Design and Use of GIS-based Transportation Database Models ...

Nyerges GIS Database Primer GISDBP_chapter_4_v19.doc

Chapter 4 Design and Use of GIS-based Transportation Database Models

Abstract

This chapter is about transportation database models for urban and regional environmental concerns. GIS database analysts face many choices about transportation database model design for a GIS project. TIGER/Line files have been used for developing transportation network information. However, other transportation attribute information is needed if the focus is about transportation database model development. In each of the sections we differentiate database model needs among planning, improvement programming, and implementation decision problems as a way of dealing with the issue that database design depends on the context of the decision situation. This chapter describes transportation database models suitable for use in urban-regional applications of GIS. Remember that a data model language implemented in a particular context results in a database model – the (schema) model of a particular database – specified in terms of entity-object classes. The database models in this chapter focus on transportation issues issues for three decision situations – planning, improvement programming, and project implementation. 4.1 Motivations for Transportation Planning, Programming, and Implementation Databases Urban-regional infrastructure concerns in planning, improvement programming, and project-level assessment get reflected in infrastructure database models. Infrastructure and capital improvements to that infrastructure have an important role in the overall scheme of growth management. A transportation element of a growth management plan for the City of Seattle is presented in Figure 4.1. The plan establishes a long-term vision. Improvement programs are budgeted activities that put a plan into action. Implementation activities carry out the actual development of those programs. Unfortunately, the connection among these decision processes with regard to GIS databases is not so simple and clear because different units within organizations, as well as different organizations are responsible for one or the other processes. Generally this is the current situation with most public facilities. Hence, there is plenty of opportunity for integration activities supported by GIS databases. Let us first consider the nature of the need for public facilities, of which transportation infrastructure is a key component, before we address the character of transportation database models. Community growth problems often surface in the form of shortfalls of public facilities, for example, congested streets and highways, overcrowded schools, deficient water supply, failing sewage treatment plants. These are noticeable because such problems affect the daily lives of residents. Decisions about public services are reflected in considerations about significance of facility for sustaining health, safety and welfare. Further consideration includes efficiency of service delivery through large linked systems, e.g.,, transportation, water, storm sewer, sanitary sewer, or individual facility, e.g., library or airport. When offering such services, an ability to assign service cost is very important. This can be accomplished through (1) incremental metered use, e.g., water, (2) lumped through common use, e.g., access to city hall, (3) size and complexity of facility or system, e.g., a reservoir (4) lengthy periods of design and construction,

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e.g., a light rail line, and/or (5) shorter term improvements, e.g., additional buses. Furthermore, who is providing the facilities and services is quite important. Service responsibility could lie with (1) local public works within City Government, (2) authorities functioning as arms of local government, e.g., air pollution authority, (3) quasi-public agencies, e.g., the Port, (4) independent authorities, e.g., transit operating across jurisdictions, and/or (5) state agencies. Voters have choices as a group; usually choice by area, but not by individual. Such votes set policy indirectly.

Figure 4.1 Transportation Element (Principal Arterials Map) of the City of Seattle Comprehensive Plan 2000 (City of Seattle 2000). Controlling growth means local government must have control over facility infrastructure. Local governments can control their future development by controlling development of infrastructure

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systems, and/or influence supply and quality of infrastructure service. Governments set standards for local roads, require developers to abide by certain standards, e.g., development concurrency, operate water and sewer systems and encourage or restrict hook-ups, have sewer and water districts abide by certain restrictions on hook-up plans, and/or establish plans that define permitted uses and densities, design guidelines for building (Porter 1997). Such items provide a fundamental plan for infrastructure action – proactive growth management. Tracking the need for improvements can be done through concurrency management. (Porter 1997). Concurrency management assesses the adequacy of public facilities to meet capacity before a development project is approved. Concurrency management is one of most common growth management techniques today for evaluating adequate facilities provision. This is the case in many states and communities engaged in planning and improvement efforts within growth management guidelines. The King County, Washington Department of Transportation develops concurrency maps that show land management planning areas in terms of the current transportation conditions to focus concerns about land use permit approval or denial (Figure 4.2). A transportation adequacy measure is computed for subareas in terms of level of service regarding traffic flow for that subarea (See Table 4.1). The transportation adequacy measure is developed as a way to link land use and transportation conditions, which is why it is useful for concurrency assessment. Subareas are deemed over, under, or near a threshold for transportation service. In Figure 4.2, areas labeled over threshold are not considered for permits because the roads are highly congested. Nyerges (2004) provides more details of how the transportation adequacy measure is computed.

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Figure 4.2 Concurrency transportation management in King County, WA using a transportation adequacy measure based on level of service (King County 2002).

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Table 4.1 Traffic Levels of Service (LOS) (Washington State Department of Transportation 2006)

LOS is reported on a scale of “A” through “F”. The general definition of each LOS is as follows:

LOS “A” – This is a very high service level in which the roadway and associated features are in excellent condition. All systems are operational and users experience no delays.

LOS “B” – This is a high maintenance service level in which the roadway and associated features are in good condition. All systems are operational. Users may experience occasional delays.

LOS “C” – This is a medium maintenance service level in which the roadway and associated features are in fair condition. Systems may occasionally be inoperable and not available to users. Short term delays may be experienced when repairs are being made, but would not be excessive.

LOS “D” – This is a low maintenance service level in which the roadway and associated features are kept in generally poor condition. Systems failures occur because it is impossible to react in a timely manner to all problems. Occasionally delays may be significant.

LOS “F” – This is a very low service level in which the roadway and associated features are kept in poor and failing condition. A backlog of systems failures would occur because it is impossible to react in a timely manner to all problems. Significant delays occur on a regular basis.

Many facilities are upgraded on a 6-year cycle through capital improvements programs (CIP). A CIP is a cycle of scope, design, and build (2 years each), with new facilities put on the list every two years. Each facility has its own “performance characteristics”, e.g. Chula Vista Threshold Standards (Table 4.2) for air quality, police, fire, water, sewer, traffic, etc. Providing facilities raises issues about existing infrastructure capacity and potential impacts of proposed projects, which are important in decision analysis.

Table 4.2 Threshold standards for Chula Vista, CA (adapted from Porter 1997, Figure 2.1)

Air Quality Annual report required from Air Pollution Control District on impact of growth on air quality.

Fiscal Annual report required to evaluate impacts of growth on city operations, capital improvements, and development impact fee revenues and expenditures.

Police Respond to 84% of the Priority I emergency calls within 7 minutes and maintain average response time of 4.5 minutes. Respond to 62% of Priority II urgent calls within 7 minutes and maintain average response time of 7.0 minutes.

Fire/EMS Respond to calls within 7 minutes in 85% of cases.Schools Annual report required to evaluate school district's ability to accommodate new growth.

Library Provide 500 square feet of library space adequately equipped and staffed per 1,000 population.

Parks and Recreation Maintain 3 acres of neighborhood and community parkland with appropriate facilities per 1,000 residents east of Interstate 805.

Water Annual report from water service agencies on impact of growth and future water availability.

Sewer Sewage flows and volumens shall not exceed City Engineering Standards. Annual report from Metropolitan Sewer Authority on impact of growth on sewer capacity.

Drainage Storm flows and volume shall not exceed City Engineering Standards. Annual report reviewing performance of city's storm drain system.

Traffic Maintain Level of Service (LOS) "C" or better as measured by observed average travel speed on all signalized arterial streets, except, that during peak hours, an LOS "D" can occur for no more than 2 hours of the day.

Chula Vista's Threshold Standards

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Carlsbad CA uses a three-tier growth management program to set, guide, and implement plans. The three tiers are as follows; 1) performance standards at policy level, 2) facility siting processes, 3) facility financing (Table 4.3). Each of these should be incorporated into the decision process. Table 4.3 Three-Tier Growth Management Program (adapted from Porter 1997, Table 2.1)

Citywide Facilities Improvement Plan Zone Plans

Individual Development Applications

Performance Standards

Establishes standards Shows how standards will be complied with as development occurs

Must demonstrate that standards will be maintained

Provisions of Facilities

Shows existing inventory and future buildout needs

Shows how and when new facilities will be funded and constructed to accommadate growth

All condiditons of approval must be complied with, and specific facilities must be constructed concurrent with development

Funding of Facilities

Outlines various funding options

Proposes specific financing mechanisms for each facility

Funding must be provided prior to final map approval, grading permit, or building permit, whichever occurs first

Carlsbad's Three-Tiered Growth Management Program

Among the most important issues in capital improvement programming is finance, i.e. the source of funds for the improvements. A standard approach involves; 1) a general property tax fund, 2) issuance of municipal bonds as a sort of a loan, backed by investors, and 3) state and federal funding programs. A newer approach involves exactions, i.e., developers are required to contribute resources towards infrastructure. The contribution can be in the form of land dedicated for facilities, of actual facility construction, or financing facility construction for local streets, sewer and water, parks etc. Another financing approach is through impact (also development) fees charged for improvements, but not tied to improvement. This funds the same kind of facilities as above, but may go for other mitigation efforts. Under this funding scenario, legal defensibility is important because the finances are not tied to specific facility. A spatial analysis of a growing area sets the fee. Special taxing districts are another approach used to fund facilities. Districts, which must be approved by state law, are used more frequently in some states than others, e.g., California, Florida, Illinois, Pennsylvania, Texas, and Washington State. Most are single purpose districts, e.g., road construction, junior colleges, soil conservation. There are several advantages of special districts; 1) useful for funding new development, 2) avoids taxing the general public for specific facility development, and 3) funds go where they are needed because facilities are place-

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based. However, there are also disadvantages of special taxing districts. They weaken local government power through proliferation of district authority and may promote inefficiency and inequity in government, and/or leads to confusion and complexity in governance. Local governments can seek legislation from their State Legislature so districts act in accordance with local comprehensive plans. Due to the many alternatives for tax revenue generation, local governments understand that funding will likely come from a variety of sources. Both private and public sources of funds are needed for efficient, effective and equitable growth management. The interest in improving infrastructure (as capital improvements) within a community under growth management guidelines leads us to ask about the importance of such infrastructure in sustainability. Deakin (2001) demonstrates how transportation is one of the key issues in making cities more sustainable (Table 4.4). Public transit has been of growing interest in the US., but not quite gaining in popularity as in Europe. Nonetheless, the investments in public transit are gaining on investments in single occupancy vehicle transportation because more people are realizing the futility of simply expanding concrete for cars.

Table 4.4 Strategies for Sustainable Development Land Use and Community Development ♦ Preservation, Rehabilitation, and Redevelopment of Central Cities and High Density Inner Suburbs

♦ Infill in Cities and Suburbs — Increased Density, Mixed Use

♦ Reusing Brownfields, Recycling Buildings

♦ TODs and PODs as the Paradigm for New Developments

♦ Quality of Life: Attention to Crime / Schools / Services / Amenities

♦ Recycling / Precycling / Composting Programs Transportation ♦ Access vs. Mobility — Basic Concepts

♦ Bike- and Pedestrian-Friendly Cities

♦ Transit, Paratransit, Ridesharing

♦ Telecommuting / Teleconferencing

♦ New Technologies for Improved Efficiency: Traffic Control Systems, Transportation Information Systems

♦ Prices and Subsidies Aligned with Sustainability Housing and Other Building Designs ♦ A Range of Choices

♦ Energy Efficient Buildings

♦ Edible Landscaping

♦ Natural / Indigenous Plants Business/Job Creation ♦ Business Leadership

♦ Community Economic Development

♦ Clean / Safe Technologies Social Equity

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♦ Aligning Taxes and Subsidies with Sustainable Development

♦ Equitable Distribution of Resources 4.2 Feature Classes in Transportation Database Models Linking planning efforts with improvement programming efforts can be addressed practically by focusing on the database models that address transportation projects. Although GIS has been used for many years to foster coordination across scales, nonetheless, there remain some challenges. One such challenge at the current time and for the near future is the growing recognition that planning and programming treat the concept of a “transportation project” differently. Not only is this a problem within each jurisdictional scale, but it is even more significant across jurisdictional scales. Different organizations conceptualize the planning process within different geographic domains, even though the domains are overlapping. Long-range plans contain the concept of a project in a general sense with general impacts. Programming requires more specificity to fund an “improvement project”, and implementation requires more detail yet because of the social, economic, ecological, and physical impacts that such projects will have at specific sites and in the surrounding area. Transportation projects often appear as symbols on a planning map, but the full character of the projects are not defined. For example, transportation modeling software that is used to estimate changes in vehicle flow characterizes a network as node intersection linked to node intersection, i.e., an abstraction of the real network. Consequently, transportation projects are conceptual in character because the physical details of the projects are not necessary to estimate logical flows. Such conceptual projects can be depicted on maps without much of the detail being worked out. More detail about transportation projects is required in transportation improvement programs because the main consideration is funding the implementation of projects. When funding is involved, more of the details of a project must be enumerated, e.g., from what start reference marker and ending reference marker on a highway will the lanes be physically changed. The improvement programming process identifies funds for scoping, designing and building projects. Because funding is the main concern, many of the impacts of implementation are left for scoping and designing phases. The implementation phase is where the social, economic, ecological, and physical impacts of the transportation project are identified in detail, i.e., commonly in the scoping phase of a project as funded within a transportation improvement program. The impacts of a project are compared against the current conditions of the system. This usually occurs on a one by one basis, that is, cumulative effects are seldom addressed because it is still a rather big challenge to get all of the planning, programming, and implementation issues resolved as related to individual projects. It is not unusual that the advent of GIS data processing has brought about institutional changes in the way information in transportation organizations is handled. There is a growing recognition that project inconsistency exists in the planning, programming, and implementation process, because GIS databases for long-range planning, medium-term programming, and short-term implementation do not always agree. Separate groups are responsible for planning,

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programming, and implementation situations, hence separate databases are generated. This is not new, because separate hardcopy reports always existed. It is simply more evident now that we have the ability to integrate data across decision situations. The same projects are not described as in the same way across databases, because the databases are developed under different mandates and by different units within transportation organizations. This challenge will become increasingly evident, and subsequently addressed, as organizations move toward implementing GIS applications for transportation improvement programming. There is thus tremendous potential for information integration across decision situations. Addressing this circumstance can actually lead to more sustainable transportation decisions, as only one among many capital improvement decision processes. 4.3 Examples of Transportation Database Models for Planning, Programming, and Projects As a basis for addressing growth management concerns, (e.g., developing concurrency measures in transportation) it is important to have an adequate land records (covered in section 2.2.4) and transportation database model, the topic of this section. As before, adequacy of a database model involves asking questions about the size of the geographic domain and the resolution of the features needed to be able to process the appropriate level of information from the data categories. That is, an analyst should consider whether large areas, subareas, or individual parcels are needed. To address that, we turn to examples of transportation database models that are being used as templates for database design. Planning-level database models support the four-step planning cycle. We frame the planning level database models by first looking at scales in growth management transportation planning based on a study of metropolitan planning organization (MPO) and regional transportation planning organization (RTPO) information needs in Washington State (Figure 4.3).

Figure 4.3 Institutional scales in transportation planning (from Nyerges and Orrell 1992).

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A database model was developed for regional growth management/transportation planning (Figure 4.4a and 4.4b). GIS managers from many of the MPOs in Washington State contributed their perspectives on its development. It incorporates all transportation modes as well as the transportation system network. The half of the database model in Figure 4.4a addresses the trip distribution aspect of data to feed a transportation demand forecasting model. The half of the database model in Figure 4.4b addresses trip mode assignment on a network.

Figure 4.4a MPO Planning database model for travel directed at developing trip distributions in a growth management context. (Adapted from Nyerges and Orrell 1992 p. 26)

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Figure 4.4b MPO Planning database model for trip mode assignment to a network in a growth management context. (Adapted from Nyerges and Orrell 1992 p. 27) A number of groups have developed database models that provide examples for planning, programming, or projects (Figure 4.5). Such database models are too general for a specific application, thus they need to be enhanced.

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Figure 4.5 Street theme portion of urban data model (ESRI 2006b).

In transportation planning, a “link and node” network must include connectivity to support path tracing. As such, a number of topological data constructs are useful in transportation-oriented GIS database models as depicted in Figure 4.6)

Figure 4.6 Geospatial constructs for transportation database models For improvement programming, a group of GIS experts in the transportation industry worked with ESRI-oriented object models to create the UNETRANS database model (Figure 4.7). The

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model contains minimal project descriptions, and is meant for state Departments of Transportation.

Figure 4.7 Transportation assets portion of the UNETRANS database model, excerpted from the pages available at ESRI (2006). A much simpler version of an improvement programming-oriented database model appears in the basemap theme excerpted from of the urban database model (Figure 4.8)

Figure 4.8 Improvement project features in the urban basemap data model (ESRI 2006b).

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The King County (transportation network) TNET can serve as an example of a transportation project-level database model (Figure 4.9). The database model focuses on public transit, e.g. applications for scheduling and routing. The King County TNET database diagram has a rich set of US codes for links to designate differences in link type.

Figure 4.9 King County TNET Overview Diagram (King County 2006).

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4.4 Comparing and Contrasting Transportation Database Models A GIS analyst should consider whether large areas, subareas, or individual parcels are needed when determining database models for transportation projects. Considering Figure 4.6, let us review some of the basic data constructs in terms of transportation data. Are topological construct categories needed for transportation applications? Is spatial topology (for highway infrastructure connection) enough or do we also need to represent functional flow for example for turning and one-way streets? 1) Requirements for a data model to support planning-level database model Even in transportation planning, a “link and node” network is essential. GIS database models feed a transportation modeling software system. This showed up in the King County TNET – that is, the idea of a transportation link was expressed in terms of the ESRI “edge” construct 2) Requirements for a data model to support improvement programming-level database model A shapefile data model is a minimal requirement, but coverage and geodatabase data models can be used to represent connectedness if some analysis is to take place. Such analysis is not occurring much in any improvement programming GIS applications to the authors’ knowledge. Whatever information is being created, it is being borrowed from the planning-level GIS context. 3) Requirements for a data model to support project-level database model Coverage or geodatabase data models are needed because topology, i.e., connectedness, adjacency, and containment, is requried to perform analysis

- about routing, or - about environmental impacts, or - about mobility and accessibility improvements.

4.5 Summary This chapter describes several transportation database models suitable for use in urban-regional GIS applications. Transportation planning databases commonly focus on the movement of people and goods across the transportation system. Multiple modes are involved. Transportation programming often considers the various modes when making investments in transportation improvement, but some investment money is dedicated to funding only certain modes (call it the “flavor” of money). Transportation databases for project level work would require higher level resolution information than for planning and for programming. Transportation project databases require all of the nuances of landscape features in a particular area to make sure impacts are considered. Small area planning databases are often used as the start of transportation project databases. A variety of geospatial data types are used in transportation databases to reflect the “level of intelligence”

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needed to model the behavior of transportation relative to the particular planning, programming, or project-level issues at hand. Consequently, there are several transportation database models that have been developed as generic templates for assisting with transportation database design. 4.6 References Deakin, Elizabeth, 2001. Sustainable Development and Sustainable Transportation: Strategies for Economic Prosperity, Environmental Quality, and Equity, http://www-iurd.ced.berkeley.edu/pub/WP-2001-03.PDF, accessed June 23, 2005. ESRI (Environmental Systems Research Institute) 2006. Transportation Data Model. http://support.esri.com/index.cfm?fa=downloads.dataModels.filteredGateway&dmid=14, last accessed November 15, 2006. King County 2002. Concurrency management program King County Washington, http://www.metrokc.gov/kcdot/tp/concurr/attachmentA32002.pdf, used with permission King County 2006. Transportation Network – TNET Database Design Diagram http://www.metrokc.gov/gis/Projects/TNET/TNET_images/Pictures/TNET_dBase_Design_Diagram.pdf, last accessed November 15, 2006. Nyerges, T. and Orrell, J. 1992. Using GIS for regional transportation planning in a growth management context. Final Report, WA-RD 285.1, Washington State Department of Transportation, Olympia, Washington. Nyerges, T. 2004. Geographic Information Systems in Transportation Planning in Susan Hanson and Genevieve Giuliano (eds.), The Geography of Urban Transportation, 3rd edition, Guilford Press, pp. 163-195. Washington Department of Transportation 2006 Source: Washington Department of Transportation Levels of Service http://www.wsdot.wa.gov/maintenance/mgmt/service.htm last accessed August 24, 2006. 4.7 Review Questions 1. Of what significance is infrastructure and capital improvements in the overall scheme of growth management? 2. Why is "concurrency" an important issue in growth management? 3. What is sustainable transportation for a livable city? 4. What constitutes the Feature Classes of a Transportation Database Model? 5. What is an example of Transportation Database Model organization?

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6. How would a transportation planning database model differ from a transportation improvement programming database model? 4.8 Glossary concurrency management – maintaining a relationship between the level of service for a facility or infrastructure and whatever generates influences on that facility or infrastructure database model – A schema and data dictionary associated with the outcomes of a particular database design process. level of service – an expected and/or measured amount of activity as related to public facilities for example transportation or libraries schema – description of data categories plus data fields that characterize features (entities) about some portion of the world in a database model. topology, spatial – the study of relationships including connectedness, adjacency and containment among points, lines, polygons.

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