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Jack Faucett Associates, luc. Final Report March 1997 4. CONCLUSIONS Ad SUGGESTED SEARCH A major conclusion reached in this study is that the problem of classification of data should be sorted between two uses: A. Data for internal administrative purposes held by each transportation planning organization. Data of external significance for planning organizations that needs to be shared in an integrated and standardized mode. The first group of data means that the vast volume of data held by the planning organizations is under their internal control and subject to their separate and innovative organizational procedures. However, it offers an opportunity to each organization to learn efficient methods as practiced by other organizations, to accommodate innovative practices oftheir own and to provide data to support the unique transportation infrastructure and modal operations in their area. One opportunity is to assess their internal and external needs by applying the Business Model which identifies their principal missions and avoids duplication of data among its planning departments. The second group of data to be shared by planning organizations means that the efficiency of sharing the data can be greatly enhanced by agreements on what is essential to be exchanged and on the definitions and standardized measures to be employed. The agreements would also need to specify the scope of the organizations for exchange conformity (among contiguous areas of planning or extending to state and national domains). Further research is appropriate to work with six or eight different size planning organizations including at least three that are state DOTs and the remainder are Metropolitan Planning Organizations. The purpose would be to investigate in detail what integrated and standardized data would be agreed to be exchanged among the planning organizations and to what extent state DOTs would agree to establish a Task Force as outlined in this report. A research task could involve an intensive investigation with a selected sample of Metropolitan Planning Organizations of how they assign priorities to specific data collections and to assess the resource savings that may be possible by reducing the duplication in data collected by integrating these data collection activities. This would involve extensive review of data files and interviews with professionals in each department to obtain their suggestions on how cooperation among the users of the data could lead to integrated data collections with significant resource savings. An extensive report on these findings should be synthesized and reviewed with the top planners in each planning organization. Their conclusions and advice on the feasibility and valise of limiter! or extensive reorganization of the data files would be presented in the final report. , . i. .. ~ _ ~ . NCHRP-Multimodal Transportation 132 Project8-32(5) Planning Data

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JackFaucettAssociates,lnc. FinalReport Other Conclusions ifIarch 1997 The strategic assessment of data needs should be targeted on the most important functions of the transportation planning agency as stated or implied in its mission goals and objectives. Data needs should be assessed based on the information needs to accomplish its goals and objectives. The relative economic value of the data sets depends on the costs of collecting the data sets and how much useful information can be developed from each set, and its direct relevance to the mission objectives. Priorities in data development depend upon whether its application is to short-term objectives, or to long-term objectives. The relative importance of information depends upon the relative importance of mission objectives, and how much the information can contribute to achieving the objectives. Since individual preferences differ on the value of various objectives, end the cost tradeoffs in meeting the objectives, the relative values accorded to each depend upon consensus of public opinions and political options. Examples of objectives with preferential value differences include: 1. economic benefits of improving transportation efficiency 2. social and aesthetic benefits of alleviating community disruption from transportation infrastructure construction 3. health benefits from reducing air pollutants. Studies that attempt to measure the economic tradeoffs among these objectives can be used to inform the public of information gained that can be factored into public decisions that result in resource allocations among options in the construction of transportation infrastructure. In the case of passenger transportation, individuals are to a considerable extent aware of these tradeoffs and exert political pressure to do this research and provide useful results for their evaluation. In the case of goods movement, individuals only rarely experience the logistical problems associated with intermodal freight transportation. Thus, they have not exerted much pressure on transportation planning agencies to improve the efficiency in intermodal goods movement and for the public to understand the economic tradeoffs in public policy formulation affecting freight transport. Need for Data on Freight Transportation Efficiency Improvements in intermodal freight transport productivity have a large payoffpotential in reductions in delivered costs of freight. Intercitv freight across all modes accounts for over $300 billion in annual freight costs. The value of freight transported over S00 miles amounts to over $1750 billion NCHRP Multimodal Transportation 133 Planning Data Project 8-32~5)

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Jack Faucett Associates, Inc. Final Report March 1997 annually.48 Thus efficiency increases in intercity freight transport of a modest 1 0 percent could yield $30 billion in annual savings. A productivity increase in goods production (at least traveling distances eligible for intermodal freight connections) of 1 percent could save at least $ 1 7.5 billion annually (representing the indirect impacts of transport efficiency improvements on manufacturing productivity). Over a 6-year period these two sources of savings would amount to nearly $300 billion. This would represent more then the funds to be budgeted for the renewal of ISTEA this year, only part of which would be invested in intermodal freight transport improvements. These illustrative numbers point up the potential value of the development of data and information on the ways to improve freight transport efficiency and the resulting payoffs from appropriate investments in infrastructure and methods. It would be important to quantify the estimates of the potential payoff in investments in freight transport efficiency. Several research avenues and topics are suggested as follows: 1. Further research on the indirect effects of efficiency gains in freight transport as measured by industrial productivity. Current estimates vary widely and an in-depth analysis based on scientifically selected sample case studies would be appropriate. 2. Research on impediments to interstate trade and development of estimates of their costs in losses in transport efficiency. Examples include incompatible regulations among states and inefficiencies in taxation systems and procedures. 4. 5. Identification of lack of reasonable carrier competition in selected transport links, and the economic costs and development of economically efficient approaches to alleviating the problems. Examples include lack of third party trackage rights on rail segments and lack of foreign competition in coastal water freight (the Jones Act). Measurement of the costs of delays in freight transfer at intermodal connections and the benefit/costs of changes in operating procedures (EDI, flexible work rules at ports, improved technology for loading/unloading). Measurement of the impacts of dedicated truck throughways in large urban areas on congestion problems of all traffic. These are only examples of further needs for data and information for planning purposes for improvements in freight transportation and intermodal freight connections. 48Eno Transportation Foundation, Inc., Transoortation in America 1996 n. 40 NCNRP Mllllrimodal Transportation 134 Planning Data Project 8-32~5)

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