Case Study:

SPARTACUS

Application

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

Source: Commission of the European Communities, 1998.

Figure 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

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:

• The most effective policies to improve the sustainability indicators were pricing policies. Increases in car operating costs of 50 to 100 percent decreased car mileage by roughly 15 to 25 percent. Pricing policies led to some negative side effects, but these could be offset by other policies. For example, reductions in accessibility to the city center could largely be offset by land use policies and public transportation investments. Car pricing increases greater than 100 to 150 percent had negative marginal economic and social benefits.
• Land use policies alone did not have significant effects, and some land use policies (such as moving either population or employment to balance jobs/housing ratios in zones) did not improve the indicators. The authors concluded that land uses had already adapted to the transportation system. However, redistribution of both population and employment resulted in some positive impacts, particularly in combination with other policies.
• Some measures to decrease travel demand, such as carpooling, teleworking, and some public transport scenarios, led to long-term increases in car mileage. This is because these policies initially resulted in less congestion, which allowed households to move to more peripheral areas where car use increased.
• Investment programs alone had limited potential to improve environmental and social indicators. In some cases, the effects of individual projects in an overall investment package worked in opposite directions so there was little net effect on the indicators.
• Reducing car speeds had positive environmental and social effects, but these were outweighed by the negative impacts on mobility.
• Some results, such as the equity of exposure to noise and pollution, were not consistent among the three cities. This suggests that local conditions are important in determining these measures.

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.

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