List of Tables
Recent implementation of market-based spatial competition models in the United States 1
Description of categories in Figure 4 10
Comparison between SacMEPLAN2 and SacMEPLAN3 15
Model outputs for the year 2025 by scenario 26
Model outputs for 2050 by scenario 28
Shifts in location of households and employment for 2025 34
Shifts in location of households and employment for 2050 35
Total changes, from the Base Case, in economic benefits (in 2000 dollars) to commuters by income class and to all non-commercial travelers in 2050
Per trip changes, from the Base Case, in economic benefits (in 2000 dollars) to commuters by income class and to all non-commercial travelers for 2050
Total a.m. peak period and per trip changes, from the Base Case, in economic
benefits (in 2000 dollars) to all non-commercial travelers in 2050, with full auto ownership costs added in 41
Effective urban planning and infrastructure investment rely on the ability to assess future needs today. Models are instruments for obtaining projections of what future conditions will be.
The ability of sophisticated urban and regional models to analyze policy alternatives is growing. As these models have improved, their use has been expanded.1 Presently, many large Metropolitan Planning Organizations (MPOs) and state departments of transportation are developing, for the first time, integrated land use and transportation models. Table 1 presents a list of recent integrated land use and transportation modeling activities specifically for market- based spatial competition models. These agencies are responding both to local needs, expressed by their constituent cities and counties, and to external legal requirements, such as the National Environmental Policy Act (NEPA) and the Clean Air Act air quality conformity modeling rule. Driving this movement is the desire to create the ability for metropolitan regions to test alternative policies (both land development policies and transportation policies) and make more informed decisions regarding future impacts of current policies.
Table 1 Recent implementation of market-based spatial competition models in the United States
The Sacramento region has been a leader in developing urban models. In the fall of 2002 the Sacramento Area Council of Governments (SACOG) became one of the first MPOs in the United States to adopt a fully integrated land use and transportation model for policy purposes. This adoption was preceded by extensive model-demonstration exercises aimed at showing the usefulness of these models for urban and regional policy analysis. Researchers at the University of California, Davis, together with the consulting firms HBA Specto and Modelistica, implemented the first set of models with the cooperation of SACOG. These exercises were largely academic in nature.2 The Sacramento Model Test Bed study was a side-by-side comparison of the SACOG travel model, a land use model that was used in conjunction with the SACOG travel model, and two fully integrated land use and travel demand models of which MEPLAN was one. The purpose of the study was to test and compare each model's policy analysis ability.3 Each model was given identical data from the Sacramento region for model calibration. A trend scenario was run, as well as three policy scenarios. While the test bed study did not explicitly recommend one model over another, the findings, together with the previous model demonstration studies, led to MEPLAN being adopted by SACOG for regional policy analysis. SACOG implemented and funded the third incarnation of the Sacramento MEPLAN model, the model used in this study.
The purpose of this report is to present results from policy scenarios run with the third installment of the MEPLAN model in the Sacramento region. These policy scenarios were obtained via outreach work with two Sacramento-based citizens groups: the Environmental Council of Sacramento (ECOS) and Sacramentans for Transportation Equity (SAC-TE). It was anticipated that by giving these citizens groups access to the model, a greater diversity of policies would be evaluated and greater weight would be given to their positions.
The remainder of this report will proceed as follows. The following section will briefly detail the current modeling exercise taking place in the Sacramento Region and its relevance to this study. Next, the improvements of the current Sacramento MEPLAN model over past versions of this model will be discussed. This is followed by an explanation of the MEPLAN model itself. The Analysis and Methods section details the citizen outreach associated with this report as well as a description of the policy scenarios that were modeled. Results are then presented and discussed. Finally, a Conclusions section summarizes the findings in this report and discusses the limitations of our findings.
the sacramento blueprint project
In the Sacramento, California region, the Sacramento Area Council of Governments (SACOG) is undertaking one of the more innovative modeling campaigns in the country. Titled the "Sacramento Region Blueprint Project," it is the bringing together of a suite of planning models to enable citizens, cities and counties to better plan for future growth, land development, traffic and air quality conformity within the six county (El Dorado, Placer, Sacramento, Sutter, Yolo and Yuba) SACOG region. This process included numerous citizens meetings at the neighborhood, county and region-wide levels.
At these meetings, the citizens, planners and local elected officials were able to test different land development policies and receive real-time feedback on the implications of each policy using the Place3s (PLAnning for Community Energy, Economics, and Environmental Sustainability) planning tool. (Place3s is a GIS-based tool that allows users to experiment with alternative land use patterns and returns selected indicators to evaluate each alternative.) Throughout the process, organizers sought for collaboration between the SACOG staff and the various local planning staffs and elected officials. Once the region-wide policy scenarios were selected, they were to be fed into a market-based spatial competition model to allow a more robust evaluation of each alternative. The research reported here is from a parallel modeling process undertaken by the Urban Modeling Lab at the University of California, Davis under funding from the Mineta Transportation Institute.
Figure 1 The Sacramento region
In the Sacramento Region Blueprint Project, citizens are able to rearrange only two characteristics of the region, the density and the use of land. This was for a variety of reasons, not the least of which would be simplification of the process. Also, the SACOG staff has retained the elements of their current regional transportation plan, with some minor modifications.
The work reported here differs from the Blueprint Project in several ways. First, the outreach for this project is not as comprehensive. Specific community organizations were selected for this project rather than the public-at-large. Community organizations were selected in an effort to give voice to underrepresented groups. Second, the selected community organizations were given full access to the model (street networks, bus and light rail transit systems, land use, densities and growth boundaries). Third, rather than start at the neighborhood level and move successively up in scale, the citizen representatives were encouraged to think regionally in developing their policy scenarios. Finally, the results of this process were put directly into MEPLAN without Place3s as an intermediate step.
The intermediate model (Place3s) was not used for several reasons. First, it does not model the impacts of policy changes. It is a simple accounting tool that allows the users to make changes and then returns results. Second, no checks are made for feasibility. Unrealistic combinations of land uses can be input, whereas MEPLAN requires land uses to be socioeconomically related.
The MEPLAN modeling framework is described in Hunt and Simmonds' 1993 article for Environment and Planning. The basis of the framework is the interaction between two parallel markets, a land market and a transportation market. This interaction is illustrated in Figure 2. Behavior in these two markets is a response to price and price-like signals that arise from market mechanisms. In the land markets, price and generalized cost (disutility) affect production, consumption, and location decisions by activities. In the transportation markets, money and time costs of travel affect both mode and route selection decisions.
Figure 2 System of two types of markets: Markets in land and in transportation and the interactions between them form the basis of the MEPLAN framework. Reproduced with permission from Abraham, 2000.
the social accounting matrix (SAM)
The cornerstone of the land market model is a spatially-disaggregated social accounting matrix (SAM)4 or input-output table5 that is expanded to include variable technical coefficients and uses different categories of space (e.g., different types of building and/or land). Discrete choice (logit) models of location choice are used to allocate volumes of activities to geographic zones in the different sectors of the SAM. The attractiveness or utility of zones is based on the cost of inputs (which include transportation costs) to the producing activity, location-specific disutilities, and the costs of transporting the resulting production to consumption activities. The resulting patterns of economic interactions among activities in different zones are used to generate origin-destination matrices of different types of trips. These matrices are loaded to a multi-modal network representation that includes nested logit forms for the mode choice models and stochastic user equilibrium for the traffic assignment model (with capacity constraints). The resulting network times and costs affect transportation costs, which then affect the attractiveness of zones and the location of activities, and thus the feedback from transportation to land use is accomplished.
The framework is moved through time in steps from one time period to the next, making it "quasi-dynamic." In a given time period, the land market model is run first, followed by the transportation market model, and then an incremental model simulates changes in the next time period (see Figure 3). The transportation costs arising in one period are fed into the land market model in the next time period, thereby introducing lags in the location response to transport conditions. See Hunt and Simmonds for descriptions of the mathematical forms used in MEPLAN, and Johnston et al.'s 2001 publication for a more complete explanation of the model structure.
The specific structure of the Sacramento MEPLAN model is shown in the diagram in Figure 4, and Table 2 defines the categories in the diagram. The large matrix in the middle of the diagram lists the factors in the land use submodel and describes the nature of the interaction between factors. A given row in this matrix describes the consumption needed to produce one unit of the factor, indicating which factors are consumed and whether the rate of consumption is fixed (f) or price elastic (e).
Figure 3 MEPLAN submodels and their interactions: Temporal dynamics are simulated by ordering the sequence of interactions among the program modules at adjacent points in time. Reproduced with permission from Abraham, 2000.
Table 2 Description of categories in Figure 4
The Sacramento MEPLAN model uses eleven industry and service factors that are based on the SAM and aggregated to match employment and location data. Households are divided into three income categories (high, medium, and low) based on the SAM and residential location data.
The consumption of households by businesses represents the purchase and supply of labor. This MEPLAN model does not have the number of workers by education level in the households, which would result in a more accurate matching of workers with jobs. This disaggregation can be done, but it was not part of our study proposal. The consumption of business activities by households represents the purchase of goods and services by consumers.
Industry and households consume space at different rates and have different price elasticities, and thus there are seven land use factors in the model. Constraints are placed on the amount of manufacturing land use to represent zoning regulations that restrict the location of heavy industry. Each of these land uses (except agricultural land use) locates on developed land represented by the factor URBAN LAND. Two factors are used to keep track of the amount of vacant land available for different purposes in future time periods (MANUF VAC LAND and TOTAL VAC LAND), and the development process converts these two factors to URBAN LAND. The MONEY factor is a calibration parameter that allows differential rents to be paid by different users of the same category of land, meaning that a firm would pay a higher rent for the same category of land in the downtown zone than it would in a more rural zone.
The single-row matrix, just above the large matrix in Figure 4, shows activity that is demanded exogenously, which includes exporting industry, retired households, and unemployed households. This corresponds to the "basic" economy in a Lowry model.6
The matrix directly above, at the top of the diagram, shows the structure of the incremental model that operates between time periods. The r's for the industry and household factors indicate the economic growth in the region, and the r's for the land use factors show how vacant land is converted to urban land.
The matrix on the left, below the large matrix, indicates the structure of the interface between the land use and transportation submodels. Each row represents one of the matrices of transportation demand and indicates the producing factors (in the corresponding columns in the matrix above) whose matrices of trades are related to that flow.
The remaining three matrices, at the bottom, show the structure of the transportation model. Five modes are available, and each mode can consist of several different types of activity on different types of links. The matrix directly to the right shows that all modes are available to all flows (m). The matrix below this, on the right, indicates the travel states (s) that make up each mode. The matrix on the left shows which travel states are allowed on each transportation network link and whether capacity restraint is in effect (a) or not (w). The design of the mode choice and assignment models is based on the Sacramento Regional Travel Demand model. A more detailed description of the Sacramento MEPLAN model design can be found in Abraham's 2000 dissertation.
improvements from sac-meplan2 to sac-meplan3
In past research, a four-county (El Dorado, Placer, Sacramento, and Yolo counties) version of MEPLAN was used, with a 1990 base year. Some of the needed input data was not available when this model was calibrated, which led to a less than ideal model calibration. Many inputs, for example, represented expert "best guesses." In addition, the travel networks also suffered as a result of inadequate budget. The past version of the Sacramento MEPLAN model (SacMEPLAN2) ran on a "sketch" travel network that omitted most collector streets. By limiting the road network, it is conceivable that the travel outputs would be too sensitive to road improvements.
The current model being used for this report, SacMEPLAN3, is not an academic/research model but was funded by SACOG. This incarnation of the model enjoyed a more complete calibration and longer evaluation period. This model contains all six counties (adding Yuba and Sutter counties) in the SACOG region. It represents the full travel network and has better input data. The calibration of this model was closely scrutinized by SACOG staff and the authors of this report. The combination of better inputs, better model calibration, better travel network representation, and a more distant horizon year make this newer installment of the model more relevant to policy analysis. Table 3 summarizes several of the improvements from SacMEPLAN2 to SacMEPLAN3.
Table 3 Comparison between SacMEPLAN2 and SacMEPLAN3
This version was also calibrated on more accurate floor space rent data than were the earlier two versions. Lead researcher Robert Johnston re-interviewed real estate experts in the region and read private reports from leasing firms on rents for industrial, office, and apartment properties. These data were assembled for display in a geographic information system and a one-day meeting of real estate experts was held at the SACOG offices. Average monthly per-square-foot rental rates by zone for industrial, office, retail, multifamily and single-family floor space types were projected in sequence onto a screen, and the real estate professionals were asked if the mean seemed correct and if the research had missed zones with high rents as well as low rents. The model requires a range of rents to see where demand is high or low. The researchers did the residential land uses separately for the three household income classes, and also asked for the number of acres available for redevelopment in each zone and which zones were "hot" for future development of each type. This process resulted in a greater range of rent values for each activity type and a better grasp of the level of demand for each zone.
community outreach
There are numerous community organizations in the Sacramento area that are concerned with a variety of issues. For this study, researchers wished to work with groups that were concerned with issues related to land use/development and transportation. To optimize time and the impact of this project, umbrella groups (large organizations that are made up of several member groups, each having their own citizen/concern base) were considered. The Environmental Council of Sacramento (ECOS), a regional umbrella group that represents environmental and social equity organizations, was ultimately chosen. This research group has worked less formally with ECOS in the past.
Frequent meetings were held during the fall of 2003 and winter of 2004 (roughly two a month) with ECOS, and Sacramentans for Transportation Equity (SAC-TE), a transportation equity group. Researchers from the Urban Modeling Lab attended roughly half of these meetings. The meetings had varying purposes and attendance, ranging from one-on-one meetings with selected individuals to larger presentations and discussions. Attendance at these meetings ranged roughly from 15 to 35 participants.
In the initial meetings, a cursory overview of the MEPLAN model was provided to the groups as well as an outline for this project. Their challenge was to create a comprehensive and cohesive vision for the Sacramento region that we would then convert into an operational scenario to input into the model. The groups divided the tasks into a land use vision and a transportation vision. These task groups met independently and presented their ideas at the collective meetings. These citizens groups generated the policy scenarios modeled and reported here.
These meetings produced several important outcomes. First, a set of maps was produced with new transit lines (region-wide bus rapid transit, for example). Second, an urban growth boundary and zoning changes (to allow higher-density infill) were decided upon. Third, the process facilitated both groups in developing region-wide vision statements for land use and transportation. Prior to this exercise, ECOS was primarily concerned with Sacramento County, as opposed to the entire six-county metropolitan area.
There was an early struggle among the participants over how this process would take place. Some of the participants wanted to know all model inputs and exactly how the model worked in an effort to achieve the best or most desired outputs. While these community organizations were given access to the model inputs and outputs, we discouraged this approach of simply "gaming the model" in favor of a visioning process in which the participants came up with a set of ideals or goals and then worked on operationalizing them into useable scenarios.
Another challenge faced by the participants was the sequencing of road projects, transit improvements, land policies, and transportation policies. The SACOG six-county MEPLAN model has a 2050 horizon year. With 50 years of sequencing to work with, numerous possibilities were presented by the citizens and discussed in these meetings. The outcomes of these meetings are represented in the five scenarios that were modeled and are explained below.
The Base Case scenario was similar to the SACOG trend scenario used in the Blueprint process, and represents a relatively unconstrained land use scenario. In 2000, over 800,000 vacant acres are zoned for development. By 2050, only roughly 250,000 of these have been developed, leaving more than a half-million vacant acres zoned for development. This allows households and employment to locate wherever market demand influence them, in other words, few regulatory land use controls are in place in the Base Case scenario. This is not unlike the actual condition in the six-county region. This land use scenario was derived from the local general plans of the cities and counties in this region and efforts were made to represent their land use strategies.
All scenarios used virtually the same internal populations in the various model years: 1.9 million in 2000, 2.7 million in 2025, and 3.6 million in 2050. Household size is projected to fall from 2.66 to 2.36, and so household formation will be more rapid than population growth. This is a fairly rapidly-growing region with prime agricultural lands in Sutter, Yolo, and Sacramento counties and with important Sierra foothill habitats in Yuba, Placer, and El Dorado counties. The region is in air quality nonconformity for ozone and particulates. Like most rapidly growing regions, this one will have difficulty showing air quality attainment in the future.
Citizens groups are questioning low density, single-use developments on previously undeveloped land for increasing travel and emissions. El Dorado County has historically allowed this type of development, while Placer County has kept growth confined mostly to the I-80 corridor in the past, but recently has begun to grow in outlying areas. Sutter and Yuba counties protected agricultural lands in the past but seem to be rezoning large areas for development now. Sacramento County has seen its urban growth boundary come under fire more often in recent years, but so far has withstood these pressures. However, new cities have incorporated recently, and they are annexing lands for growth. Yolo County has historically been the most resistant to these pressures in this region.
The travel networks consist of the current network (as it was in 2000) with incremental additions in the years 2005, 2015, 2025, 2035, and 2050. These travel networks were obtained directly from the travel demand model currently being employed by SACOG. Appendix A contains an abbreviated description of the major network improvements by model year.
Our outreach and modeling work was being conducted in 2004. Projects scheduled in the model for a 2005 completion date were considered too far along to be impacted by this work; therefore, they were not on the table for the citizens groups to manipulate and are not detailed here since many of them are already on the ground.
The overarching idea behind the scenarios generated by these citizens groups was to improve the quality of life in the region for both the current 1.9 million residents and the estimated 1.7 million additional residents expected by 2050. They felt strongly that travel options needed to be improved and that air quality issues and the rapid conversion of undeveloped land to urban uses needed to be addressed. To do this, several strategies were adopted: 1) limit roadway expansions, including limiting HOV lane additions; 2) dramatically improve transit service; 3) use a strict urban growth boundary, and promote infill development; and 4) introduce parking charges and higher fuel taxes.
Scenario 1: Transit Improvements
Scenario 1 consists of massive improvements to the transit service and facilities in the region. All road projects beyond 2005, including HOV lanes, were canceled (removed from the model). Numerous new bus rapid transit (BRT) lines were added and most of the BRT lines from the Base Case were carried over as well. For our purposes, BRT refers to high-speed bus lines with signal preemption that make a limited number of stops and at times have dedicated lanes. For roadways with three or four lanes in one direction, a lane was taken and dedicated as a BRT lane. On roadway sections with only two lanes, a BRT lane was added. All BRT lines were given an initial headway of 20 minutes, with the exception of 50BR1/50BR2, which maintained its 10-minute peak hour headway from the Base Case. Appendix B contains a summary of these transit improvements.
The citizens attempted to create a transit-only scenario with roughly the same capital costs as the Base Case, to make the comparison easier and fair.
Figure 5 shows the LRT extensions and new BRT lines in this scenario. BRT runs to major outlying cities (Davis, Marysville/Yuba City, Auburn, and Placerville), as well as throughout the urbanized central area of Sacramento County.
Figure 5 Scenario 1: Light rail extensions and new bus rapid transit lines
Scenario 2: Urban Growth Boundary
Scenario 2 utilized the Base Case networks, including all roadway improvements and adds a tight urban growth boundary. The massive capital improvements of the Base Case travel networks improve travel accessibility in nearly every zone. This increased accessibility means that people and jobs can move farther from the central business district (CBD) without incurring increased travel costs. In other words, the roadway and transit improvements may facilitate sprawl-type growth in the outer zones by reducing travel times from the outer zones to all other zones and especially to the CBD.
In order to prevent this, an urban growth boundary (UGB) was implemented by restricting the amount of land available for development in the outer zones. Figure 6 presents the growth boundary. The shaded zones represent zones in which development was allowed to occur. The non-shaded zones had all developable land converted into a protected classification that did not allow development of any kind. In total, over 480,000 acres of developable land were removed from the rural zones. This still left plenty of developable land within the urban growth boundary. At the conclusion of model year 2050, there was still land available in every category within the urban growth boundary.
It should be noted that the shaded area can be misleading. The shaded area represents zones where growth was allowed to occur. Within each zone the actual amount of land available varies. For example, the two westernmost zones are located in Yolo County, which already had strict growth controls, so while growth is allowed to go into these zones, the amount of land available within these zones for development is quite modest. This is in contrast to the easternmost zones in the region, which have no growth controls, and large amounts of these zones are actually zoned for development.
In order to make this project comparable with the work that SACOG has undertaken in the Sacramento Area Blueprint Project, the citizens groups designed the UGB to replicate SACOG's most growth-controlled scenario (Scenario D). It should be noted, however, that while SACOG's Scenario D does allow small amounts of growth in the more rural parts of the region, the UGB modeled here does not.
Figure 6 Urban growth boundary shown in the shaded zones
Scenario 3: Transit Improvements with Urban Growth Boundary
Scenario 3 utilizes the networks created for Scenario 1 (transit improvement scenario), adding the tight urban growth boundary created for Scenario 2. The massive capital improvements of Scenario 1 (transit improvements, as opposed to the roadway improvements of the Base Case) improve travel accessibility within the zones affected by the creation of these new, high-capacity, high-speed transit modes. This increased accessibility means that people and jobs can move farther from the CBD without incurring increased travel costs. In other words, the transit improvements may facilitate some sprawl growth in the outer zones, although in different patterns from the Base Case, by reducing travel times from the outer zones to other zones and especially to the CBD. In order to combat this, the urban growth boundary created for Scenario 2 was modeled in tandem with the travel networks of Scenario 1.
Scenario 4: Transit Improvements with Pricing
One of the reasons vehicle miles traveled (VMT) continues to rise is due to the relatively low cost of driving per mile.7 Once the car has been purchased, it becomes a sunk cost and therefore isn't typically considered when deciding which mode to take (e.g. SOV, HOV, transit, walk, or bike), which destination to select (how far to drive), or whether or not the purpose of the trip is worth the cost. Because of this, the community organizations decided that a pricing scenario was needed to see if policies such as mandatory parking charges or higher gasoline taxes could be effective tools to limit the amount of driving and, therefore, the amount of air pollution generated by automobiles.
In this scenario, the networks that were created for Scenario 1 were used in combination with a gas tax and a parking charge. The parking charge was applied at the destination of work trips in the amounts of $6.00 per trip in the central business district and $2.00 per trip everywhere else. This parking charge was only applied to work trips. Charging for work-trip parking is likely to occur in the next decade or two for a variety of reasons, especially in regions with adequate transit service. The gas tax was the equivalent of $1.00 per gallon and was applied globally to all automobile trips. In reality this wouldn't have to be a gas tax per se. If the actual price of gasoline rose by one dollar a gallon, it would have the same effect (or any combination of rising fuel prices and taxes). This is a modest price increase over 50 years, given that most experts predict a real price increase by 2020. If the economies of China and India continue to grow rapidly, and if Russia's economy recovers, demand will push up prices sooner.
Scenario 5: Transit Improvements with Pricing and Urban Growth Boundary
In Scenario 5 the previous two scenarios were combined to create a scenario with improved transit service, parking prices and an increased gasoline tax, and the urban growth boundary.
MEPLAN is a quasi-dynamic model in which the interplay between the various policies can only be understood with the model explicitly accounting for the effects of them collectively. For this reason, the fifth scenario was run.
The methods used to calculate the various results are described within the discussion of the results, so the methods will be clearly related to the obtained results.
It should be noted that for the base case and all scenario runs reported here the population and employment totals (year-by-year) remained relatively constant from one scenario to the next. (MEPLAN allows additional people to enter or leave the region if conditions warrant it, typically in relatively small amounts.) All changes reported here are a result of behavioral changes of the households and employers being modeled. MEPLAN allows households and employers to change both location and travel choices in response to changes in the land market and travel conditions. Efforts were made to maintain the base case assumptions in each scenario except for the policy changes noted previously in the description of each scenario. The MEPLAN travel model only includes travel for the three-hour a.m. peak period (hours between 7 and 10), and so we are not accounting for all daily trips. In addition, work trips are a high proportion of modeled trips.
travel changes
Base Case vs. Scenario 1: Transit Improvements
First, note that overall transit share falls in 2050, compared to 2025, in the Base Case. This is due to the availability of cheap land beyond the reach of transit coverage. As more and more households move to this land, transit becomes less available.
This (Scenario 1 versus the Base Case) is really a comparison of a large number of road improvements versus a large amount of transit improvements. As can be seen in Tables 4 and 5, halting road expansions while dramatically improving transit service lowers vehicle miles traveled (VMT) in 2025 and 2050. Mode shares also change as more households choose to take transit and fewer are driving.
These results demonstrate that, given the choice, many households that now choose to drive may actually be amenable to transit. In this region, as in many others, the inability of transit to connect many trip origins and destinations in one transfer or less makes it a poor substitute for the automobile. By improving the level of service and availability of transit, this scenario demonstrates the ability of transit improvements to draw drivers out of their vehicles and onto mass transit, particularly high-speed transit modes such as bus rapid transit and light rail transit.
Table 4 Model outputs for the year 2025 by scenario
(SOV=Single-occupant vehicles; HOV=High-occupant vehicles)
Base Case vs. Scenario 2: Urban Growth Boundary (UGB)
The UGB scenario is different from the others considered here. While transit improvements and pricing strategies entice people to move closer or switch from auto to non-auto modes, the UGB scenario is a regulatory action that does not allow development in certain zones. Sacramento County already has an urban services boundary, but the UGB modeled here is stronger and extends to all six counties.
The UGB has the strongest effect on the location decisions of households and firms (see Tables 6 and 7). It also draws travelers to transit, although not quite as strongly as the transit scenario (Tables 4 and 5). Given that this scenario has all of the road improvements of the base case, it provides a strong argument for the importance of land use planning as a tool to reduce VMT and improve transit's viability. In contrast to Scenario 1, which utilized massive capital expenditures in transit to bring about land use and travel changes, the creation of a UGB is a low-cost alternative, which local governments may find appealing. Scenario 2 is similar to SACOG's Scenarios C and D from their Regional Blueprint visioning process, and so one can conclude that Scenario 1 reduces VMT more, especially in 2050. It can also be observed that in Scenario 3, where the model improves only transit and keeps the UGB policy, VMT are reduced by twice as much as Scenario 2.
Table 5 Model outputs for 2050 by scenario
Base Case vs. Scenario 3: Transit and Urban Growth Boundary
The combination of transit improvements and a UGB reduce VMT by roughly 15 percent by 2025 and roughly 20 percent by 2050 (see Tables 4 and 5). Not surprisingly, this combination also produces strong downward shifts in the mode share of single- and high-occupant vehicles while improving the mode share of transit, walk, and bike. This scenario is clearly better than Scenario 2, which is very close to the scenario adopted by SACOG in its charrette process in 2004, at lowering VMT and the auto shares.
It is interesting to note that the impacts of this scenario are nearly the sum of the impacts of its components. This suggests either one of two behaviors. First, the two strategies might be affecting different groups. For example, the transit improvement scenario wouldn't have much impact on households locating in the outer zones (people to whom the availability of transit is limited, even in this scenario). Conversely, the UGB scenario may bring people into zones where they can choose transit, but, because it doesn't improve transit, those who might be affected by the transit scenario are unaffected. Notice that the UGB scenario lowers VMT, which lowers congestion on the travel network, which would have an opposite impact on people already living within the urban core and using cars, but who are teetering on the transit/auto choice.
The other plausible explanation for the cumulative relationship of these policy scenarios is that it is simply chance, meaning that the combined effects raise the utility of taking transit, which raises its mode share and lowers VMT. The fact that they are nearly double is merely an artifact of the model structure. One might expect a dampening effect, as additional policies are modeled together. At least in this case, the dampening effect does not appear. Also, there may be a synergism where there is some substitution but also may be complementary over time.
Base Case vs. Scenario 4: Transit and Pricing
Due to the market-based nature of MEPLAN, researchers supposed that the pricing policies modeled in Scenario 4 would have large impacts on the model outputs. As can be seen in Tables 4 and 5, while the VMT impacts are similar to Scenario 3, the shifts in mode to transit are more pronounced. The utility of driving an auto is directly tied to its costs. This scenario significantly raises the costs of driving. Table 6 shows that location changes are not as high as those caused by the UGB scenario. This suggests that a market-based solution, such as parking charges and gasoline taxes, may have similar VMT and even better mode share impacts than a regulatory policy like a UGB. Readers should note, however, that the UGB appears to reduce growth in the outer zones more than the pricing scenario. Thus, it may be more appealing in terms of impacts on habitats and agricultural lands. Researchers note that this peak-period travel model in MEPLAN has a high proportion of work trips, which are affected by the parking charges. Daily mode shares and VMT would not be affected as much.
Base Case vs. Scenario 5: Transit, UGB and Pricing
By far the largest reduction in VMT and the greatest shifts away from automobile modes occur when all three policies are modeled together. Tables 4, 5, 6, and 7 demonstrate fairly radical changes in travel and location decisions. In response to these policies, households and employment are moving closer to the urban core and travelers are choosing to take other travel modes, including transit, walking, and bicycling in greater numbers. Researchers expect synergism among these policies, as transit has high service levels and so can handle the travelers priced out of cars. Again, the pricing will have strong effects because of the peak period.
Looking at all scenarios, travel changes were larger in 2050 than in 2025, as some transit improvements occur in 2035 and there is more time for transit and the other policies to affect land development and locators. Also, the scenarios were ranked in a reasonable fashion, according to theory and compared with previous simulations by us and by others. In addition, the changes in development across zones seemed broadly reasonable, given what we know about this region's land markets. All elasticities for travel behaviors were within acceptable ranges, in terms of total travel and mode choices. Floor space rents were also reasonable, as well as travel times.
congestion
The researchers defined congestion as those links with a volume/capacity ratio of 1.00 or greater. Due to the complexity of the network and the software, we calculated congestion only on freeway and expressway links. It was assumed that congestion on the other roads correlates strongly with freeway/expressway congestion, due to the assignment model being capacity-restrained and equilibrated to convergence and the whole model set also being equilibrated. MEPLAN uses an a.m. peak model, so this is the most congested period.
Anyone can make a model produce less VMT with various policies. The truly interesting issue is whether one can do this without worsening congestion. In theory, it is expected that pricing scenarios will reduce congestion, due to the higher cost of travel and parking. In fact, these two scenarios have the lowest lane-miles of congestion in 2025. They also have the highest increases in speed of all scenarios (along with HOVs in Scenario 2), in 2025. It is very significant to also note that all the other scenarios decrease congestion or keep it the same in 2025. All scenarios also have higher average auto speeds in 2025, except Scenario 1. The poorer performance of Scenario 1 is probably due to the reduced freeway lane-miles and transit not yet having short enough headways in 2025.
Things are more complex in 2050. All scenarios have higher congestion levels than in 2025 and all have roughly the same congestion levels. This is due to all the freeways leading into the CBD being saturated and traffic being shunted to surface streets, which we are not measuring. The slightly higher congestion in Scenario 1 is probably due to the increase in CBD employment in this scenario. Scenarios 3 and 5 also have congestion levels about the same as the base case, also probably due to increases in CBD employment. All scenarios increase average auto speeds, compared to the base, except Scenario 1, as occurred in 2025. This indicates that building even a very strong transit system will not work well with a sprawl land use plan for the region. These are small changes and it is believed that the strong benefits for the low-income travelers and the large reductions in VMT and emissions make this scenario beneficial overall. Scenarios 4 and 5 have the highest average speed for autos.
emission changes
As part of the research for this publication, the California emissions model was not run. Past studies have revealed that emissions are very strongly correlated with VMT and the percentage differences from the Base Case are very close to the percentage differences in VMT.8 So, the emissions reductions will be very similar to the VMT reductions. Daily emissions rankings would be the same as the rankings for the a.m. peak period. Percentage differences from the Base Case would also be similar.
This region may have difficulty showing conformity in the next Metropolitan Transportation Plan, due to a new state emissions inventory with more sport utility vehicles (SUVs) and light duty trucks in the region's current and future vehicle fleets, and also due to increases in most types of emissions per mile for the higher vehicle speed classes. There is considerable interest among citizens and elected officials in this region in reducing VMT and emissions.
a comment on sacog's blueprint scenario D
In the recent Regional Blueprint process, SACOG did a series of neighborhood design charrettes throughout the region and then did countywide charrettes. Finally, they did a regional meeting with over 1,000 attendees. At this meeting, the audience chose two scenarios with UGBs, but with the adopted regional transportation plan networks (which are our Base Case networks). So, researchers performed Scenario 2 with a UGB and the Base Case policy of new freeways and freeway widenings with moderate transit improvements, to represent the SACOG Scenario D. Scenario 3 represents the ECOS environmental group's transit-only transportation networks, along with the same UGB. It is noted that Scenario 3 reduces VMT and emissions in both years about twice as much as does Scenario 2. Scenario 3 is slightly more congested and has slightly lower average auto speeds than does Scenario 2, but these costs may be acceptable if the region needs to reduce emissions substantially. If the outer counties do not cooperate and adopt UGBs, then Scenario 4 (transit with pricing) could become necessary.
In this current study, as in past ones, we find that transit by itself is only moderately effective in reducing VMT and emissions. To be really effective, transit needs to be supported by land use policies or by pricing policies.
location changes
The key advantage of using MEPLAN over a traditional travel demand model is that households and employment can change location in response to changes in the travel network (e.g., road improvements and changes in the coverage and level of service of mass transit). Given that this is a 50-year modeling exercise, location shifts are an important factor in forecasting how the Sacramento region will grow and change in response to the policies being modeled. In past work, this research has shown that an urban model more strongly differentiates among scenarios than does a travel model, due to synergistic land use effects.9 The scenarios run in this study produced fairly substantial shifts in the location of households and employment. From a travel perspective, the closer activities are to each other, the less travel demand is placed on the travel network. From a conservation perspective, the closer activities are together, the less land is needed, thus preserving habitats and agricultural lands.
Figure 7 Super zones created for location shift analysis
In order to facilitate the presentation of the land use shifts, the 71 internal zones used by the MEPLAN model were grouped into three superzones (Figure 6). The zone comprising the central business district (CBD) of Sacramento was left alone due to its unique density, land use pattern, and accessibility by freeway and transit. Next, all of the zones that comprise the bulk of the current urban development in the region were grouped into a second zone (urban/suburban zone). Readers should note that these two groups are also the zones that are included within the UGB created for Scenarios 2, 3, and 5. The final zone is the rural zone, comprising the zones with limited, scattered rural development. These zones were excluded from the UGB because of the desire to maintain open space and preserve agricultural lands. The shifts of total employment and population by these superzones (not just new growth) are shown in Tables 6 and 7.
Table 6 Shifts in location of households and employment for 2025
Notice that the UGB accounts for the largest location changes. The mode shifts from driving to transit, discussed in Tables 5 and 6, are facilitated by location shifts. Employment and households shifting from the rural zone to the urban/suburban and CBD zones make transit more accessible to a greater percentage of the total population. The CBD loses employment in Scenario 1 in 2025, compared to the base case, because we reduced radial freeway capacity to the CBD. Also, the transit system is complete but does not yet have short headways, so access to the CBD is worse than in the base case. In Scenario 4, the CBD loses employment in 2025 because of the higher parking charge in this zone ($6 vs. $2 elsewhere). The CBD gains employment in all scenarios in 2050, due to the freeways being more congested throughout the region and transit having short headways and serving the CBD well.
Note that the transit scenario (1) reduces households and employment in the outer ring less than 1 percent, and the transit plus pricing scenario (4) reduces them one to four percent in 2025. In 2050, these losses in the outer ring are a few percent, due to the higher road congestion and better transit service in the CBD and urban/suburban zones. The UGB scenarios, of course, cause large reductions in the outer ring in both years.
The detailed data for changes in total households and employment by zone are given in Appendices C and D, to give the reader an idea of this critical model output at this level.
Table 7 Shifts in location of households and employment for 2050
economic benefits for travelers
The researchers calculated changes in traveler economic welfare, using the compensating variation (CV) measure. See Rodier and Johnston's 1998 publication for a description of this method, as applied with the SACOG travel model. In MEPLAN, this indicator was obtained by multiplying the disutilities (log sums by flow type) for all trips (all origin/destination pairs) from the mode split model by the flow volumes for each flow type for all trips. The log sums take into account all costs of travel (time and money) between zone "i"and all zones "j." The CV is really a weighted disutility that allows the analyst to see the total amount of effort, in time and money, being expended for travel purposes.
This calculation was made for each scenario and compared with the base case (Policy Scenario minus Base Case). A lower CV in total or by flow type indicates that people are spending less time and money on travel and therefore are receiving economic benefits. Small and Rosen (1981) show that the disutility log sums from a mode split model can be used to obtain economic welfare measures for travelers. Basically, this measure is similar to consumer surplus, meaning it measures the benefits for the consumer (of travel) above what they are willing to pay, sometimes called net benefits. Consumer surplus is widely used by agencies throughout the world for the evaluation of public projects of all sorts.
The model gives separate benefits for three traveler income classes for the worktrip. This allows the understanding of vertical equity (equity by income classes) for our scenarios. The benefits for the non-work trips probably have the same signs as the benefits for the work trips, so it is believed that this measure can be used as a surrogate for overall traveler equity for the a.m. peak period.
For all other trip purposes, it is possible to get the measure only for all travelers together, so the total benefit for all trips is provided. This measure is then the indicator for aggregate economic welfare in the scenario.
All economic measures are the difference of the policy scenario minus the base case, or the region-wide net benefits of the policy scenario, expressed as a three hour a.m. peak-period value (for an average weekday), including about 1.9 million trips. From past experience, it is believed that the daily welfare measures would rank the same, but would be three-to-four times as great for aggregate welfare. Per trip welfare would be somewhat smaller, due to lower congestion and time costs, in non-peak periods.
The better measure of economic welfare in an urban model would be locator surplus, but this measure in MEPLAN is not theoretically sound, so it is not used. Locator welfare captures changes in household and firm welfare in consuming space and includes changes in traveler welfare in the design of the indicator. There may be cases where the traveler experiences an increase in traveler welfare, but a loss in overall locator welfare. UGB scenarios may fall into this category, because some middle- and upper-income households cannot consume space in the outer zones and, instead have to locate in the middle ring. So, they lose locator welfare but gain traveler welfare through shorter trips. The number of such households is relatively small, however. If the moral position is taken that a UGB is desired regardless of effects on locator surplus, then those losses are not consi