Updating, Upgrading, Refining, Calibration and Implementation of Trade-Off Analysis Methodology Developed for INDOT

As part of the ongoing evolution towards integrated highway asset management, the Indiana Department of Transportation (INDOT), through SPR studies in 2004 and 2010, sponsored research that developed an overall framework for asset management. This was intended to foster decision support for alternative investments across the program areas on the basis of a broad range of performance measures and against the background of the various alternative actions or spending amounts that could be applied to the several different asset types in the different program areas. The 2010 study also developed theoretical constructs for scaling and amalgamating the different performance measures, and for analyzing the different kinds of trade-offs. The research products from the present study include this technical report which shows how theoretical underpinnings of the methodology developed for INDOT in 2010 have been updated, upgraded, and refined. The report also includes a case study that shows how the trade-off analysis framework has been calibrated using available data. Supplemental to the report is Trade-IN Version 1.0, a set of flexible and easy-to-use spreadsheets that implement the tradeoff framework. With this framework and using data at the current time or in the future, INDOT’s asset managers are placed in a better position to quantify and comprehend the relationships between budget levels and system-wide performance, the relationships between different pairs of conflicting or non-conflicting performance measures under a given budget limit, and the consequences, in terms of system-wide performance, of funding shifts across the management systems or program areas

Assessing and Updating INDOT\u27s Traffic Monitoring System for Highways (2 volumes)

This study evaluates the existing resources and procedures of INDOT’s current traffic monitoring program, with the objective of transforming this program into a comprehensive Traffic Monitoring System for Highways (TMS/H). Reliable traffic data is a valuable input for studies and decision-making at various levels and in various phases of highway management including planning and design, finance and taxation, legislation and safety. The nature and scope of the various components comprising a TMS/H for any state were identified in available literature such as the Federal Register and Traffic Monitoring Guide. A complete inventory of the existing resources (personnel and equipment) and procedures used for field data collection and office-based data processing were compiled and evaluated for adequacy and/or accuracy and appropriateness by matching them with requirements stated in available literature to ensure compliance with ISTEA recommendations. In general, the existing traffic monitoring program was found to be adequate in meeting the needs of most management systems, with the exception of vehicle classification monitoring at sections having ‘abnormal’ traffic conditions. For the Continuous Count program, a large number of additional classification ATR stations are recommended while very few additional WIM sites are needed. All HPMS sample sections and NHS segments are covered under the existing program, although a lack of resources sometimes limits the frequency of data collection. Also, a new schedule for coverage counts is proposed to place greater emphasis on NHS roads and high-growth areas of the state. A new database system is recommended to effectively address data management issues. Also, documentation of field operations and office factoring procedures was carried out in this study. With the recommendations from this study, INDOT intends to streamline its overall data collection activities and to improve the accuracy, adequacy, timeliness, and delivery of data to the end-users

Developing Statistical Limits for Using the Light Weight Deflectometer (LWD) in Construction Quality Assurance

The traditional methods of evaluating the compaction quality of pavement subbase and subgrade construction require considerable time and resources. Therefore, there is a need for a safe, reliable, rapid, and cost-effective field measurement technique for compaction testing of unbound pavement layers. The Light Weight Deflectometer (LWD) is one such mechanism that offers field measurement of deflections and stiffness of unbound pavement layers under a given load. The LWD is gaining increased attention for quality control and quality assurance (QC/QA) during pavement construction. The Indiana Department of Transportation (INDOT) is planning on implementing the LWD in field QA/QC for unbound layers of pavements. As such, this research investigates the feasibility of developing statistical limits for the compaction of specified combinations of subbase and subgrade materials in terms of their maximum allowable LWD deflections. Statistical limits were developed for six of the most common subgrade, subbase, or subgrade-subbase combinations that are used for highway pavement construction in Indiana: lime modified, cement modified, natural subgrade and No. 53 crushed stone (53CS) subbase overlaying these subgrades. For the subbase layers, these statistical limits are applicable only to six inches of subbase over subgrade and may not be applicable to a different layer configuration in terms of the number of lifts or thickness of lifts. The ultimate goal is for the developed statistical limits to replace the need for site-specific LWD limits derived from the onsite test sections, ultimately saving time and money. Due to variability in the data and data limitations, caution must be exercised when generalizing the findings published in this report. Compared to the data from the acceptance test sections, the data collected from test sections saw less variability between projects, for any given material type. The test section data yielded maximum allowable deflections that did not vary significantly between projects for cement- and lime-modified subgrade, non modified subgrade, and six inches of #53 crushed stone over lime-modified subgrade. Generally, within any specific contract location (project site), the data indicates adequate confidence that the test pads generate control measurements that can be used reliably to check the adequacy of compaction at that contract location. However, across different contact locations, even for the same material type, so much variability was observed that it is not possible to guarantee that the control measurements generated from a limited number of test sections (pads) can be confidently transferred to another site of the same material type

Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories

With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems. The virtual simulation test (VST) has become a prominent approach for testing autonomous driving systems (ADS) and advanced driver assistance systems (ADAS) due to its advantages of fast execution, low cost, and high repeatability. However, the success of these simulation-based experiments heavily relies on the realism of the testing scenarios. It is needed to create more flexible and high-fidelity testing scenarios in VST in order to increase the safety and reliabilityof ADS and ADAS.To address this challenge, this paper introduces the "Transfusor" model, which leverages the transformer and diffusor models (two cutting-edge deep learning generative technologies). The primary objective of the Transfusor model is to generate highly realistic and controllable human-like lane-changing trajectories in highway scenarios. Extensive experiments were carried out, and the results demonstrate that the proposed model effectively learns the spatiotemporal characteristics of humans' lane-changing behaviors and successfully generates trajectories that closely mimic real-world human driving. As such, the proposed model can play a critical role of creating more flexible and high-fidelity testing scenarios in the VST, ultimately leading to safer and more reliable ADS and ADAS.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar

Increasing Bridge Deck Service Life: Volume I—Technical Evaluation

Deterioration of bridge decks is a primary factor limiting the lifespan of bridges especially in cold climates where deicing salts are commonly used. While controlling deck cracking or decreasing the permeability and porosity of concrete can improve performance and service life, chloride and moisture ingress as well as cracking cannot be eliminated. Full-depth cracks which are caused by restrained shrinkage allow for corrosive conditions at early ages for both the top and bottom reinforcement mats. Therefore, the use of corrosion-resistant reinforcement is essential to mitigate deterioration of bridge decks. The objective of this research program to examine the efficacy of using alternative materials in a bridge deck from both technical and economic perspectives. For the technical evaluation (Volume I), a three phase experimental investigation was conducted considering a wide range of corrosion-resistant reinforcing materials. These materials included stainless steels, microcomposite steel, and coated steels considering a variety of metallic and nonmetallic coatings. The first phase evaluated the bond between corrosion-resistant reinforcement and concrete using lap splice tests. The second phase evaluated the cracking behavior of slabs reinforced with corrosion-resistant reinforcement. Finally, the third phase evaluated corrosion resistance under uncracked and cracked conditions using macrocell test specimens. Transverse steel was also tied to the longitudinal steel to simulate actual bridge deck conditions. Recommendations are provided on development and splice lengths for both conventional black and corrosion-resistant reinforcing steel, control of cracks widths, as well as the selection, design, and construction of corrosion-resistant reinforcement. For the economic evaluation (Volume II), a decision support methodology and associated spreadsheet tool for robust analysis of the cost-effectiveness of alternative material types for bridge deck reinforcement was developed. The two evaluation criteria are agency and user costs, and the input data that influence this criteria include the deck service life, material process, discount rate, detour length, and bridge size. The methodology incorporates analytical techniques that include life cycle analyses to evaluate the long-term cost and benefits of each material over the bridge life; Monte Carlo simulation to account for the probabilistic nature of the input variables; stochastic dominance to ascertain the probability distribution of the outcome that a specific reinforcement material is superior to others; and analytical hierarchical process to establish appropriate weights for the agency and user costs. The study methodology is demonstrated using a case study involving three reinforcement material alternatives: traditional (epoxy-coated) steel, zinc-clad steel, and stainless steel. Through this study, it is demonstrated that the use of corrosion-resistant reinforcing materials can significantly increase bridge deck life, reduce agency and user costs associated with bridge deck rehabilitation and maintenance, and thus lower the financial needs for long-term preservation of bridges

Impact of Vehicle Travel Characteristics on Level of Service: A Comparative Analysis of Rural and Urban Freeways

The effect of trucks on the level of service is determined by considering passenger car equivalents (PCE) of trucks. The current version of Highway Capacity Manual (HCM) uses a single PCE value for all tucks combined. However, the composition of truck traffic varies from location to location; therefore a single PCE-value for all trucks may not correctly represent the impact of truck traffic at specific locations. Consequently, present study developed separate PCE values for single-unit and combination trucks to replace the single value provided in the HCM on different freeways. Site specific PCE values, were developed using concept of spatial lagging headways (the distance from the rear bumper of a leading vehicle to the rear bumper of the following vehicle) measured from field traffic data. The study used data from four locations on a single urban freeway and three different rural freeways in Indiana. Three-stage-least-squares (3SLS) regression techniques were used to generate models that predicted lagging headways for passenger cars, single unit trucks (SUT), and combination trucks (CT). The estimated PCE values for single-unit and combination truck for basic urban freeways (level terrain) were: 1.35 and 1.60, respectively. For rural freeways the estimated PCE values for single-unit and combination truck were: 1.30 and 1.45, respectively. As expected, traffic variables such as vehicle flow rates and speed have significant impacts on vehicle headways. Study results revealed that the use of separate PCE values for different truck classes can have significant influence on the LOS estimation

PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information

Intelligent vehicle anticipation of the movement intentions of other drivers can reduce collisions. Typically, when a human driver of another vehicle (referred to as the target vehicle) engages in specific behaviors such as checking the rearview mirror prior to lane change, a valuable clue is therein provided on the intentions of the target vehicle's driver. Furthermore, the target driver's intentions can be influenced and shaped by their driving environment. For example, if the target vehicle is too close to a leading vehicle, it may renege the lane change decision. On the other hand, a following vehicle in the target lane is too close to the target vehicle could lead to its reversal of the decision to change lanes. Knowledge of such intentions of all vehicles in a traffic stream can help enhance traffic safety. Unfortunately, such information is often captured in the form of images/videos. Utilization of personally identifiable data to train a general model could violate user privacy. Federated Learning (FL) is a promising tool to resolve this conundrum. FL efficiently trains models without exposing the underlying data. This paper introduces a Personalized Federated Learning (PFL) model embedded a long short-term transformer (LSTR) framework. The framework predicts drivers' intentions by leveraging in-vehicle videos (of driver movement, gestures, and expressions) and out-of-vehicle videos (of the vehicle's surroundings - frontal/rear areas). The proposed PFL-LSTR framework is trained and tested through real-world driving data collected from human drivers at Interstate 65 in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability and high precision, and that out-of-vehicle information (particularly, the driver's rear-mirror viewing actions) is important because it helps reduce false positives and thereby enhances the precision of driver intention inference.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar