Rural Interstate Research Articles

Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

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.

Assessment of Large Trucks Crash Severity on a Rural Interstate Road in Wyoming Using Decision Trees and Structural Equation Model

Assessment of Large Trucks Crash Severity on a Rural Interstate Road in Wyoming Using Decision Trees and Structural Equation Model

Modeling the lagged impacts of hourly weather and speed variation factors on the segment crash risk of rural interstate freeways: Applying a space–time-stratified case-crossover design

In the realm of traditional roadway crash studies, cross-sectional modeling methods have been commonly employed to investigate the intricate relationship between the crash risk of roadway segments and variables including roadway geometrics, weather conditions, and speed distribution. However, these methodologies assume that the explanatory variables and target variable are only associated within the same time period. Although this assumption is well-founded for static factors like roadway geometrics, it proves inadequate when dealing with highly time-varying variables related to weather conditions and speed variation. Recent investigations have unveiled that these time-varying variables may exhibit lagged impacts on segment crash risk, necessitating the adoption of more comprehensive time-series modeling methods. This study employs two interpretable statistical methods, namely the distributed lag model (DLM) and the distributed lag nonlinear model (DLNM), to elucidate meaningful and interpretable patterns of the lagged impacts of weather and speed variation factors on segment crash risk. Empirical evidence based on crash data collected from rural interstate freeways in the state of Texas demonstrates coherent and interpretable lagged impact patterns of these variables. This study's results serve as strong support for the existence of lagged impacts on roadway segment-level crash risk, emphasizing the need for considering time-series effects in future crash modeling research. Furthermore, these findings could offer practical implications for the design of real-time crash warning systems and the effective implementation of variable speed limits to enhance road safety.

A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates

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.

Analysis Framework to Assess Crash Severity for Large Trucks on Rural Interstate Roads Utilizing the Latent Class and Random Parameter Model

Truck freight has high importance to the national economy as it handles more cargo than other types of freight transportation. To improve the safety of commercial vehicles, several studies have focused on determining factors leading to crashes. Parametric models have been extensively employed to explain crash causal factors for heavy trucks. Unlike studies in the past, a comprehensive framework was proposed in this study to compare crash underlying factors utilizing several statistical approaches. The structural equation modeling approach was used to assess latent factors affecting the crash severity of large trucks. In addition, ordinary (binary) logistic and random parameter models were employed to assess the direct effect of the observed data, and the heterogeneity in parameter means was also estimated. Three crash categories were investigated: single-truck crashes, multi-vehicle truck crashes, and total truck crashes. A total of five years of crash data from 2015 to 2019 were analyzed. The results showed that multiple observed variables were factored to measure crash severity. Direct and indirect effects were identified, in which challenging roadway conditions had an indirect effect on crash severity for single-truck crashes. Random parameter logit models indicated that roadway geometry and adverse weather conditions were among the significant contributing factors increasing crash severity for trucks. This study recommends that improving the situational awareness of truck drivers, providing more frequent rest stops, updating variable speed limit algorithms, and integrating roadway geometry information into connected vehicle applications in Wyoming could be considered to assist stakeholders in promoting safety on rural interstate corridors.

Applying Explainable Machine Learning Techniques in Daily Crash Occurrence and Severity Modeling for Rural Interstates

Conventional traffic crash analysis methods often use highly aggregated data, making it difficult to understand the effects of time-varying factors on crash occurrence. Although studies have used data with small aggregation intervals, they typically analyze the effect of a single factor on crash occurrence. In this study, we investigate the collaborative effect of roadway geometry, speed distribution, and weather conditions on crash occurrence and severity using explainable machine learning methods on daily level crash data. The data were collected on rural Interstate highways in Texas. Four machine learning methods: random forest, AdaBoost, XGBoost, and deep neural network, were tested on the dataset. The results showed that XGBoost performs the best on the imbalanced dataset. The study used the synthetic minority oversampling technique (SMOTE) method to mitigate the data imbalance issue. The XGBoost model was trained separately on all crash occurrences and severe crash occurrences. Finally, the SHAP (SHapley Additive exPlanation) method was applied to investigate the contribution of all variables to the model’s output. The results showed that weather condition factors have a significant contribution to all crash occurrences. Speed distribution factors have a stronger impact on severe crash occurrences.

A Preliminary Comparison of Drivers’ Overtaking behavior between Partially Automated Vehicles and Conventional Vehicles

The study investigated drivers' overtaking behavior in partially automated vehicles and conventional vehicles under different traffic density conditions. Twelve participants "drove" a simulated vehicle on a 10-mile straight, two-way rural interstate highway in 4 scenarios (2 (traffic density: Low, High) x 2 (vehicle type: Partially automated vehicle, Conventional vehicle)) in random orders. Participants' driving performance, eye movement, brain activity, and workload were collected and analyzed. Results indicated that vehicle type significantly affects speed standard deviation, pupil dilation and workload. Results also showed that traffic density significantly affects average speed and workload. The findings should help improve the design of automated vehicles and thus improve traffic efficiency.

Charging needs for electric semi-trailer trucks

Battery-electric vehicles provide a pathway to decarbonize heavy-duty trucking, but the market for heavy-duty battery-electric semi-trailer trucks is nascent, and specific charging requirements remain uncertain. We leverage large-scale vehicle telematics data (>205 million miles of driving) to estimate the charging behaviors and infrastructure requirements for U.S. battery-electric semi-trailer trucks within three operating segments: local, regional, and long-haul. We model two types of charging—mid-shift (fast) and off-shift (slow)—and show that off-shift charging at speeds compatible with current light-duty charging infrastructure (i.e., ≤350 kW) can supply 35 to 77% of total energy demand for local and regional trucks with ≥300-mile range. Megawatt-level speeds are required for mid-shift charging, which make up 44 to 57% of energy demand for long-haul trucks with ≥500-mile range. However, demand shifts from mid-shift to off-shift charging as the range for battery-electric trucks increases and when off-shift charging is widely available. Finally, we observe geographic trends in charging demand, finding that local trucks have greater demand within urban areas, whereas long-haul trucks have more demand along rural interstate corridors. As the range for battery-electric trucks increases, we show that charging demand shifts from rural to urban locations due to observed vehicle dwell tendencies.

Developing Statewide Safety Performance Functions for Commercial Trucks Transporting Hazardous Materials on Interstate Rural Roads in Wyoming

Truck crashes in Wyoming are considered a major issue. Nearly 26% of crashes on rural interstate roads involve hazardous materials (HAZMAT) trucks. Wyoming has a high rate of activities related to the energy industry, as it is considered among the top energy producing states in the U.S.A., and mainly relies on the trucking industry for transportation. A crash involving a HAZMAT shipment might have a catastrophic impact because of the nature of the HAZMAT shipment. Therefore, it is crucial to identify the traffic safety performance of HAZMAT trucks, so suitable countermeasures can be identified to reduce the frequency and severity of these crashes. This study aims to develop safety performance functions (SPFs) for crashes involving HAZMAT utilizing traditional negative binomial (NB) models, as well as variations of the NB model, namely, NB-1 and NB-P. The results indicate that HAZMAT truck crashes are associated with vehicle miles traveled, truck percentage, horizontal and vertical characteristics of road geometry, pavement type, and speed limit. The findings from this study show that the NB-P models outperformed the traditional NB models based on likelihood ratio tests, information criteria, and prediction measures. Relevant insights are made on traditional countermeasures, such as road geometry, warning signs, slippery road surface warnings, and climbing lanes, as well as non-traditional countermeasures including updating variable speed limit (VSL) algorithms, adding variable message signs, and integrating roadway geometry information into connected vehicle applications in Wyoming. These could be considered to assist stakeholders and emergency management agencies in better decision making toward safer operations of HAZMAT trucks.

A framework for transportation infrastructure cost prediction: a support vector regression approach

ABSTRACT Highway agencies and Departments of Transportation (DOTs) often face a challenge in estimating annual maintenance expenditures (AMEXs) to allocate available funds to various divisions in their jurisdictions. Few AMEX models were developed in the past, which are based on traditional statistical methods. This study introduces a support vector regression (SVR) approach to predict AMEX using data on the U.S. rural interstate highway pavement performance. Results show that the best SVR model is based on the Gaussian kernel and sequential minimal optimization algorithm. The developed SVR model can reliably predict the AMEX at the state level with a root mean squared error of 1.28. Entropy analysis results indicate the existence of mutual information (MI) between all the considered variables and the AMEX, with the highest MI value of about 79% between the AMEX and the number of lane miles with IRI values greater than 1.5 m/km.

Integrated Herfindahl–Hirschman Index, Compromise Programming, and ε-Constraint Method For Multicriteria Performance-Based Transportation Budget Allocation

This paper introduces a new method for multicriteria performance-based transportation budget allocation. First, the Herfindahl–Hirschman Index (HHI) that has long been used in economics for measuring the level of monopolization in a marketplace is employed to derive the relative weights of multiple non-commensurable transportation performance criteria. Next, a compromise programming (CP) model is formulated to help transform the multicriteria optimization formulation for transportation budget allocation to a single-objective optimization model solvable by minimizing the Chebyshev distance to the ideal levels of performance targets associated with individual performance criteria. Finally, the ε-constraint trade-off analysis method is incorporated into the combined use of HHI and CP framework to iteratively derive the optimal decision outcome. Six-year data on candidate investment projects proposed for a U.S. state-maintained rural Interstate highway system along with data details of the available budget is used in a computational study for method application. Comparative analysis of decision outcomes is conducted with the STEP method for cross validation. It has revealed that the proposed HHI-CP ε-constraint method outperforms the existing method and can be adopted by state and local transportation agencies to carry out effective budget allocation.

Truck platooning in the U.S. national road network: A system-level modeling approach

Truck platooning enables a group of trucks to move close together, which helps reduce truck fuel use and increase effective road capacity. In this paper, a system-level equilibrium model is developed to characterize spontaneous truck platooning with coexistence of non-platooning vehicles in a network, by explicitly accounting for the interlocking relationship among platoon formation time, truck fuel saving, and increase in effective road capacity. To equilibrate the relationships, an algorithm is proposed which involves a diagonalization approach and a bush-based algorithm to solve decomposed subproblems. The condition of proportionality is imposed to obtain unique traffic flows for each class of vehicles on road links. In addition, a spatially constrained multivariate clustering technique is employed to construct origin/destination zones that are smaller than the coarse Freight Analysis Framework (FAF) zones, while maintaining reasonable computational burden for network traffic assignment. Model implementation in the U.S. shows that platooning could lead to 7.9% fuel saving among platoonable trucks in 2025 and a comparable increase in effective capacity of platoonable road links, which would account for 60% of rural interstate roads. The fuel saving and road capacity improvement translate into an annual cost reduction of $868 million for the U.S. intercity trucking sector and reduced road infrastructure investment needs worth $4.8 billion. Extensive sensitivity analysis further reveals that fuel saving of platoonable trucks increases with platoon size but decreases with inter-truck distance in a platoon. Fuel saving potential suggests that priority should be given to rural rather than urban roads in deploying platooning technologies. As expected, greater market penetration of platooning technologies means higher fuel saving and greater increase in effective road capacity.

Connected vehicle real-time traveler information messages for freeway speed harmonization under adverse weather conditions: Trajectory level analysis using driving simulator

This paper employed a high-fidelity driving simulator to investigate the impacts of the Wyoming Department of Transportation (WYDOT) Connected Vehicle (CV) Pilot’s Traveler Information Messages (TIMs) on drivers’ speed selection and the safety benefits of their speed harmonization. Three driving simulator experiment scenarios were developed to simulate the typical traffic and weather conditions on the rural Interstate 80 (I-80) in Wyoming. A total of 25 professional drivers from the WYDOT and trucking industry were recruited to participate in the driving simulator experiment. Participants’ instantaneous speeds at various locations were collected to reveal the effects of CV TIMs on their speed selection. The results showed that average speed profiles under CV scenarios were generally lower than under baseline scenarios, particularly for winter conditions (snowy and severe weather). The variance of speed under CV scenarios was found to be significantly lower than the baseline scenarios, indicating that CV TIMs have the potential to harmonize the variations in speed. In addition, for the work zone driving simulator experiment, this research revealed that the mean time-to-collision (TTC) under baseline scenario is approximately 40 % lower than CV scenario, and the mean deceleration to avoid a crash (DRAC) under baseline scenario is approximately 19.3 % higher than CV scenario. These findings suggest that CV TIMs can reduce the risk of crashes. Research findings would provide the WYDOT with early insights into the effectiveness of CV TIMs, which could assist with developing more efficient transportation management strategies under adverse weather conditions.

Safety evaluation of drowsy driving advisory system: Alabama case study

Introduction: The present study discusses roles, characteristics, and safety assessment of a drowsy driving advisory (DDA) system, implemented on rural interstates of Alabama. The DDA system is an engineering countermeasure designed to reduce the likelihood of drowsy driving crashes. It consists of a series of roadside signs with warning and advisory messages for drowsy drivers. The DDA system was implemented upstream of rural rest areas based on a comprehensive crash analysis. Method: A post-implementation study was conducted three years after the DDA system implementation to assess its safety effects. An empirical bayes (EB) method along with predictive methods of the Highway Safety Manual was used in the safety assessment. To overcome the underreported issue of drowsy driving crashes in the crash analysis, the present study used a concept called, expanded definition of drowsy driving (EDD) crashes. Result: The analysis found that the DDA system could reduce total and EDD crashes by 64% and 49%, respectively. It is important to note that such huge crash reduction effects are due to a combined effect of both rest areas and the DDA system, not because of a single treatment. The safety effect of a rest area itself, without considering the effect of the DDA system, was also investigated. Results show that total and EDD crashes would increase about 12–45% and 5–33%, respectively if there is no presence of a rest area. Conclusion: Our findings conclude that the DDA system could significantly reduce both total and drowsy driving crashes when it cooperates with a rest area facility. Practical Application: The findings also provide the guidance of using the DDA system on high-speed roads as a safety countermeasure of drowsy driving crashes. Readers can find details of the DDA system used in this study with its layout, dimension, and roadside safety messages.

Identifying the principal factors influencing traffic safety on interstate highways

This study aims at identifying the principal factors influencing fatal, nonfatal injury and non-injury traffic crashes on urban and rural interstate highway segments using a statistical approach called principal component analysis. Initially, fourteen explanatory variables including segment length, annual average daily traffic (AADT), weekday/weekend, hour of the day, urban–rural designation of the segment, median type, pavement surface condition, roadway geometric characteristics, weather, number of lanes, and drivers’ age and gender, and the accident year were considered. Separate principal component analyses are performed for the three crash categories and the aggregate dataset. The results of the analyses show that, regardless of the crash categories used, seven principal components accounting for over 70% of the variances in the original datasets were retained. In addition to the overall dimensional reduction of the original dataset by 50%, the results suggest that the key variables contributing to the crashes across the categories of the accident types observed on the freeway segments are insignificant. The retained principal component loadings of the factors PC1, PC2, PC3, and PC5 revealed the fact that the number of lanes, the median type, and the AADT of the segments are highly correlated and represented by the first factor (PC1). Similarly, other interrelated factors such as the prevailing weather and the pavement surface condition (wet, dry, snow), the hour of the day and the lighting condition, the drivers’ age and gender are well represented by PC2, PC3, and PC5 respectively.

Analysis of Rural Interstate Fatality Rates in Consideration of Recent Increases in Maximum Statutory Speed Limits

The relationship between traffic safety and speed limits has been an area of significant research. Since the repeal of the National Maximum Speed Law in 1995, states have full autonomy in establishing maximum statutory speed limits. Since 2001, at least 25 states have increased their maximum limits to speeds as high as 85 mph. This study examines changes in rural interstate fatalities from 2001 to 2016 in consideration of such increases. Speed limit policy data include the maximum speed limit for each state–year combination, as well as the proportion of rural interstate mileage posted at each speed limit in each state. Random parameter negative binomial models are estimated to control for unobserved heterogeneity, as well as time-invariant effects unique to each state. The results show that increasing the mileage of rural interstates posted at 70, 75, or 80 mph by 1% is associated with fatality increases of 0.2%, 0.5%, and 0.6%, respectively. These increases are more pronounced than when considering only the maximum statutory limits in each state. The study also examines the influence between these higher limits and the frequency of fatal crashes involving speeding and driver distraction. At the highest limits of 75 and 80 mph, the increases among these subsets of crashes are greater than the increases in total fatalities. Ultimately, this study provides important empirical evidence in support of continuing speed limit policy discussions, in addition to identifying salient analytical concerns that should be considered as a part of longitudinal analyses of state-level fatality data.

Competing risks models for the deterioration of highway pavement subject to hurricane events

Various methods have modeled failures due to hazard events using joint survival functions by assuming independent failure among the events. This paper evaluates the impact of two competing risks events: “natural” crack deterioration, and hurricane failure (Hurricane Categories 1, 2, and 3), on 6702 highway pavement sections using the nonparametric survival probability (Kaplan–Meier estimates) and the cumulative incidence function (CIF). The risks are compared using the Logrank Test (to indicate if the survival probabilities of the risks are significantly different) and the hazard ratio (ratio of hazard rates based on time to failure covariate). From the results, it was observed that the contribution of the Hurricane Category 3 as a competing risk was significantly higher and different from that of crack deterioration. For example, the hazard ratio of the Hurricane Category 3 was twice that of the crack deterioration for the inland urban interstates roadways. Also, the hazard ratio between hurricane category 3 and crack deterioration was about one hundred and sixty percent for rural interstates and slightly above one hundred percent for urban non-interstates and rural non-interstates. The hazard ratios and CIF plots showed that impact of hurricanes on coastal roadways is more significant compared to the inland segments.

Safety Evaluation of Change in Posted Speed Limit from 65 to 70 mph on Rural Virginia Interstate System

Effective July 1, 2010, the Virginia Department of Transportation (VDOT) increased the maximum posted speed limit on interstates and similar facilities from 65 to 70 mph, if recommended following an engineering study. As a result, VDOT performed engineering studies on selected rural interstates posted at 65 mph. By November 2010, VDOT had increased the speed limit from 65 to 70 mph for approximately 670 centerline miles of select rural interstates. This paper presents the results of an empirical Bayes before–after study into the safety and operational effects of the speed limit increase. The analysis focused on total, injury, run-off-road, and truck-related crashes. Comparison segments were used to develop annual adjustment factors, account for regional differences, and identify underlying crash trends in the period before the speed limit increase. At the aggregate level, the results indicated no increase in any of the focus crash types after the increase. Focusing on sites without other changes, the increased speed limit did not increase or decrease any of the crash types. The disaggregate analysis provided further insight into the circumstances in which the change in posted speed limit had more and less pronounced impacts; specifically, that segment type (base or interchange) influenced safety: interchange segments observed statistically significant increases in total, run-off-road, and truck-related crashes. The disaggregate analysis also showed that roadway improvements may help to offset the safety impact of increasing the posted speed limit.

Land Development and Traffic Composition at Rural Interstate Highway Interchanges in Ohio

AbstractHighway interchanges, particularly those in rural areas, offer fertile opportunities for highway-oriented land development such as gas stations, convenience stores, restaurants, and hotels....

Vehicle NOx emission plume isotopic signatures: Spatial variability across the eastern United States

AbstractOn‐road vehicle nitrogen oxide (NOx) sources currently dominate the U.S. anthropogenic emission budgets, yet vehicle NOx emissions have uncertain contributions to oxidized nitrogen (N) deposition patterns. Isotopic signatures serve as a potentially valuable observational tool to trace source contributions to NOx chemistry and N deposition, yet in situ emission signatures are underconstrained. We characterize the spatiotemporal variability of vehicle NOx emission isotopic signatures (δ15N‐NOx) representative of U.S. vehicle fleet‐integrated emission plumes. A novel combination of on‐road mobile and stationary urban measurements is performed using a field and laboratory‐verified technique for actively capturing NOx in solution to quantify δ15N‐NOx at hourly resolution. On‐road δ15N‐NOx upwind of Providence, RI, ranged from −7 to −3‰. Simultaneous urban background δ15N‐NOx observations showed comparable range and variations with on‐road measurements, suggesting that vehicles dominate NOx emissions in the Providence area. On‐road spatial δ15N‐NOx variations of −9 to −2‰ were observed under various driving conditions in six urban metropolitan areas and rural interstate highways during summer and autumn in the U.S. Northeast and Midwest. Although isotopic signatures were insensitive to on‐road driving mode variations, statistically significant correlations were found between δ15N‐NOx and NOx emission factor extremes associated with heavy diesel emitter contributions. Overall, these results constrain an isotopic signature of fleet‐integrated roadway NOx emission plumes, which have important implications for distinguishing vehicle NOx from other sources and tracking emission contributions to NOx chemistry and N deposition.