Interstate Research Articles

Performance of a Thin CRCP Inlay Designed for a Five-Year Life: a Case Study

The steep uphill section of an interstate highway had experienced considerable damage in the form of serious rutting due to slow-moving heavy vehicles. The asphalt pavement had had to be rehabilitated every 4 years and a decision was made to mill and replace the top 180mm of the slow lane with a CRCP inlay. The inlay design was based on an anticipated 6 million equivalent 80 kN axles over five years and it was envisaged that a concrete overlay would be placed over the full width of the road at the end of this period. However, at the end of six years only approximately 0.25% of the area of the inlay had shown serious distress. It was decided to determine the remaining structural life of the pavement with a view to repairing failed areas permanently, rather than temporarily, if the remaining structural life was found to be significant. This paper describes the testing of behaviour and performance on a relatively highly cracked section of the inlay using the Heavy Vehicle Simulator (HVS). Although the test section had narrowly spaced transverse cracking prior to testing, no punch-outs could be created using the HVS. A detailed survey of the 26 lane km of inlay showed that failure did not necessarily occur at closely spaced cracks, but was more associated with edge loading, loss of slab support and poor quality concrete. It also became very clear that a high variation in concrete characteristics (and in particular concrete strength) resulted in a high risk of structural failure. The results were translated into transfer functions that have been included in cncPave, a mechanistically-based design method for concrete pavements developed for South African conditions.

CONCRETE PAVEMENT RECONSTRUCTION ON THE INTERSTATE SYSTEM

This article reviews and discusses the design and reconstruction of concrete pavement projects on the U.S. Interstate System, focusing on the use of Portland cement concrete. The adoption of the 1982 Surface Transportation Assistance Act's 4R fund has facilitated significant advancements in reconstruction methods. Various projects across different climates in the United States are examined, including complete replacements, full-depth unbonded and partial-depth bonded overlays, and the recycling of old concrete as aggregate for new pavements. The paper highlights innovative equipment and techniques that have emerged as a result of increased funding and legislative support, aimed at addressing the challenges posed by heavy commercial traffic and the need for durable road infrastructure. Economic factors, life-cycle costing, and material reuse are also discussed as critical considerations in the decision-making process for pavement reconstruction. The effectiveness of these projects is assessed in terms of technical feasibility and long-term economic benefits, emphasizing the importance of continued funding and research to support future infrastructure demands. (Abstract generated by AI tool ChatGPT 4)

PLAIN PAVEMENT; AN ECONOMIC ALTERNATIVE

The paper "Plain Pavement; An Economical Alternative" by Ralph C. Blum and Clinton E. Solberg examines the economic pressures faced by state transportation departments and the innovative measures implemented to reduce costs in highway construction. Focusing on the Wisconsin Department of Transportation (WDOT), the study details the transition from steel-reinforced concrete pavement to non-reinforced plain concrete pavement (PCP) in a 20-mile segment of Interstate Highway 43. This shift resulted in a significant cost reduction of $1.6 million. The authors discuss the historical context and economic analysis that influenced the decision, emphasizing the need for cost efficiency due to the declining purchasing power of state dollars and increasing construction material costs. The paper explores the construction process, the performance and maintenance of plain versus reinforced pavements, and the long-term economic and operational benefits. It concludes that the use of PCP not only offered immediate financial relief but also maintained quality and durability, making it a viable economic alternative for future highway projects. The findings suggest that PCP could potentially offer a sustainable model for managing limited resources while addressing the need for infrastructure development. (Abstract generated by AI tool ChatGPT 4)

New Psychoactive Substances Discharged at Rest Areas along Interstate Highway in Kentucky - Wastewater Analysis

New Psychoactive Substances Discharged at Rest Areas along Interstate Highway in Kentucky - Wastewater Analysis

Impact of speed on injury severity in single-vehicle run-off-road crashes: Insights from partially temporal constrained modeling approach

Single-vehicle run-off-road crashes accounts for approximately 35% of all the traffic fatalities in the U.S during the period of 2019–2021. This paper explores the association between driving speed and injury severity outcomes of single-vehicle run-off-road crashes. The single-vehicle run-off-road crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods of pre-, during-, and post-COVID-19 pandemic. The partially constrained temporal and temporal unconstrained random parameters logit models are developed considering three injury severity outcomes: no injury, minor injury and serious injury/fatality. Multiple variables in terms of driver, vehicle, roadway, environmental, crash, and temporal attributes are observed to significantly affect the injury severity. Moreover, temporal instability and transferability issues are validated through likelihood ratio test and out-of-sample prediction. In the partially constrained models, numerous variables such as indicators of new vehicle, male driver, and restraint-protected driving consistently yield identical parameter values across all periods, whereas various variables clearly illustrate the distinct differences across the three periods and three speed intervals. The marginal effects in the unconstrained models also display the obvious differences across three periods and three speed intervals. Moreover, the findings corroborate the increased risk outcomes linked to larger speed differences and the COVID-19 pandemic period. These results provide better understanding of the risk mechanisms underlying run-off-road crashes and furnish valuable direction for the formulation of effective safety interventions.

Noncombustion Emissions of Organic Acids at a Site near Boise, Idaho.

Gas-phase organic acids are ubiquitous in the atmosphere with mixing ratios of several species, such as formic acid and acetic acid, often as high as several parts per billion by volume (ppbv). Organic acids are produced via photochemical reactions and are also directly emitted from various sources, including combustion, microbial activity, vegetation, soils, and ruminants. We present measurements of gas-phase formic, acetic, propionic, pyruvic, and pentanoic acids from a site near Boise, Idaho, in August 2019 made by iodide-adduct chemical ionization mass spectrometry (CIMS). The site is adjacent to a major interstate highway and beyond the urban/suburban core is surrounded by national forests to the north and northeast and by farmland to the west and south. Maximum mixing ratios of formic, acetic, propionic, and pentanoic acid were typically near 10, 3, 0.4, and 0.2 ppbv, respectively. Observed daytime concentrations of these acids were mostly consistent with other studies, but concentrations were persistently the highest at night between 20:00 to 8:00 (local standard time). Such elevated nighttime concentrations are unlike most other reported organic acid measurements. Although there were times when organic acid concentrations were enhanced by mobile source emissions, the organic acid concentrations appear to be mainly controlled by noncombustion surface primary emissions. Source apportionment by positive matrix factorization (PMF) supports the importance of significant noncombustion, nonphotochemical emissions. Two agricultural surface sources were identified and estimated to contribute to greater than half of total observed concentrations of formic and acetic acid. In contrast to the other measured organic acids, but in agreement with all other reported measurements in the literature, pyruvic acid concentrations peaked during the daytime and were largely controlled by photochemistry.

Modeling Crash Risk on Roadway Networks Using Bayesian Regression Trees

Statistical modeling of vehicle crashes leads to a better understanding of how and why such crashes occur. Due to the irregular network structure of roadways, analyses are typically confined to a single roadway rather than considering the entire network collectively. Here, we present methodology to model crash risk of vehicle crashes on irregular roadway networks and estimate how that risk varies with road characteristics. We model vehicle crashes observed on a road network as a Poisson point pattern with a piecewise linear intensity surface. Further, we combine Bayesian additive regression trees (BART) and spatial data analysis to accurately explain the intensity surface allowing inference on the effect of road characteristics on crash risk. We illustrate the methodology using a dataset of vehicle crashes on Interstate highways in Utah.

Using statewide transportation planning model to forecast demand for electric vehicle charging at stations along intercity highways

The availability of Charging Stations (CSs) with adequate capacity is critical for the growth in ownership and usage of Electric Vehicles (EVs). In the United States, the state Departments of Transportation (DOTs) are responsible for planning CSs along the Interstate, national, and state highways. While the state DOTs have their Statewide Transportation Planning Models (STPMs), most of these models do not represent EVs internally. The state DOTs can only rely on the existing STPMs to forecast the demand for EV charging at specific CS locations in a state’s highway network. This research proposes a methodology that utilizes an existing STPM, EV and non-EV vehicle registration data, and hourly traffic volumes at strategic counting stations to predict the hourly demand for EV charging (charging port occupancy) at proposed CSs. This approach first estimates the volume of all passenger cars that will pass the vicinity of a CS along intercity highways, and identifies the Traffic Analysis Zones (TAZs) of their trip origins. Based on the EV adoption rates of the TAZs, EV range, and EV owner’s charging behavior, the methodology forecasts the volume of EVs that pass the vicinity of a CS and the number of EVs that will stop at the CS to charge every hour of the day. An example problem demonstrated the application of the methodology in the State of Texas. It not only forecasted the hourly occupancy at CSs along three major corridors, but also demonstrated how to determine the capacities of the CSs. The challenges of using the existing STPM and their solutions are discussed. This proposed methodology enables the state DOTs to forecast the demand for EV charging and the capacities at the proposed CSs along intercity highways, thus allowing the DOTs to apply for federal funds to accelerate the deployment of the CSs.

Development of a Large-Scale Horizontal Curve Inventory for Safety Analysis Using Open GIS Data

Abstract Horizontal curves are crucial for the safety and mobility of roadways. To enhance the understanding of these curves, transportation agencies are investigating methods to accurately determine their spatial locations, geometric attributes, and conditions within their extensive road networks. Leveraging the widespread availability of Global Positioning System (GPS) and Geographic Information System (GIS) data, significant advancements have been made in developing automated algorithms that identify horizontal curves from GPS trajectories and GIS basemaps. This study introduces a novel dataset—the Horizontal Curve Inventory—that encapsulates detailed features of horizontal curves. We employed a modified automated cycle regression algorithm designed for large-scale road network analysis to extract horizontal curve data. This method was applied nationally to process an expansive network utilizing open-source GIS basemaps, encompassing over 300,000 miles across interstate, U.S. highway, and state highway systems. The inventory includes comprehensive data on curve radius, rotation angle, start and end point coordinates, and the curve’s center points. These data are essential for supporting safety research pertaining to road alignment. Our approach demonstrated robust performance across various open GIS data sources and provides the first accurate, efficient, and comprehensive inventory of horizontal curves for all major highway systems in the United States. The dataset is available at http://aichengbo.com/research/national-horizontal-curve-inventory/.

Short-term freeway traffic speed multistep prediction using an iTransformer model

Accurate prediction of the freeway traffic speed is important for enhancing intelligent transportation management and assisting with route planning. Current traffic speed prediction studies usually neglect predictions extending to an hour or longer. Thus, to address this gap, the inverted Transformer (iTransformer) model is utilized to predict freeway speeds across intervals from 5 to 150 minutes. The iTransformer model provides a global view by encapsulating the entire time series from each detector into variate tokens, allowing it to capture the complex patterns and dependencies that change over time. Additionally, a multi-head attention mechanism is employed to identify short-term and long-term speed patterns. This study validates the prediction performance of iTransformer by using the traffic speed data from an interstate freeway in Minnesota, comparing it against traditional traffic prediction methods and two other Transformer models. Results indicate that the iTransformer model outperforms these benchmark approaches, particularly when predicting speed over 60 minutes for peak hour periods.

Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.

Analyzing Rear-End Crash Counts on Ohio Interstate Freeways Using Advanced Multilevel Modeling

This study presents a new modeling approach for rear-end crash counts on Ohio’s interstate freeways based on a dataset for 2021 that contains 2745 rear-end crashes. The analysis encompasses 20 interstate freeways, comprising 1833 homogeneous segments and extending over approximately 1313 miles. These interstate freeways exhibit varying safety performances, indicating a significant degree of heterogeneity. A unique rear-end crash risk rate was devised for each interstate, capturing diverse risk profiles. Three distinct models were developed: a standard negative binomial model, an uncorrelated two-level negative binomial model, and a correlated two-level negative binomial model. The correlated two-level negative binomial model demonstrated superior fit, as evidenced by the likelihood ratio test, Akaike information criterion, and Bayesian information criterion. The correlated two-level negative binomial model exhibited enhanced forecasting precision, as measured by the Root Mean Square Error. A significant finding is that the rear-end crash risk rate significantly improves the fit of the models. The study also reveals that rear-end crashes are expected to occur more frequently in urban segments of interstate freeways with high rear-end risk rates. However, rural segments experience no such significant variations in the rear-end crash risk rate. However, an increase in the inner shoulder width is associated with a decrease in expected rear-end crashes. This research offers a valuable methodology for modeling rear-end crashes on interstate freeways, providing insights into the contributing variables that could inform targeted safety improvements.

Mapping Tornado Hotspots in the U.S.: Spatial and Temporal Analysis Across the U.S.

Tornadoes rank among the most damaging weather events in the US. They put lives at risk, wreck infrastructure, and upset economic systems. This study examines data from 1950 to 2022 using Geographic Information Systems (GIS) to uncover tornado hotspots, pinpoint high-risk areas, and grasp why some regions face bigger threats. States such as Texas, Alabama, and Oklahoma proved to be risk zones due to high tornado activity, which leads to major damage, deaths, and money losses. Texas tops the list as the state tornadoes hit hardest, with the most recorded events and severe economic blows. Season patterns show tornadoes strike most in late spring and early summer creating a pressing need to get ready during these months. The research also sheds light on the toll on key infrastructure, such as U.S. interstate highways. These results offer key insights to steer disaster management and mitigate risks for the most exposed areas.

Exploring Kriging metamodeling to alleviate speed reading dependence in Bridge Weigh-In-Motion predictions

Bridge Weigh-In-Motion (BWIM) systems can be installed and serviced on highway bridges to provide measurements of moving vehicle’s gross weight, axle load, axle separation, and so on, without interrupting traffic. This data can be used to provide information of the heavy traffic over the bridge to management. This article presents the application of Kriging metamodeling to BWIM through the simulation of traffic streams. The Kriging methodology, with its prediction of metamodel variance, allows for the strategic selection of vehicles to train the BWIM metamodel. The proposed approach extends the BWIM concept of influence area from response time histories to geometric input spaces from the strain response wave (RW). The simulated dynamic response of simply supported bridges in the US Interstate Highway System were performed using Finite Element Modeling (FEM) of moving loads, generating RWs captured in different sensor locations. Three independent input spaces based on geometric properties of the RWs are the input to train the Kriging metamodel of a defined training truck group. In this approach, the measurement of the vehicle speed is not needed as an improvement of traditional BWIM methods. The results show that the Kriging prediction can fulfill at least the Type II WIM Systems performance defined in the ASTM’s Standard, using a comparable number of training runs to the number of calibration runs defined in the standard.

Modeling human activity-related spread of the spotted lanternfly (Lycorma delicatula) in the US.

The spotted lanternfly (Lycorma delicatula) has recently spread from its native range to several other countries and forecasts predict that it may become a global invasive pest. In particular, since its confirmed presence in the United States in 2014 it has established itself as a major invasive pest in the Mid-Atlantic region where it is damaging both naturally occurring and commercially important farmed plants. Quarantine zones have been introduced to contain the infestation, but the spread to new areas continues. At present the pathways and drivers of spread are not well-understood. In particular, several human activity related factors have been proposed to contribute to the spread; however, which features of the current spread can be attributed to these factors remains unclear. Here we collect county level data on infestation status and four specific human activity related factors and use statistical methods to determine whether there is evidence for an association between the factors and infestation. Then we construct a network model based on the factors found to be associated with infestation and use it to simulate local spread. We find that the model reproduces key features of the spread 2014 to 2021. In particular, the growth of the main infestation region and the opening of spread corridors in the westward and southwestern directions is consistent with data and the model accurately forecasts the correct infestation status at the county level in 2021 with 81% accuracy. We then use the model to forecast the spread up to 2025 in a larger region. Given that this model is based on a few human activity related factors that can be targeted, it may prove useful to incorporate it into more elaborate predictive forecasting models and in informing management efforts focused on interstate highway transport and garden centers in the US and potentially for current and future invasions elsewhere globally.

The Relative Probability of Facebook Friendship in the United States

We mapped Facebook’s Social Connectedness Index (SCI) between adjacent counties in the Contiguous 48 U.S. States. The index is calculated as the number of Facebook friends between counties, divided by the product of active Facebook users in the two counties. The results follow regional science principles that tell us that fewer flows may occur across political (administrative) borders such as state boundaries, and between economic zones, including transition zones between metropolitan areas and hinterland boundaries. We also found low connectivity between adjacent counties that are divided by interstate highways and low connectivity within densely populated areas. High connectivity is found in rural areas, and areas of cultural significance, such as highly African American regions in the U.S. South and isolated regions in Appalachia.

Prediction and interpretive of motor vehicle traffic crashes severity based on random forest optimized by meta-heuristic algorithm

Providing accurate prediction of the severity of traffic collisions is vital to improve the efficiency of emergencies and reduce casualties, accordingly improving traffic safety and reducing traffic congestion. However, the issue of both the predictive accuracy of the model and the interpretability of predicted outcomes has remained a persistent challenge. We propose a Random Forest optimized by a Meta-heuristic algorithm prediction framework that integrates the spatiotemporal characteristics of crashes. Through predictive analysis of motor vehicle traffic crash data on interstate highways within the United States in 2020, we compared the accuracy of various ensemble models and single-classification prediction models. The results show that the Random Forest (RF) model optimized by the Crown Porcupine Optimizer (CPO) has the best prediction results, and the accuracy, recall, f1 score, and precision can reach more than 90 %. We found that factors such as Temperature and Weather are closely related to vehicle traffic crashes. Closely related indicators were analyzed interpretatively using a geographic information system (GIS) based on the characteristic importance ranking of the results. The framework enables more accurate prediction of motor vehicle traffic crashes and discovers the important factors leading to motor vehicle traffic crashes with an explanation. The study proposes that in some areas consideration should be given to adding measures such as nighttime lighting devices and nighttime fatigue driving alert devices to ensure safe driving. It offers references for policymakers to address traffic management and urban development issues.

The Road to Crime: An Unintended Consequence of the Interstate Highway System

Abstract The necessity to overhaul national infrastructure has become one of the key issues of US domestic policy in recent years. While the benefits of infrastructure spending are often straightforward, its unintended consequences are less well understood. This paper studies the impact of transportation infrastructure on local crime, focusing on the construction of the Interstate Highway System (IHS), the largest public works project in US history. Using a staggered difference-in-differences design and a county-by-year panel dataset spanning all US counties between 1960 and 1993, we find that a highway opening in a county led to an 8% rise in total index crime. This effect is driven by property crime (burglary, larceny, and motor vehicle theft), although some evidence suggests an increase in violent crime as well. Exploring potential mechanisms, we show that the positive effect of highways on crime is concentrated in counties with low pre-existing stock of police resources where increased connectivity may have raised the returns to crime. Moreover, we find that highway construction induced higher local employment, average firm size, and the local population size, thus increasing the returns to criminal activity, and ultimately the risk of victimisation in the newly-connected counties.

Vegetation composition and organic carbon content along right-of-ways on Interstate Highway 35 in Bexar County, Texas

Roadside habitat through major urban areas may offer remnant examples of natural grasslands. These habitats may be subjected to frequent as mowing and exposure to automobile emissions and runoff. This study was conducted on interstate highway right-of-ways in San Antonio, Texas, U.S.A. We compared the vegetation species and diversity, and the organic carbon of leaf litter, plants, and soils. Two non-native grasses accounted for 50.9% cover, while all native forbs and grasses accounted for only 9.8% cover. The mean biomass of non-native grasses was 4.5 times greater than that of all native species. Organic carbon content of leaf litter, plants, and soils was variable among the study sites, possibly due to management practices and a drought that occurred during the study. The mean organic carbon content in the upper 20 cm soil was 5.0 kg·C/m2 and organic carbon content was greater in the upper 10 cm than organic carbon in the 10.1 to 20 cm portion. Cynodon dactylon and Bouteloua curtipendula exhibited the greatest photosynthesis efficiency indicating these grasses are more adaptable to hot summer temperatures found in Central Texas. The organic carbon content along a major interstate was 13793 kg·C/ha for leaf litter, vegetation, and the upper 20 cm of the soil. We conclude that interstate highways provide habitat for some native species, but the vegetation along right-of-ways is dominated by two non-native grasses. It appears unlikely that roadside habitat can be restored to resemble native grasslands without large scale and costly restoration efforts.

Securing Your Eggs in Multiple Baskets — Assuring a Resilient and Secure Supply Chain

AbstractThe global supply chain is a complex system of systems made up of and relying on other complex systems of systems (SoS) to achieve its goals. To take a typical example, Enterprise A is supplied essential parts on a regular basis to manufacture its products. To place the order requires global financial systems, integrated email systems, the internet, multiple telecommunications systems, and supply software provided by large companies. To deliver the parts may require air and maritime transportation systems, the rail network, interstate highway systems, road haulage companies, state and local transportation systems and so forth. When any of these complex systems fail, the impact can be global, and the results catastrophic. Recent examples include the shortage of Personal Protective Equipment (PPE) during the COVID pandemic, computer chip shortages delaying the assembly and sales of cars, and, most recently, the baby formula shortage. These were due to disruptions in the supply chain caused by an overreliance on single sourced suppliers who failed to deliver, transportation disruptions, outsourcing of critical parts, supplies, medicines to distant countries, and/or an overreliance on “Just In Time” for inventory management. This is the case of placing too many eggs in too few baskets, and often just one basket. Counterfeit or substandard parts and products can enter the supply chain via graft, breaks in chain of custody, or carelessness. This has included critical mechanical parts on aircraft, chips containing spyware, and substandard or out of date medicines substituted for the real thing resulting in serious illness and death. This complex SoS needs to be examined, studied, and understood in the same way as a mission critical system; threats, vulnerabilities, and risks need to be identified and mitigated and assurance cases defined to ensure a solid and reliable supply chain. This paper will look at the supply chain of an example factory system to determine how some of these problems can be predicted, prevented, mitigated, and solved using the UAF, RAAML and assurance case techniques.