Transportation Agencies Research Articles

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/.

PS‐InSAR post‐processing for assessing the spatio‐temporal differential kinematics of complex landslide systems: A case study of DeBeque Canyon Landslide (Colorado, USA)

AbstractThe complex superimposition of different kinematics and nested sectors within landslide systems amplifies the challenge of interpreting their heterogeneous displacement pattern and targeting effective mitigation solutions. As an example of such peculiar spatio‐temporal behaviour, the DeBeque Canyon Landslide (Colorado, USA) is emblematic of the application of interferometric post‐processing analysis for a detailed, remotely‐based investigation. We employed a multi‐geometry Persistent Scatterers (PS) InSAR dataset to provide continuous information on the spatio‐temporal scale and achieve a solid representation of the segmented kinematics and timings. Using an updated geomorphological map of the landslide system, we performed a two‐dimensional decomposition of the Persistent Scatterers (PS) dataset to determine the displacement orientation and inclination for each internal sector of the landslide system. We then conducted statistical analyses on the displacement vector characteristics and time series data. These analyses enabled us to spatially characterize the segmented activity patterns of the landslide system and identify abrupt changes in trends associated with preparatory and triggering factors. A clear differentiation of the rotational or translational kinematics within the landslide system was accomplished solely using surface displacement measures. Moreover, the application of a Bayesian model on the bi‐dimensional vector time series leads to the identification of significant differences in the deformational behaviour of each sector with respect to precipitation and temperature factors. Our approach represents a replicable method for local‐scale characterization and monitoring of landslides exhibiting complex spatio‐temporal displacement patterns and providing an effective, low‐cost solution for transportation agencies from a risk‐reduction perspective.

Evaluating Mobile LiDAR Intensity Data for Inventorying Durable Tape Pavement Markings.

Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Study protocol of the safe driving program for the prevention of car accidents: A randomized controlled trial protocol.

Driving cessation is strongly associated with adverse health outcomes in older adults. Although numerous studies have focused on driving skill interventions for safe driving, the effects of interventions on car accidents remain unclear. We designed a randomized controlled trial to examine the effects of driving skill training on the prevention of car accidents among community-dwelling older adults using on-road training. A total of 1408 community-dwelling older drivers aged ≥65 years participated in the randomized controlled trial with blinded endpoint assessment. Participants randomized to the intervention group (n = 697) underwent four sessions, with 200 min re-training focused on the problem of driving in the older people. Controls (n = 697) received one classroom education session. On-road driving performance was assessed by certified driving school instructors in a driving school. The primary endpoint is an incident car accident, which will be detected based on the Japanese national car accident report data, after 24 months. This study has the potential to provide the first evidence of the effectiveness of on-road driving skill training regarding the prevention of car accidents. If our trial results show a lower number of car accidents due to driving skill training, this kind of intervention will provide an effective method for maintaining safe driving. These results will be disseminated to the appropriate national transportation agencies to improve or modify safe driving policies and possibly extend the validity of driver's licenses for older adults. UMIN-CTR (identification number: UMIN000034709). Registered 31 October 2018. Geriatr Gerontol Int 2024; ••: ••-••.

Pavement Distress Detection Based on the Staged Deep Learning Classifier Approach

Pavement crack detection is a critical task that is essential to ensure the safety of roads and highways. Accurate detection and classification of pavement cracks can help transportation agencies to identify potential maintenance needs and plan appropriate interventions. In this project, we propose a staged deep learning classifier approach for pavement crack detection to provide accurate and comprehensive information about the pavement condition. In this proposed pavement crack detection model, there are four main phases: pre-processing, feature extraction, feature selection, and crack detection. Firstly, raw images are pre-processed through median filtering and histogram equalization for noise removal and contrast enhancement. Next, features such as the improved local binary pattern, gray-level co-occurrence matrix, and gray-level run length matrix are extracted from the pre-processed images. The optimal features are selected using a hybrid optimization model, the tuna customized honey badger optimization algorithm, which combines the honey badger algorithm and tuna swarm optimization. This new hybrid optimization model will enhance the search strategy for solving optimization problems. The loss function of an augmented convolutional neural network (A-CNN) is modified to compute the root mean square error instead of the entropy-based loss function. The final detected outcomes (presence or absence of cracks) are acquired from the A-CNN.

Boater’s Preference for Coral Reef Restoration and Sustainable Resource Management in Florida

The Florida Reef is under intense pressure from excessive human disturbance and climate change. A group of Florida residents including boaters and fishers was surveyed about their concerns and willingness to pay (WTP) for restorative services. A discrete choice valuation experiment was conducted, involving nearly 1,300 respondents, to estimate WTP for coral reef restoration, wide beaches, and clean water. The boaters demonstrated a somewhat higher WTP for a more extensive beach size and coral restoration. The analysis allowed quantification of maritime transportation passengers’ preferences for sustainable resource management. The findings provide useful insights for maritime transportation and coastal management agencies wishing to finance conservation initiatives. Furthermore, the study offers valuable methodological contributions and policy relevant information to incorporate ecosystem service valuations into the decision-making process beyond the study areas.

Real-Time Freeway Crash Detection Framework using Connected Vehicle Waypoint Data

Accurate and timely detection of traffic accidents is crucial for transportation agencies seeking swift responses and effective traffic management, particularly on freeways where crash severity and traffic flow disruptions are amplified. While several methods have been developed using cameras or infrastructure-mounted sensors, these approaches may face challenges in geographical scalability. To address this issue, a novel approach has been developed that leverages real-time connected vehicle trajectories at a market penetration rate of 4%–9%. The method involves extracting five key features from individual journeys that sense the speed and acceleration fluctuations. The results demonstrate promising outcomes, achieving an overall accuracy of 63.5% with 4.1% false detections and 33.4% non-detections. This accuracy surpasses the speed-alone model using the same data by 24%. Furthermore, higher traffic volumes lead to even greater accuracy, exceeding 80% at level of service D and 90% at level of service E. The developed algorithm also exhibits precision in crash detection, with a mean latency of only 2.5 min after the actual incidents. The entire processing time may additionally add up to 1 min. The robustness of the model is tested for a single day on one of the interstates, providing promising results. This study highlights the potential of connected vehicle data in enhancing crash prediction methods and underlines its value for the safety and efficiency of future transportation systems.

Quantitative Analysis Toward Guidance for Implementing E-Ticketing for Asphalt Paving Operations

Previous research efforts into e-ticketing for asphalt paving operations have qualitatively identified benefits and concerns. However, there is minimal academic literature detailing the quantitative impact of e-ticketing in the post-COVID-19 pandemic era. This research addresses this gap by gathering information from the Kentucky Transportation Cabinet (KYTC) about e-ticketing practices to aid in determining potential implementation steps. Quantitative project information was collected from KYTC projects to compare the traditional weigh ticket collection process with e-ticketing processes for both project engineers and inspectors. The analysis shows statistically significant time savings for field employees but not project management personnel. Further, factors affecting e-ticketing implementation for both project engineers and inspectors were also investigated. For inspectors, the dataset was analyzed to determine how the project type and quality assurance responsibilities of the inspector altered the e-ticketing impact. For engineers, the dataset was analyzed to determine how the project type altered e-ticketing impacts and how e-ticketing affected the engineer reviewing and storing project data. Cluster analysis was completed for both project engineers and inspectors to group project types where e-ticketing impact was maximized, and a decision matrix was created to aid agencies in creating implementation plans. The primary contributions to the body of knowledge include a modified action research methodology applied to electronic bulk material tickets to incorporate practitioner expertise into implementation guidelines and quantitative findings indicating that state transportation agencies should first implement e-ticketing on large asphalt-resurfacing projects, such as interstate rehabilitations, where inspectors have strenuous quality assurance responsibilities to maximize benefit.

When Segment Anything Model Meets Inventorying of Roadway Assets

Automating the identification, localization, and monitoring of roadway assets distributed widely in the roadway network is critical for the traffic management system. It can efficiently provide up-to-date information in supporting transportation asset management. Collecting videos with vehicle-mounted cameras and processing the data with computer vision-based deep learning methods is garnering increased attention from transportation agencies. While promising, challenges arise due to the lack of high-quality annotations for roadway assets in images, difficulties in identifying these assets, and limited solutions. The Segment Anything Model (SAM), a visual foundation model, demonstrates robust zero-shot capability for general image segmentation under various prompts. This study evaluates SAM’s applicability and efficiency in extracting roadway assets from images. Specifically, it examines the impacts of model size and prompt quality on SAM’s performance in segmenting roadway assets. Five state-of-the-art semantic segmentation models are trained and compared with SAM. Results show that a lightweight SAM with human-rendered prompts outperforms the five semantic segmentation models. Based on the evaluation results, future work will explore incorporating SAM into transportation asset management applications, promoting collaboration between human experts and artificial intelligence.

Feasibility of LoRa‐based Infrastructure‐to‐Vehicle (I2V) communication for road weather information systems

SummaryA road‐weather information system (RWIS) is an attractive application in the context of intelligent transportation systems (ITSs), which is used where climatic conditions and meteorological events worsen traffic safety. The primary goal is to significantly reduce potential accidents by sending early warning messages to prompt immediate action. For this purpose, road weather information stations are placed at critical points on the highways. In addition to responsible transportation agencies, enabling direct warning message transfer from roadside infrastructure to vehicles can significantly improve the effectiveness of a typical RWIS application. Dedicated short range communication (DSRC) and cellular vehicle to everything (C‐V2X) technologies are extensively used for data exchange between vehicles (V2V) and between vehicles and road infrastructure (V2I or I2V) under the umbrella term of vehicle‐to‐everything (V2X) communications. Latency and coverage are key performance indicators of the vehicular network that determine how drivers react to risks in adverse road and weather conditions. V2X‐based connectivity mainly aims to offer low latency in message delivery, but it is not possible to obtain a wide‐range coverage due to significant propagation loss in the dedicated 5.9 GHz ITS frequency band. In this study, we presented a long range (LoRa)‐based RWIS application that uses I2 V communication at sub‐GHz bands. Our main contribution is proposing a novel method for RWIS implementations, where the LoRa gateways run in a half‐duplex way to collect information from road weather information stations in the uplink channel and then broadcast warning messages to vehicles in the downlink channel. To validate the applicability of the proposed method, we presented a feasibility study that includes transmission range and latency analysis. Our theoretical calculations' results validate the more extended transmission range and applicability of LoRa connectivity on RWIS applications, especially for common scenarios like black ice where the climate risks do not occur suddenly. We also showed that our methodology would be beneficial due to the ability to offer wide coverage, especially in the areas where cellular base stations have not yet existed or V2X road infrastructure has not yet been established.

Enhancing work zone crash severity analysis: The role of synthetic minority oversampling technique in balancing minority categories

Road work zones are becoming increasingly common due to the aging infrastructure and the need for capacity enhancement. They present significant safety risks due to narrow lanes, uneven traffic flow, lower speed, and reduced visibility. It is particularly important to understand the role of human behavioral factors in WZ crash injury severity due to difficulty navigating such areas. Furthermore, the crash injury data available is mostly imbalanced, primarily due to the lower incidence of high-cost fatal and severe injuries, and can benefit from the use of emerging analysis techniques. This research study examines a unique dataset comprising 7,855 WZ crashes in Tennessee from 2018 to 2022 as a case study to provide useful insight into the behavioral factors associated with injury severity and how they change after adjusting for the underrepresented fatal and serious injuries within the dataset. The study applies frequentist methods and a machine learning technique enhanced with the Synthetic Minority Oversampling Technique (SMOTE), addressing the data imbalance (relatively fewer fatal and serious injuries) for useful inferences and predictions. The study results indicate that aggressive driving, overspeeding, and drunk driving significantly elevate injury severity. Additionally, after balancing the minority categories of crash injury severity levels, the importance of contributing factors changes. The study offers engineers and data analysts a framework for analyzing imbalanced data, a prevalent issue in crash injury severity analysis. By exploring key behavioral factors responsible for injury severity in WZ crashes, the study provides useful insight and valuable information to traffic safety engineers, transportation agencies, and policymakers to implement enhanced safety measures in WZ design and management, ultimately aiming to mitigate injury severity and to improve overall safety for road users.

Network-level optimisation approach for bridge interventions scheduling

This paper introduces a novel extension of the multi-system optimisation method, known as the 3C concept, tailored for optimising budget allocation for bridge interventions at the network level. This extended methodology accounts for the interdependencies among bridges due to their spatial proximity within the network. It incorporates direct and user costs, bridge performance indicators, and a bridge deterioration model. A real-world case study involving a portfolio of 555 bridges demonstrates the practicality of the methodology, efficiently determining the optimal intervention sequence. Over an 18-year analysis period, the proposed methodology achieved a 23% reduction in total costs by combining repairs for bridges with high to severe damage and maintenance for the others. This represents a significant improvement compared to the traditional approach, used by bridge management agencies, which relies exclusively on maintenance. The optimised procedure outperforms human intuition in managing complex bridge networks, particularly over extended periods. This methodology can assist transportation agencies in implementing and exploring various scenarios by adjusting the time between consecutive interventions and budget constraints, supporting comprehensive analysis and informed decision-making.

Safety Culture and Worker Perception in Highway Maintenance Operations: A Survey of Alabama Department of Transportation Maintenance Technicians

The survey entitled “Field Survey of Alabama Department of Transportation (ALDOT) Transportation Maintenance Technicians (TMTs) Perception of Worker Safety” investigates the safety perceptions and concerns of maintenance workers within ALDOT through a multifaceted analysis approach. The findings reveal critical insights that can significantly enhance ALDOT’s safety practices, training programs, and overall safety culture. Disparities in safety perceptions across different ALDOT’s areas were found, emphasizing the need for ALDOT to tailor its strategies to meet the unique needs of each area, ensuring that safety measures are both relevant and effective. The study emphasizes the importance of implementing targeted interventions focused on high-risk activities and prevalent safety concerns, allowing ALDOT to prioritize resources effectively. Additionally, the research highlights the necessity for continuous improvement by regularly evaluating safety initiatives based on feedback and best practices. This ongoing evaluation ensures that safety measures remain relevant and effective in addressing the evolving needs of TMTs. By integrating these insights into its safety management practices, ALDOT can cultivate a more proactive and resilient work environment that not only addresses immediate safety concerns but also establishes a sustainable culture of safety prioritizing employee well-being. The study provides a roadmap for future improvements in workplace safety and initiates discussions on the implications and recommendations for enhancing ALDOT’s and other transportation agencies’ safety management practices.

Towards development of a roadway flood severity index

Flooding is among the costliest and deadliest weather disasters. Moreover, different types of flooding have significant impacts on the transportation network and infrastructure including flash, riverine, urban, coastal, and storm surge. The variety of flooding scenarios makes it challenging to quantify the impacts of flooding on transportation across spatial scales; however, such metrics would be beneficial both prior to and after the event. Pre-flood metrics can promote enhanced impact-based decision-support guidance and hazard communication, while post-flood metrics may include larger regional disruptions located away from the most inundated areas and their associated secondary societal impacts. This study developed a retrospective Roadway Flood-Severity Index (RFSI) from 1982 to 2020 capable of integrating geo-located hydrometeorological data and transportation mobility information across localized and multi-state, sub-national regions to (1) categorize larger-scale, flood-related transportation disruptions, (2) understand the origins of those disruptions, and (3) identify severity risk levels of individual road segments and broader regions of transportation disruption during flood events. The fundamental question is, as flooding events unfold, can past hydrometeorological inundation information be coupled with transportation system network and mobility data to identify the most vulnerable roadway segments and regions? The overall mobility impacts of flooding on transportation were highly variable and relatively uncommon throughout the study period. Given this variability in other mobility data (e.g., vehicle speeds), hydrometeorological parameters were used exclusively as model inputs and crowdsourced Waze flood reports were used as the target response variable. A logistic regression based RFSI was found to best align with the dataset providing a “no flood” or “flood” classification. Eventually, this retrospective analysis will be extended to provide predictive capability as well. The RFSI is intended to provide transportation agencies with a quantitative metric to classify, categorize, and communicate the potential impacts of flood events throughout the transportation network.

Assessing the impact of driver compliance on traffic flow and safety in work zones amid varied mixed autonomy scenarios

The safety of work zones is a critical issue for drivers, transportation agencies, and governing authorities. In particular, the vehicles that perform lane changes in the proximity of the work zones involving lane closure, pose a significant threat to the safety of the public and the work zone workers, as they need to complete a forced merging. Yet, there is no comprehensive simulation framework to examine the work zone traffic safety under different compliance distributions of the drivers to the warning delivery for the work zone in a mix-autonomy operation of autonomous and human-driven vehicles. To fill this void, we present an integrated microsimulation framework to assess the correlation between the number of vehicles that perform late merge at the taper (LMT) and traffic mobility and safety under different empirical compliance distributions of the drivers to the warning delivery for the downstream work zone.We employ different work zone configurations to illustrate the relationship between late merges at the taper and performance indicators for traffic mobility and safety of the work zone under a variety of work zone configurations. Simulation results show that compliance distribution significantly impacts the number of late merges at the taper (LMTs) and thereby traffic safety and efficiency. Our findings demonstrate that when human-driven vehicles exhibit high compliance behavior to the merging warning signs, it can offset the impact of the lower percentage of market penetration rate (MPR) levels for autonomous operation to achieve comparable traffic safety and efficiency. We further employ the conflation of microsimulation observations and data-driven models to design a regression model to predict LMTs as an indicator for traffic conditions using the work zone configuration as input variables. In particular, we address the heterogeneity induced by the compliance distribution of drivers by sampling the data points from the distribution to capture the diversity in compliance behaviors of the drivers. Our findings can provide insights for practitioners and researchers regarding the optimal compliance distribution using the performance measurements demonstrated in this work.

Large Scale Evaluation of Normalized Hard-Braking Events Derived from Connected Vehicle Trajectory Data at Signalized Intersections, Roundabouts, and All-Way Stops

Intersection safety has been traditionally evaluated using three to five years of crash data. Recent literature suggests that connected vehicle (CV)-derived hard braking (HB) events can provide a surrogate for crashes with only a few weeks or months of data collection. This study used CV trajectories to derive HB events. Then, the HB events were normalized as the ratio of HB events to sampled CV trajectories. The normalized HB ratios were evaluated and compared at 435 signalized intersections, roundabouts, and all-way stops in Indiana. The analysis showed that signalized intersections and roundabouts had the highest counts of HB events, and all-way stops had the highest HB ratios. Through movements at signalized intersections showed the lowest HB ratios, whereas left turns at all-way stops had the highest ratios. A density analysis of the geospatial occurrence of HB events concluded that they tend to occur closest to the intersection center at all-way stops, but are more evenly distributed at signalized intersections. Additionally, a speed analysis indicated that HB events at signalized intersection through movements tend to occur at higher speeds, roughly between 26 and 36 MPH, perhaps due to the driver reaction during the onset of yellow. The findings presented in this study provide transportation agencies with insights on the occurrence of normalized HB ratios at three different intersection types. The data provided in this paper provide a framework for agencies to use HB ratios to screen different types of intersections for further evaluation.

Market Penetration Rate Optimization for Mobility Benefits of Connected Vehicles: A Bayesian Optimization Approach

The advancements of connected vehicle (CV) technologies promise significant safety, mobility, and environmental benefits for future transportation systems. These benefits will largely rely on the market penetration rate (MPR) of CVs and connected infrastructure. However, higher MPR is not guaranteed to result in greater benefits in a transportation system in some cases even if we do not consider the deployment cost of CVs. Therefore, understanding the optimal CV MPR to achieve the best system benefits is informative and can provide some guidance for transportation agencies to use appropriate incentives or other policies to potentially affect the speed of CV adoption. Instead of using the traditional incremental method, this paper proposed a simulation-based approach combined with Bayesian optimization to determine the optimal CV MPR that achieves the highest performance benefits for a freeway segment. The proposed methodology is tested in the I-210 E (in California, U.S.) simulation freeway segment built and calibrated in Simulation of Urban Mobility software as a case study. The weighted sum of the average total travel time on the mainline and the average queue length of on-ramps is formulated as the objective function to optimize the CV MPR. Different weight combinations are tested as different scenarios. The optimization results of these scenarios show that, when the weight of total travel time is high, the optimal CV MPR tends to be high. On the contrary, when the weight of queue length increases, higher CV MPRs may not guarantee higher benefits for the traffic system. The globally optimal CV MPR can be as low as 3%. The case study also confirms the effectiveness of optimizing the CV MPR based on microsimulation and Bayesian optimization.

Age-related driver injury occurrence from crashes at curve–grade combined segments

Objectives Due to their relatively complex roadway characteristics, horizontal and vertical curve segments are associated with decreased visibility and a higher risk of rollovers. Multiple studies have identified the associated risk of young and older drivers separately in such complicated driving environments. This study investigated the relationship between driver age and injury occurrence from crashes occurring along curve–grade combined segments. Methods Crash data recorded in Ohio State between 2012 and 2017 were used in this study. Driver age was categorized into 3 groups: teen (age <20 years), adult (age 20–64), and older adult (age >64). Descriptive statistics were summarized using random forest, gradient boosting, and extreme gradient boosting (XGBoost) to estimate the probability of a driver incurring an injury in case of a crash at curve–grade combined segments. The area under the receiver operating characteristics curve (AUROC) was used to select the best performing model. Partial dependence plots (PDPs) were used to interpret the model results. Results The probability of injury occurrence is different for older drivers compared to teen and adult drivers. Although teen and adult drivers showed a higher probability of sustaining injuries in crashes with an increase in the degree of curvature, older drivers were more likely to sustain injuries in roadways with higher annual average daily traffic (AADT), steeper grades, and more occupants in the vehicle. Older drivers were observed to have a higher probability of sustaining injuries during peak hours and when unrestrained compared to teen and adult drivers. Conclusions The results emphasize the significance of tailored education and outreach countermeasures, particularly for teen and older drivers, aimed at decreasing the likelihood of injuries in such driving environments. This research adds to the expanding body of knowledge concerning the age-related occurrence of driver injuries resulting from crashes at curve–grade combined segments. The study findings provide insights into the potential over- or underrepresentation of certain age groups in analyzing crash injury occurrence. The insights gained from the machine learning analysis could also assist policymakers, transportation agencies, and traffic safety experts in developing targeted strategies to enhance road safety and protect vulnerable age groups.

The Effectiveness of the Transportation Agency's Role in Monitoring Illegal Travel

The Effectiveness of the Transportation Agency's Role in Monitoring Illegal Travel