Transportation agencies receive large volumes of free-form public comments describing infrastructure conditions, safety concerns, and service issues. These comments are often processed manually for downstream operational actions, which is time-consuming, inconsistent across reviewers, and difficult to scale, thereby limiting their value for operational decision-making. This study presents a machine learning and Large Language Model (LLM) framework for automated triage of free-form public comments, assigning each report to a three-level hierarchical taxonomy consisting of Category, Subcategory, and Final Decision. The proposed framework uses agency historical data together with retrieval-based evidence, where semantically similar past comments are provided to the LLM as contextual support to better align predictions with agency-specific labeling practices. The framework was evaluated using TF-IDF with Logistic Regression, TF-IDF with Linear SVM, embedding-based kNN with cosine similarity, few-shot LLM prompting, and retrieval-based LLM prompting. Results show that retrieval-based prompting achieved the best overall performance, with the highest accuracy at both the Category and Subcategory levels. At the Final Decision level, retrieval-based prompting slightly outperformed kNN, while few-shot prompting performed worse. Error analysis showed that many misclassifications were semantically plausible alternatives, reflecting the overlap across infrastructure-related complaint categories. When a second candidate label was allowed, further improving performance. Latency analysis also indicated that the framework can process more than 2000 comments in under 30 min, supporting faster and more consistent agency workflows.

Intersections present considerable safety risks for bicyclists, where road space shared with vehicles frequently triggers conflicts. Although connected technologies promise safety benefits, they may trigger additional stress and adverse behavioral changes, making it important to investigate their impact on bicyclist–vehicle conflicts. This study introduces a novel experimental framework that integrates a high-fidelity digital twin of a one-way signalized arterial (Delaware Avenue in Newark, DE, United States) with a dual human-in-the-loop simulator, allowing real-time interaction between a human-driven vehicle and a human-operated bicycle. The digital twin combines CARLA, used for immersive participant simulation, and PTV Vissim, which replicates realistic background traffic conditions. The first step was to identify potential conflict situations by considering traditional surrogate safety measures and validating them as real conflicts with self-reported feedback and physiological stress responses. Results confirmed that the experimental setup produced events meeting both kinematic and stress-based conflict criteria. The second stage focused on how connected technologies influence user experience, specifically whether collision warning information evokes additional stress, given its novelty and limited adoption in real-world contexts. Finally, we examined behavioral changes by analyzing approach and postconflict speeds, revealing how connectivity affects speed choices in conflicting situations, with drivers increasing approach speeds and bicyclists becoming more cautious. This research demonstrates the value of combining digital twins, dual human-in-the-loop simulators, and physiological sensing for studying connected safety technologies. The findings provide new insights into how connectivity influences user stress and behavior, helping the design and evaluation of future connected multimodal traffic systems.

This study evaluates the On-Demand All-Red Interval (ODAR) at signalized intersections to address red-light running (RLR) issues. Traditional fixed all-red intervals fail to adapt to dynamic traffic conditions, leading to potential safety risks and unnecessary delays. This study introduces a novel approach for dynamically extending the all-red interval on demand to enhance intersection efficiency while maintaining safety by eliminating unnecessary clearance intervals when no risk exists. Utilizing software-in-the-loop simulation, the study assesses the effectiveness of the ODAR method compared to conventional fixed-duration and Dynamic All-Red Extension (DARE) methods, allowing realistic controller testing without field deployment. The ODAR method adapts to real-time traffic conditions by incorporating vehicle speed and signal timing, ensuring vehicles with high collision risk clear the intersection safely. The study is conducted using a microsimulation model based on the Washington Street arterial network in Lake County, Illinois, validated against real traffic conditions. The results demonstrate that ODAR increases throughput and, in specific scenarios, reduces delays and stop occurrences compared to FAR and DARE strategies, based on a field-calibrated microsimulation dataset of a real-world arterial corridor. Importantly, these efficiency improvements are achieved while maintaining comparable intersection safety outcomes, as measured by red-light-running events, conflict frequency, and conflict severity.

Non-motorized users play a pivotal role in advancing sustainable transportation systems. To effectively promote and improve conditions for these modes of travel, the provision of high-quality infrastructure is a fundamental requirement. It was observed that among the methodologies for assessing sustainability from the aspect of traffic, hardly any methodology adequately assesses the state of non-motorized users, especially from the aspect of conditions in the traffic flow, that is, the infrastructure offered. The paper presents the development of a methodology for evaluating conditions for non-motorized users regarding the manuals "HCM 2010" and "Multimodal Quality/Level of Service Handbook" (2023). The purpose of the developed methodological framework is to show the condition of non-motorized users through the criteria of the Level of Service, i.e. to show the extent to which the infrastructure of the street network satisfies pedestrians and cyclists.