Study of Iowa's Current Road Use Tax Funds (RUTF) and Future Road Maintenance and Construction Needs, 2006

The 81st General Assembly of the Iowa Legislature, in Section 85 of House File 868, required the Iowa Department of Transportation (DOT) to conduct a study of current Road Use Tax Fund (RUTF)revenues, and projected roadway construction and maintenance needs

It's about time: Investing in transportation to keep Texas economically competitive - Appendices

APPENDIX A : PAVEMENT QUALITY (Zhanmin Zhang, Michael R. Murphy, Robert Harrison), 7 pages -- APPENDIX B : BRIDGE QUALITY (Jose Weissmann, Angela J. Weissmann), 6 pages -- APPENDIX C : URBAN TRAFFIC CONGESTION (Tim Lomax, David Schrank), 32 pages -- APPENDIX D: RURAL CORRIDORS (Tim Lomax, David Schrank), 6 pages -- APPENDIX E: ADDITIONAL REVENUE SOURCE OPTIONS FOR PAVEMENT AND BRIDGE MAINTENANCE (Mike Murphy, Seokho Chi, Randy Machemehl, Khali Persad, Robert Harrison, Zhanmin Zhang), 81 pages -- APPENDIX F: FUNDING TRANSPORTATION IMPROVEMENTS (David Ellis, Brianne Glover, Nick Norboge, Wally Crittenden), 19 pages -- APPENDIX G: ESTIMATING VEHICLE OPERATING COSTS AND PAVEMENT DETERIORATION (by Robert Harrison), 4 page

Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks

The accurate prediction of forest road pavement performance is important for efficient management of surface transportation infrastructure and achieves significant savings through timely intervention and accurate planning. The aim of this paper was to introduce a methodology for developing accurate pavement deterioration models to be used primarily for the management of the forest road infrastructure. For this purpose, 19 explanatory and three corresponding response variables were measured in 185 segments of 50 km forest roads. Logistic regression (LR) and artificial neural networks (ANNs) were used to predict forest road pavement deterioration, Pothole, rutting and protrusion, as a function of pavement condition, environmental factors, traffic and road qualify. The results showed ANNs and LR models could classify from 82% to 89% of the current pavement condition correctly. According to the results, LR model and ANNs predicted rutting, pothole and protrusion with 83.5%, 83.00% and 81.75%, 88.65% and 85.20%, 80.00% accuracy. Equivalent single axle load (ESAL), date of repair, thickness of pavement and slope were identified as most significant explanatory variables. Receiver Operating Characteristic Curve (ROC) showed that the results obtained by ANNs and logistic regression are close to each other

PAVEMENT MANAGEMENT ANALYSIS USING RONET: CASE OF THE FREE STATE PROVINCE

Published ThesisCurrently, more than 40% of roads in the Free State are in very poor condition as a result of underfunding, lack of technical capacity, lack of maintenance, increased vehicle tyre pressure, increased traffic volumes, and more. Moreover, it was discovered that local municipalities do not have a tool to strategize their maintenance expenditure. This research study was undertaken in an attempt to address this challenge, and with the intention that RONET be introduced to the Free State road network at some stage. This would be done in an effort to improve the conditions of this road network by addressing maintenance and rehabilitation backlogs. The study was limited to roads in the Free State province as the data was available to the researcher. This research study presents the application of the World Bank’s model, the Road Network Evaluation Tools (RONET), to perform a strategic network level analysis of the road network of the Free State province. As already mentioned, the condition of this network deteriorated considerably during the early 2000s due to under-financing of operations and maintenance, increased vehicle tyre pressure, increased traffic volumes, etc. In recent years, financing for the road sector has gradually increased, focusing on the dangerous and highly trafficked sections of the road network. However, the overall budget for the road sector remains inadequate to maintain the entire road network in a stable condition (Free State Department of Roads, 2002). The primary goals of RONET are to design and obtain an optimum maintenance and rehabilitation strategy and related budget, estimate the impact of different funding levels on the future quality and estimate the economic consequences of budget constraints. The application of the RONET model will lead to an optimal maintenance and rehabilitation strategy with a good balance between rehabilitation, periodic and recurrent maintenance. The implementation of an optimal maintenance and rehabilitation strategy would cause major improvement compared to the current condition of the network. Implementation of higher than optimal maintenance and rehabilitation strategies would lead to higher costs and subsequently lower net benefits, while implementation of lower than optimal maintenance and rehabilitation standards would lead to considerably worse network conditions for slightly lower agency costs. In other words, even minor budget constraints would result in considerably higher total road transport costs, impacting on the province’s economy. The undertaking of appropriate road maintenance of even a small road network is difficult without some form of road maintenance management plan, hence the study to investigate RONET in an attempt to enable road authorities to formulate a feasible business plan to curb the maintenance and rehabilitation backlog. Decision makers can use the Road Network Evaluation Tools model to appreciate the current state of the network, determine its relevant importance to the economy and compute a set of monitoring indicators to assess the performance of the road network. RONET assesses the performance of the road network, over time, under different road maintenance standards. It determines, for instance, the minimum cost of sustaining the network in its current condition and estimates the savings or the costs to the economy for maintaining the network at different levels of service. RONET further determines the allocation of expenditure among routine maintenance, periodic maintenance and rehabilitation road works. Moreover, it determines the optimal maintenance standard for each road class (highest Net Present Value) and compares it with the current (budget constraints) and other maintenance standards. Lastly, it determines the “funding gap”, which is defined as the difference between current maintenance spending and required maintenance spending (to maintain the network at a given level of service), and the effect of under-spending on increased transport costs. The new Road User Revenues module estimates the level of road user charges required (e.g. fuel levy.) The application of RONET will lead to an optimal Maintenance and Rehabilitation (M & R) strategy with a good balance between rehabilitation, periodic and routine maintenance. Implementation of the “Optimal” maintenance and rehabilitation strategy will result in an improvement to the current condition of the network. Implementing RONET will alleviate the backlog and bad conditions of the Free State road network, which was caused by the lack and/or shortage of experienced technical staff in government. RONET will also be used to assess the current characteristics of the road network and its future performance depending on different levels of network funding. The future performance of the road network under different funding levels will also be simulated

Spatial analysis of passenger vehicle use and ownership and its impact on the sustainability of highway infrastructure funding

Across the United States, the sustainability of highway funding is at risk due to increasing need and uncertainty in the factors that drive revenue. Past studies on highway funding sustainability have identified that the root cause of changing highway revenue are the shifts in social demographics and economic characteristics. Unfortunately, from the revenue perspective (the focus of this dissertation), the ability of previous research to account for these factors has been rather limited in two ways; first, the inability to accurately assess current regional vehicle use (a typical prerequisite for statistical modeling of highway revenues) due to difficulties associated with collecting data for local roads; second, the inability to directly account for the spatial dependence and heterogeneity that inherently characterize vehicle use, vehicle ownership, and socioeconomic attributes. ^ In addressing these issues, this dissertation focuses on revenue uncertainty and investigates the socioeconomic factors that influence passenger vehicle use and ownership and, by extension, the revenue generated from this class of vehicles. Spatial econometric models were used to capture the complex spatial trends that characterize the relationship between the influential factors and vehicle use and ownership. The models were used to estimate the impact of long-term socioeconomic changes on highway revenue from passenger vehicles. ^ This dissertation developed a unified framework incorporating spatial econometric modeling of regional vehicle use and ownership. This dissertation showed that vehicle use and ownership exhibit spatial dependence and heterogeneity which is caused by the influence of neighboring regions and unobserved spatial factors. Therefore, the research accounted duly for spatial heterogeneity and dependence, resulting in a more accurate and unbiased estimation. Also, the research yielded results suggesting that vehicle use and ownership are a function of the characteristics of a region as well as it neighbors. ^ The unified framework includes a robust methodology to estimate the current vehicle miles traveled (VMT) for all roads within a geographic region. The methodological approach uses spatial interpolation to impute unknown road segment values, overcoming an issue that typically impairs the traditional link-specific approach for estimating VMT. ^ This dissertation determines that, in order for the current level of funding from state gas tax revenue to be sustainable, the gas tax would have to be annually increased between 2.59% to 3.41%, depending on the forecast socioeconomic conditions. This annual increase in gas tax would allow agencies to recoup the effective fuel tax losses due changing vehicle use and ownership, inflation, and increased fuel economies. Unlike revenue from fuel taxes, revenue from passenger vehicle VMT fees is not susceptible to changing vehicle fuel efficiencies. To ensure funding sustainability, an annual VMT fee increase between 1.66% to 2.48%, depending on the socioeconomic conditions, is required; this would account for fluctuations in vehicle use and counteract the impact of inflation. The dissertation also determined that, in the likely event that a state is unable to collect VMT fees from out-of-state drivers (vehicles registered outside of the state), the fees would need to be increased by 12% to ensure funding sustainability

A methodological framework for quantifying impacts of truck traffic on regional network with implications to transport policy

Increased global trade has promoted the importance of shipping industry and the introduction of mega-ships has created an opportunity to be more cost-effective. Because of this, the expected change in freight transportation influences the operating regimes and schedules at the port terminals. Trucks being the predominant mode of transportation used to carry the freight transport, there is a growing concern about the impact of trucks in the region. The problems are further expected to grow as the improvements to resolve them are hindered by funding shortfalls. Public agencies are therefore involved in developing comprehensive state freight plans that outline immediate and long-range plans for freight-related transportation improvements. However, for states to develop and implement investment policies that can adequately address challenges, there is a need for a policy framework that can evaluate the impact of freight. The lack of the framework makes it difficult for state/metropolitan planning organizations to implement investment strategies in the best possible way. The proposed framework in the dissertation tries to fill the gap by developing a methodological framework, which can help agencies to evaluate multiple policies and their impact on local communities. Additionally, the framework can ascertain the magnitude of impacts that the infrastructure or policy in conjunction with the change in truck traffic might have on a regional level. The developed framework thus can help decision makers to prioritize policies that will benefit both public and freight transportation needs. Three demand models are used in the framework, which is built on the principle of behavioral route choice and mode-choice assignment problem. The outputs from the demand models are further used to quantify the impact in terms of cost-benefit analysis. The dissertation includes a real-world case study demonstrating how the framework can be used to evaluate alternative policies and its impact on a regional level. To this end, the developed framework in the dissertation addresses the research questions to present stakeholder\u27s complex implications that policy can have on the region. It also answers the question of how much the change in truck demand affects the region regarding monetary costs such as safety, congestion, environment, and pavement damage. The research further provides an insight of the change in travel behavior as a result of policy decision and its effect on communities

Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks

The accurate prediction of forest road pavement performance is important for efficient management of surface transportation infrastructure and achieves significant savings through timely intervention and accurate planning. The aim of this paper was to introduce a methodology for developing accurate pavement deterioration models to be used primarily for the management of the forest road infrastructure. For this purpose, 19 explanatory and three corresponding response variables were measured in 185 segments of 50 km forest roads. Logistic regression (LR) and artificial neural networks (ANNs) were used to predict forest road pavement deterioration, Pothole, rutting and protrusion, as a function of pavement condition, environmental factors, traffic and road qualify. The results showed ANNs and LR models could classify from 82% to 89% of the current pavement condition correctly. According to the results, LR model and ANNs predicted rutting, pothole and protrusion with 83.5%, 83.00% and 81.75%, 88.65% and 85.20%, 80.00% accuracy. Equivalent single axle load (ESAL), date of repair, thickness of pavement and slope were identified as most significant explanatory variables. Receiver Operating Characteristic Curve (ROC) showed that the results obtained by ANNs and logistic regression are close to each other