Ohio Department of Transportation

Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary across time-of-day patterns. This study is conducted on the Ohio Department of Transportation (ODOT) traffic count data and corresponding ODOT crash records. This work utilizes a Spatio-Temporal Transformer (STT) model with Adaptive Conformal Prediction (ACP) to produce multi-horizon forecasts with calibrated uncertainty. We propose a piecewise Coefficient of Variation (CV) strategy that models hour-to-hour traveltime variability using a log-normal distribution, enabling the construction of a per-hour dynamic adjacency matrix. We further perturb edge weights using incident-related severity signals derived from the ODOT crash dataset that comprises incident clearance time, weather conditions, speed violations, work zones, and roadway functional class, to capture localized disruptions and peak/off-peak transitions. This dynamic graph construction replaces a fixed-CV assumption and better represents changing traffic conditions within the forecast window. For validation, we generate extended trips via multi-hour loop runs on the Columbus, Ohio, network in SUMO simulations and apply a Monte Carlo simulation to obtain travel-time distributions for a Vehicle Under Test (VUT). Experiments demonstrate improved long-horizon accuracy and well-calibrated prediction intervals compared to other baseline methods.

This one-day, multi-state field trip visits trace-fossil sites in Ashtabula County, Ohio and western Pennsylvania, USA, that allow a direct comparison of ichnological elements of the Chagrin Member of the Ohio Shale in Ohio with those of the Northeast Shale through Chadakoin Formation of western Pennsylvania. Some of these rocks contain numerous Rusophycus-like megaburrows as well as smaller Lingulichnus and Diplocraterion trace fossils; others contain smaller phyllocarid traces belonging to the genus Protovirgularia M’Coy, 1850, previously identified as Rusophycus or as Chagrinichnites (the latter currently considered a junior subjective synonym of Protovirgularia). Sites with large trace fossils are found, in part, near covered bridges, so these bridges will be briefly described, as will the stream anticlines that are abundant in this area, sand barrens along a beach ridge, and an agricultural research station focusing on grape growing, contextualizing the traces within the broader geological and cultural milieu of northwest Pennsylvania and northeast Ohio.

Road safety and traffic management face significant challenges due to secondary crashes, which frequently cause increased traffic, delays, and collisions. Traditional methods for anticipating secondary crashes often overlook the importance of different road types, resulting in suboptimal predictions and response plans. This research presents a novel method that combines a hybrid machine-learning model with a functional class-based weighting strategy to classify secondary crashes. The functional classes in the dataset are categorized as interstates, arterial roads, collector roads, and local roads. The dataset also includes comprehensive crash narratives and various road attributes. Each functional class is assigned a weight reflecting its proportional importance in the likelihood of a subsequent crash, based on historical data and road usage patterns. This weighting technique is integrated into a hybrid model architecture that trains a Random Forest (RF) model on structured data to utilize road-specific features and a Long Short-Term Memory (LSTM) network on crash narratives to capture contextual and sequential information. A meta-classifier, which leverages the strengths of both methods, combines these models to produce a more comprehensive forecast. The functional class-based weighting increases the model's sensitivity to high-risk road types—such as interstates and arterial roads—which are more likely to experience secondary crashes. By achieving higher precision, recall, and F1 scores than individual models, the hybrid model demonstrates significantly improved performance. The findings reveal that integrating functional class weights with hybrid models resulted in a validation accuracy of 98%, providing crucial insights for traffic management. This strategy enables transportation authorities to effectively manage secondary crash risks, allocate resources more efficiently, and improve overall road safety.

Due to its location along Lake Erie and to Lake Erie’s connection with the Erie Canal, stones from diverse locales outside of Pennsylvania, USA, were used for large buildings in the heart of Erie beginning early in its history, notably the use of Vermont marble for its 1839 Customs House. Medina sandstone (Grimsby Sandstone) from New York State quarried near the Erie Canal was used for mansions, a cathedral, and roadway paving later in the 1800s. Berea Sandstone and Sandusky bluestone from northern Ohio and a great variety of European and North African decorative stones also found use in Erie in the nineteenth and twentieth centuries. Railway connections eventually allowed for use of more North American stones. Use of local blue stone (Panama Sandstone) was primarily restricted to foundations. This field trip identifies, using both commercial and geological rock unit names, major stones used for two dozen structures, many of them historic, in the downtown area of Erie.

Background Soil-bearing spread footings (SBSF) are emerging as an increasingly attractive option for supporting highway structures. SBSF offers numerous benefits compared to deep foundations, including cost-effectiveness, accelerated construction, straightforward design, environmentally friendly characteristics, and reduced maintenance requirements. Objective The primary objective of this study is to assess the performance of highway structures that SBSF supports at four specific locations in Ohio. The findings from this assessment will serve as valuable recommendations for the future application of spread footings while identifying potential usage constraints. Methods The project team conducted a comprehensive review of documented performance data, assessed the effectiveness of existing footings, and compared these assessments against established structural performance criteria. Additionally, the team analyzed calculated settlements, provided an estimate of acceptable structural settlement, compared measured settlements with predicted values, and examined the relationship between performance and soil conditions. Results The data gathered, and field assessments conducted at all four sites encompassed in this investigation affirm that SBSF structures function as designed, displaying no signs of settlements or cracks associated with rideability. Both measured and calculated settlements fall within acceptable tolerances. While all structures exhibited acceptable levels of differential settlement, one encountered a differential settlement of approximately 54 mm between substructures, attributed to cohesive soil (A-6) in deeper layers. Conclusion SBSF structures were constructed by the manuals and guidelines established by the Ohio Department of Transportation (ODOT) and exhibited satisfactory performance. This paper also offers recommendations for settlement monitoring methods and plan notes that should be incorporated into the Ohio Department of Transportation's practices.