponsor,

National Science Foundation, Duration 7/1/2008-‐6/30/2011. • “Optimizing Regional Earthquake Mitigation Investment”, PIs: R. Davidson and L. Nozick,

Sponsor: National Science Foundation, Duration 7/1/2006-‐6/30/2010. • “Modeling Support for the Operations Research and Computational Analysis (ORCA) Group”, PIs:

Mark Turnquist and L. Nozick, Sponsor: Sandia National Laboratory, Duration 3/1/05-‐9/1/09. • “Modeling Interdependent Infrastructures and Optimizing Investments”, PIs: Linda Nozick and

Mark Turnquist, Sponsor: NSF, Duration 8/04-‐8/06. • “Forecasting part demands and Building Production Schedules”, PIs. L. Nozick, and M. Turnquist,

Sponsor: General Motors, Duration 1/03-‐12/05. • “Managing Portfolios of Projects Under Uncertainty with Application to Construction Activities”;

Sponsor: NSF, PIs. L. Nozick and M. Turnquist, Duration: 9/02-‐6/05. • “Managing Projects Under Uncertainty” Sponsor: General Motors; PIs M. Turnquist and L.

Nozick, Duration: 1/02-‐9/02. • “GIS-‐Based Decision Support for Gas Distribution Systems,” Sponsor: Keyspan Energy, Inc., PIs:

T. O’Rourke and L. Nozick, Duration: 1/2001-‐1/2003. • “Analytic and Mathematical Tools for Planning” Sponsor: Sandia National Labs. PIs: M.

Turnquist and L. Nozick, Duration: 2/97-‐2/01. • “Value of Information in Integrated Supply Chain Management,” Sponsor: General Motors, PIs:

M. Turnquist, L. Nozick, Duration: 1/2000-‐8/2000. • “The Integration of Education & Research in Transportation Engineering (in the area of Routing

and Scheduling),” Sponsor: National Science Foundation, CAREER/PECASE Award, PI: L. Nozick, Duration: 7/97-‐6/2002.

• “Benefit Evaluation of Advanced Information Technology For the Peace Bridge, Sponsor: Peace Bridge Authority, PI: Dr. Linda Nozick, Co-‐PIs: Drs. M. Turnquist, F. Wayno (Cornell-‐Industrial and Labor Relations), G. List (RPI), Duration: 7/97-‐12/98.

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• “Effective Marketing of Transit Systems and High Occupancy Vehicles: Case Study Syracuse New York Metropolitan Area,” Sponsor: New York State Department of Transportation, PIs: A. Meyburg (Cornell), L. Nozick, Duration: 7/95-‐12/97.

• “Route Verification for Oversize/Overweight Vehicles,” Sponsor: Region II-‐University Transportation Research Center, PIs: M. Turnquist, G. List, L. Nozick, Duration: 9/93-‐9/94.

Joan Walker joined UC Berkeley in 2008 as an Assistant Professor in the Department of Civil and Environmental Engineering and a member of the interdisciplinary Global Metropolitan Studies initiative. She received her Bachelor's degree in Civil Engineering from UC Berkeley and her Master's and PhD degrees in Civil and Environmental Engineering from MIT. Prior to joining UC Berkeley, she was Director of Demand Modeling at Caliper Corporation and an Assistant Professor of Geography and Environment at Boston University. She is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) – the highest honor bestowed by the U.S. government on scientists and engineers beginning their independent careers. She also serves in prominent professional positions, including the Chair of the Travel Demand Forecasting Committee (ADB40) of the Transportation Research Board of the National Academies.

Fig. 6 Screenshot of a smartphone-‐enable stated preference survey

Dr. Walker’s research focus is behavioral modeling, with an expertise in discrete choice analysis and travel behavior. She works to improve the models that are used for transportation planning, policy, and operations. In terms of data collection, Dr. Walker has been working on several projects dealing with new methods (“mobile laboratories”) for gathering information to study travel behavior. An example of her work that is relevant for this project is the combination travel diaries and tracking data for analyzing transit satisfaction in San Francisco. This project at investigating the link between objective, quantifiable measures of travel quality and customer satisfaction at a personal level by using smartphone data to capture respondents’ transit travel experiences and connecting them with satisfaction surveys. Another

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example is Dr. Walker’s work on transportation impacts of information provision and data collection via smartphones, for instance to determine the “value of green” or the subjective valuation of emission reductions (Fig. 6). Relevant Projects of Dr. Walker (with Clients): • Creating Mobile Laboratories for Studying Human Behavior: Is Unhealthy Eating a Matter of

Price or Preference?. Agency: Center for Information Technology Research in the Interest of Society (CITRIS), 2011-‐2012, (Co-‐PI)

• NetDiary: The Travel Behavior Data System, Agency: University of California Transportation Center, 2011-‐2012, (PI)

• XLab mobile – Creating Mobile Laboratories for Studying Human Behavior, Agency: UC Berkeley seed money from three sources – XLab, Associate Vice Chancellor for Research, and Dean of Social Science Research, 2011, (Co-‐PI)

• Revolutionizing Transportation Modeling due to a Revolutionized Data Collection Environment, Agency: Hellman Family Faculty Fund, 2010-‐2011, (PI)

• Revisiting the Use of Traveler Information to Induce Mode Shifts, Agency: University of California Transportation Center, 2010-‐2011, (PI)

• Sustainable Transportation: Technology, Mobility, and Infrastructure, Agency: UC Multicampus Research Programs and Initiatives, 2009-‐2011, (Co-‐PI)

• Employing Lessons from Behavioral Economics to Promote Sustainable Behaviors and Improve Travel Demand Models, Agency: University of California Transportation Center, 2008-‐2010, (PI)

• Drawing Linkages Between the Use of Wireless Infrastructure and Long-‐Range Transportation Planning, Agency: UC Berkeley Volvo Center of Excellence, 2008-‐2010, (PI)

• CAREER: Taking Attitudes Seriously: A Multi-‐Contextual Approach to Behavioral-‐Modeling, Faculty Early Career Development (CAREER) Program, Presidential Early Career Award for Scientists and Engineers (PECASE), Agency: National Science Foundation, 2007-‐2013, (PI)

• US-‐Netherlands Workshop: Frontiers in Transportation: Social and Spatial Interactions, Amsterdam, The Netherlands, Agency: National Science Foundation, 2005-‐2007, (PI)

6. Organization, Staffing and Schedule

6.1. Principal Investigator and Project Manager Ricardo A Daziano ([email protected]) David Croll Fellow Assistant Professor School of Civil and Environmental Engineering Cornell University 305 Hollister, Ithaca NY 14853

6.2. Key Personnel

In addition to the project manager, the team is completed with the following research members:

Oliver Gao ([email protected]) Associate Professor School of Civil and Environmental Engineering Cornell University

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Linda Nozick ([email protected]) Professor School of Civil and Environmental Engineering Cornell University Joan Walker ([email protected]) Associate Professor Department of Civil and Environmental Engineering Center for Global Metropolitan Studies University of California, Berkeley 2 Graduate Research Assistants (TBD) School of Civil and Environmental Engineering Cornell University Fig. 7 provides details of the project team, highlighting the expertise of team members that is relevant for successful completion of the project.

Fig. 7 Project Team

6.3. Coordination and Management Plan Each research team member leads expertise in different dimensions of transportation systems analysis, so close collaboration within the whole team for all tasks is key for successful completion of the project. Biweekly team meetings are planned (using WebEx technology for remote meetings with Berkeley). Smaller sub-‐groups focusing on particular tasks will meet more frequently as needed. Overall

Ricardo DazianoProject Manager

Transportation Behavioral ModelsConsumer Preferences

Web SurveysDiscrete Choice Experiments

Energy, Safety, Security, Reliability

Oliver GaoExpert

Emission Inventories and PostprocessingTransportation Systems AnalysisAir Quality and Climate Change

Pollution-related Health HazardsTransportation Infrastructure

Linda NozickExpert

Mathematical Models for Complex SystemsCivil Infrastructure Networks

Transportation PlanningManagement of Natural Disasters (Evacuation)

Movement of Hazardous Materials

Joan WalkerExpert

Transportation Planning, Policy, and OperationsNetDiaries

New Data Collection EnvironmentsBehavioral Impacts of Travel Information

Wireless Infrastructure and Long-Range Planning

Graduate Research Assistant 1 Graduate Research Assistant 2

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coordination of the work will be lead by the Principal Investigator. Dr. Daziano, who will also act as the main contact with NYMTC. The 2 Graduate Research Assistants involved in the project will be current PhD students of the Transportation Systems Analysis program of Civil and Environmental Engineering at Cornell. Dr. Daziano will directly advise one of the students, while the second student will be advised by Dr. Gao. Other members of the team will belong to the dissertation committee of the students. This project will provide an excellent learning opportunity to introduce the students not only to the subject problem of interest, but also to how to perform high-‐quality, exhaustive literature and practice surveys.

6.4. Schedule The project duration is 6 months. Scheduled times for tasks, subtasks, milestones, and deliverables (as detailed in subsection 4.3 Workplan) are presented in the Gantt chart below. Completion of milestones and deliverables (as the form of a draft, first, and final versions) will be used as metrics of success of each of the 4 identified tasks.

Fig. 8 Gantt Chart

Periodic reporting to clients will be scheduled. In particular, after each deliverable draft has been generated, conference calls with NYMTC will be scheduled to check whether the project outputs are meeting the expected requirements and needs. Revised versions of each document will be prepared by addressing NYMTC’s comments and suggestions. Table 1 shows the individuals share of effort of each team member. The Berkeley sub-‐contract is justified by the expertise that Dr. Walker adds to the proposing team. More specifically, Dr. Walker will work on identifying and summarizing the expected impacts of novel data collection methods on transportation behavioral models, with

1 2 3 4 5 6

1 Review,of,Practice,and,Research

Milestone ,,Kick

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a special focus on the state-‐of-‐the-‐art and state-‐of-‐the-‐practice in the San Francisco Bay area. This work will include a review of the literature as well as summarizing research insights from Dr. Walker’s own research projects.

Table. 1 Individual Share of Effort for each Team Member per Task

TasksRicardo Daziano

Task #1 - Review of Practice and Research 36%

Task #2 - Anticipating the Impact of New Travel Data on the New Generation of Large Scale Transportation Planning Models 18%

Task #3 - Cost Effectiveness ande Efficacy of Emerging Travel Survey Techniques 27%

Task #4 - Developing Recommendations for NYMTC's Data Collection Activities 18%

Total 100%

INDIVIDUAL SHARE OF EFFORT PER TASK

Oliver Gao

18%

9%

14%

9%

50%


Linda Nozick

9%

5%

7%

5%

25%


Joan Walker - subaward

18%

9%

14%

9%

50%


Student A

36%

18%

27%

18%

100%


Student B

36%

18%

27%

18%

100%


CV – Ricardo Daziano

CVs Ricardo Alvarez Daziano David Croll Fellow Assistant Professor Cornell University School of Civil and Environmental Engineering 305 Hollister Hall Ithaca, NY 14853 Email: [email protected] Phone: (607) 255-‐2018, Fax: (607) 255-‐9004 Education PhD in Economics, Université Laval, Québec, Canada, 2010 Majors: Econometrics, Industrial Organization Dissertation: A Bayesian Approach to Hybrid Choice Modeling Advisor: Prof. Denis Bolduc MSc in Civil Engineering, subject area: Transportation, Universidad de Chile, Santiago, Chile, 2001 Graduated with distinción máxima (Highest title of honor in Chilean universities, equivalent to summa cum

laude) Majors: Transportation Economics and Discrete Choice Modeling Thesis: Correlated Errors in Discrete Choice Models Advisor: Prof. Marcela Munizaga Professional degree* in Industrial Civil Engineering, Universidad de Chile, Santiago, Chile, 2001

(*A degree type granted after 2 years of additional coursework following successful completion of a BSc) Graduated with distinción máxima (Highest title of honor in Chilean universities, equivalent to summa cum

laude) BSc in Industrial Civil Engineering, Universidad de Chile, Santiago, Chile, 1999 Minor: Civil Engineering, subject area: Transportation Graduated with distinción (Title of honor in Chilean universities, equivalent to cum laude) Professional Appointments David Croll Fellow Assistant Professor (tenure track), Cornell University, 2011-present Graduate Fields: Civil and Environmental Engineering, Systems Engineering, Regional Science; Atkinson

Center Faculty Fellow Visiting Scientist, Università degli Studi Roma Tre (Roma Tre University), Facoltà di Scienze Politiche,

Centro Interdipartimentale di Ricerca sull’Economia delle Istituzioni (Interdepartmental Research Center on the Economics of Institutions), Rome, Italy. May-June 2013

Visiting Scientist, Transportation Sustainability Research Center, Institute of Transportation Studies, UC Berkeley. May-June 2012

Visiting Scientist, Zentrum für Europäische Wirtschaftsforschung (ZEW, Centre for European Economic Research), Mannheim, Germany. June-July 2011, December 2013

Academic Honors and Awards National Science Foundation CAREER Award, 2013 David Croll Sesquicentennial Faculty Fellowship, 2012 (Gift to launch Cornell's Faculty Renewal

Initiative. Donor: Trustee David Croll '70.)


Barry McNutt Award in recognition of the 2008 TRB paper (Bolduc, Boucher, and Daziano, 2008) that best met the standards and spirit fostered by Barry McNutt. The award recognizes outstanding contributions to transportation and energy policy analysis and to the development of efficient and effective federal policies related to the automotive sector. This award is given annually by the Energy and Alternative Fuels Committees of the Transportation Research Board of the National Academies.

Affiliations Atkinson Center for a Sustainable Future, Cornell University, Faculty Fellow Centre for Data and Analysis in Transportation CDAT and Groupe de recherche en économie de l'énergie,

de l'environnement et des ressources naturelles GREEN, Université Laval European Association of Environmental and Resource Economists EAERE Government of Canada Scholars’ Alumni Association GCSAA Other Studies and Qualifications Venice International University, Venice, Italy, July 2009 EAERE-FEEM-VIU European Summer School in Resources and Environmental Economics: Economics,

Transport and Environment. Alma Mater Studiorum-University of Bologna, Bologna, Italy, June 2009 EAERE International Summer School Program 2009: “Discrete Choice Models: Theory and Applications

to Environment, Landscape, Transportation and Marketing”, advanced module. Publications (Student co-authors highlighted in bold) Peer-reviewed Daziano, RA and M Achtnicht. 2014. Accounting for uncertainty in willingness to pay for environmental

benefits. Energy Economics 44, 166-177. Daziano, RA. 2013. Conditional-logit Bayes estimators for the valuation of electric vehicle driving range.

Resource and Energy Economics 35(3), 429-450. Daziano, RA and M Achtnicht. 2013. Forecasting adoption of ultra-low-emission vehicles using Bayes

estimates of a multinomial probit model and the GHK simulator. Transportation Science, DOI 10.1287/trsc.2013.0464.

Daziano, RA and D Bolduc. 2013. Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent attributes. Transportation 40(3), 647-670.

Daziano, RA and D Bolduc. 2013. Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian Hybrid Choice Model, Transportmetrica A: Transport Science 9(1), 74-106.

Daziano, RA and E Chiew. 2013. On the effect of the prior of Bayes estimators of the willingness-to-pay for electric-vehicle driving range. Transportation Research Part D: Transport and Environment 21, 7-13.

Daziano, RA, L Miranda-Moreno and S Heydari. 2013. Computational Bayesian statistics in transportation modeling: from road safety analysis to discrete choice. Transport Reviews 33(5), 570-592.

Tudela, A, RA Daziano and JA Carrasco. 2013. El papel de los factores contextuales, socioeconómicos y sicológicos en la elección de modo. Un estudio de caso en Concepción. Revista Ingeniería de Transporte. In press. (In Spanish)

Daziano, RA. 2012. Taking account of the role of safety on vehicle choice using a new generation of discrete choice models. Safety Science 50, 103-112.

Daziano, RA and E Chiew. 2012. Electric vehicles rising from the dead: data needs for forecasting consumer response toward sustainable energy sources in personal transportation. Energy Policy 51, 876-894.

Daziano, RA and E Chiew. 2012. Analyzing a probit Bayes estimator for flexible covariance structures in discrete choice modeling. Transportation Research Record 2302, 42-50.

Raveau, S, R Alvarez Daziano, MF Yáñez, D Bolduc and J de D Ortúzar. 2010. Sequential and simultaneous estimation of hybrid discrete choice models: some new findings. Transportation Research Record 2156, 131-139.

Bolduc, D, N Boucher and R Alvarez-Daziano. 2008. Hybrid choice modeling of new technologies for car choice in Canada. Transportation Research Record 2082, 63-71. (2009 TRB Barry McNutt Award)


Munizaga, MA and R Álvarez Daziano. 2005. Testing mixed logit and probit by simulation. Transportation Research Record 1921, 53-62.

Papers submitted Lapierre, N, RA Daziano, P Barla and M Herrmann. Reducing Automobile Dependency on Campus:

Evaluating the Impact of TDM Using Stated Preferences. (Submitted to Canadian Public Policy) Books Vanek, F, L Angenent, J Banks, RA Daziano and M Turnquist, 2014. Sustainable Transportation Systems

Engineering. McGraw-Hill Professional, 1st Edition (May 16, 2014). Book chapters Alvarez Daziano, R and E Rivera, 2003. El ABC del Transporte en Santiago. In P. Lanfranco ed., Muévete

por tu ciudad: una propuesta ciudadana para transporte con equidad, LOM Ediciones, Santiago, Chile. (In Spanish)

Peer reviewed conference proceedings Bolduc, D and R Alvarez-Daziano. 2010. On estimation of Hybrid Choice Models. In S. Hess and A. Daly

(Eds.), Choice Modelling: the state-of-the-art and the state-of-practice. Proceedings from the Inaugural International Choice Modelling Conference, Emerald, England, 2010.

Videla, J and R Álvarez Daziano. 2004. Percepción e Imagen de los Modos de Transporte Público en la Ciudad de Concepción. Proceedings of the XIII PANAM Conference of Traffic and Transportation Engineering, Albany, New York. (In Spanish)

Videla, J and R Álvarez Daziano. 2003. Introducción de variaciones en los gustos determinísticas en Preferencias Declaradas multimodal. Actas del XI Congreso Chileno de Ingeniería de Transporte, Santiago. (In Spanish)

Alvarez Daziano, R and MA Munizaga. 2002. Modelación flexible de elecciones discretas: una revisión ilustrada. Actas del XII Congreso Panamericano de Ingeniería de Tránsito y Transporte, Quito, Ecuador. (In Spanish)

Munizaga, MA and R Álvarez Daziano. 2002. Evaluation of mixed logit as a practical modelling alternative. Proceedings European Transport Conference, Cambridge, UK.

Alvarez Daziano, R and MA Munizaga. 2001. Modelos mixed logit: antecedentes teóricos y aplicaciones. Proceedings of the IX Chilean Transport Engineering Conference, Concepción, Chile. (In Spanish)

Munizaga, MA and R Álvarez. 2000. Modelos mixed logit: uso y potencialidades. Proceedings of the XIII PANAM Conference of Traffic and Transportation Engineering, November, Gramado, Brazil. (In Spanish)

Working Papers Daziano, RA and Achtnicht, M. 2013. Forecasting adoption of ultra-low-emission vehicles using the GHK

simulator and Bayes estimates of a multinomial probit model. Discussion Paper 12-017 Centre for European Economic Research ZEW, Mannheim.

Lapierre, N., RA Daziano, P Barla and M Herrmann. Reducing Automobile Dependency on Campus: Evaluating the Impact of TDM Using Stated Preferences. Québec: Cahier de recherche/Working Paper 2012-3 Center for Research on the economics of the Environment, Agri-food, Transports and Energy.

Munizaga, MA and R Álvarez Daziano. 2001. Mixed MNL models: a comparison with nested logit and probit. Working paper presented at the 5th tri-annual Invitational Choice Conference, Asilomar, California.

Munizaga, MA and R Álvarez Daziano. 2001. A mixed logit equivalent to a nested logit. Working Paper. Civil Engineering Department, Universidad de Chile.

On-Going Research Statistical inference on functions of the taste parameters of discrete choice models, with Esther Chiew (PhD

Student, Cornell University). Cancellation behavior in air travel, with Laurie Garrow (Georgia Tech) and Esther Chiew. A normalization approach to discrete choice models in willingness-to-pay space.


Implementation of a maximum likelihood estimator for a mixed multinomial logit model with exogenous latent explanatory variables.

Research Grants and Awards

Sponsored – Granted Title: Forecasting evacuation behaviors of coastal communities in response to storm hazard information.

Agency: New York Sea Grant (NYSG). Program: Coastal Storm Awareness Program (CSAP). Role: PI. Amount: $150,000 (Daziano’s portion: $132,218). Period: 01/01/2014-12/31/2015.

Title: Analyzing Willingness to Improve the Resiliency of New York City’s Transportation System.

University Transportation Research Center (UTRC), Region II (New York, New Jersey, and Puerto Rico). Role: PI. Amount: $80,000 (Daziano’s portion: $80,000). Period: 03/01/2014-02/31/2014.

Title: CAREER Advanced demand estimators for energy-efficiency in personal transportation. Agency:

National Science Foundation (NSF). Program: Faculty Early Career Development (CAREER), Environmental Sustainability, Chemical, Bioengineering, Environmental, & Transport Systems Division (CBET). Role: PI. Amount: $409,565. Period: 02/01/2013-12/31/2018.

Title: Data collection and econometric analysis of the demand for nonmotorized transportation. Agency:

University Transportation Research Center (UTRC), Region II (New York, New Jersey, and Puerto Rico). Role: Sole PI. Amount: $80,000. Period: 10/01/2012-12/31/2013.

Unsponsored – Funded Title: Electric Car Objective, Behavioural Choice Analysis for Transport (ECO BEST) – Modelli di

acquisto di auto elettriche e a carburanti alternativi: analisi degli aspetti comportamentali, tecnologici, ambientali e valutazione dell’impatto delle politiche tramite analisi di scenario. Agency: Roma Tre University, Trieste University. Role: Collaborator (PI: Edoardo Marcucci, Roma Tre University). Amount: €12,000 (Daziano’s portion: €6,000). Period: 04/01/2012-04/01/2014.

Title: Exploring Mechanisms for Improving Resiliency of the Transportation System of New York City.

Agency: ELI Undergraduate Research Funds, Cornell University. Role: Faculty Advisor. Amount: $1,000. Period: 10/01/2013-12/31/2013.

Title: Econometric analysis of vehicle ownership and usage. Agency: ELI Undergraduate Research Funds,

Cornell University. Role: Faculty Advisor. Amount: $1,200. Period: 10/01/2012-12/31/2012. Travel Funds: CEE delegation to the Smart Transportation - A CEAA Smart Cities Event, Cornell

Engineering Alumni Association, New York City. 10/15/2012-10/16/2012. Amount: $900. Travel Funds: Cornell delegation to the Technion-Cornell Built Environment workshop, New York City.

10/15/2012-10/16/2012. Amount: $1,250. Invited Speaker in Seminars and Workshops National Graduate Institute for Policy Studies, Tokyo, Japan, December 2014 (scheduled). The University of Queensland, School of Economics, School Seminar Series, Brisbane, Australia, November 2014 (scheduled). Rochester Institute of Technology, Golisano Institute for Sustainability, Weekly Speaker Series, March

2014. Cornell University, Energy Seminar, March 2014. Zentrum für Europäische Wirtschaftsforschung (ZEW, Centre for European Economic Research),

Mannheim, Germany, December 2013. Georgia Tech, National Center for Transportation Systems Productivity and Management, School of Civil

and Environmental Engineering, Transportation Weekly Speaker Series, November 2013. Cornell University, Charles H. Dyson School of Applied Economics and Management, 1st Cornell

Environmental and Energy Economics ‘Boot Camp’, August 2013.


Università degli Studi Roma Tre (Roma Tre University), Facoltà di Scienze Politiche (Faculty of Political Science), Rome, Italy, May 2013.

Cornell Engineering Alumni Association, Smart Transportation - A CEAA Smart Cities Event, New York, NY December 2012.

Cornell University, Center for Applied Mathematics Colloquium, November 2012. Cornell University, School of Civil and Environmental Engineering, Environment Seminar, November

2012. UC Berkeley, School of Civil and Environmental Engineering, May 2012. UC Davis, School of Civil and Environmental Engineering, June 2012. 91st Transportation Research Board of the National Academies, Annual Meeting, Workshop on recent

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