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2012 Annual Meeting

Housing and Transportation Affordability (HTA) Index 
Housing affordability is most often measured by the percentage of household income devoted to housing-specific costs.  Although housing is the single biggest expense for most households, transportation is the next largest.  On average, approximately 20 percent of household income is spent on transportation, but for many working-class households, transportation expenses canexceed housing costs. 
Within metropolitan areas, the cost of housing tends to decline the farther a household moves away from the core city and job centers, promoting a culture of “drive ‘til you qualify.”  However, housing decisions based on housing cost alone often fail to consider related transportation costs.  This means the potential savings from moving away from the metropolitan core may be lost to transportation costs.  In fact, an analysis of 337 metro areas by the Center for Neighborhood Technologies (CNT) indicates that housing cost savings are generally negated by increases in transportation costs at commute distances of 10 miles or more.
 
This trade-off indicates that families who decide where to live based only on the traditional measure of housing affordability—housing costs as a percentage of income—may actually be paying more toward housing and transportation combined even as they move away from center cities in pursuit of affordable housing.  This traditional definition of affordability may also be undermining HUD’s strategic goal of increasing affordability in HUD-assisted communities, as HUD programs currently do not consider transportation cost burden when making funding decisions.
 
Research by CNT and others is showing how transportation costs and travel behavior are driven by household and neighborhood characteristics, thus making it possible to estimate transportation costs for a given household type based on the location of a home.  HUD’s Office of Sustainable Housing and Communities has engaged the Manhattan Strategy Group (MSG), who will work with CNT to develop a Housing and Transportation Affordability (HTA) Index that measures the combined cost of housing and transportation as a share of household income.  The HTA Index will help consumers and policy-makers better understand the relationship between housing and transportation costs, leading to more informed decisions.  It will also open up the potential for integrating this understanding of transportation costs into HUD programs, allowing HUD to promote affordability beyond what is now possible.
 
The HTA Index will build on CNT’s existing transportation cost model, which utilizes six independent neighborhood variables and three independent household variables to estimate travel behavior and costs. Incorporating new research and data as well as input from housing and transportation experts, community stakeholders and HUD program staff, the project team will reengineer, update and expand CNT’s existing work to create the HTA Index.  The team will also provide data, tools, technical support, marketing, and stakeholder engagement to help HUD strengthen linkages between housing and transportation policy and investments at both the federal and local level.  This will include extensive consultation with program offices to understand the role that affordability currently plays in HUD programs.


TPW12-001

Urban Data Integration: New Frontiers in Urban Transportation Data Management
Tony Qiu, University of Alberta, Canada, presiding
Sponsored by Committee on Urban Transportation Data and Information Systems (ABJ30)

With the explosion of numerous new transportation data sources and data collection technologies, data integration issues are of crucial importance. This workshop will review the state of the practice in urban data integration and next steps in relevant research and practice including ongoing efforts on state, regional and local levels, public and private sector roles and academic research with large scale data integration system development and deployments. 

Setting the Stage for Understanding Urban Data Integration (P12-5670)
     Vladimir Livshits, Maricopa Association of Governments
Data Integration "Lessons Learned" by FHWA (P12-5671)
     Vicki Miller, Federal Highway Administration
Integrating Arterial, Transit and Freeway Data for the Urban Environment (P12-5672)
     Karl Petty, Berkeley Transportation Systems
Application of data integration efforts for travel behavior modeling: the merits and potential pitfalls (P12-5677)
     Ram M. Pendyala, Arizona State University
WisTransPortal Support for Emerging Traffic Operations Data Integration Requirements in Wisconsin (P12-5675)
     Steven Parker, University of Wisconsin, Madison
The Good, the Bad and the Ugly - What you should know about SaaS in Data Warehousing and Integration (P12-5680)
     Ben Chen, Midwestern Software Solutions
Smarter Utilization of Urban Data: the City Of Edmonton Experience (P12-5682)
     Stevanus Tjandra, City of Edmonton, Canada

Session Type: Workshop (W)
Subject Areas: 
Data and Information Technology


Setting the Stage for Understanding Urban Data Integration (P12-5670)
     Vladimir Livshits, Maricopa Association of Governments

Vladimir Livshits is a System Analysis Program Manager at the Maricopa Association of Governments – Metropolitan Planning Organization for the Greater Phoenix Area. Vladimir has 28 years of experience in the fields of Transportation Modeling, Transportation Planning, Transportation Economics and Transportation Data Management. Vladimir has published numerous papers on systems analysis, transportation forecasting and data management. He is a member of the ABJ30 TRB Committee, past member of the ADB10 TRB Committee and a member of technical expert groups for the FHWA Strategic Highway Research and Exploratory Advanced Research programs. Vladimir has his Ph.D. in Transportation Planning and Economics and M.Sc. in Computer Science from the Moscow State Technical University -MADI.

Abstract. Recent technological applications in transportation data collection led to explosion of the new data sources and new approaches to data collection. Region and State wide speed data is available from a number of commercial sources. Cell phones and Bluetooth based origin-destination and speed data is being collected, smart phones are used for a variety of applications to collect data on travel demand, speed and route choice in multimodal environments. GPS – based technologies are opening new possibilities for household travel and transit on-board travel surveys as well as for truck movement data collections. Computer vision technologies are being applied in traffic and travel data collections. However synergy between the different data has not been duly exploited. This circumstance results in persistent inefficiencies in data utilization and lack of data integrity. For many organizations a point is reached where investments in data integration is a prerequisite for continuous efficient data management and effective decision making. The paper suggests a classification of transportation planning data integration processes. Based on this classification an incremental approach to transportation planning data integration is proposed. An overall methodological framework for the transportation planning data integration is outlined with illustrations of the data integration implementations from the regional agency for the metropolitan Phoenix area.   

Application of data integration efforts for travel behavior modeling: the merits and potential pitfalls (P12-5677)
     Ram M. Pendyala, Arizona State University

Ram M. Pendyala is a Professor of Transportation Systems in the School of Sustainable Engineering and the Built Environment at Arizona State University.  He conducts research primarily in the areas of activity-travel behavior modeling and travel data analysis.  Ram has published nearly 100 articles and serves on the editorial boards of several journals.  He is currently the Chair of the Travel Analysis Methods Section of the Transportation Research Board, and is the immediate past Chair of its Committee on Traveler Behavior and Values.  He is also the Chair and past Vice-Chair and Secretary/Treasurer of the International Association for Travel Behaviour Research (IATBR).  Ram has his PhD and Masters degrees from the University of California at Davis.

Abstract.  Travel behavior models are increasingly being expected to address complex policy issues and behavioral phenomena of interest. The ability to model such complex inter-related behavioral processes calls for the ability to integrate data sets from disparate sources.  For example, activity-travel engagement is inextricably tied to how people spend time both inside and outside the home.  How people spend time engaging in various activities is closely linked to willingness-to-pay and actual monetary expenditures that are incurred in the pursuit of activities.  Attempts are being made to link measures of travel exposure to safety outcomes to better understand how safety considerations can be integrated in transportation planning processes.  The estimation of mode choice and destination choice models inevitably involves the use of land use and network level of service measures as explanatory variables in behavioral models.  In these and many other examples, one finds that data come from a variety of different sources and these data are available at different scales of space, time, and behavioral units.  There is a need for robust and rigorous methods for data fusion, i.e., for integrating data across different sources so that comprehensive data sets can be compiled for modeling the myriad inter-relationships that govern activity-travel demand.  This presentation will provide examples of recent attempts to integrate a variety of data sets for a range of travel behavior modeling applications including time use and monetary expenditure data, travel survey and crash data sets, and travel survey and built environment data sets.  Results from several data integration efforts will be provided to highlight the merits and potential pitfalls associated with such attempts.      

WisTransPortal Support for Emerging Traffic Operations Data Integration Requirements in Wisconsin (P12-5675)
Steven Parker, University of Wisconsin, Madison

Steven Parker is the IT Program Manager for the Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison.  His research interests include Intelligent Transportation Systems (ITS), traffic control room operations, transportation data management, and GIS in transportation.  Since 2005, he has been the chief project manager for the WisTransPortal, a statewide transportation data hub sponsored by the Wisconsin Department of Transportation (WisDOT).  He has also been the Principal Investigator of over 20 projects on ITS and traffic operations funded by WisDOT and FHWA.  Steven has his Ph.D. and Masters degrees in Computer Sciences from the University of Wisconsin-Madison.

Abstract. This presentation describes how the WisTransPortal system at the University of Wisconsin-Madison Traffic Operations and Safety Laboratory has evolved to support emerging data integration requirements at the Wisconsin Department of Transportation Statewide Traffic Operations Center (STOC).  Originally developed to archive freeway operations detector data, the WisTransPortal now includes statewide lane closure data, traffic incident data, planning data, maintenance data, support for 511, ITS inventory data, and others.  This evolution has been pushed along, in large part, by emerging STOC business processes not covered by traditional Advanced Traffic Management System (ATMS) control room software capabilities.  The overall WisTransProtal ITS Architecture and associated information flows are presented, along with discussion about the nature of these emerging datasets and the challenges that exist in their integration.  The status of current integration efforts within the WisTranPortal with respect to GIS, database, and user interface development are also discussed.

The Good, the Bad and the Ugly - What you should know about SaaS in Data Warehousing and Integration (P12-5680)                              Ben Chen, Midwestern Software Solutions

Ben Chen is a Principal of MS2 located in Ann Arbor, Michigan.  Ben has extensive experience in developing innovative data collection methods and management systems, including the widely used web-based Transportation Data Management System (TMS).  More than 140 agencies in 23 states and Canada, such as state DOTs, MPOs, counties, and cities, are using the TMS to manage transportation data and streamline business processes.  The web-based system makes it possible for all partner agencies to share data and maximize productivity.  The system is seamlessly integrated with Google Maps and ArcGIS Server to offer both easy user interface and powerful spatial analysis.  Through Software as a Service (SaaS), the enterprise system delivers a great economy of scale by the sharing of best practices, application development, and system resources. Ben received his M.S. in Civil Engineering from the New Jersey Institute of Technology. He is a licensed Professional Engineer (PE) in Michigan and a certified Professional Traffic Operations Engineers (PTOE).  For more information about MS2, visit www.ms2soft.com.

Smarter Utilization of Urban Data: the City Of Edmonton Experience (P12-5682)
     Stevanus Tjandra, City of Edmonton, Canada

Stevanus A. Tjandra received his Ph.D. in Discrete Optimization in 2003 from the Department of Mathematics, University of Kaiserslautern, Germany. He is currently the Senior Analytical Coordinator with the Office of Traffic Safety, Transportation Services, City of Edmonton. His responsibilities include supervising all analysis programs relating to automated enforcement, managing external consultants, and directing/guiding research and traffic safety analytical programs/policies in partnership with leading global academic practitioners. He is also the Co-Chair of the City of Edmonton Traffic Data Coordination Committee that brings together stakeholders from various offices within the City of Edmonton, including the Transportation Department, Community Services, Information Technology, and the Edmonton Police Service.

Abstract.  The world urban population is expected to increase by 84 per cent by 2050, from 3.4 billion in 2009 to 6.3 billion in 2050, while the world rural population is expected to reach only 2.9 billion in 2050[1]. Furthermore on May 2011, the United Nation (UN) launched the UN Decade of Action for Road Safety 2011-2020[2]. To save 5 million lives from 2011-2020 based on the forecast of roadway traffic fatalities, the UN proposed Five Pillar Plan with the first pillar, Building Capacity for Road-Safety Management, recognized the need of a smarter utilization of data. The shift of rural to urban world population and UN Decade of Action highlight the importance of a smart urban data system that fosters cross-sector data collection and analyses to improve the livability of urban areas.  The City of Edmonton (COE) has collected a huge amount of various urban roadway traffic data; however the ability to extract relevant, meaningful, and accurate information in a timely manner has been a challenge. To overcome this challenge, in the second quarter of 2010 the City of Edmonton Transportation Services has led the establishment of the Traffic Data Coordination Committee (TDCC) with representation across traditional organization and information silos within the City of Edmonton, including various areas within the Transportation Services, the Edmonton Police Service, Information Technology and Community Services.  The mission of this committee is to provide leadership for the creation of an integrated traffic data system that provides timely, consistent, complete, accurate, and accessible information that meets strategic, tactical and operational needs of all stakeholders. To further the vision and goals of TDCC, a traffic data integration project has been created to develop a prototype solution that will assist in the clarification of the TDCC’s collective business requirements and to create a five-year plan for the use and development of data and analytics.  By aggregating all relevant traffic data into a standard format and enabling users to select and compare different data-sets, more in depth super data crunching can be done to allow us to do more comprehensive analysis, reporting, and research. Such a smart data system would allow data to be converted into information, knowledge, and finally the intelligence to achieve the efficient and effective optimization of the traffic system and provide a single version of the truth, as part of a wholistic transportation approach. Furthermore, a smart data system would connect different fields that may likely contribute to livability of urban areas, including crime, medically-at-risk drivers and impaired drivers.  The data coordination initiative and data integration project were the highlights of COE’s proposal for the IBM Smarter City Challenge http://smartercitieschallenge.org/ that won the $400k IBM grant. Through this data initiative, the IBM team recognized that the City of Edmonton is well-positioned to be a global leader in smarter urban traffic safety.  The presentation will discuss the coordination and integration processes and challenges, including the cross-sector data collection, analyses, and research, business prioritization, system architecture development, and the integrated system design and construction. 



[1] United Nations Department of Economic and Social Affairs/Population Division. World Urbanization Prospects: The 2009 Revision. New York, 2010.

[2] World Health Organization. Global Plan for the Decade of Action for Road Safety 2011-2020. Retrieved from http://www.who.int/roadsafety/decade_of_action/plan/english.pdf on September 24, 2011

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Marcelo Simas,
Dec 12, 2011, 12:35 PM
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Marcelo Simas,
May 5, 2012, 1:55 PM
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Marcelo Simas,
May 5, 2012, 1:44 PM
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Marcelo Simas,
May 5, 2012, 1:44 PM
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