LEAST COST PLANNING: A TOOL FOR METROPOLITAN TRANSPORTATION DECISION MAKING
by Dick Nelson and Don Shakow
This paper describes a new approach to transportation investment planning and a prototype computer model. The model was developed to assist metropolitan transportation planners and decision makers in meeting the new federal and state planning requirements. Based on two decades of experience in electrical energy planning, the model incorporates the principles of least cost planning and full cost accounting. It attempts to promote an efficient search for investment and policy options that enhance regional benefits, while reducing social costs.
An application of the model for the Puget Sound metropolitan region was carried out by comparing a limited number of options. These include a set of study options associated with a proposed light rail system; two commuter rail options; an option featuring the construction of an ambitious bicycle and pedestrian network; a highway construction option; and a series of options emphasizing public and private incentives directed toward reduced single occupancy vehicle use.
Our analysis suggest that a well-coordinated set of demand management measures in conjunction with modest investment in infrastructure may be cost-effective relative to major construction projects. A published version of this paper may be found in the Transportation Research Record, #1499, 1995
Under new federal and state planning requirements, regional planners and decision makers must assess the cost-effectiveness of a broad selection of transportation modes and policy options. Demand management strategies must be given equal consideration to highway and transit capacity enhancements. Non-motorized modes must be allowed to compete on an equal basis with motorized modes. Costs, including indirect social and environmental costs, must be fully accounted for. And planning must recognize the reality of increasingly constrained revenues.
Traditional planning and decision making tools were not designed to accomplish the comprehensive and integrated analysis now required. New tools must be devised that allow a broad comparison of modes and management strategies to identify the most cost-effective alternatives.
Metropolitan planning organizations and transportation decision makers face difficult challenges as they begin to address the requirements of the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA), the Clean Air Act Amendments of 1990 and, in an increasing number of states, legislation directed at management of growth. These challenges include provision for expanding mobility and access needs, management of congestion, integration of transportation investments and land use policies, and mitigation of air quality and other environmental impacts. All of this must be accomplished in the context of fiscal constraints, especially with respect to the available level of federal assistance which continues to decline as a share of all public transportation expenditures.
Planners and decision makers must also adjust to the ISTEA requirement that funding be more flexible. This will probably mean that transit and alternative travel modes, such as ridesharing, walking, bicycling, and telecommuting, will receive a larger share of available revenues. The concept of flexible funding must encompass the reality that traditional solutions involving major capacity investments, even transit investments, which use up scarce resources may make less costly, but more beneficial, solutions impossible to finance.
The ISTEA, in fact, recognizes this imperative by requiring that the metropolitan planning process analyze the cost-effectiveness of alternative investments in meeting transportation demand and the ways transportation needs may be met by using existing transportation facilities more efficiently. The cost analysis requirements for major investments in highways and transit systems are even more specific. A major investment study (MIS) must evaluate the cost-effectiveness of alternative investments or strategies, and it must consider the indirect as well as the direct costs of reasonable alternatives. The MIS must take into account social, economic, and environmental effects, operating efficiencies, land use, economic development, and energy consumption, among other factors.
Clearly, cost must be an object of metropolitan transportation planning in a way that it hasn't been previously. New analytic tools must be devised that allow for cost and benefit comparisons across all feasible alternatives, whether new capacity investments or management strategies. External and indirect costs, as well as direct development and operation costs, must be accounted for.
A number of analysts of urban transportation have voiced a need for a least cost approach that fully accounts for the societal and environmental costs of transportation (1 - 5). Several make reference to the effective use of least/full cost methods in the energy sector.
Recent studies have compared the application of least cost methods to energy planning with its potential application to transportation (6 - 8). These authors, while pointing to differences as well as similarities between energy and transportation, give encouragement to the belief that the least cost methodology, which has been highly refined and widely applied to energy, could be successfully translated to transportation.
Two authors who have contributed to the development of least cost planning as it is practiced in the Pacific Northwest electric power industry, Ed Sheets and Dick Watson, observe a number of important analogies between the domains of energy and transportation which make both fitting candidates for least cost planning (8). Both energy and transportation can benefit greatly from an analytical process by which demand-side resources are given equal consideration in comparison to the construction of facilities and infrastructure. In both cases, a full survey of options would highlight approaches to system design and management which could result in far lower costs than merely expanding capacity.
In addition, energy and transportation both require a full accounting of levelized life-cycle costs including direct capital costs, environmental costs, time costs, and preference costs. Least cost planning mandates this degree of rigor in cost accounting.
Sheets and Watson also suggest that both energy and transportation must deal with an uncertain future. Demand for both transportation services and electricity are subject to unknown changes in technology, behavior, fiscal and economic constraints. Transportation planning could benefit from the flexibility to adapt to uncertainties that has been incorporated into electricity least cost planning.
As noted earlier, a major challenge for transportation planning under the new planning rules is to assess the impact of a broad set of options on mode choice and then to fully assess costs over all mode choices. The aggregate social cost associated with various options can then be computed.
A new approach that can treat alternatives and costs in this way has been outlined by federal highway and transit researchers (9,10). Patrick DeCorla-Souza and Ronald Jensen-Fisher note that an integrated approach has been impeded historically for highways and transit by the use of different measures of effectiveness and cost for each mode. Also, significant costs have been omitted. Transit cost accounting omits the cost of the use of roadways by buses, while highway cost accounting excludes vehicle ownership costs and the costs of parking. External social and environmental costs are excluded in all instances. The authors stress the importance of full-cost accounting to avoid favoring certain modes.
Least cost planning is beginning to attract the attention of transportation decision makers. The Washington State Legislature in 1994 enacted legislation that requires regional transportation planning organizations to use a least cost planning methodology in formulating their regional transportation plans (11). The methodology must identify the most cost-effective facilities, services, and programs.
Similar legislation was considered by the Wisconsin Legislature (12). The Wisconsin bill would have required the state department of transportation to develop a statewide transportation plan which assesses in a least cost integrated manner the benefits and costs of all reasonable options for supplying facilities and managing the demand for the movement of people and goods. Under this legislation, costs and benefits would include public safety and environmental benefits and costs as they apply to the public and to private persons.
If a least/full cost approach is to be useful, it must be more than just a planning tool; it must be capable of assisting decision makers who often must make tough choices in a highly political environment.
This paper describes a new least/full cost tool for application to metropolitan area transportation planning and investment decisions. With appropriate data, the tool would also be useful for sub-area and corridor decision analyses.
FRAMEWORK OF THE LEAST COST PLANNING MODEL (LCPM)
The LCPM (13, 14) is designed to identify a package of transportation options for a study area satisfying the following criteria:
1. The package meets the access needs of the area for a variety of trip purposes and special populations.
2. The package results in a maximum net-reduction in social cost as compared to a no-action base case;
3. Costs are inclusive of private costs, government subsidies, environmental and pecuniary externalities, congestion, and other travel time costs.
4. The range of options surveyed is complete, inclusive of TSM and TDM options, and mindful of various ride sharing, transit, low-powered and non-motorized modes.
5. The optimal package accounts for synergies among options and for the time path over which the options are implemented.
In so far as the model is designed to reduce the cost of meeting transportation needs, it can be regarded as a tool to enhance net social benefit, rather than simply least cost. The term "least cost model" provides terminological continuity with energy prototypes, but suggests a too limited notion of what this model aims to achieve.
A schematic description of the model is shown in Figure 1. The exogenous driver is access which we define as a condition where individuals with the requisite economic means overcome the limitations of space which would otherwise impede the fulfillment of an economic objective. Access, in the context of this definition, is defined in units of potential trips. In a typical instance, access involves movement and takes the form of mobility. The LCPM is nonetheless mindful of situations where access is achieved without resort to mobility. An expanding telecommunications infrastructure facilitates telecommuting or other activity allowing access to the worksite while lessening the mobility requirements associated with traditional commuting.
The LCPM allows for the possibility of achieving access through non-mobility or reduced mobility options. For each work purpose or special population (shown in Figure 1), accessibility is discounted by variables which reflect the future incidence of means to achieve access without resort to mobility. The generalized form of these relations is as follows:
Mobility, as implied in the equation is measured as a vector of trips by trip purpose.
Our objective is to compare costs over the universe of option packages. Costs are typically determined as the product of some measure of transportation activity and the cost per unit of this activity. A crucial question arises in this regard: what is the measure of activity appropriate to a least cost transportation model? Three candidates suggest themselves including (i) the number of trips; (ii) vehicle counts; (iii) person miles of travel (PMT) defined as the product of trips and average trip length; and (iv) vehicle miles of travel (VMT) defined for our purposes as PMT divided by the average occupancy rate per vehicle.
In distinguishing these four (and other) measures, it is necessary to keep in mind the practical distinctions which occur among options. For example, VMT is not adequate as a sole basis for measuring transportation activity since it would fail to distinguish adequately among options which highlight vehicle occupancy rates such as those which require HOV lanes. By contrast, where congestion costs are at issue, traffic volumes and vehicle counts or, in some instances, VMT, are more appropriate. Options which highlight trip reduction in the face of constant access would focus on number of trips. Thus, no single measure of transportation activity is appropriate in a least cost model, but rather a vector of measures.
Trips multiplied by trip length yields PMT in aggregate (across modes). Trip length is a crucial variable in the efficient search for a least cost package in that many long-term transportation options focus on land use regulation. Growth management policies which limit the extent of development and which emphasize mixed-use and higher density living implicitly target trip length reduction as a goal.
Mode choice is a major consideration in defining transportation options. We distinguish among twenty modes including three ridesharing modes for automobiles, vanpooling, taxis, non-motorized modes, and three commercial modes. The complete list is shown in Figure 2. The model allocates PMT by trip purpose among modes utilizing a multinomial logit specification for each distinct trip purpose (or special population). It computes the probability of an individual selecting a given mode for a particular trip purpose. This probability is a function of the following variables:
(i) Direct internal cost of travel per PMT by mode
(ii) Travel time per PMT by mode
(iii) Real income
Once mode choice probabilities are determined, total PMT as computed in the previous module is allocated among modes. Information on occupancy rates per vehicle allows the inference of VMT.
The LCPM is currently being elaborated to allocate trips rather than PMT among modes. This accords with traveler behavior in that mode choice is made on a trip, rather than a PMT basis. In the context of an "end use" framework involving multiple trip purposes and special populations, this requires the elaboration of many more variables.
Estimation and Treatment of Costs--Sources and Methodological Observations
The LCPM is a full cost model which attempts to account for all costs, internal and external, public and private, monetized and non-monetized. (Travel time is an instance of a significant cost which is not monetized.) This objective raises the level of uncertainty associated with model outcomes. While some cost elements are easily computed, others are subject to controversy. The elements subject to the most variability include the various components of environmental cost, land use costs, congestion and travel time cost, and costs related to the achievement of such social objectives as equity. A complete list of cost categories and their categorization is shown in Figure 2.
Some observers have suggested that costs other than those associated with real-life monetary transactions be omitted from policy oriented analyses due to their inherent uncertainty and poorly understood theoretical foundations. While conceding the embrionic nature of work in this area, it would seem reasonable to include as many of these costs as possible. Transportation investment decisions involving the allocation of many billions of dollars of public and private funds are being made continually. The attempt to quantify environmental, social, and temporal costs reflects a need to assess their social importance in comparison to those costs which are quantified explicitly by market mechanisms. In the absence of such quantification, transportation and land use decisions are necessarily biased in favor of those factors which enter an explicit market calculus. Since internalized private costs are not of intrinsic relative social importance they must not, in our view, be alloted a special planning status. Where reasonable persons might disagree over the specific magnitude of costs, social mechanisms can be devised to achieve compromise and consensus within specific planning jurisdictions.
This class of imperfectly understood costs are widely estimated (15 - 20). A recent study sponsored by the Conservation Law Foundation (16) estimates costs per PMT across modes for the components employed within LCPM. Other recent studies by Litman (17) and Miller and Moffat (18) cover similar ground, though estimates differ. The existence of multiple estimates has presented the authors with the problem of choosing among three or more competing estimates. The current data base is based primarily on Litman since he alone attempts to quantify travel time costs which we view as central both to the understanding of mode choice behavior and to the assessment of transportation costs and benefits. Litman has recently (20) compiled a revised version of his earlier estimates and the authors are currently revising the database in order to employ these data, and they intend to update estimates as new studies are published.
The costs for each category in dollars per PMT are summarized in Table 1. These numbers are continually being revised as new studies and theoretical arguments come to light. The numbers shown here reflect only a current "snapshot," rather than a definitive set of results.
A significant issue concerns the reckoning of travel time as a social cost. The Conservation Law Foundation Study omits all non-congestion travel time costs arguing that "when deciding to make a trip, a driver implicitly considers his or her own time costs of the travel" (16, p.12). In general, economists do not account as costs the time required to perform such personal tasks as mowing the lawn and washing the dinner dishes. In the case of travel, however, there are compelling reasons to break with this tradition and to impute a cost to the time required to travel.
Congestion costs which involve delays and inefficiencies in the transport of both persons and goods are clearly a cost to society. Non-congestion costs are also reckoned by consumers as costs. Given a choice among two alternative modes which require significantly different travel times, the traveler is likely to choose the more expeditious mode all other things equal. This is an economic calculation.
It has been claimed that the inability of public transit to increase its proportional share of ridership lies in the common perception (and often the reality!) that transit trips (including access times, wait times, and transfer times) absorb minutes by comparison to automobile trips. Such non-delay related travel time is appropriately factored into the overall calculation of transportation-related social cost. If such costs were omitted, the social cost of, say, land use patterns which encourage home-based work trips of ever-increasing length and travel time is likely to be underestimated.
A condition where one-quarter to one-third of non-sleep, non-work time is devoted to travel for many commuters cannot be regarded with indifference from an economic perspective. The LCPM, for these reasons includes travel time as a cost and attempts to monetize these costs. It taking this position, the study team has followed the general practice (while rejecting the specific estimates) of the British Columbia Ministry of Transportation (19) which accounts travel time costs for commercial/non-commercial drivers and for passengers of various age groupings.
Congestion cost is an important constituent of total social cost. Indeed, the public often perceives the level of congestion as a principal index of how a regional transportation system functions. Moreover, the existence of congestion cost as a classic instance of market failure has been recognized and acknowledged by economists for many years.
Estimating congestion cost presents (at least) two difficulties in the context of a least-cost planning model. A first problem involves translating from minutes to dollars. Since congestion is defined as the cost of delay, it is difficult to quantify. This cost clearly differs from person to person and from situation to situation. Bus riders are likely to represent lower income travelers as compared to SOV riders. Does this imply that the congestion cost of the former (approximated by their lost wages) is lower than that of the latter? In principle, it would be appropriate to stratify this cost by traveler characteristics, but for the present iteration of the model, the study team has employed an estimate from Litman which averages over a range of characteristics.
A second problem involves the estimation of hours of delay for the region. The LCPM attempts this without employing a detailed network or zonal model. We recognize that this is a major output of conventional travel demand modeling and assume that the apparatus of such models could be appropriated and integrated with a least cost model. This integration has been discussed in . In our simplified approach, regional congestion is identified for a finite number of the highest-volume corridors as a function of total vehicular traffic which is estimated from VMT, trip length, and mode choice. Congestion in these limited instances is estimated as a function of these volumes relative to published present and future capacities. These estimates are employed as an index for approximating congestion for the overall study area. Our decision to abstract from the network detail is based on the view that congestion in practice is concentrated in well-defined corridors and that the margin of error associated with the omission of estimates for less-congested corridors does not significantly affect the overall calculation of congestion cost for the study area.
The net social benefits associated with a package of options is defined as savings less capital and operation and maintenance cost. Savings are calculated relative to a base "no action" case. The model first calculates real discounted cost for the base case over a user-defined planning horizon. It then introduces a series of options in succession. Options in some instances may combine several technological, policy, or institutional elements. The net benefit of all options are then ranked. Options are then introduced in succession working down an "option stack" until marginal net benefit is no longer positive.
Disaggregation of Trip Purposes and Modes
The set of trip purposes (special populations), modes, and costs incorporated in the LCPM are shown in Figure 2. We have attempted to be as inclusive as possible. It is necessary to account realistically and thoroughly for the factors which underlay the response of transportation users to a set of options.
The most recent decade has witnessed fundamental changes in the structure and characteristics of families and households. Two earner households are increasingly the norm and single parent households are far less uncommon than in the past. This holds significant implications for transportation choices. Multi-car ownership is often a matter of necessity, while travel patterns are often dictated by child care needs. The LCPM recognizes these trends by specifying child care as a distinct trip purpose.
Child care, moreover, is likely to result in significant trip chaining. Neglect of the phenomenon of chaining is apt to bias transportation planning in favor of public transit options. The choice of transit for chained trips is likely to involve significant travel time costs since the traveler must embark and disembark at least twice. If the chain involves child care as an element, trip quality must be taken into account as well, since parents may be reluctant to carry their child onto a bus or train.
Chaining occurs in many other contexts as well. Travel to work is often combined with shopping. Eating out (perhaps in fast-food drive-ins) on the way home from work and shopping result in multiple links on the chain.
Our analysis of the Puget Sound Transportation Panel, National Personal Transportation Survey and other empirical sources suggest the importance of chained trips as a percentage of all travel. If we define chained trips as trips which occur within 30 minutes of one another and where the destination of the second trip is different from the origin of the first, we observed, for instance, for the first wave of the Puget Sound Panel a total of 8,527 chained trips of a total of population of 31,342.
As noted, we believe that the existence of chained trips is likely to reduce the incidence of non-SOV mode choice. In order to factor chained trips in our model more precisely, we have defined three chained trip purposes: (1) work/shop; (2) work/child care; (3) work/shop/child care.
In listing trip purposes, the importance of special populations must be emphasized. The determinants of transportation choices for elderly or disabled persons are clearly different as compared to young, able-bodied individuals. The LCPM distinguishes special populations in estimating access requirements, trips, trip length, and mode choice. This in turn allows consideration of options which target populations, e.g., on-demand transit for elderly and disabled persons.
The LCPM aims in its choice of modes to be as inclusive as possible. Aside from single occupancy vehicles and the principal public transit modes including bus and (commuter, rapid, and light) rail, the model considers a spectrum of ridesharing modes, taxi, non-motorized (bicycle and pedestrian), and foot ferry (a viable option for the central Puget Sound region). Commercial modes are also considered, distinguishing fleet vehicles and (heavy and light) trucks.
A conceptual ambiguity arises in defining modes and options. A subset of significant options are specifically aimed at enabling particular modes, e.g., construction of a light rail system. In the absence of this class of options a number of modes are inoperative. Mode choice is thus contingent on the governing transportation investment strategy.
MODEL IMPLEMENTATION: OPTIONS AND PORTFOLIOS
Application of the model to the Puget Sound metropolitan area was accomplished by employing a limited set of options (Table 2). The eighteen options will be supplemented as additional data becomes available. The options were selected because they represent a range of supply and demand side measures that are under active discussion in the State of Washington and the central Puget Sound region, or in other states and regions.
The system design, ridership forecasts, and costs for the bus emphasis, light and heavy rail emphasis, and commuter rail options were developed by the Central Puget Sound Regional Transit Authority. Each of the demand-side options required that a cursory design be accomplished and estimates of costs and performance be made. No attempt was made to perfect the design of these options to the extent that would be necessary to propose them for adoption by decision making bodies. The options were sufficiently outlined such that they would be accepted as reasonable by transportation practitioners.
Similarly, obvious linkages and synergies between options were ignored in this test run. A real world application would require the design of comprehensive programs involving these options as elements.
The LCPM searches among a set of distinct transportation options in order to configure a "least cost" package. These options range widely from major transportation investment projects, to enhancements and expansion of existing infrastructure, to TSM and TDM measures which are individually modest in scope, but which offer a significant impact when bundled with other measures.
Accounting for the Political Environment
In many cases, choices among options are not determined on efficiency grounds alone. Political and other factors may require that some options be "forced" into a mix regardless of cost. In certain cases, the public favors the implementation of a given option regardless of cost deeming it a solution to such perceived problems as congestion. In other instances, a highway may be located to serve the needs of a favored constituency; a light rail system might be routed to avoid disrupting a locality which would otherwise delay construction by litigating. The LCPM is designed to allow for these situations, optimizing under the political and other constraints to design a "second best" package of options. Where this involves the inclusion of particular options without subjecting them to a benefit-cost calculus, we term these portfolios.
Portfolios are also useful in assessing the relative economic merits of option bundles currently "on the table." In the central Puget Sound region, public discussion currently centers on the relative merits of a rapid rail, light rail, monorail, bus, TSM, and TDM investment strategies, with other interests emphasizing highways, still others non-motorized--i.e. pedestrian and bicycle infrastructure, and others the need to insure equity among high and low-income families in regards transportation. The LCPM can be applied to various portfolios which are constrained a priori to support these emphases and interests. The model here assumes a number of options which highlight the bias in question and then optimizes over the remaining options noting the synergies (in the form of reduced costs) which occur when certain measures are already in place or are assured in the investment strategy.
As indicated above, the LCPM proceeds first by computing costs associated with a base case in which no new options or measures are adopted, but present trends are assumed to continue over the thirty-year planning period. The social benefit of introducing options singly is then computed by comparing the full life-cycle cost of meeting regional access requirements under the option with the comparable estimate for the base case. This estimate of social benefit is computed net of the life cycle cost associated with implementing the option. Options are then combined into option packages and comparisons are made over option combinations.
Some results associated with a typical application are shown in figures 3 to 8. Figure 3 highlights the distinction between the access requirements of a region and its corresponding mobility requirements. In a growing number of instances--as the relevant infrastructure becomes widespread and affordable--individuals can gain access to work, shopping, social/recreational opportunities, commercial venues, etc. without actually taking trips. Telecommuting is an exemplar of these opportunities which are infrequent in today's environment, but which must be accounted for in planning over a thirty-plus year horizon. In our base case we have provided conservative estimates of this potential, indicated by the divergence between MAXTRIPS--the total access requirement and TRIPS--which discounts for trips not taken as access is achieved through telecommunications or through some other means. Conservative estimates were used because some researchers, principally Niles (22) and Mokhtarian (23), have cautioned that telecommunications may also stimulate travel.
Figure 4 (divided into two frames for the sake of clarity) describes personal miles traveled disaggregated into twenty personal and commercial modes for the base case. The salient result in this instance is that, despite public policy initiatives in favor of transit and ridesharing, single occupancy vehicle (SOV) use dominates personal transportation choices. Since SOV use is very costly from a social perspective, the benefits which accrue to the various transportation options result from the displacement of SOV riders in favor of other, less costly modes.
Figure 5 enumerates the implementation costs associated with the ten options described in Table 1. These life-cycle costs include both capital and operations and maintenance costs over the thirty year planning period. Options associated with the implementation of an ambitious transit proposal (14, 15 and 16) are most costly; those involving the enhancement of existing infrastructure (19, 20, 33, 48, 75) are intermediate in cost; while such TDM measures as 114 and 120 involve the lowest implementation cost.
The model presently has the capability of assessing the impact on travel demand of any single option or group of options. An example is illustrated in Figure 6 where single occupancy vehicle PMT are compared for the base case and for a grade separated rail option, the most ambitious and costly to implement option. While some SOV displacement is indicated, the magnitude of this displacement is relatively small.
Figures 7 and 8 indicate the net benefits associated with the options. Figure 7 shows the breakdown between gross benefit and option cost, while Figure 8 illustrates the net cost.
The model indicates that the preferred combination among the ten options considered would involve significant expansion in bus service with limited light rail (Option 14); highway enhancement (Option 33); expansion of bicycle and pedestrian infrastructure (Option 48); implementation of a traveler information system (Option 75); and a telecommuting tax incentive for employers (Option 120). The results validate the proposition that there is no single dominating "fix" for an impending condition of excess transportation demand and excess social and environmental cost. Rather, a number of undramatic, but well coordinated, synergistic measures should be implemented in combination.
The LCPM is currently being refined and enhanced. The current objective is to proceed from a model prototype to an operational tool which might be adopted for planning. Model improvements are directed to the following areas:
(1) Elaborating the specification of trip purposes, with a special emphasis on chained and linked trips.
(2) Refining the search algorithm to compare decision packages more efficiently. In the Northwest Power Planning Council algorithm on which the current "optimization" is based, options are "stacked" in order of increasing net social cost. They are then implemented in order of the net social benefit generated. It is important to extend the search among option combinations since the Council's method, while perhaps appropriate for energy planning purposes, is quite restrictive in the context of transportation planning.
(3) Providing a more exact accounting of cost synergies associated with option combinations. The model formally allows for this at present, but there is little theoretical and/or empirical basis for actual estimates.
(4) Accounting more accurately for the timing of investments through a truly dynamic optimization procedure.
(6) Accounting more precisely for the direct, indirect, and induced benefits of options due, for example, to increased economic activity.
(7) Accounting for variability in travel demand--population, economic activity, trip length, and mode choice.
(8) Providing a more accurate basis for estimating regional congestion.
(9) Providing a useful analog to the supply and demand curves employed in least cost energy planning.
(10) Refining cost estimates--especially for non-market costs.
CONCLUSIONS AND RECOMMENDATIONS
Our work to date has satisfied us that the objective of identifying transportation options which are of maximum benefit to a metropolitan region is a feasible one. Moreover, the role of such analytic tools as LCPM to assist in the search process is essential. In the absence of such a model, the danger exists that no objective standard can be invoked to compare widely disparate options. In the transportation field, perhaps more so than in other venues, there exists an intensity of conviction among advocates which may dampen objectivity. The sheer magnitude of the expenditures involved in building a new freeway or constructing a rapid or light rail system suggests that a rational standard should be invoked before scarce public and private funds are committed to these very costly projects. Indeed such a cost-based methodology is required by statute under ISTEA. Once funds are committed and spent they cannot be unspent.
Professor Martin Wachs has recently asserted: "...transport policy making is primarily a political exercise, and ... analytic approaches by technical experts are invariably less influential than the pull and tug of influential interest groups" (23). A least cost planning approach attempts to provide a neutral basis which would mediate among such interest groups. Yet, for this to be achieved, a coherent analytic foundation is essential.
Support for this work was provided by The Energy Foundation, The Bullitt Foundation, The Medina Foundation, and the Cascade Bicycle Club. The authors wish also to express appreciation to Professor Jerry Schneider and John Niles whose comments improved this paper.
1. I. L. McHarg. Design With Nature. Conservation Foundation. Washington, D.C. 1969.
2. D. G. Burwell, K. Bartholomew and D. Gordon. Energy and Environmental Research Needs, in Transportation Research Board, Special Report 231, TRB, National Research Council, Washington, D.C., 1991., pp. 83-99.
3. A. Lovins. Least-Cost Transportation Planning. Rocky Mountain Institute, Snowmass, Colorado. Presented at the First International Conference for Auto-Free Cities, May 3, 1991. Published in Solar Mind, June-July 1992, pp. 3 and 20.
4. D. Morris. Getting From Here to There: Building a Rational Transportation System. Institute for Local Self-Reliance, Washington, D.C. Presented at the Twelfth International Pedestrian Conference, Boulder, Colorado, October 3, 1991.
5. M. A. Birk and P.C. Zegras. Moving Toward Integrated Transport Planning--Energy, Environment and Mobility in Four Asian Cities, In Shifting Gears Toward a New Planning Paradigm, International Institute for Energy Efficiency, Washington, D.C., 1993.
6. R. L. Steiner. Least Cost Planning for Transportation? What Can We Learn About Transportation Demand Management from Utility Demand-Side Management, Department of City and Regional Planning, University of California at Berkeley. Submitted to the Transportation Research Board, 1992.
7. E. L. Hillsman. Transportation DSM--Building on Electric Energy Experience, Energy Division, Oak Ridge National Laboratory, February 19, 1993.
8. E. W. Sheets and R. H. Watson. Least Cost Transportation Planning: Lessons From The Northwest Power Planning Council. University of Washington Institute for Public Policy and Management, The Economic Development Council of Seattle & King County, and The Buillitt Foundation, January 1994.
9. P. DeCorla-Souza. Comparing Cost-Effectiveness Across Modes. Presented at the Fourth National Conference on Transportation Planning Applications, May 1993.
10. P. DeCorla-Souza and R. Jensen-Fisher. Comparing Multi-Modal Alternatives in Major Travel Corridors. U.S. Department of Transportation, 1993.
11. Washington State Legislature. Substitute House Bill 1928, 1994.
12. Wisconsin Legislature. Committee Report No.18 to the Legislative Council. Special Committee on Transportation Development, Bill Draft LRB-4429/3, 1993.
13. D. Nelson and D.Shakow. Applying Least Cost Planning to Puget Sound Regional Transportation: Development of the Conceptual Model for a Least Cost Transportation Planning Process, Phase I Report, Institute for Transportation and the Environment, Seattle, February 1994.
14. D. Nelson and D. Shakow, Applying Least Cost Planning to Puget Sound Regional Transportation: Development of a Prototype Least Cost Planning Model and its Initial Application, Phase II Report, Institute for Transportation and the Environment, Seattle, December 1994.
15. M. E. Hanson. Results of Literature Survey and Summary of Findings: The Nature and Magnitude of Social Costs of Urban Roadway Use, Prepared for FHWA by Resource Management Associates, Madison, Wisconsin, July 1992.
16. Apogee Research, Inc. The Costs of Transportation: Final Report. Prepared for the Conservation Law Foundation, Cambridge, Massachusetts, March 1994.
17. T. Litman. Transportation Cost Analysis: Techniques, Estimates and Implications, Draft 2, Victoria, BC, May 13, 1994.
18. P. Miller and J. Moffat. The Price of Mobility: Uncovering the Hidden Costs of Transportation, Natural Resources Defense Council, 1993.
19. W.G. Waters. Value of Time Savings for the Economic Evaluation of Highway Investments in British Columbia. B.C. Ministry of Transportation and Highways, Victoria, March 1992.
20. T. Litman. Transportation Cost Analysis: Techniques, Estimates and Implications, Victoria, B.C., October, 1994.
21. D. Nelson and D. Shakow. Least Cost Planning Model: Interface with PSRC 4-step Model, Memorandum to K. Cushman, Puget Sound Regional Council, March 21, 1994.
22. J. Niles. Beyond Telecommuting: A New Paradigm for the Effect of Telecommunications on Travel, DOE/ER-0626, U.S. Department of Energy, Office of Energy Research and Office of Scientific Computing, Washington, D.C., September 1994.
23. P. L. Mokhtarian. A Typology of Relationships Between Telecommunications and Transportation. Transportation Research A, 24A(3), pp.231-242, 1990.
24. M. Wachs. Transportation Research A, 27A(4), p.337, 1993.
Return to the Least-Cost Transportation Planning index page
Last modified: May 3, 1996