Search Results (8)

Most urban areas have Traffic Management Centers that rely partially on communication with 9-1-1 centers for incident detection. This level of awareness is often lacking for rural interstates spanning several 9-1-1 centers. This paper presents a novel approach to extending TMC visibility by automatically monitoring regional 9-1-1 dispatch channels using off-the-shelf hardware and open-source speech-to-text libraries. Our study presents a proof-of-concept study servicing 71 miles of rural I-65 in Indiana, successfully monitoring four county dispatch centers from a single location, and efficiently transcribing live audio within 60 s of broadcast. This work’s primary contribution is demonstrating the feasibility and practical value of automated incident detection systems for rural interstates. This technology is implementation-ready for extending the visibility of Traffic Management Centers in rural interstate segments. Further work is underway for developing scalable procedures for integrating multiple remote sites, extracting more diverse keyword sets, investigating optimal speech-to-text models, and assessing the technical aspects of the experimental procedures of this manuscript. Full article

Traffic crashes contribute significantly to non-recurrent congestion, thereby increasing delays, congestion pollution, and other challenges. It is important to have tools that enable accurate prediction of incident duration to reduce delays. It is also necessary to understand factors that affect the duration of traffic crashes. This study developed three machine learning models, namely extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and a light gradient-boosting machine (LightGBM), to predict crash-related incident clearance time in Louisiana rural interstates and utilized Shapley additive explanations (SHAP) analysis to determine the influence of factors impacting it. Four ICT levels were defined based on 30 min intervals: short (0–30), medium (31–60), intermediate (61–90), and long (greater than 90). The results suggest that XGBoost outperforms CatBoost and LightGBM in the collective model’s predictive performance. It was found that different features significantly affect different ICT levels. The results indicate that crashes involving injuries, fatalities, heavy trucks, head-on collisions, roadway departure, and older drivers are the significant factors that influence ICT. The results of this study may be used to develop and implement strategies that lead to reduced incident duration and related challenges with long clearance times, providing actionable insights for traffic managers, transportation planners, and incident response agencies to enhance decision-making and mitigate the associated increases in congestion and secondary crashes. Full article

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level. Full article

This study presents a new modeling approach for rear-end crash counts on Ohio’s interstate freeways based on a dataset for 2021 that contains 2745 rear-end crashes. The analysis encompasses 20 interstate freeways, comprising 1833 homogeneous segments and extending over approximately 1313 miles. These interstate freeways exhibit varying safety performances, indicating a significant degree of heterogeneity. A unique rear-end crash risk rate was devised for each interstate, capturing diverse risk profiles. Three distinct models were developed: a standard negative binomial model, an uncorrelated two-level negative binomial model, and a correlated two-level negative binomial model. The correlated two-level negative binomial model demonstrated superior fit, as evidenced by the likelihood ratio test, Akaike information criterion, and Bayesian information criterion. The correlated two-level negative binomial model exhibited enhanced forecasting precision, as measured by the Root Mean Square Error. A significant finding is that the rear-end crash risk rate significantly improves the fit of the models. The study also reveals that rear-end crashes are expected to occur more frequently in urban segments of interstate freeways with high rear-end risk rates. However, rural segments experience no such significant variations in the rear-end crash risk rate. However, an increase in the inner shoulder width is associated with a decrease in expected rear-end crashes. This research offers a valuable methodology for modeling rear-end crashes on interstate freeways, providing insights into the contributing variables that could inform targeted safety improvements. Full article

This paper investigated factors influencing injury severity of crashes involving median traffic barriers, including the impact of barrier characteristics and their geometric features in Wyoming. Combining field data of inventoried median barriers with crash data on Wyoming interstates highways, a random parameters multinomial logit (mixed logit) model of injury severity was estimated. This methodological approach allowed for the possibility of estimated model parameters to vary randomly across crash observations to account for heterogeneity with respect to driver characteristics, roadway attributes, and vehicle characteristics. The estimation results indicated concrete barriers installed on front side-slopes and box beam barriers were associated with severe injury crashes. It was also found that median barrier crashes involving sports utility vehicles, pickups, and improperly restraint vehicle occupants are complex and vary significantly across observations. Other statistically significant variables found to increase the likelihood of severe injury crashes were rural interstate roads, concrete barriers installed on a front side-slope, box beam barriers with lateral offset less than 2 feet, and rollover crashes. These parameters were fixed across observations. The findings of this research point to the need to further investigate the impacts of sport utility vehicles, pickups, and rollover crashes on median barrier crash injury severity. Full article

Accurate demographic data are critical for comprehending and treating cutaneous T-cell lymphoma (CTCL). Our research aimed to determine the demographics and incidence trends of CTCL patients in Arkansas compared to those of the national CTCL population to recognize the underlying disparities. We collected data from 143 CTCL patients at the University of Arkansas for Medical Sciences (UAMS) and national CTCL patient data from the Surveillance, Epidemiology, and End Results (SEER) database. Our analysis revealed that males are affected more than females across all ages and races. CTCL incidence and mortality data show that CTCL has a steady increase at the national level and in Arkansas while disproportionately affecting the young black male population. In Arkansas, more than one-third of black patients presented at an advanced stage (IIB+) compared to one-fifth in the white population, and the mean age of death was more than a decade younger for black (60 years) than for white patients (74.6 years). Nationally, black male patients had the greatest mortality rate (0.5) compared to 0.32 for white males. CTCL is 2.23 and 2.38 times more prevalent in urban versus rural areas in Arkansas and nationally, respectively. Most Arkansas patients reside near major interstates and chemical-emitting sites. In conclusion, our demographic analysis of Arkansas and national CTCL patients verifies recent trends toward more aggressive presentations in young black male patients, and our geographic findings suggest possible environmental risk factors. Full article

The Apalachicola–Chattahoochee–Flint (ACF) basin is arguably the most litigated interstate river system in the eastern United States. Given the complicated demands for water use within this basin, it has been difficult to ascertain if the recent multi-decadal decline in streamflow is a product of human disturbance, changing climate, natural variability, or some combination of the above factors. To overcome these challenges, we examined unimpaired streamflow and precipitation within and adjacent to the ACF basin, upstream of the Apalachicola River at Chattahoochee, and the Florida streamflow station (ARCF), which has historically been identified to be representative of hydrologic variability in the ACF basin. Several of the upstream, unimpaired, streamflow stations selected were identified in rural watersheds where land-cover changes and human disturbance were minimal during the study period. When applying a series of statistical evaluations, ARCF streamflow variability generally reflects the natural variability of the ACF basin. Additionally, unimpaired streamflow variability from the neighboring Choctawhatchee River compared favorably with ARCF variability. The recent multi-decadal decline was consistent in all records, with the 2000s being the most severe in the historic record. Full article

Background: Drunk-driving is a major crash risk factor, and crashes resulting from this risky behavior tend to be serious and have significant economic and societal impacts. The presence of passengers and their demographics and activities can influence risky driving behaviors such as drunk-driving. However, passengers could either be an “enabling” factor to take more risks or could be an “inhibiting” factor by ensuring safe driving by a drunk-driver. Objective: This study examines whether the presence of passengers affects the contributing factors of single-vehicle (SV) drunk-driving crashes, by presenting a severity analysis of single- and multi-occupant SV drunk-driving crashes, to identify risk factors that contribute to crash severity outcomes, for the effective implementation of relevant countermeasures. Method: A total of 7407 observations for 2012–2016 from the crash database of the State of Alabama was used for this study. The variables were divided into six classes: temporal, locational, driver, vehicle, roadway, and crash characteristics and injury severities into three: severe, minor, and no injury. Two latent class multinomial logit models—one each for single- and multi-occupant crashes—were developed, to analyze the effects of significant factors on injury severity outcomes using marginal effects. Results: The estimated results show that collision with a ditch, run-off road, intersection, winter season, wet roadway, and interstate decreased the probability of severe injuries in both single- and multi-occupant crashes, whereas rural area, road with downward grade, dark and unlit roadway, unemployed driver, and driver with invalid license increased the likelihood of severe injuries for both single- and multi-occupant crashes. Female drivers were more likely to be severely injured in single-occupant crashes, but less likely in multi-occupant crashes. A significant association was found between severe injuries and weekends, residential areas, and crash location close (<25 mi ≈40.23 km) to the residence of the at-fault driver in multi-occupant crashes. Sport utility vehicles were found to be safer when driving with passengers. Conclusions: The model findings show that, although many correlates are consistent between the single- and multi-occupant SV crashes that are associated with locational, roadway, vehicle, temporal, and driver characteristics, their effect can vary across the single- and multi-occupant driving population. The findings from this study can help in targeting interventions, developing countermeasures, and educating passengers to reduce drunk-driving crashes and consequent injuries. Such integrated efforts combined with engineering and emergency response may contribute in developing a true safe systems approach. Full article