Transportation Agencies Research Articles

Comprehensive Investigation of Roundabout Safety Challenges in the Southeast United States: Findings and Recommendations from Kentucky’s Survey Analysis

Recognizing the relatively limited studies about roundabout perception in the Southeast of the United States, this study took the initiative and presented the results from a comprehensive survey, supported by the Kentucky Transportation Cabinet (KYTC), identifying the main concerns and difficulties encountered by Kentucky residents when navigating roundabouts. Thorough questions were prepared that encompassed various aspects, such as drivers’ sociodemographic characteristics, general driving-related questions, roundabout-specific questions, crash-specific questions, and open-ended roundabout safety concerns and recommendations. A total of 6,688 in-state responses were gathered. In general, Kentuckians are in favor of roundabouts, where 51.6% of respondents either “liked” or “strongly liked” navigating roundabouts. The chi-square ( x 2 ) test of independence revealed that age, level of education, and years of driving experience were significantly correlated with the overall opinion of roundabouts. Interestingly, there existed a significant difference between the proportion of younger (16–55 years) and older (55+ years) age groups in relation to their preference for roundabouts in Kentucky, with the younger age group having a significantly higher proportion (53%) than did the older age group (49%). Furthermore, the younger age group consistently had a significantly higher proportion of correct answers for the roundabout knowledge tests than the older age group had. The lack of roundabout-related education (39.3% of the responses) was reported to be Kentuckians’ largest roundabout safety concern, followed by driver behavior (27.4%). In addition to KYTC, other states’ departments of transportation and other agencies could use the study results to improve public opinion about roundabouts and better plan for newer roundabout locations.

Leveraging Physics with Deep Learning: Physics-Informed Neural Networks (PINN) for IRI Prediction in Flexible Pavements

This study presents a Physics-Informed Neural Network (PINN) framework for predicting the IRI of flexible pavements, utilizing the LTPP database. A total of 390 observations from 74 pavement sections across the United States were utilized. The architecture of the PINN combines an MSE loss function with a custom Physics-Informed MSE (PMSE) loss function. It integrates mechanistic IRI equations sourced from the Mechanistic-Empirical Pavement Design Guide (MEPDG) into the training process. Comparative analysis was performed against standard regression models and the MEPDG IRI prediction equation. Results indicate that the PINN model significantly improves prediction accuracy, achieving a Coefficient of Determination (R²) of 0.743 and a Root Mean Squared Error (RMSE) of 26.57, outperforming traditional methods. Furthermore, a closed-form equation from the model was derived. By utilizing this new simplified equation, transportation agencies can allocate resources and prioritize maintenance activities efficiently through the IRI forecast PINN Model for asphalt pavements.

Illinois Tollway Specification Management and Resource Tool

Transportation agencies require special provisions to issue the requirements for various elements of highway and bridge construction, which must frequently adapt to changes in field conditions, innovations, and techniques conducted through research. A major challenge for designers is that these special provisions must be included in the specification book assembled as part of the contract documents. It is a common occurrence that specification books often omit special provisions, related special provisions, or required pay items, owing to human error or unfamiliarity with the requirements. These errors can lead to unbalanced bidding or cause change orders during construction. As a result, this paper is focused on the development and implementation of a tool to assist designers in streamlining the process of selecting special provisions and assembling a specification book for contracts bid by the Illinois Tollway using a web-based tool. The tool, named the Specification Management and Resource Tool (SMART), allows designers to input the itemized list of pay items and obtain a complete list of required special provisions needed to assemble the specification book. SMART also has search capabilities that enable designers to quickly identify special provisions, access the document, and compile a complete list of special provisions and associated pay items derived from the specification requirements. This allows designers to improve their quality control processes and reduce the likelihood of errors that cause cost increases or overruns to the agency. The tool is available at: https://smart.wspis.com/ .

Automated Digitization Approach for Road Intersections Mapping: Leveraging Azimuth and Curve Detection from Geo-Spatial Data

Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to GDP and economic development. Accurate intersection mapping forms the foundation of effective road asset management, yet traditional manual digitization methods remain time-consuming and prone to gaps and overlaps. This study presents an automated computational geometry solution for precise road intersection mapping that eliminates common digitization errors. Unlike conventional approaches that only detect intersection positions, our method systematically reconstructs complete intersection geometries while maintaining topological consistency. The technique combines plane surveying principles (including line-bearing analysis and curve detection) with spatial analytics to automatically identify intersections, characterize their connectivity patterns, and assign unique identifiers based on configurable parameters. When evaluated across multiple urban contexts using diverse data sources (manual digitization and OpenStreetMap), the method demonstrated consistent performance with mean Intersection over Union greater than 0.85 and F-scores more than 0.91. The high correctness and completeness metrics (both more than 0.9) confirm its ability to minimize both false positive and omission errors, even in complex roadway configurations. The approach consistently produced gap-free, overlap-free outputs, showing strength in handling interchange geometries. The solution enables transportation agencies to make data-driven maintenance decisions by providing reliable, standardized intersection inventories. Its adaptability to varying input data quality makes it particularly valuable for large-scale infrastructure monitoring and smart city applications.

AI-Driven Intelligent Driving Assessment System: A Comprehensive Analysis of Driving Skills using Multi Sensor Data

To make roads safer, we need new ways to test driving skills and make transportation systems better than ever. This study presents an Intelligent Driving Assessment System that uses multiple sensors to track important driving metrics in real time. The device checks the driver's skill by looking at how much pressure they put on the accelerator pedal. When you combine data from several sensors, you get a full picture of how a driver acts and performs. The pressure on the accelerator pedal shows how well the driver controls the throttle and how quickly they speed up, which are both important for judging driving skill and fuel efficiency. The driver's alertness and focus are tracked by sensors that watch for blinking. The MEMS sensor steering control study also looks at how well drivers stay in their lanes and how safe their cars are. Advanced AI algorithms use machine learning to look at and understand sensor data in real time to judge each driving trait. These algorithms look at a driver's past driving habits, the weather, and how traffic changes to make a personal and accurate judgment of their performance. The AI-powered system combines data from many sensors to give a percentage score for safer driving and licensing choices. A system like this could help both AZS drivers and society as a whole. The technology lets drivers know how good they are at driving by showing them where they need to improve and making them more aware of their own driving. People who use the road more responsibly and safely are those who are watched and given feedback all the time. The system helps transportation and regulatory agencies check how well people can drive, give out licenses, and make sure safety standards are followed in a fair and systematic way.

The Role of the Mandailing Natal Regency Transportation Office in Increasing Regional Original Revenue (PAD) Parking Management in Panyabungan City

The low parking revenue in Mandailing Natal Regency over the past two years has hurt Regional Original Revenue (PAD). Observations show that this problem is caused by a lack of public awareness of paying parking fees and a lack of knowledge about parking services. This study aims to analyze the role of the Mandailing Natal Regency Transportation Office in increasing PAD through parking management in Panyabungan City. The approach used is qualitative with descriptive type, and data is collected through observation, interviews, and documentation conducted at the Transportation Agency office. The results showed that the agency has three leading roles in parking management to increase PAD: planning, mobilizing, and supervising. However, internal and external obstacles complicate parking management, such as the lack of parking lots in Panyabungan City that could disrupt nearby shops and the unavailability of terminals for rural transportation and buses. Therefore, although the role of the Transportation Agency is already underway, further optimization is needed to increase PAD through better parking management in Panyabungan City.

The Effect of Career Development and Work Motivation on Employee Performance Transportation Agency

Work motivation and career development are important factors that influence employee performance in government environments. This study aims to analyze the influence of career development and work motivation on employee performance Transportation Agency. The research population consisted of 124 employees, and a sample of 40 respondents was selected using purposive sampling. Data collection involved interviews, questionnaires, and documentation, utilizing both primary and secondary data. The analysis was conducted using multiple linear regression with SPSS version 26. The findings show that career development and work motivation simultaneously have a positive and significant effect on employee performance. Effective career development such as structured training, clear career paths, and skill development opportunities enhances employee motivation and responsibility, which in turn improves organizational productivity. Meanwhile, strong work motivation, whether intrinsic or extrinsic, also contributes positively to performance. When both variables are optimized together, employees tend to demonstrate higher productivity, discipline, and goal orientation. The results highlight the importance of managerial strategies in developing employee potential and sustaining motivation. Organizations, particularly in the public sector, must prioritize career development and motivational strategies to ensure consistent performance improvement and the achievement of institutional goals.

Mapping the patterns of cost and time overruns in the U.S. highway construction projects: a regional risk analysis

Highway construction projects are inherently complex, with time and cost as risk factors requiring effective management for successful completion. Regional factors—such as climatic conditions and workforce availability—influence cost overruns and time delays in highway projects; thus, addressing these factors through robust risk management is essential for risk mitigation. As part of that goal, this research aims to map the cost and time overrun risk patterns across various regions in U.S. highway projects and examine how transportation agencies can leverage this risk mapping. Data from 186 highway construction projects across the four AASHTO regions were analyzed statistically to evaluate the risks of time delays and cost overruns. A focus group discussion with experts was conducted to validate the findings and identify strategies for leveraging risk mapping. The findings indicated no significant differences among the four regions regarding the overall risks of cost overruns and time delays. However, the northeastern states exhibited the highest incidence of cost overruns, while the western states experienced the most considerable time delays. The southern and midwestern regions demonstrated moderate risk levels in both categories. Eight strategies were identified to address these cost overruns and time delays arising from regional factors through the risk mapping.

Exploring Relationships between Summary Resilient Modulus, California Bearing Ratio, and Light Weight Deflectometer Modulus for Unbound Coarse Materials in Idaho

Mechanistic–empirical pavement design (using AASHTOWare Pavement ME Design software) incorporates inputs at three different levels (i.e., Level 1, Level 2, and Level 3). Level 1 input is preferred over Level 2 and Level 3 in AASHTOWare Pavement ME Design software. Level 1 inputs are project- or job-specific, and are obtained through direct measurements or laboratory testing. Level 1 input measurements, such as the resilient modulus ( M R ), might require additional resources to those required for Level 2 inputs. Many transportation agencies develop Level 2 inputs, which are more region-specific. For unbound materials, M R is considered one of the fundamental material properties. Measuring M R through laboratory testing requires advanced equipment and trained technicians, and the test is time-consuming. This study is an evaluation of the correlations between the resilient modulus of commonly used unbound materials in Idaho and the California bearing ratio (CBR) to develop Level 2 inputs. In addition, the recently introduced light weight deflectometer (LWD) modulus was correlated with the resilient modulus. The results of this study demonstrate a good and promising correlation between resilient modulus and LWD modulus, and a fair correlation between resilient modulus and CBR.

Analysis of the Influence of Competency, Work Motivation, and Discipline on Employee Performance in South Labuhanbatu Regency Transportation Agency

Performance in human resource research is always a reference or choice of topics with quite high intensity, so that almost all academics and universities in the world are favorites to choose this topic. The purpose of this study was to determine and analyze the effect of competence, work motivation, and work discipline on employee performance at the South Labuhanbatu Regency Transportation Agency. The number of people registered at this Agency is 67, consisting of Civil Servants and contracts with sampling using a census. The data collection technique used a questionnaire (Google Form). The data analysis tool used is path analysis with SmartPLS. The results of the hypothesis testing show that the variables of competence and work motivation in this study do not have a significant effect on employee performance at the South Labuhanbatu Regency Transportation Agency. At the same time, the work discipline variable is a variable that tends to strongly influence employee performance at the South Labuhanbatu Regency Transportation Agency.

School Transportation Planning Using PTV Visum 2025

The problem of school transportation is a serious concern. This can be seen from the low interest of students in using public transportation due to suboptimal service and poor fleet conditions. This study aims to plan school transportation for students. The stages of this study consist of data collection, school transportation analysis and interpretation of results. The results of the analysis show the potential demand for school transportation of 13,416 students, resulting in 7 routes serving 36 schools. These routes are between 4.75 km and 23.6 km long and use the Isuzu NQR 71 fleet with a capacity of 33 passengers, operating 6 days a week with 3 shifts: morning (06.00–07.30), afternoon (11.45–15.00) and evening (15.01–17.30), speed 35 km/hour and target load factor 100%. Travel time ranges from 5-60 minutes, vehicle circulation time 13-105 minutes, number of rit 1-4 rit, with headway between 1-4 minutes depending on shift, and number of fleet 1-76 fleets (Isuzu NQR 71). The results of this analysis are expected to provide a significant contribution to the Samarinda City Transportation Agency in planning school transportation.

Safety Performance Functions and Crash Modification Factors for Skid Resistance on Interstate and Non-Interstate Highways

Pavement friction is crucial for road safety, especially in adverse weather. This study investigates the relationship between pavement friction—measured as skid number (SN)—and crash frequency on Utah highways. Using data from 2016 to 2019 for I-15 (interstate) and US-89 (non-interstate), negative binomial models were estimated to establish safety performance functions (SPFs) and crash modification factors (CMFs). The models accounted for traffic volume, segment length, and roadway geometric characteristics, examining various crash types, including dry- and wet-weather, property damage only, and injury-related crashes. Results show a significant negative association between SN and crash frequency for all crash types on both highway types. Higher SN values (more friction) were linked to fewer crashes. A 10-unit increase in SN was associated with a 7% to 8% reduction in dry-weather crashes on both highway segments. For wet-weather crashes, the same increase in SN resulted in a 14% decrease on non-interstate and a 21% decrease on interstate highways. The impact of SN on reducing crashes was particularly strong on interstate highways during wet conditions, indicating that pavement friction is vital for safety in these scenarios. Additionally, the safety impact of skid resistance was greater on segments with more curved portions. These findings suggest that enhancing pavement friction through measures such as high friction surface treatments could significantly improve traffic safety. The results support the continued collection of skid data by transportation agencies to identify high-risk locations and prioritize friction improvement efforts to enhance roadway safety.

Applications of UAV Technologies in Assessment of Transportation Infrastructure Systems

As transportation infrastructure systems continue to expand, the demand for unmanned aerial vehicle (UAV) technologies in the assessment of urban infrastructure is expected to grow substantially, due to their strong potential for efficient data collection and post-processing. UAVs offer numerous advantages in infrastructure assessment, including enhanced time and cost efficiency, improved safety, and the ability to capture high-quality data. Furthermore, integrating various data-collecting sensors enhances the versatility of UAVs, enabling the acquisition of diverse data types to support comprehensive infrastructure evaluations. Numerous post-processing software applications utilizing various structure-from-motion (SfM) techniques have been developed, significantly facilitating the assessment process. However, researchers’ efforts to find the potentialities of this technology will be in vain if its applications are not utilized effectively in the practical field. Therefore, this study aims to determine the adaptation condition of UAV technologies in different Department of Transportation (DOT) and Federal Highway Administration (FHWA) agencies to assess transportation infrastructure systems. This study also explores the quantitative analysis of benefits and challenges/barriers, expectations for every UAV and post-processing software, and the cutting-edge features that should be integrated with UAVs to effectively evaluate transportation infrastructure systems. A comprehensive survey form was distributed to all 50 DOTs and the FHWA, and 35 complete responses were recorded from 27 DOTs and the FHWA. The survey results show that 25 agencies currently use UAVs for roads or highways, and 23 DOTs for bridges, confirming these as the most commonly assessed infrastructure systems. The top benefits found in this study include safety, cost effectiveness, and time efficiency (mean ratings: 3.95–4.28), while weather, FAA regulations, and airspace restrictions are the main challenges. Respondents emphasize the need for longer flight times, better automation, and advanced data tools, underscoring growing adoption and highlighting the need to overcome technical, regulatory, and data privacy challenges for optimal UAV integration within transportation infrastructure systems management.

Analysis of Employee Creativity, Competency, Work Motivation and Work Quality on Employee Achievement in the Medan City Transportation Department

Using work quality as an intervening variable, the study aims to ascertain and examine the degree to which the work creativity, competence, and motivation variables impact the job performance of employees at the Medan City Transportation Agency. Eighty employees of the Medan City Transportation Agency served as the study's subjects. The study's independent variables included work creativity, competence, and motivation; its dependent variables included work performance; and its intervening variables included work quality. the outcomes of data analysis using SPSS 22's multiple linear regression analysis. Observation, surveying respondents, and documenting studies are the methods used to gather data. Data collection methods include questionnaire distribution and observation, and the study methodology is a quantitative descriptive approach of data analysis utilizing the multiple linear regression method. The study's findings indicate that the variables of work originality, competence, and motivation have a partial and simultaneous impact on the job performance of Medan City Transportation Agency personnel.

Leveraging probe data to assess left-turn driver behaviour under varying permissive signals

Flashing yellow arrows (FYAs) and circular green signals (CGs) are prevalent permissive left-turn indications at intersections. These signals can significantly influence drivers’ left-turn behaviour and, in turn, affect intersection safety. Despite this, few studies have investigated differences in left-turn behaviour under these indications. This study leverages probe data to assess speeds, accelerations, and decelerations at 106 FYA and 116 CG intersection approaches in Tucson, Arizona, while controlling for geometric and temporal factors. Results showed that FYA approaches generally exhibited lower speeds and higher decelerations than CGs. Vehicles in the outer lanes of dual left-turn approaches with FYAs showed greater acceleration fluctuations. Additionally, vehicles on dual left-turn lanes, particularly with FYAs, exhibited lower average speeds than those on single left-turn approaches. These findings enhance the understanding of how FYAs and CGs affect driver behaviour at approaches with varying geometric configurations, aiding transportation agencies in making informed decisions to improve intersection safety.

Paving Over the Past: Oregon Historical Markers, White Innocence, and Manifest Destiny

In this article we examine how Oregon’s highway markers represent settler colonization and White supremacy. The data consist of 141 historical highway markers developed by the Oregon Travel Information Council (OTIC), a state-led tourism and transportation agency. We coded the data set based on the extent to which markers acknowledge White supremacy or settler colonization or represent multiculturalism. Although the markers employ multiple narratives, we found that the vast majority deny histories of White supremacy and colonization. We argue that one reason for this is because hegemonic cultural memory is invested in preserving White innocence, which is central to the legitimacy of the White nation. There are numerous ways that White innocence is produced, including centering Whiteness; obscuring Black, Indigenous, Mexican, and Asian American history; erasing settler violence; and celebrating and normalizing settler colonization. Within the data set, Manifest Destiny is the most fully articulated discourse of White innocence. Our findings illuminate how White supremacy and Manifest Destiny are foundational to Oregon and are expressed through state-sponsored White innocence to absolve racial violence and dispossession.

Evaluating Project Selection Criteria for Transportation Improvement Plans (TIPs): A Study of Southeastern U.S. Metropolitan Planning Organizations

Metropolitan Planning Organizations (MPOs) are required to prepare a Transportation Improvement Plan (TIP) that outlines a fiscal strategy over a four-year period in order to qualify for federal funding. However, the growing population and limited financial resources available often pose significant challenges for transportation agencies in aligning their needs with available budgets. This article examines the project selection criteria used by 20 MPOs in the Southeastern United States to identify the best practices for prioritizing projects in TIPs. Using document analysis, this study categorizes the most commonly used criteria into nine broad groups: safety and security; environmental impacts; mobility, accessibility, and connectivity; preservation; environmental justice; equity; economic factors; alignment with other plans; and local support. Many of these categories are further divided into subcategories and metrics. Despite variations in criteria, weighting, scoring, and methodologies across these MPOs, the study identifies several shared factors that support effective decision-making in regional transportation planning. These findings can help transportation planners and policymakers refine their project prioritization strategies, promote consistency, and lead to improved decision-making frameworks for future TIP development.

Highway-Transportation-Asset Criticality Estimation Leveraging Stakeholder Input Through an Analytical Hierarchy Process (AHP)

Transportation agencies face increasing challenges in identifying and prioritizing which infrastructure assets are most critical to maintain and protect, particularly amid aging networks, limited budgets, and growing threats from climate change and extreme events. However, existing prioritization approaches often lack consistency and fail to adequately incorporate diverse stakeholder perspectives. This study develops a systematic, stakeholder-informed method for ranking transportation assets based on their criticality to the overall transportation system. As a novel approach, we use the analytical hierarchy process (AHP) and present a case study of the applied approach. Six criteria were identified for ranking assets: annual average daily traffic (AADT), redundancy, freight output, roadway classification, Social Vulnerability Index (SoVI), and tourism. Stakeholder input was collected via an AHP-based survey using pairwise comparisons and translated into weighted rankings. Thirty complete responses (13.2% response rate) from experts (i.e., engineers, analysts, planners, etc.) were analyzed, with the resulting ranks from highest to lowest priority being AADT, redundancy, freight output, roadway classification, SoVI, and tourism. Stability analysis confirmed that rankings were consistent with a minimum of 15 responses. The resulting method provides a practical, replicable tool for agencies to perform statewide vulnerability/resiliency assessments ensuring that decision-making reflects a broad range of expert perspectives.

Risk factors and safety strategies for mitigating violations, harassment and assault in taxi and ride-hailing services

ABSTRACT Recent evidence highlights an alarming rise in violations, assaults, and harassment targeting both drivers and passengers in taxi and ride-hailing services, prompting concerns about the adequacy of current safety measures. This systematic review identifies key risk factors and proposes strategies to enhance safety and security in these services. We analysed documents from Scopus, Web of Science, and PubMed, published between January 2014 and June 2024. Of the 1,783 documents screened, 41 met the inclusion criteria. The review identified several risk factors contributing to increased incidents for both passengers and drivers, including nighttime operations, intoxicated passengers, high-crime areas, lack of surveillance, racial and gender biases, anonymity, inadequate training, and high-stress environments. Strategies to mitigate these risks include installing video and audio recording systems, enhancing app features (e.g. emergency alerts, real-time tracking, verification systems), offering women-only services, keeping interior lights on during dark hours, and displaying signage about surveillance. Training programmes focused on preventing sexual harassment and improving communication were also recommended. Video and audio recordings were identified as particularly effective but must adhere to local legal frameworks. Proper handling and storage of recordings by transportation agencies were emphasised to ensure security and privacy compliance. This paper underscores the need for comprehensive, legally compliant guidelines to address safety concerns in both taxi and ride-hailing services.

Quality of service of “Electric Suroboyo Bus” as an urban public transportation system in Surabaya

The global environmental crisis has prompted various sectors, including the government, to support sustainability efforts. In response, the Surabaya City Government introduced the "Electric Suroboyo Bus" as an environmentally friendly and customer-oriented public transportation innovation. This initiative, managed by the Surabaya City Transportation Agency and operated by PT Yutaka, aims to provide a safe, comfortable alternative to private vehicles while promoting the shift to electric-based public transit in line with the SDGs 7 goal of pollution reduction. This study employed a qualitative descriptive approach to evaluate the service quality of the Electric Suroboyo Bus across five dimensions: responsiveness, reliability, assurance, empathy, and tangibles. Data were collected through purposive sampling involving key stakeholders, including the Transportation Agency, PT Yutaka, and Surveyor Indonesia, using interviews, observations, and documentation at relevant sites. The results indicate that the Electric Suroboyo Bus demonstrates strong service performance in responsiveness, reliability, assurance, and empathy. However, certain tangible elements, particularly infrastructure such as bus stop facilities and the use of digital applications, require further development. Enhancing collaboration among managing parties is crucial to optimize the use of digital platforms and improve overall service quality, ensuring greater public adoption and contributing more effectively to sustainable urban mobility.