Bismark R. D. K. Agbelie

Impacts of Signal-Related Characteristics on Crash Frequency at Urban Signalized Intersections

The effect of signals at urban intersections is critical in reducing death, injuries, and property loss. Unfortunately, the signal-related factors that can effectively reduce crash frequencies have not been adequately explored. This study investigates the impacts of signal-related, traffic, and highway geometric characteristics on crash frequency at urban signalized intersections. Seven-year crash data from 381 intersections in Illinois, United States of America, was used to estimate a random-parameters negative binomial model. The estimation results showed that a unit increase in the number of signal phases would increase crash frequency by 0.4. Additionally, the ratio of traffic volume on the major road to the traffic volume on the minor road was shown to unambiguously increase crash frequency. On the other hand, the number of approach lanes and the maximum green time of different approaches in a signal revealed an ambiguous effect, whereas increasing crash frequency on 76.33%, and 93.32% and decreasing it on the remaining 23.67%, and 6.68% of the signalized intersections, respectively. Although, this study is exploratory in nature, the results showed that ignoring these factors at urban signalized intersections would lead to the design of ineffective safety-related countermeasures, and the impacts could be underestimated by 5% to 25%.

Modeling Deterioration of Bridge Components with Binary Probit Techniques with Random Effects

Highway agencies use deterioration models to monitor the performance of bridge components (deck, superstructure, and substructure) and to predict their remaining service lives on the basis of the attributes of the bridges and their operating environments. Most bridge deterioration models are deterministic in nature. However, as a result of recent federal legislation, agencies are paying more attention to risk-based performance evaluation and decision making and are seeking models that incorporate stochastic elements. Probabilistic models provide more robust predictions of conditions. This paper describes the development of ordered binary probit (BP) models that duly account for observation-specific effects. The models describe the bridge component deterioration trends, specifically the probability that the component condition will drop from one state to another. This paper also acknowledges past similar or related efforts in this area of research but presents new insights and simplifies the complexity associated with BP models. To demonstrate the application of the models, data from over 5,000 in-service bridges were accessed; included were component age, superstructure material type, type of service under bridge, highway functional class, truck traffic, climate severity, rehabilitation history, condition switching state in last inspection period, and current condition rating. With the use of the developed BP models, a simulation was conducted to predict the probability of the component condition dropping from one state to another, where the predicted future condition is based on simulation involving the predicted probability and the current condition. The paper also presents visualizations of the deterioration trend simulation for each bridge component.

Linking Interstate Pavement Preservation Investment to Performance: An Exploratory Aggregate Analysis

AbstractFor purposes that include oversight and accountability, the U.S. Department of Transportation and the General Accountability Office continue to emphasize the need for regular systemwide monitoring of transportation infrastructure performance in response to system expenditures. To address this issue, it is necessary to ascertain the nature of the relationship between the funds expended on a jurisdiction’s infrastructure system preservation and the resulting performance. Duly recognizing past research efforts in this direction, this paper investigates such a relationship specifically for interstate highway pavements. This is an aggregate analysis and therefore does not consider site-specific design variables. Acknowledging that there exist jurisdiction-specific variables besides spending amounts that affect interstate pavement performance, this paper removes some of this bias by incorporating the system size as a normalizing variable and by considering state-specific values of the key deterioration ...

Forecasting of Highway Revenues Under Various Options

Throughout the United States, state highway revenue, which is dominated by receipts from fuel taxes, has 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. This development motivates highway agencies not only to seek revisions of existing funding structures but also to consider potential alternative sources. To establish and implement an effective and efficient financing strategy that incorporates potential new funding sources, it is necessary to model the possible outcomes of these sources in terms of their impacts on revenue stream and to study the sensitivity of these outcomes with respect to changes in key revenue factors such as vehicle-miles of travel and fuel price. In addressing this issue, this study utilizes data on amounts of travel, fuel price, and other primary information to enhance the existing models for state highway revenue forecasting in Indiana. To facilitate implementation of the study results, the existing revenue forecasting software package has been enhanced to include traditional and new revenue sources, to estimate revenue under several different scenarios, and also to analyze sensitivity of revenue to changes in input factors such as fuel price, per capita income, gross domestic product, driving age population, and traffic growth rate. The package provides annual forecasts for both existing and alternative highway revenue sources in Indiana. Short range forecasts for fuel tax revenues are also estimated.

Costs and Revenues Associated With Overweight Trucks in Indiana

This study estimated highway pavement and bridge damage costs, and analyzed the adequacy of permit revenues to cover these costs. The study began with an extensive review of the literature on the subject, thus facilitating identification of the gaps in the existing practice and research. The developed framework includes the establishment of asset families, realistic types and timings of reconstruction, rehabilitation, and maintenance, traffic volumes and growth projections. The cost of damage was estimated for each asset family and age group, and the sensitivity of asset damage cost with respect to key policy and analysis variables was explored. Finally, the study examined cost and operational issues associated with the enforcement of overweight truck policies. In addressing the gaps in existing research, this study showed that the damage cost of highway assets due to overweight trucks is influenced significantly by the asset type and age. For pavement assets, the pavement damage cost estimates were found to range from

The effect of gender on two-passenger vehicle highway crash-injury severity: A mixed logit empirical analysis

0.006 per Equivalent Single Axle Load (ESAL)‐mile on Interstates to

Efficiency Measurement of Bridge Management with Data Envelopment Analysis

0.218 per ESAL‐mile on non‐national highways. The study also showed that non‐consideration of reconstruction or maintenance cost can result in underestimation of the actual pavement damage cost by 79% and 83%, respectively. The analysis also showed that the unrealistic approach of considering only rehabilitation treatments applied at fixed intervals can lead to as much as 86% underestimation of the actual pavement damage cost. The results also suggest that pavement damage cost is highly sensitive to the pavement life‐cycle length, interest rate, rest period, and the costs and service lives of rehabilitation treatments. For the bridge assets (classified on the basis of their superstructure material type), the incremental methodology was found to be suitable to estimate the cost of bridge damage due to overweight vehicles. This methodology determines and assigns bridge damage cost to all vehicle classes on the basis of vehicle axle configurations and usage frequency (vehicle‐miles travelled). Incremental designs were carried out and cost functions were developed using Association of State Highway and Transportation Officials’ (AASHTO) design vehicles. Each Federal Highway Administration (FHWA) vehicle weight group was classified into an equivalent AASHTO loading regime using the modified equivalent vehicle (MEV) model which is based on gross vehicle weight, axle loading and axle spacing. The results were used to incrementally assign bridge cost to each vehicle class. For each class of overweight vehicles, the bridge damage cost was computed as the ratio of the cost responsibility and the volume of these vehicles. The bridge damage cost was estimated under two permit fee options and three user charging scenarios. The results suggest that approximately 22% of total bridge cost can be considered attributable to overweight vehicles. Also, it was observed that the bridge damage cost is not just a factor of gross vehicle weight but a function of all the three vehicle‐related variables: gross vehicle weight, axle spacing and axle loads. It was concluded that the adoption of a permit structure on the basis of gross vehicle weight only, will result in certain vehicle classes underpaying by as much as 92% of their actual contribution to bridge damage. Finally, the study examined cost and operational issues associated with the enforcement of overweight truck policies and identified a number of locations that could be considered for establishing new weigh stations and other enforcement facilities.

Indiana State Highway Cost Allocation and Revenue Attribution Study and Estimation of Travel by Out-of-State Vehicles on Indiana Highways

ABSTRACT This study investigates the effect of gender on two-passenger vehicle crash-injury severity. Using detailed police-reported data from Washington State for the year 2011 along 150 highway segments, a mixed logit method was applied to unravel the effect of gender on two-passenger vehicle driver crash-injury severity (no injury, injury and fatal) at highway segments, while accounting for other factors including weather, traffic characteristics, and highway geometric. More than 57% of the variables produced statistically significant normally distributed random parameters. The results indicate that the effects were consistent in directions across gender but had significant differences in magnitudes. For example, snow fall weather condition, defined for no-injury function, was found to be statistically significant, and the estimated parameters indicate that for 61.4% (of the male drivers) and 74.52% (of female drivers), the presence of a snowfall weather condition decreases the likelihood of no-injury crash. Thus, restricting these parameters to be fixed across observations will result in inconsistent and erroneous conclusions. To assess the effects of the factors on two-passenger vehicle highway segment crash-injury severity, the direct and cross elasticity (pseudo-elasticity) values were computed for each explanatory variable, and the effects significantly varied from 0.08% to 34.03%.

Updates to Indiana Fuel Tax and Registration Revenue Projections

This study explored the feasibility of data envelopment analysis (DEA) to measure objectively the efficiency of Interstate bridge replacement and rehabilitation programs of the state highway agencies in the United States. Past applications of DEA indicated that this method showed promise for such assessments because of the ability to take into account the effect of uncontrollable factors and scale efficiency. The current study developed a modified input-oriented variable returns to scale model that treated the uncontrollable variables as uncontrollable. The two variables selected as outputs were (a) the change in area of structurally deficient bridges and (b) the change in area of functionally obsolete bridges between two consecutive years. The six variables selected as inputs were (a) expenditures on Interstate bridge replacement and rehabilitation, (b) average daily traffic on Interstate bridges per deck area, (c) proportion of Interstate truck vehicle miles traveled (VMT) to Interstate total VMT, (d) average age of Interstate bridges, (e) annual precipitation, and (f) annual freeze–thaw cycles. For each state, the DEA efficiency score and the corresponding target states were identified. The performances of the states and the possible reasons for inefficiencies were assessed. The Malmquist productivity index model was applied to calculate the technical efficiency change (catch-up effect) and the technological change (frontier shift). Over the analysis period, an overall improvement was observed in the frontier of the studied industry; this improvement suggested within this period a general improvement in the efficiencies of the Interstate highway bridge replacement and rehabilitation programs at highway agencies in the United States.

An empirical analysis of three econometric frameworks for evaluating economic impacts of transportation infrastructure expenditures across countries

This study was commissioned by Indiana Department of Transportation (INDOT) to investigate the cost responsibility and the revenue contribution of highway users with regard to the upkeep of Indiana’s state and local highway infrastructure (pavements, bridges, safety assets, and mobility assets). The costs consisted of expenditures on construction, preservation, maintenance, and operations of the highway infrastructure. For revenues, user and non‐user sources were considered. The highway users were represented by the 13 Federal Highway Administration (FHWA) vehicle classes, and the study was based on 2009‐2012 data on expenditures and revenues. The study framework duly recognized the dichotomy between attributable and common costs. For allocating the attributable costs to the vehicle classes, equivalent single axle loads (ESALs), American Association of State Highway and Transportation Officials (AASHTO) loading equivalents, and passenger car equivalents (PCEs) were used; for allocating common costs, vehicle miles traveled (VMT) was used. For each vehicle class, the share of revenue contribution was compared to the share of cost responsibility to determine respective equity ratios and thus to ascertain the extent to which vehicles in each class may be underpaying or overpaying their cost responsibilities at the current time. The study also determined the distribution of fuel purchases and travel by out‐of‐state vehicles on Indiana’s highways; this analysis was required to further refine the results of the cost allocation and also to quantify the magnitude of any imbalance between the out‐of‐state travel and share of consumption on Indiana’s infrastructure and the revenue from such out‐of‐state vehicles. The outcome of this research is a systematic documentation of the sources and extents of highway revenues and the areas of expenditures at the local and state levels in Indiana. Pavement and bridge expenditures were found to have a dominant share of the overall expenditures on Indiana’s highway system. Classes 2 (automobiles) and 9 (5‐axle combination trucks) were found to have a dominant share of the cost responsibilities. It was determined that the user revenue sources contributed approximately 63.5% of the total state funding for highway expenditures and 36.5% were from non‐user revenue sources. The inability of user revenue sources to cover the total highway expenditure and the consequent partial reliance on non‐user sources seem to constitute a rather unstable funding situation particularly because the non‐user sources are characterized by significant variability. On the basis of the expenditures and revenues associated with the various user groups (vehicle classes) over the analysis period, this study found that inequities exist, albeit in varying degrees, among the highway user groups. Of the 13 vehicle classes, classes 1–4 were found to be overpaying their cost responsibilities while classes 5–13 are underpaying. For example, vehicle class 2 is overpaying its cost responsibility by 10% while vehicle class 9 is underpaying by 19%. The results of the equity analysis are generally consistent with those of studies carried out at other states. Also, it was estimated that the travel by out‐of‐state vehicles on Indiana’s interstates, national highway system (NHS) non‐interstates, non‐NHS and local roads are 21%, 10%, 9%, and 7% respectively, of the total travel as a percentage of VMT on those families of highway systems.