Pavement and Bridge Management and Maintenance

Recent increases in motorcycle fatalities, in the rate of motorcycle fatalities per vehicle mile of travel (VMT), and in the ratio of estimated motorcycle VMT to motorcycle registrations have resulted in greater attention to motorcycle safety and to related motorcycle statistics. Technologies that are currently used for collecting counts of vehicles by vehicle class or that have been proposed for use in counting motorcycles are reviewed. Available information about the quality of motorcycle counts produced by these technologies is discussed. Several recommendations for improving the quality of motorcycle counts and count-based VMT estimates for Class 1 vehicles are included. Also discussed are some factors that are likely to have contributed to the observed divergence between the growth rates of motorcycle registrations and those of motorcycle VMT estimates. The use of registration or insurance company data, or both, for estimating VMT of these vehicles without the use of count data is briefly discussed.

A study of interstate motor carrier vehicle miles of travel

An integrated land-use�transportation model was used to simulate the impact that an urban growth boundary would have on vehicle miles of travel in a small metropolitan community over a forty-year modeling horizon. The results of the modeling effort indicate that even in an area with low to moderate population growth, there is the potential to reduce vehicle miles of travel per person by as much as 25% from a business-as-usual scenario over a forty-year period. The reduction would result primarily from a shift of driving alone to carpooling or walking for many trips. A scenario in which growth is concentrated in a single urban core would also benefit from shorter average trip lengths; a scenario with multiple village centers would not have shorter trip lengths, but would still have significant improvements in total vehicle miles of travel.

While voluminous empirical studies have examined the impact of land use on travel behavior, few have relied on longitudinal data and an analytical approach. With data from two activity travel surveys (1997 and 2006) conducted in Austin, Texas, this paper develops a longitudinal multilevel model for estimating the change in the effect of land use on vehicle miles of travel (VMT) over time and the long-range land use effect on VMT reduction. Results suggest that the influences of land use mixture and street density on VMT would vary between 2 years. The effects of VMT reduction on land use policies by raising population and street densities are salient in the short run but insignificant in the long run, whereas those of mixed-use policies are even larger in the long run. These findings validate the importance of longitudinal data and analysis in land use–travel studies and suggest that the short-run elasticity of land use derived from cross-sectional analyses may be inappropriate for assessing the long-run effect of land use–based mobility strategies for reducing VMT.

This report examines the sensitivity of annual vehicle miles of travel (VMT) of light-duty vehicles to the price of gasoline, commonly referred to as the elasticity of demand for VMT to the price of gasoline; the fuel-economy-related rebound effect is generally assumed to be of the same magnitude as the VMT elasticity of gas price or driving cost. We use detailed odometer readings from over 30 million vehicles in four urban areas of Texas, over a six-year period. We account for economic conditions over this period, as well as vehicle age. Following the literature we include fixed effects by vehicle make and individual vehicle, as well as the effect of adding an instrument to predict monthly gasoline price independent of any influences of demand for gasoline on its price.

Transportation planning has historically relied on statistical models to analyze travel patterns across space and time. Recently, an urgency has developed in the United States to address outdated policies and approaches to infrastructure planning, design, and construction. Policymakers at the federal, state, and local levels are expressing greater interest in promoting and funding sustainable transportation infrastructure systems to reduce the damaging effects of pollutive emissions. Consequently, there is a growing trend of local agencies transitioning away from the traditional level-of-service measures to vehicle miles of travel (VMT) measures. However, planners are finding it difficult to leverage their investments in their regional travel demand network models and datasets in the transition. This paper evaluates the applicability of VMT forecasting and impact assessment using the current travel demand model for Dane County, Wisconsin. The main finding is that exploratory spatial data analysis of the derived data uncovered statistically significant spatial relationships and interactions that planners cannot sufficiently visualize using other methods. Planners can apply these techniques to identify places where focused VMT remediation measures for sustainable networks and environments can be most cost-effective.

Relationships between highway capacity and induced vehicle travel

Historically, the State of California assessed the environmental impacts of proposed developments based on how it was projected to affect an area’s level of service (LOS). However, as LOS focused on traffic delays, many agencies simply widened roads, which was an ineffective way to reduce greenhouse gas emissions (GHGs). With the passage of Senate Bill (SB)743 in 2013, LOS was replaced by Vehicle Miles Traveled (VMT) as a more appropriate metric by which to gauge the environmental impacts of proposed development. Additionally, SB 743 presented an opportunity for off-site VMT mitigation strategies through banking and exchanges– allowing multiple development projects to fund a variety of strategies to reduce VMT elsewhere in the city or region. While the shift from LOS to VMT has generally been lauded, concerns remain about how to apply SB 743 effectively and equitably. This study aimed to: 1) understand how local governments are addressing this shift toward VMT while ensuring equity, including its approaches to off-site VMT mitigation; and 2) evaluate the various built environment factors that impact VMT, which should be considered by local governments, using both qualitative and quantitative research designs. The study posited that both micro and macro level aspects of the built environment needed to be considered when evaluating the impacts of proposed development on VMT, not only to ensure higher accuracy VMT models, but also because of the potential equity implications of off-site mitigation measures. Using multiple linear regression, the study shows that macroscale built environment features such as land use, density, housing, and employment access have a statistically significant impact on reducing VMT (35%), along with transit access (15%), microscale features such as sidewalks, benches, and trees (13%), and income (6%). More notably, a four-way interaction was detected, indicating that VMT is dependent on the combination of macro and micro level built environment features, public transit access, and income. Additionally, qualitative interviews indicate that transportation practitioners deal with three types of challenges in the transition to VMT impact mitigation: the lack of reliable, standardized VMT measure and evaluation tools; the lack of a strong legal foundation for VMT as a component of the California Environmental Quality Act (CEQA); and the challenge of distributing off-site VMT mitigation equitably. Overall, findings support a nuanced, multi-factor understanding of the context in which new developments are being proposed, both in terms of modeling VMT, but also when considering whether offsite mitigation would be appropriate. The results of this study can help California ensure equitable VMT mitigation that better aligns with the state’s climate goals.

Previous literature has shown mixed findings on whether bike or e-scooter share programs, collectively called shared micromobility, play a role in reducing vehicle travel. In this study, we focused on two types of shared micromobility (i.e., bikes—both regular and e-bikes—and e-scooters). We examined their impact on vehicle travel in urbanized areas in the United States between 2012 and 2019. Employing the difference-in-differences (DID) approach, we quantified the collective effect of shared micromobility on daily vehicle miles of travel (VMT) per capita at the urbanized area level. Our findings suggest that bike shares could reduce VMT while e-scooter share alone might not have a significant impact on lowering vehicle travel. Our results also indicate that there may be a synergistic effect of bike and scooter shares in VMT reduction when both types of shared micromobility are available in an urbanized area. Our findings also demonstrate regional variations in the impact of shared micromobility on vehicle travel.

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.

The Taiwan Bridge Management System (TBMS) has been online for 15 years and has an inventory of 33,275 bridges, including all kinds of bridges and culverts in Taiwan. Currently, the number of fields in all tables in the databases of TBMS is around 6,500 with more than 3 million data records in its databases. Meanwhile, bridge inspection data and maintenance data are increasing at a speed of 15,000 records annually. Thus, the TBMS databases are deemed as “Big Data.” There are more than 9,500 bridges that are over 20 years old with another 7,200 bridge having unknown built years in the TBMS. The bridges in Taiwan have stepped into the stage where maintenance is crucial and frequently required. Therefore, this research aims at analysing the database in the TBMS using “Big Data” approach for determining maintenance strategies for these bridges. This paper describes results of the first year's research efforts. Relevant literature in bridge maintenance, prioritization, and life-cycle bridge management were firstly reviewed. Concepts, theories, techniques, and available software for analysing “Big Data” were also intensively examined and summarized. In next year, functions will be programmed and applied to the TBMS databases using appropriate “Big Data” software to obtain useful information in bridge deterioration, repair methods, and maintenance costs.

Problem: Congestion pricing and land use planning have been proposed as two promising strategies to reduce the externalities associated with driving, including traffic congestion, air pollution, and greenhouse gas emissions. However, they are often viewed by their proponents as substitutive instead of complementary to each other. Purpose: Using data from a pilot mileage fee program run in Portland, OR, we explored whether congestion pricing and land use planning were mutually supportive in terms of vehicle miles traveled (VMT) reduction. We examined whether effective land use planning could reinforce the benefit of congestion pricing, and whether congestion pricing could strengthen the role of land use planning in encouraging travelers to reduce driving. Methods: VMT data were collected over 10 months from 130 households, which were divided into two groups: those who paid a mileage charge with rates that varied by congestion level (i.e., congestion pricing) and those who paid a mileage charge with a flat structure. Using regression models to compare the two groups, we tested the effect of congestion pricing on VMT reduction across different land use patterns, and the effect of land use on VMT reduction with and without congestion pricing. Results and conclusions: With congestion pricing, the VMT reduction is greater in traditional (dense and mixed-use) neighborhoods than in suburban (single-use, low-density) neighborhoods, probably because of the availability of travel alternatives in the former. Under the same land use pattern, land use attributes explain more variance of household VMT when congestion pricing is implemented, suggesting that this form of mileage fee could make land use planning a more effective mechanism to reduce VMT. In summary, land use planning and congestion pricing appear to be mutually supportive. Takeaway for practice: For policymakers considering mileage pricing, land use planning affects not only the economic viability but also the political feasibility of a pricing scheme. For urban planners, congestion pricing provides both opportunities and challenges to crafting land use policies that will reduce VMT. For example, a pricing zone that overlaps with dense, mixed-use and transit-accessible development, can reinforce the benefits of these development patterns and encourage greater behavioral changes. Research support: This project was supported by the Mineta Transportation Institute, where the authors are research associates.

The objectives of the present study are to explore potential changes expected in highway infrastructure expenditures and revenues, and develop a robust framework that will facilitate highway cost allocation when connected and autonomous vehicles (CAVs) are in operation. The scope of this study covered both federal and state expenditures and revenue sources. The highway cost allocation elements covered in the paper include vehicle miles of travel, highway expenditures highway revenues, and equity analysis. In the day of CAVs, significant changes are expected in all these elements, and these changes are incorporated into the proposed highway cost allocation framework. Pavement-related expenditures would increase due to the addition of CAV-related infrastructure integrated into the pavement infrastructure to ensure it is an intelligent pavement. Bridge-related expenditures are expected to increase due to the infrastructure technologies on the bridges making them smart bridges, and the design loads of these bridges may have to be increased due to potential platooning on long span bridges. It is expected that many of the CAVs on the road will be electric vehicles. All these changes are incorporated into the proposed highway cost allocation framework.

Highway revenues both at the federal and state levels have failed to keep up with expected investments required for infrastructure preservation and improvement. The reasons for this trend include the increasing fuel efficiency of vehicles, slowing of the growth in vehicle-miles of travel, and the erosion of the purchasing power of the dollar due to inflation. Past studies on the issue of highway revenue forecasting for Indiana highways were conducted under different economic conditions than what exists today. The present study updates the revenue projections of particularly with the recognition of new CAFE Standards. The present study also updates the equations for estimating vehicle miles of travel. Impacts of alternative options for changing the fuel tax rate structure are also investigated. The present study predicted fuel tax revenues from 2012 to 2025 under the existing fuel tax rate structure and also considered possible options for changes in fuel tax rates. Fuel tax revenue from existing rate structure indicated a continuous annual decrease from 2012 to 2025 by 2.96% to 3.49% in real terms. Adopting one of the four fuel tax rate modifications would provide additional short- term revenue for a variable number of years. A 1-cent increase would offset the decline in the total fuel tax revenue only for a year after which it will continually decline every year. A 3-cent increase would provide a substantial increase in revenue in the short term but will continually decline, however, the 2025 revenue from 3-cent increase would be a little higher than the 2012 revenue level. Both inflation indexing and an ad valorem tax would also provide substantial increase in fuel tax revenue.

The Auto Sector Forecasting (ASF) Model presented is designed to be a tool for describing the effects of alternative automobile fuel-economy policies. It produces the future gasoline consumption, vehicle miles of travel, new car sales, fleet size, and fleet composition which are projected to result from a given fuel-economy policy posture. These postures include the use of excise taxes and rebates applied to new-car fuel economy, imposition of sales-weighted fuel-economy standards by manufacturer, and manipulation of the price of gasoline through taxation or other measures. This document presents the mechanics of using this model. It is a user's guide, not a discussion of the model's motivation or statistical properties.