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Toolbox for Regional Policy Analysis Report (2000)

Case Study: Spartacus

Overview

Some of the primary benefits of the SPARTACUS approach include modeling of the feedback between transportation and land use, and the disaggregation of air quality, noise, and land use data to allow the detailed assessment of socioeconomic impacts.

SPARTACUS (System for Planning and Research in Towns and Cities for Urban Sustainability) is a European project undertaken to analyze the implications of urban land use and transportation policies. The SPARTACUS research project included model development and policy testing in three European cities: Helsinki, Naples, and Bilbao. The project has successfully demonstrated a number of analytical approaches that can be used to model metropolitan-level policies not only in Europe, but in the United States as well.

The SPARTACUS system is based on an integrated transportation-land use model, MEPLAN. A Geographic Information Systems (GIS) Raster module is used to process many of the outputs of MEPLAN and to calculate and display micro-scale indicators. Additional analysis tools are used to process other model outputs, calculate impacts, and aggregate the results into economic, environmental, and social indices.

Figure 1. Traffic Noise Levels, Helsinki

Fig. 1 Traffic Noise Levels, Helsinki

Note: Large parts of the Helsinki Metropolitan Area are affected by high traffic noise levels. Noise impact areas are large in outlying areas around main roads and highways. However, only a few people live along the noisy links in these areas. In the baseline scenario, about 29 percent of the metropolitan population would feel disturbed by traffic noise in the peak hour.

Context

The SPARTACUS project was initiated in 1996 by a consortium of regional planning agencies and consultants in Europe. Its purpose is to assess the sustainability implications of urban land use and transport policies. The approach taken by the SPARTACUS project team was to develop a modeling system for forecasting various indicators of sustainability, and to apply this modeling system to three typical European cities. Their objectives were twofold: first, to compare the effects of similar policies in different cities; and second, to demonstrate technical methods that can be applied locally to test policies in other cities.

Funded in part by the European Commission under its Environment and Climate Programme and in part by local agencies, the SPARTACUS projects reflect a growing interest in Europe in developing indicators of sustainability and measuring progress on those indicators. Consistent with the 1992 Rio Declaration on Environment and Development, all European Union member states have committed themselves to preparing national strategies for sustainable development and to submit progress reports to the United Nations Commission on Sustainable Development.

The SPARTACUS project team measured "sustainability" through a variety of environmental, social, and economic indicators. The team recognized that they could not measure the sustainability of an urban area in an absolute sense. Nonetheless, they could measure progress on a variety of indicators. From this perspective, progress toward sustainability can be viewed as maximizing social and economic benefits while at the same time minimizing negative environmental impacts and meeting desired social objectives.

The range of indicators (or impacts) tested by the project is relatively comprehensive and includes:

The range of policies tested in the SPARTACUS project includes:

Three test cities were selected: Helsinki, Finland; Naples, Italy; and Bilbao, Spain. These areas were selected because of the interest of local partners, the availability of data and modeling tools, and because they represent a diversity of environmental and policy settings. The test city, or regional or national authorities, participated in the project by financing a portion of the work or by participating as a Client-Partner. Client-Partners contributed to developing the system, formulating the indicators, selecting policies for testing, and participating in the definition of weights and value functions for the indicators.

The consortium that conducted the project includes LT Consultants Ltd, Finland; Marcial Echenique & Partners Ltd, UK; Marcial Echenique y Compania SA, Spain; TRT Trasporti e Territorio srl, Italy; and the Institut fur Raumplanung, Universitat Dortmund, Germany. Further work is now underway to expand the SPARTACUS methodology to additional European cities.

Methodology

Analytical Framework

The SPARTACUS analytical framework is illustrated in Figure 2. The system includes four main components:

  1. The "engine" incorporates the modeling tools, MEPLAN and the Raster module. MEPLAN is a computer software package based on a land-use transport interaction modeling framework. The Raster module is a GIS-based method to calculate indicator values at a spatially disaggregate level, based on a grid of 100m x 100m cells. These tools carry out the policy simulation and much of the basic mathematical computation.
  2. A set of input files that provide data to the models and translate policy packages into a form suitable for model simulation.
  3. An integrated GIS module, MEPLUS, that provides tools for policy analysis and reporting of results. MEPLUS includes facilities for storing, manipulating, and screen-browsing of selected model outputs, as well as calculation of indicators and background variables based on the output of MEPLAN and the Raster module.
  4. The USE-IT (Urban Sustainability Evaluation and Interpretation Tool) module, a decision support tool that allows the user to define indicators; give weights and value functions to create overall indices of economic, environmental, and social sustainability; and view results in tables or graphical forms.

Figure 2. Analytical Framework

Fig. 2 Analytical Framework

The principles of the MEPLAN model and Raster module, as well as their use in calculating various impacts, are discussed in more detail below. MEPLUS and USE-IT are not discussed here, but more information on these tools can be found on the SPARTACUS web site.

Land Use - Transportation Model

Structure

MEPLAN was used as the land use-transportation model in the SPARTACUS study. (Each of the three metropolitan areas in the SPARTACUS study had an existing, operational MEPLAN model.) The model has three interrelated modules:

  1. The land use module produces a spatial allocation of activities (such as employment and population) and produces trade flows between zones;
  2. The transport module assigns flow matrices to different transportation modes and routes and calculates the resultant transportation disutilities; and
  3. The interface module converts land use trade matrices into transportation flow matrices and, conversely, converts transportation disutilities into trade disutilities.

MEPLAN is based on the principles of economic input-output models as well as random utility models from the transportation sector. Three key theories drive the model:

  1. Interactions between activities are determined by input-output analysis (for example, coefficients state how much of an input factor is required by households in different socioeconomic groups);
  2. Locational choices, mode choices, and route assignment are determined by random utility models; and
  3. Capacity restrictions in the land and transportation markets alter prices and thus affect disutilities.

The three MEPLAN modules are applied dynamically, with land use affecting transportation and vice-versa; feedback typically occurs in five-year increments. The interface module is used in such a way that land use changes produce immediate changes in the demand for transport, whereas transport changes have only gradual effects upon the pattern of land uses and trades.

Data Requirements and Calibration

The MEPLAN model is custom-built for each area for which it is applied, based on the availability of information and on relationships which best reflect the area and the users' interests. Typical inputs for the land use submodel include population, employed persons, land and floorspace area, incomes, land prices, and goods vehicle travel by area. Employment must be separated into an exogenous (basic) and an endogenous (non-basic) element to drive the input-output mechanism.

The transportation submodel inputs, which describe the transportation network and trip patterns, are similar to those for typical four-step transportation modeling systems. The land use and transportation models may be built on different zonal structures; for example, economic data may be available at the jurisdictional level for the land use module, while transportation data may be provided at the traffic analysis zone (TAZ) level for the transportation module. The interface module translates between these different zone structures.

Additional information on the MEPLAN model is provided in the Sacramento case study.

Outputs

A large volume of information is generated by running the model. Typically, this includes:

The model also offers an economic evaluation module that can be used to compare alternative policies or scenarios. This module is based on flows of money (e.g., transit fares) as well as other benefits such as time savings. Using principles of consumer surplus, the model provides a measure of the distribution of benefits among firms, households, the government, transport operators, and landowners/developers. In the SPARTACUS study, net economic benefits are divided by the population of the region to achieve an overall economic indicator value.

Comparison with Other Land Use/Transportation Models

MEPLAN is one of a number of packages available for modeling the interactions between land use and transportation. The overall level of effort as well as the sophistication of these packages varies considerably. Among models that have been applied in the U.S., DRAM/EMPAL and HLFM II+ require somewhat fewer data inputs and a lower level of modeling effort. MEPLAN, TRANUS, and UrbanSim are examples of models that require more data and effort to develop, but at the same time can model a broader range of relationships with greater sophistication.

Models such as MEPLAN, TRANUS, and UrbanSim have a strong economic foundation, and business and residential location decisions are modeled based on a range of factors. Land markets are key to the modeling framework, with prices driving development. This is in contrast to models such as DRAM-EMPAL that re-allocate land use only on the basis of transportation accessibility. UrbanSim advances the state of the practice by using the logit model structure, typically used to predict transportation mode choice, to model a broad range of decisions such as business and residential location.

At the same time, some of the economic data required to calibrate MEPLAN or other large-scale urban models may not be readily available. For example, in many urban areas, a single database on factors such as land prices. Data on goods movement - such as truck trip generation, truck flows, or goods flows - are also typically poor. Commonly available data sources may not provide a breakdown of exogenous versus endogenous employment at less than the county level. As a result, any region considering implementing a land use/transportation model should carefully consider the availability of data as it relates to modeling requirements, in addition to other factors such as staff resources.

GIS Raster Model

Overview

The modeling of locally important environmental processes and their social implications requires the exact spatial location of input information and output data. Raster cells or pixels can be used as a spatial reference framework for environmental and social impact models. Most available data and most urban models, however, are spatially aggregate and work within a zonal (polygon) framework. The Raster module disaggregates zone-level and network-level data into individual raster cells. In the SPARTACUS project, each cell is 100 meters (328 feet) square. This dimension was selected to allow a sufficiently detailed level of analysis but not to overwhelm the memory and speed of the computer.

For a typical U.S. application, disagreggation of data can be done using tools such as ESRI's ArcView Spatial Analyst or MapInfo Professional and SpatialWare software. While disaggregation can be performed with a simple command, some data processing may be required, depending on the form of the data. For example, zonal population data must be converted into densities by dividing by the area of each zone, then disaggregated to assign the densities to grid cells. Finally, the density of each grid cell is multiplied by the area of the grid cell to obtain the total population in each grid cell. The SPARTACUS study used more sophisticated simulation techniques to disaggregate land use data, as described below.

The Raster representation of households and employment data has a number of functions in the SPARTACUS modeling system, including:

To identify equity impacts, households can also be disaggregated by socioeconomic group. In the SPARTACUS study, three socioeconomic groups were defined, based on occupation. The definition of socioeconomic groups could also rely on other characteristics, such as income, race, or mobility limitations.

The relationships among the databases, MEPLAN output, Raster module, and calculation of environmental and social indicators are shown in Figure 3.

Figure 3. Raster Structure

Fig. 3 Raster Structure

Disaggregation of Households and Employment

The disaggregation process begins with zone-level household and employment data for the base year and for the forecast year for all policy scenarios tested. If no information on the distribution of population and employment within the zone is available, data can be allocated equally to each grid cell within the zone (represented in the GIS as a polygon). If some information on the distribution within the zone is available, the disaggregation can be performed with greater accuracy. This is done by assigning a weight to each grid cell, which is used to allocate total population or employment proportionally (Figure 4). If sufficient data are available, population can be given different weights by socioeconomic group.

Disaggregation of data in the SPARTACUS was performed using microsimulation techniques. Each single activity (such as place of residence or work) was assigned a raster cell by applying Monte Carlo Simulation. The simulation technique avoids the problem of small numbers, i.e., how to allocate 50 households to 300 raster cells. Different densities within each zone were also assumed, using information on land use types.

In the U.S., sources of information on the distribution of activity within the zone might include:

For the forecast years, the distribution of development within the zone may change. If additional development (as measured in floorspace) is forecast in the zone, it is allocated to both developed and undeveloped cells. In zones where the amount of floorspace decreases, the allocation weights remain the same and overall activity is reduced. If specific information is available on the locations of development planned for construction or elimination, this can also be used.

A uniform disaggregation of zonal data to grid cells can be done with a minimum amount of effort. As the number of judgments to be made in the disaggregation scheme increases (e.g., assigning weights by zoning classification), the level of effort will increase as well. The amount of time required for non-uniform disaggregation will also vary considerably depending upon the format and quality of the GIS databases used for the disaggregation and the amount of data processing involved.

Figure 4. Disaggregation of Polygon Data to Grid Cell Level

Fig. 4 Disaggregation of Polygon Data to Grid Cell Level

Source: Commission of the European Communities, 1998.

Disaggregation of Networks

Network data are disaggregated by linking MEPLAN output with the information in the GIS database. For each link in the MEPLAN network, the number of cars, trucks, and buses, the link type, and average speed are transferred from the model. The GIS database contains the model node numbers and the alignment for each link; the node numbers are used to merge the GIS information and the model output. If the network is not contained in a GIS format, it can be approximated by considering links as straight lines between the network nodes.

Once the information is transferred, the raster grid is laid over the network. Each cell touched by a network link receives the information assigned to the link; if the cell is touched by more than one link, the loads are summed (Figure 5).

Figure 5. Conversion of Network Data to Grid Cell Data

Fig. 5 Conversion of Network Data to Grid Cell Data

Source: Commission of the European Communities, 1998.

In addition to trips on the network contained in the urban model, neighborhood trips are also estimated for each grid cell. Trip-ends are assigned to each cell in proportion to the total activity in each cell; each trip is assumed to go straight to the nearest raster cell that belongs to a link (limited-access highways excluded). All raster cells touched by this trip increase their load by one vehicle. Households are used to assign trip origins, and employment is used to assign trip destinations. A similar technique is used to assign intrazonal trips.

The result is five raster layers representing urban traffic: the raster load for cars, trucks, and buses; dominant link type, and average speed for each cell. The cells are considered as point sources for emissions in the environmental models.

Land Use Cover

The proportion of land cover which is impermeable is an important environmental indicator for a number of reasons. Impermeable cover increases runoff in drainage and sewerage and can affect water quality in streams and rivers in the watershed, as pollutants such as oil and sediment are collected in the runoff. Impermeable cover also decreases the renewal rate for groundwater, increases the risk of floods, and indicates the area and space taken for human use at the expense of plant and animal species.

Within the Raster module, the percentage of impermeable cover in each cell can be estimated based on land use categories as well as population and employment. In the SPARTACUS project, functions relating impermeable land to land use type (residential, mixed-use, or industrial) and to total population plus employment density, based on recent German research, were used. Since actual land coverage factors vary considerably, this provides only a rough estimate, but one that should at least be suitable for assessing differences among alternative policies and land use scenarios.

Exposure To Air Pollution

The Raster module in the SPARTACUS study was set up to model the chain from emission to exposure using 1) emission functions for different vehicle types, 2) an air dispersion model, and 3) GIS to overlay concentration and population data.

Functions relating emissions of particulate matter (PM), oxides of nitrogen (NOx), and carbon monoxide (CO) to vehicles by speed and type are applied to the traffic characteristics of each raster cell. (These functions can be estimated for vehicle fleets in U.S. cities by running U.S. EPA or California Air Resources Board emission models under local conditions, for a variety of speeds.) The emission is then fed into a Gaussian air dispersion model, which was developed based on German technical guidance for air quality. The model includes meteorological parameters to describe a predominant wind speed and direction. A separate equation is applied to PM to account for PM sedimentation. The dispersion model computes the concentration (in milligrams per cubic meter) of pollutant at every other raster cell as a result of the emission cell. This calculation is repeated for all emission cells, and the results are summed to estimate the total concentration for each cell. A similar procedure, utilizing a dispersion model developed in the U.S., is described in the Envision Utah case study.

To measure population exposure, the total population is summed across the raster cells for which concentrations exceed air quality standards. For a variety of reasons, this does not measure actual exposure. People may spend varying levels of time in their residential zone; pollutant levels indoors and outdoors may differ; and pollutant levels and diffusion patterns may vary from day to day depending upon the weather. The measure also does not consider the exposure of people outside of their zone of residence; for example, at the workplace, or in other activity centers such as schools, hospitals, and shopping areas. The indicator does, however, provide a relative measure of exposure under different scenarios. In addition, the proportion of households from each socioeconomic group subject to unacceptable pollution levels can be compared to assess the equity of alternative policies.

Modeling of NO2 levels in Helsinki (Figure 6) shows that in 2010 concentrations will exceed guidelines in only 4.9 percent of the metropolitan area, but that 10.6 percent of the total population and 12.6 percent of lower-income groups will live in these areas of exceedance.

Figure 6. NO2 Concentrations

Fig. 6 NO2 Concentrations

Exposure to Noise

The impact of a traffic noise source ranges from a few meters to a few kilometers depending on the amount of noise being generated and on local circumstances. Most noise analysis models rely on spatially disaggregate information on topography, built form, and the distribution of population to estimate noise propagation and exposure. A simplified version of a German noise propagation model was applied in the SPARTACUS framework to make use of the available data.

Noise is treated similarly to pollutant emissions in the raster environment. The noise emission from each raster cell is treated as coming from a point source as a function of traffic volume, percentage of trucks, and a speed correction factor. The resulting noise level at a receptor cell is then calculated as a function of the segment length in the emission cell, distance from the emission cell, and absorption due to buildings. This last factor is estimated from land use categories and population and employment densities based on recent German research (Lee, 1998). The noise levels for each receptor cell are then aggregated.

To calculate social impacts of noise, two approaches can be taken. First, the number of households living in areas that have noise levels exceeding guidelines can be calculated. Alternatively, the percentage of households disturbed by noise levels can be assumed to increase as a function of the noise level. For example, Finnish researchers estimate that for a noise level between 55 and 64 dB(A), 33 percent of the population is disturbed; for levels between 65 and 69 dB(A), 50 percent is disturbed, and for levels of 70 dB(A) the entire population is assumed to be disturbed. As with pollutant emissions, the distribution of noise impacts across socioeconomic groups can also be calculated. For an approach to estimating noise impacts in the U.S., see the Waterloo case study.

Application

Indicators

For the SPARTACUS project, 19 indicators were selected under five broad categories: air pollution; consumption of natural resources; and three social categories, including health, equity, and opportunities. Weights were assigned to each indicator based on input from local officials. Weights were then assigned for each indicator category, so that two summary index values could be computed: environmental and social. Five economic indicators were also developed, showing the distribution of monetized costs/benefits among user groups. Total economic benefits were measured in monetary terms and divided by population to obtain an economic index value. The indicators used are shown in Table 1.

Table 1. Indicator List

Environmental Indicators
Air Pollution
  • Emissions of greenhouse gases from transport
  • Emissions of acidifying cases from transport
  • Emissions of organic compounds from transport

Consumption of Natural Resources

  • Consumption of mineral oil products
  • Land coverage
  • Consumption of construction materials
Social Indicators
Health
  • Exposure to particulate matter in the living environment
  • Exposure to nitrogen dioxide in the living environment
  • Exposure to carbon monoxide in the living environment
  • Exposure to noise in the living environment
  • Traffic deaths
  • Traffic injuries

Equity

  • Justice of exposure to particulates
  • Justice of exposure to nitrogen dioxide
  • Justice of exposure to CO
  • Justice of exposure to noise
  • Segregation

Opportunities

  • Total time spent in traffic
  • Level of service of public transport and slow modes
  • Vitality of city center
  • Accessibility to the center
  • Accessibility to services
Economic Indicators
Costs/Benefits By Type
  • Transport user benefits
  • Transport resource cost savings
  • Transport operator revenues
  • Investment financing cost
  • External cost savings

Overall Indicators

  • Total net benefits (sum of costs/benefits by type)
  • Economic Indicator (total net benefits per capita)

Policy Scenarios

To test alternative policies and policy combinations, the following approach was taken:

  1. A "Reference Scenario" was constructed, based on the existing network and committed projects as well as baseline land use forecasts.
  2. A list of policy ideas was drafted. These fell under the categories of land use, pricing, regulation, and transportation investments.
  3. The mechanism for modeling the policy in the SPARTACUS system was identified. In some cases, measures had to be tested indirectly. For example, land use controls were modeled as a redistribution in employment and population, consistent with the objectives of the controls.
  4. Individual policy elements (e.g., changes in transit fares) were tested and screened using the MEPLAN run results only. This helped demonstrate the effects of individual measures as well as check that the model system produces reasonable results.
  5. Based on the MEPLAN results, the most promising policy elements were selected for further testing using the entire SPARTACUS system to generate indicators.
  6. Combinations of policies were selected for analysis. In some cases, effective policies were combined to evaluate joint impacts. Policies were also combined when one policy could offset the negative side-effects of another policy. For example, lower transit fares and teleworking could offset some of the negative effects of higher car pricing. As policy combinations were analyzed, additional policy elements were added incrementally, while policies that had a negative or inconsequential impact were dropped.
  7. For each policy combination, the absolute or percent change for the indicators were calculated with respect to the Reference Scenario. Also, pairwise comparisons of scenarios were performed to evaluate the effects of adding or dropping individual policy elements.
  8. Alternative scenarios were also tested, based on exogenous variables, such as higher-than-projected growth in population.
  9. The results were compared for all three test cities.

Findings

Figure 7 illustrates the results of some of the policy combinations on the overall indicator values. "B" represents the baseline (Reference Scenario) indicator level. Green bars indicate a positive (desirable) change in the indicator value, while red bars indicate a negative (undesirable) change.

Figure 8 illustrates the results of a pricing scenario on the distribution of employment, while Figure 9 illustrates the results of the same scenario on the distribution of population. Note that the scenario causes employment to shift to middle-ring zones, while population shifts toward the center of the metropolitan area, compared to baseline forecasts.

Figure 7. Policy Result Graphs

Fig. 7 Policy Result Graphs

Source: Commission of the European Communities, 1998.

Figure 8. Impacts of Pricing on Distribution of Employment, Helsinki

Fig. 8 Impacts of Pricing on Distribution of Employment, Helsinki

Source: Commission of the European Communities, 1998.

Figure 9. Impacts of Pricing on Distribution of Population, Helsinki

Fig. 9 Impacts of Pricing on Distribution of Population, Helsinki

Source: Commission of the European Communities, 1998.

The comparisons showed that some policies had consistent effects in all three cities, while others had different effects depending upon local conditions. Some overall findings include:

These results, of course, may not directly apply to U.S. cities, which have different urban forms and transportation systems than their European counterparts. Also, the study did not include some important measures such as the equity of mobility/accessibility impacts. Nevertheless, the results demonstrate the power of an integrated transportation and land use modeling system to provide insights into a range of alternative policies, both individually and in combination.

Conclusions

Strengths

The SPARTACUS project was admittedly a large-scale modeling project with substantial data and resource requirements. It is not necessary to apply all elements of the approach at once, however. For example, the GIS analysis of emissions and noise exposure can be applied independently of the land use model. Also, a smaller set of policy scenarios or impact measures can be defined.

Overall, the case study illustrates a number of features that are relevant to metropolitan-level transportation and land use scenario testing in the United States. Specifically:

Equity and social justice implications of alternative scenarios can be quantified in a number of different ways. The SPARTACUS system allows the user to select any of four options for valuing equity impacts. While the selected option can impact the relative ranking of the scenarios, in the SPARTACUS study, those that performed well under one option tended to perform well under all four.

Limitations

References

Published References

Commission of the European Communities (1998). SPARTACUS Final Report.

Lautso, Kari and Yli-Karjanmaa, Sami (1999). The SPARTACUS System for Analyzing Urban Sustainability. Presented at the 78th Annual Meeting of the Transportation Research Board, Washington, D.C., January 1999.

Spiekermann, Klaus (1999). "Sustainable Transport, Air Quality and Noise Intrusion - An Urban Modelling Exercise." Paper presented at the ESF/NSF Transatlantic Research Conference on Social Change and Sustainable Transport, University of California at Berkeley, USA, 10-13 March 1999.

SPARTACUS web site: http://www.ltcon.fi/spartacus/

Contacts

Organization Person Phone
TMI SY-Arkki Co. Kari Lautso  
TMI SY-Arkki Co. Sami Yli-Karjanmaa  
Updated: 04/27/2012
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