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42 6.1 Conclusions This study resulted in the following conclusions: 1. Based on the broad review of previous studies and existing tools, this research addresses a growing need of state and local governments for resources and tools to assist in applying for state and federal program grants to address grade crossing concerns beyond just safety improvements. 2. Existing literature about when to invest in roadârail grade separations suggests that there may be better returns from expenditures made for improving a large number of existing grade crossings, rather than replacing a select few with grade separations. This factor should also be considered before prioritizing grade crossings for grade separation. 3. The costâbenefit analysis reviewed in the literature conÂ siders only costs that can be easily monetized. Social costs due to environmental impacts and noise impacts are not considered in the majority of the reviewed studies. This finding was also collaborated with the responses from state and local officials when asked about costâbenefit data sources. 4. MCA. Determining the weights to be used for each criterion to calculate a scoring for each crossing appears dependent on organizational and/or regional considerations. 5. Much of the literature emphasizes regional conditions when prioritizing the grade crossings. Determining broad criteria for use in corridors for a national study is a more challenging task. This factor is consistent with survey findings of motorist complaints as the primary motivation in making grade crossing improvements. 6. Little has been mentioned in the literature about the change in the economic value of land after the comÂ pletion of the grade separation project. 7. GradeDec, the FRA application for highwayârail grade crossing investment analysis, was developed as part of the NextÂGeneration HighÂSpeed Rail Program. None of the state or local decisionÂmakers surveyed as part of this research effort mentioned GradeDec as a tool that they use in making grade crossing investment decisions. 8. More than oneÂthird of the survey respondents indicated issues with data quality (e.g., FRA crossing inventory) or lack of accurate data (e.g., trains per day). The research team also spent considerable programming time addressÂ ing the inconsistency of data in the FRA database. Two specific items were the most time consuming to address in programming the RCAT autoÂpopulate features using FRA data: a. When the XML data is received, the number of fields for each record is not the same from state to state, as it appears that a builtÂin function eliminated fields with null values. b. The FRA database descriptor fields for cities and counties is not populated and requires the download of additional tables and a lookup process to populate the spreadsheet. 9. While survey respondents weighed safety and accident data as the most important for making grade separation decisions, current and future delays to motorists also influenced project decisions. 10. OneÂthird of respondents indicated that the lack of funding for grade separations was an issue. 11. Community livability is an evolving field in transportaÂ tion planning. While the livability module developed for the RCAT is a start in addressing how atÂgrade roadârail crossings impact communities, more research is needed in this area. 12. Additional siteÂspecific variables should be used in the USDOT accident prediction model to improve its outcome. C H A P T E R 6 Conclusions and Recommendations
43 6.2 Recommendations for RCAT Users The following recommendations were developed from the study efforts: 1. The results of the model will only be as good as the effort applied to defining the corridor to be analyzed, collecting the data, reviewing the assigned weights, and analyzing the final results. 2. Users should take time at the beginning of the analysis to define the corridor as specifically as possible. 3. Fully review the user manual, including tips and tricks before starting the model. 4. Collect as much data as time permits on the corridor. This action will save time in preparing the model and producing appropriate results. 5. Ensure that the assigned user/analyst using the model has knowledge of the area or has access to someone with knowledge of the local area to help determine inputs for the nonÂqualitative inputs requiring observations, such as land use, economic loss, and environmental justice. 6. Review the data collected from the FRA database and update as appropriate. Data elements, such as AADT and percentage of truck traffic, are not up to date. It is important to take time to review the data factors that are populated from the FRA Crossing Inventory Database and to update with the most recent information from oneâs own agency. 7. Review agency priorities and assign the userÂdefined module weights to reflect the agencyâs specific investment priorities. Users may want to run the model multiple times to test the sensitivity of the weighting scheme they have selected and adjust accordingly. 8. For users ranking different crossings on the basis of safety, the safety score should be used instead of the values from the USDOT accident prediction model. 6.3 Future Activities and Research 1. Data quality associated with the FRA HighwayâRail Crossing Inventory Data was an issue heard repeatedly from state and local agencies during the survey efforts. These agencies are key players in decisionÂmaking related to grade crossing investments. Data consistency was also an issue that took considerable programming time to address when the RCAT was being built so that users could download crossing data directly from FRA. A pooled fund effort similar to efforts undertaken by states to improve highway safety data reporting would be beneficial. 2. As noted in the conclusions, community livability is an evolving field in transportation planning. Future enhanceÂ ments to the RCAT would benefit from a best practices synthesis on this topic. 3. In this study, the USDOT accident prediction model was used because of its wide acceptance by practitioners. Other model forms (such as negative binomial or zeroÂinflated negative binomial models) to predict accident counts could be explored in the future.