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This paper introduces a semi-automated system that facilitates emergency response vehicle (ERV) movement through a transportation link by providing instructions to downstream non-ERVs. The proposed system adapts to information from non-ERVs that are nearby and downstream of the ERV. As the ERV passes stopped non-ERVs, new non-ERVs are considered. The proposed system sequentially executes integer linear programs (ILPs) on transportation link segments with information transferred between optimizations to ensure ERV movement continuity. This paper extends a previously developed mathematical program that was limited to a single short segment. The new approach limits runtime overhead without sacrificing effectiveness and is more suitable to dynamic systems. It also accommodates partial market penetration of connected vehicles using a heuristic reservation approach, making the proposed system beneficial in the short-term future. The proposed system can also assign the ERV to a specific lateral position at the end of the link, a useful capability when next entering an intersection. Experiments were conducted to develop recommendations to reduce computation times without compromising efficiency. When compared with the current practice of moving to the nearest edge, the system reduces ERV travel time an average of 3.26 s per 0.1 mi and decreases vehicle interactions.

This study assesses the impact of the introduction of connected and automated vehicles on Virginia freeway corridors. Three vehicle types: legacy vehicles (LV), automated vehicles (AV), and connected automated vehicles (CAV), were considered in mixed traffic scenarios. Previous relevant studies were reviewed and the proper operating parameters for LV, AV, and CAV identified. AV and CAV driving behavior models were developed in the VISSIM environment. According to the basic freeway test network results, AV and CAV increase road capacity by 29% and 91%. In the merging freeway test network, AV and CAV increase road capacity by 48% and 60% compared with LV, respectively. A model with diverse LV, AV, and CAV market penetration and diverse traffic demand was tested on I-95 in Virginia, where the research team tested the speed and throughput. Under the current traffic demand, the average speed was higher when there were more AV and no CAV in the traffic flow. However, the average speed of CAV in a congested segment is higher than LV. In the case of throughput, CAV shows poor performance under current traffic demand. With increased traffic demand, high penetrations of AV and CAV present better performance because of their short headway and homogeneity. Therefore, the study predicts that in the future, as the traffic demand grows, AV and CAV can reduce traffic congestion.

This study sheds light on the travel behavior of drivers when they encounter fabricated messages in work zones. Using the response of 4302 participants to a stated preference survey, we develop a multivariate ordered response model and a structural equation model to study speed change and distraction response behavior. The results of our models for fabricated announcements signify that drivers normally follow the announcement and are affected likewise. The selected socioeconomic and attitudinal variables are shown to have mixed impacts in our speed and distraction models. Some variables are statistically significant for each model, while other variables are only statistically significant for one of the models. For instance, drivers that have seen a fabricated announcement before are less likely to speed up when encountering the message, while drivers who rely on technology for their daily travels are more likely to be distracted. Higher income is shown in our models to signify undesirable behaviors: speeding up and being distracted. Contrastingly, female drivers are less likely to do nothing or be distracted by the announcement. The findings, taken together, have implications for researchers and practitioners. First, they illustrate how cyberattacks can destabilize traffic in work zone and put the life of work zone crew members in jeopardy. Second, they explain the degree of compliance with compromised dynamic message signs.

Traffic sign readability can be affected by the existence of dirt on traffic sign faces. However, among damaged signs, dirty traffic signs are unique since their damage is not permanent and they just can be cleaned instead of replaced. This study aimed to identify the most important factors contributing to traffic sign dirt. To do so, a large number of traffic signs in Utah were measured by deploying a vehicle instrumented with mobile LiDAR imaging and digital photolog technologies. Each individual daytime digital image was inspected for dirt. Location and climate observations obtained from official sources were compiled using ArcGIS throughout the process. To identify contributing factors to traffic sign dirt, the chi-square test was employed. To analyze the data and rank all of the factors based on their importance to the sign dirt, Random forests statistical model was utilized. After analyzing the data, it can be concluded that ground elevation, sign mount height, and air pollution had the highest effect on making traffic signs dirty. The findings of this investigation assist transportation agencies in determining traffic signs with a higher likelihood of sign dirt. In this way, agencies would schedule to clean such traffic signs more frequently.

One of the most discussed infrastructure issues of our time is cybersecurity. A transportation system that connects not only people but also logistics to make the community around the world closer is one of the critical infrastructures requiring cybersecurity to perform its functions. Transportation systems include aviation, maritime, pipeline, railroad, and road networks. This study focuses only on the roadway network system. Cutting-edge technologies related to highway networks, such as variable message signs, vehicular ad hoc networks, and in-vehicle networks, have been developed to improve safety and efficiency. Those technologies make transportation systems more complex and integrated, bringing many potential vulnerabilities and cyber risks. This can attract an adversary to attack and exploit the system. The number of cybersecurity attacks on transportation systems has been growing for many years. However, it is not feasible to protect against all cybersecurity attacks in the system. The risk assessment concept is proposed to prioritize risk resulting from attacks to support decision-makers in formulating appropriate policies or countermeasures. This study reviews risk assessment methods for cybersecurity attacks on road networks and intelligent transportation system applications. Three potential risk assessment methods are examined for road network systems: the National Institute of Standards and Technology Special Publication 800-30, Attack Potential and Damage Potential, and the fuzzy analytic hierarchy process.

Rural areas have the distinct opportunity to become trendsetters of innovation and technology, molding and optimizing delivery advances to operate in a way that meets their needs. Drones, autonomous delivery vehicles (ADVs), and truck-drones have the potential to improve rural delivery services significantly. However, rural environments have considerably different characteristics than urban or suburban areas. Therefore, delivery services must be evaluated specifically for the rural use case because the outcome of rural drone delivery is likely different from urban. This study conducted a systematic literature review about the current state-of-the-art drone, ADV, and truck-drone package delivery in rural areas (excluding medical or healthcare delivery). Drones can significantly reduce rural delivery emissions and costs while increasing efficiency. ADVs have the potential to greatly improve rural delivery efficiency. The truck-drone solution is one of the best mode choices for rural delivery because the truck can travel longer distances while the drone can cross challenging geography without delay. The literature review identified a general need for more drone, ADV, and truck-drone delivery research focusing on rural locations. There also is a need for research about the specific economic, environmental, and societal impacts of drone, ADV, and truck-drone delivery in rural areas. There is a significant gap in the literature on realistic rural delivery modeling and gaps about attitudes and perceptions of drone, ADV, and truck-drone delivery services in rural areas. With additional research, innovative delivery modes and technologies can become more cost-effective, accessible, and efficient in rural areas.

•Queueing evaluation for existing and future National Park entrance stations.•Application of Little’s Law and simulation for evaluating measures of performance.•Entrance station service rate estimated to be 25 seconds.•Entrance station capacity estimated to be 144 vehicles per hour.•Thresholds identified at 35 second service rate and 100 vehicle per hour capacity.•Suggested minimum distance of 47.1 meters from intersection for future station.

In this study, a GPS tracking dataset was utilized to predict the probability of a vehicle stopping along a scenic corridor in a national park setting. The Moose-Wilson Corridor (MWC) in Grand Teton National Park was evaluated for vehicle stopping/visiting determined from GPS collected data along the corridor. Four attractions were evaluated, which consisted of Death Canyon, Granite Canyon, the Laurance S. Rockefeller Preserve, and Sawmill Ponds. A decision tree analysis was implemented to determine the probabilities of visitors’ stopping patterns at park attractions. A benefit to the decision tree method is the easy to read structure and simple visual representation. An 814-observation sample set was split into training and testing datasets that resulted in model accuracies as high as 94%. A promising outcome of this methodology is that it provides a visual reference to help identify attraction relationships. Similarities in the tree structure were determined for Death Canyon and Granite Canyon owing to their child nodes being composed of other MWC attractions. Alternatively, the Laurance S. Rockefeller Preserve and Sawmill Ponds attractions determined more variability in their tree structure. The implementation of a data analysis method such as the one presented in this paper could help national park managers prepare for the incoming era of technology and data.

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment, such as a traffic light’s signal phase and timing, using vehicle-to-infrastructure communication. Several scholarly papers exist on planning strategies for DSRC RSU deployments using simulation without accounting for wireless communication constraints and environmental changes. This paper proposes an empirical-based planning strategy for a highway off-ramp in a real-world environment. The research goal focuses on developing a low-cost and structured deployment plan for DSRC RSUs with the following objectives: use free planning tools; apply the deployment strategy in a real-world environment; utilize publicly available DSRC RSU data measurements; and leverage existing intelligent transportation systems infrastructure when possible. The proposed planning strategy includes three steps: (1) conduct a virtual site survey, (2) gather baseline performance data for the DSRC RSU equipment, and (3) generate a predictive radio frequency signal. The planning strategy was successfully applied on a highway off-ramp at exit 19A of the Capital Beltway, which encircles Washington, DC.

In national parks, there is a tension between providing areas for vehicles and accommodating other visitor activities in the park. This tension often means there is more demand for vehicle parking than the supply can accommodate. This study examined one of Grand Teton National Park’s (GRTE) visitor attractions with visitor parking provided, the Laurance S. Rockefeller Preserve (LSR) in Jackson, Wyoming. With approximately 54 designated parking spots available, a study to determine the queueing theory measures of performance was conducted under observed and higher-value parameters to evaluate the system. It was determined that under the observed average arrival rate of 25 vehicles per hour (vph) and 35 vph with an average service rate (time a vehicle was parked) of 1 h and 20 min, the system resulted in good performance with a facility utilization value of 61% and 84%, respectively. However, under the higher-value parameters, the results were poorer, with facility utilization values greater than 88%. This study provides a reference for the evaluation of performance measures and can be applicable to future changes in the LSR at GRTE, other national parks parking, or in general parking areas where queueing may be anticipated.

Increasing the use of electric vehicles (EVs) has been suggested as a possible method to decrease fuel consumption and greenhouse gas (GHG) emissions in an effort to mitigate the causes of climate change. In this study, the relationship between the market share of electric vehicles and the presence of government incentives, and other influential socio-economic factors were examined. The methodology of this study is based on a cross-sectional/time-series (panel) analysis. The developed model is an aggregated binomial logit share model that estimates the modal split between EV and conventional vehicles for different U.S. states from 2003 to 2011. The results demonstrated that electricity prices were negatively associated with EV use while urban roads and government incentives were positively correlated with states’ electric vehicle market share. Sensitivity analysis suggested that of these factors, electricity price affects electric vehicle adoption rate the most. Moreover, the time trend model analysis found that the electric vehicle adoption has been increasing over time, which is consistent with theories about diffusion of new technology.

In national parks, there is a tension between providing areas for vehicles and accommodating other visitor activities in the park. This tension often means there is more demand for vehicle parking than the supply can accommodate. This study examined one of Grand Teton National Park’s (GRTE) visitor attractions with visitor parking provided, the Laurance S. Rockefeller Preserve (LSR) in Jackson, Wyoming. With approximately 54 designated parking spots available, a study to determine the queueing theory measures of performance was conducted under observed and higher-value parameters to evaluate the system. It was determined that under the observed average arrival rate of 25 vehicles per hour (vph) and 35 vph with an average service rate (time a vehicle was parked) of 1 h and 20 min, the system resulted in good performance with a facility utilization value of 61% and 84%, respectively. However, under the higher-value parameters, the results were poorer, with facility utilization values greater than 88%. This study provides a reference for the evaluation of performance measures and can be applicable to future changes in the LSR at GRTE, other national parks parking, or in general parking areas where queueing may be anticipated.

Transportation asset management helps monitor the transportation systems and optimize the construction, operation, and maintenance of assets. Many state Department of Transportations (DOTs) have already established asset management systems for high cost and low quantity assets, e.g., bridge and tunnel assets. However, due to the sheer number of traffic signs deployed by DOTs, statewide sign inventory and condition information are not well developed. Currently, using handheld devices is the most selected method by agencies to measure signs. To address safety challenge and high cost of data collection, an innovative stationary image based method has recently been proposed. This paper discusses the advantages and disadvantages of such image based method over using handheld devices in terms of the accuracy, possibility and consistency of data, speed, safety, maintenance, and cost. At its completion, this study provides suggestions to tackle the issues associated with image based method.

Traffic signs often convey critical information to drivers. To ensure visibility in nighttime or low light conditions, traffic signs must be in compliance with the minimum retroreflectivity standards outlined by the manual on uniform traffic control devices (MUTCD). Among all of the assessment methods (visual nighttime inspection, retroreflectivity measurement) and management methods (expected life, blanket replacement, and control signs) outlined in the MUTCD, expected sign life has been the most selected by agencies for maintaining compliance. In current literature, little research exists with regard to schedule sign replacement, focusing rather on the current favorite predictor, sign age. However, after collecting data on 1683 in-service traffic signs across the state of Utah, this study primarily concluded that not only sign age, but other contributing factors affect sign retroreflective performance. Aiming to determine the effects of various damage forms on sign retroreflectivity, statistical methods, including regression models, chi-square test, t-test, and odds ratio were employed to analyze traffic sign data. At the conclusion, the strong association between damage and retroreflectivity compliance of traffic signs was evident. In addition, to identify more critical damage forms, the effects of various forms on traffic sign retroreflectivity were compared. These conclusions provide insight to inform transportation agencies in the development of sign management plans and schedule sign replacement.

This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, Intelligent Driver Model (IDM), Improved IDM (IIDM), IIDM with Constant-Acceleration Heuristic (CAH), and MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) are examined with the state-of-the-art vehicle trajectory data, Highway Drone dataset (HighD), and genetic algorithm. There is no commercial level 5 AV or CAV as of 2022; therefore, the authors generate hypothetical AV trajectories based on the actual vehicle trajectories and the assumption of an ideal AV. Based on the analysis, the calibrated IIDM with CAH shows the most fit on AV behavior.

Natural gas vehicles are being developed because of increasing concerns about energy dependence, air quality and emissions, and, more recently, climate change. The major advantage of natural gas vehicles is their lower fuel cost. Several economic and technical factors such as limited range and availability of relevant infrastructure prevent widespread adoption of natural gas vehicles. A model for the financial analysis of the possibility of compressed natural gas (CNG) vehicles being competitive with gasoline-powered vehicles is offered. The model evaluates the extent to which commuters find adoption of CNG vehicles to be economically viable in the United States. The results indicate that the percentage of commuters who would adopt CNG vehicles is small, even if fueling infrastructure were fully developed and CNG vehicles were widely available for purchase. A larger number of vehicle miles traveled and increased gasoline prices encourage commuters to adopt CNG vehicles, while higher fuel economy and purchase price differentials result in lower adoption rates. In some cases, which vary in accordance with the values of the model’s parameters, commuters purchase a CNG vehicle as their second car and keep a gasoline-powered car as their first.

Among the different types of traffic sign damage, vandalism is exclusively caused by humans. Traffic sign vandalism is a serious concern, since it can lead to an increase in unsafe driving behaviors. In addition, it results in increased costs to transportation agencies to replace, repair, or maintain the vandalized signs. This paper examines the association between the local population demographics and traffic sign vandalism rates in the State of Utah. To accomplish this goal, sign data of over 97,000 traffic signs across Utah were digitally collected by an equipped vehicle. Sign damage data were obtained from the inspection of daytime digital images taken of each individual sign. Demographic data of Utah's counties, including population density, ethnicity, age, income, education, and gender, were obtained from the U.S. Census. The association between demographic groups and vandalism rates was tested using chi-square and trend tests. The results reveal that the most statistically significant variables comprise median household income, completion of at least an associate degree, and population density. According to the fitted linear regression model, a relationship exists between sign vandalism rate and local population demographic. The findings of this investigation can assist transportation agencies in identifying areas with a higher likelihood of sign vandalism, based on demographic characteristics. Such information can then be used to encourage scheduled sign inspections and to implement various countermeasures to prevent sign vandalism.

Natural gas vehicles are being developed because of increasing concerns about energy dependence, air quality and emissions, and, more recently, climate change. The major advantage of natural gas vehicles is their lower fuel cost. Several economic and technical factors such as limited range and availability of relevant infrastructure prevent widespread adoption of natural gas vehicles. A model for the financial analysis of the possibility of compressed natural gas (CNG) vehicles being competitive with gasoline-powered vehicles is offered. The model evaluates the extent to which commuters find adoption of CNG vehicles to be economically viable in the United States. The results indicate that the percentage of commuters who would adopt CNG vehicles is small, even if fueling infrastructure were fully developed and CNG vehicles were widely available for purchase. A larger number of vehicle miles traveled and increased gasoline prices encourage commuters to adopt CNG vehicles, while higher fuel economy and purchase price differentials result in lower adoption rates. In some cases, which vary in accordance with the values of the model’s parameters, commuters purchase a CNG vehicle as their second car and keep a gasoline-powered car as their first.

Vehicle fuel economy improvement has proved to be one of the most effective policies in controlling gasoline demand and greenhouse gas emissions in the transportation sector. However, such improvement also results in the decrease of fuel tax revenue, the main funding source for building and maintaining the U.S. transportation infrastructure. Conversely, fuel efficiency improvement reduces the marginal and average cost of travel, thus encouraging drivers to drive more, and that in turn increases gasoline consumption. The objective of this study was to estimate the potential loss of fuel tax revenue and greenhouse gas emissions, dependents of fuel consumption, that resulted from the fuel efficiency improvement. Accordingly, fuel consumption was modeled as a system of equations consisting of vehicle miles traveled and fuel efficiency (in mpg) as two explanatory variables. The model was estimated by the three-stage least squares method, with annual time-series data for Washington State over the period from 1976 to 2011. According to the results, Washington State will have a $106 million loss in revenue and an 8.7% reduction in emissions of carbon dioxide (CO2) in 2031.

Redundancy is vital for transportation networks to provide utility to users during disastrous events. In this paper, we develop two network-based measures for systematically characterizing the redundancy of transportation networks: travel alternative diversity and network spare capacity. Specifically, the travel alternative diversity dimensionis to evaluate the existence of multiple modes and effective routes available for travelersor the number of effective connections between a specific origin-destination pair. The network spare capacity dimensionis to quantify the network-wideresidual capacity with an explicit consideration of travelers’ mode and route choice behaviors as well as congestion effect. They can address two fundamental questions in the pre-disaster transportation system evaluation and planning, i.e., “how many effective redundant alternatives are there for travelers in the event of a disruption?” and “how much redundant capacity does the network have?” To implement the two measures in practice, computational methods are provided to evaluate the network redundancy. Numerical examples are also presented to demonstrate the features of the two redundancy measures as well as the applicability of the computational methods. The analysis results reveal that the two measures have different characterizations on network redundancy from different perspectives, and they can complement each other by providing meaningful information to both travelers and planners.

Vehicle fuel economy improvement has proved to be one of the most effective policies in controlling gasoline demand and greenhouse gas emissions in the transportation sector. However, such improvement also results in the decrease of fuel tax revenue, the main funding source for building and maintaining the U.S. transportation infrastructure. Conversely, fuel efficiency improvement reduces the marginal and average cost of travel, thus encouraging drivers to drive more, and that in turn increases gasoline consumption. The objective of this study was to estimate the potential loss of fuel tax revenue and greenhouse gas emissions, dependents of fuel consumption, that resulted from the fuel efficiency improvement. Accordingly, fuel consumption was modeled as a system of equations consisting of vehicle miles traveled and fuel efficiency (in mpg) as two explanatory variables. The model was estimated by the three-stage least squares method, with annual time-series data for Washington State over the period from 1976 to 2011. According to the results, Washington State will have a $106 million loss in revenue and an 8.7% reduction in emissions of carbon dioxide (CO2) in 2031.

This research investigated the potential of a distance-based fare structure with a case study of the Utah Transit Authority system in northern Utah. The metrics of evaluation were viewed through demand maximization within a modeling scheme for a distance-based fare structure for all fixed route transit modes. Transit users' route choices were explicitly modeled in the transit system on the time-expanded network. This modeling scheme was integrated into the lower level of the bi-level programming framework, where the upper level uncovered the optimized fare levels for the distance-based fare structure with a genetic algorithm. Through implementation of the methodology, the distance-based fare levels were evaluated for their effect on increasing transit demand. Using the market segmentation analysis, the study found that a distance-based fare with a no-base fare had the highest potential for increasing the transit demand. A $0.50 base fare was examined and was shown to be feasible in the case that a base fare was not necessary because of agency policy.

This research investigated the potential of a distance-based fare structure with a case study of the Utah Transit Authority system in northern Utah. The metrics of evaluation were viewed through demand maximization within a modeling scheme for a distance-based fare structure for all fixed route transit modes. Transit users’ route choices were explicitly modeled in the transit system on the time-expanded network. This modeling scheme was integrated into the lower level of the bi-level programming framework, where the upper level uncovered the optimized fare levels for the distance-based fare structure with a genetic algorithm. Through implementation of the methodology, the distance-based fare levels were evaluated for their effect on increasing transit demand. Using the market segmentation analysis, the study found that a distance-based fare with a no-base fare had the highest potential for increasing the transit demand. A $0.50 base fare was examined and was shown to be feasible in the case that a base fare was not necessary because of agency policy.

Since the establishment of minimum retroreflectivity levels, agencies and researchers alike have focused on determining the service life of different sheeting type and color combinations. Although deterioration curves and measured retroreflectivity are viable methods for maintaining retroreflectivity compliance, they do not ensure that a traffic sign will convey its intended message. Retroreflectivity efficiency ensures only visibility but does not properly describe the legibility of the sign. In 2011, a data collection effort was conducted by researchers at Utah State University to assess the performance of traffic signs under the Utah Department of Transportation's (DOT) jurisdiction. At its completion, 1,716 traffic signs had been recorded. The researchers determined that the sample sign population was 93% compliant with the minimum retroreflectivity levels. Even though the majority of traffic signs were performing above the minimum retroreflectivity levels, 28% were so damaged that the legibility of the sign could be questioned. Because signs under the Utah DOT's jurisdiction had rates of damage higher than rates of failure, analysis was conducted to determine the contributing factors that led to increased damage rates. Climate and location data were combined with the known location of each traffic sign with the use of geographic information system software. It was determined that average annual precipitation, elevation, seasonal temperature swing, and the exposure of the sign were all contributing factors to higher damage rates.

Since the establishment of minimum retroreflectivity levels, agencies and researchers alike have focused on determining the service life of different sheeting type and color combinations. Although deterioration curves and measured retroreflectivity are viable methods for maintaining retroreflectivity compliance, they do not ensure that a traffic sign will convey its intended message. Retroreflectivity efficiency ensures only visibility but does not properly describe the legibility of the sign. In 2011, a data collection effort was conducted by researchers at Utah State University to assess the performance of traffic signs under the Utah Department of Transportation's (DOT) jurisdiction. At its completion, 1,716 traffic signs had been recorded. The researchers determined that the sample sign population was 93% compliant with the minimum retroreflectivity levels. Even though the majority of traffic signs were performing above the minimum retroreflectivity levels, 28% were so damaged that the legibility of the sign could be questioned. Because signs under the Utah DOT's jurisdiction had rates of damage higher than rates of failure, analysis was conducted to determine the contributing factors that led to increased damage rates. Climate and location data were combined with the known location of each traffic sign with the use of geographic information system software. It was determined that average annual precipitation, elevation, seasonal temperature swing, and the exposure of the sign were all contributing factors to higher damage rates.

The Manual on Uniform Traffic Control Devices (MUTCD) specifies minimum retroreflectivity requirements that include an obligation for agencies to develop a strategy for maintaining compliance. States were given a deadline of January 1, 2012, for the implementation of an assessment or management plan, which led to an increased emphasis on sign asset management. However, a new rule was submitted to the Federal Register to extend and modify the deadlines. With budget considerations, it is important that a transportation agency implement an assessment or management plan that is efficient and provides compliance with the standards required by the MUTCD. The development of an efficient plan requires knowledge of the overall condition of an agency's assets as well as unique considerations regarding its performance. Through a review of previous data collection efforts, this paper details the development of a data collection strategy for assessing the performance of traffic signs maintained by the Utah Department of Transportation (DOT). Agency operations, site selection, and attribute collection were all considered during the development of a collection plan for an agency where limited inventory and installation data were available. Retroreflectivity measurements were taken for 1,433 Utah DOT signs. This sample provided a snapshot of current compliance and assisted in the selection of an asset management plan for maintaining sign retroreflectivity. Results from the study showed that the Utah DOT's signs were well over 90% compliant with the MUTCD standards and preliminary management strategies were presented to address vandalism and other damage.

The resiliency of infrastructure, particularly as related to transportation networks, is essential to any society. This resiliency is especially vital in the aftermath of disasters. Recent events around the globe, including Hurricane Katrina and significant seismic events in Haiti, Chile, and Japan, have increased the awareness and the importance of resiliency. Transportation systems are key to response and recovery. These systems must withstand stress, maintain baseline service levels, and be stout enough in physical design and operational concept to provide restoration to the system. Analysis of a transportation network's resiliency before a disruptive event will help decision makers identify specific weaknesses within the network so that investments and improvement projects are prioritized appropriately. Previous research in quantification of network resiliency was expanded into a proposed methodology, through which understanding and applying concepts of network resiliency could preclude many devastating effects of destabilizing events and preserve the quality of life and economic stability.

Abstract:In an effort to maximize the use of limited funding due to decreasing tax revenue, transportation agencies across the country are giving greater consideration to low cost strategies as a means of addressing transportation problems. The majority of these strategies can be grouped under what is sometimes termed Transportation System Management and Operations (TSMO) strategies. This research effort develops a decision making framework that quantifies the strengths and weaknesses of select TSMO strategies relative to specific transportation policy goals. Previously, the limited availability and quality of performance measure data for individual strategies, a lack of consensus as to what performance measures are most appropriately mapped to a particular policy goal, and a lack of consistency between the performance measures associated with individual TSMO strategies have prevented a quantifiable assessment of TSMO performance. This work has conducted an extensive search for both national and local TSMO data sources that were reduced into a usable form. A four step decision making framework is then proposed and demonstrated on an example that includes four strategies and policy goals. This work provides a clear and user-friendly approach for transportation managers and decision makers to quantify the selection of one TSMO strategy over another.

The resiliency of infrastructure, particularly as related to transportation networks, is essential to any society. This resiliency is especially vital in the aftermath of disasters. Recent events around the globe, including Hurricane Katrina and significant seismic events in Haiti, Chile, and Japan, have increased the awareness and the importance of resiliency. Transportation systems are key to response and recovery. These systems must withstand stress, maintain baseline service levels, and be stout enough in physical design and operational concept to provide restoration to the system. Analysis of a transportation network's resiliency before a disruptive event will help decision makers identify specific weaknesses within the network so that investments and improvement projects are prioritized appropriately. Previous research in quantification of network resiliency was expanded into a proposed methodology, through which understanding and applying concepts of network resiliency could preclude many devastating effects of destabilizing events and preserve the quality of life and economic stability.

The Manual on Uniform Traffic Control Devices (MUTCD) specifies minimum retroreflectivity requirements that include an obligation for agencies to develop a strategy for maintaining compliance. States were given a deadline of January 1, 2012, for the implementation of an assessment or management plan, which led to an increased emphasis on sign asset management. However, a new rule was submitted to the Federal Register to extend and modify the deadlines. With budget considerations, it is important that a transportation agency implement an assessment or management plan that is efficient and provides compliance with the standards required by the MUTCD. The development of an efficient plan requires knowledge of the overall condition of an agency's assets as well as unique considerations regarding its performance. Through a review of previous data collection efforts, this paper details the development of a data collection strategy for assessing the performance of traffic signs maintained by the Utah Department of Transportation (DOT). Agency operations, site selection, and attribute collection were all considered during the development of a collection plan for an agency where limited inventory and installation data were available. Retroreflectivity measurements were taken for 1,433 Utah DOT signs. This sample provided a snapshot of current compliance and assisted in the selection of an asset management plan for maintaining sign retroreflectivity. Results from the study showed that the Utah DOT's signs were well over 90% compliant with the MUTCD standards and preliminary management strategies were presented to address vandalism and other damage.

Many decisions about the content of an introductory transportation engineering course are complicated by a wide range of topics and skills to be presented in a limited amount of time. The information presented in this paper was compiled by a working group of educators who represented universities of varying sizes and geographic areas. This working group was charged with developing core concepts and associated knowledge tables for the introductory transportation course for the following core concept areas: traffic operations, transportation planning, geometric design, transportation finance, transportation economics, traffic safety, and transit and nonmotorized transport. Instructors can weave the knowledge tables together by explaining the ways of being of a transportation professional and the course learning outcomes. A key focus of the working group's efforts was to provide more guidance to instructors on core content versus optional content. The intent of the working group was not to dictate what exactly should be taught in a course. The group therefore created more content than could fit into a typical semester-long course so that instructors would have flexibility. Some content should be viewed as more critical to the transportation profession than other material, and the working group will prioritize it accordingly. The objective of this paper is to demonstrate the work that has been completed and to get feedback from industry partners and other academic professionals about the curriculum. The efforts of the pilot studies over the next year will help determine the amount of time needed to cover the information in the knowledge tables.

Every day dependence on transportation grows as local, regional, national, and international independence increases. Resilient transportation systems are needed to secure the highest possible level of service during disruptive events, including natural disasters and those caused by humans. To prepare for these events, decision makers need guidance to determine what investments are likely to improve the resiliency of their networks, which are often hampered by limited resources. To date, such guidance has been primarily qualitative. This paper presents a methodology to quantify resiliency, under preevent conditions, by use of a fuzzy inference approach. This methodology expands on previous work by the authors and others, by refining the definitions of key variables, adjusting model interactions, and increasing transparency between metrics. The paper includes a case study in which the methodology is applied to a disruptive event in Santo Domingo, Dominican Republic. The case study illustrates the methodology's ability to (a) evaluate the extent to which the Dominican Republic's transportation network is prepared for a disruptive event, (b) help select investments that have the potential to increase the resiliency of the network, and (c) provide outputs that will support a variety of current economic analysis strategies, allow comparison of different investment scenarios, and facilitate decision making. The paper concludes with a sensitivity analysis that shows the effects of alternative investments on the network.

Many decisions about the content of an introductory transportation engineering course are complicated by a wide range of topics and skills to be presented in a limited amount of time. The information presented in this paper was compiled by a working group of educators who represented universities of varying sizes and geographic areas. This working group was charged with developing core concepts and associated knowledge tables for the introductory transportation course for the following core concept areas: traffic operations, transportation planning, geometric design, transportation finance, transportation economics, traffic safety, and transit and nonmotorized transport. Instructors can weave the knowledge tables together by explaining the ways of being of a transportation professional and the course learning outcomes. A key focus of the working group's efforts was to provide more guidance to instructors on core content versus optional content. The intent of the working group was not to dictate what exactly should be taught in a course. The group therefore created more content than could fit into a typical semester-long course so that instructors would have flexibility. Some content should be viewed as more critical to the transportation profession than other material, and the working group will prioritize it accordingly. The objective of this paper is to demonstrate the work that has been completed and to get feedback from industry partners and other academic professionals about the curriculum. The efforts of the pilot studies over the next year will help determine the amount of time needed to cover the information in the knowledge tables.

Every day dependence on transportation grows as local, regional, national, and international independence increases. Resilient transportation systems are needed to secure the highest possible level of service during disruptive events, including natural disasters and those caused by humans. To prepare for these events, decision makers need guidance to determine what investments are likely to improve the resiliency of their networks, which are often hampered by limited resources. To date, such guidance has been primarily qualitative. This paper presents a methodology to quantify resiliency, under preevent conditions, by use of a fuzzy inference approach. This methodology expands on previous work by the authors and others, by refining the definitions of key variables, adjusting model interactions, and increasing transparency between metrics. The paper includes a case study in which the methodology is applied to a disruptive event in Santo Domingo, Dominican Republic. The case study illustrates the methodology's ability to (a) evaluate the extent to which the Dominican Republic's transportation network is prepared for a disruptive event, (b) help select investments that have the potential to increase the resiliency of the network, and (c) provide outputs that will support a variety of current economic analysis strategies, allow comparison of different investment scenarios, and facilitate decision making. The paper concludes with a sensitivity analysis that shows the effects of alternative investments on the network.

As road safety audits (RSAs) have been implemented by agencies across the United States, transportation professionals have realized that RSAs are an effective tool to proactively improve the future safety performance of a roadway during the planning and design stages and to identify safety issues in existing transportation facilities. But limited study has been done of the implementation of RSA recommendations on existing roadways. The Utah Local Technical Assistance Program Center conducted an RSA of 1300 East in Salt Lake City, Utah. The rapid implementation of the recommendations was one of the major successes of the project. In addition, a public involvement process refined the recommendations and helped develop a consensus that the safety improvements implemented would be accepted. The result of the safety audit was the implementation of countermeasures that should increase the safety of the roadway. The ability to introduce a bike lane and signalized crosswalks had a significant impact toward the goal of making roads more pedestrian- and bicyclefriendly and implementing a "complete street" solution. The roadway is now much more accessible to pedestrians and bicyclists while maintaining much of the mobility already in place for motorized vehicles.

As the National Highway System reaches the end of its serviceable life, transportation agencies increasingly need to focus on the preservation, rehabilitation, and maintenance of these roads. In light of significant increases in work zone activity, transportation officials and contractors are challenged to find ways to reduce the negative impacts on driver mobility. The key to addressing this challenge is to recognize potential impacts well in advance. One major tool used for this purpose is computer simulation. Many simulation models exist, some designed specifically for work zone analysis, including QUEWZ, QuickZone, CORSIM, and CA4PRS. This purpose of this paper is to present case studies that illustrate and evaluate these models for ease of use, data requirements, and ability to simulate and assess work zone strategies, shedding light on the relative reliability and accuracy of these simulation models as well as their user-friendliness and data requirements. This paper compares simulation results with actual work zone conditions in eight locations across New England. The results of this evaluation will be of interest to state and local transportation engineers responsible for planning and designing work zone strategies. This research has shown that some simulation models provide a low-risk, low-cost environment in which to test and analyze a variety of work zone alternatives. For example, QUEWZ and QuickZone were able to provide reasonable order of magnitude queue length estimates on Interstate highways that were comparable with observations made in the field. In addition, such estimates required few data, including hourly volume and roadway geometry information.

Driver distraction and speeding are two contributors to crashes in construction and maintenance work zones. Rumble strips can be effective and have been used in some states to alert drivers to reduce speed in advance of some change in the driving situation, such as at intersections. Previous research has examined the potential of using temporary rumble strips in advance of work zones. The purpose of this research was to compare the attention-getting characteristics of several temporary rumble strips with permanent rumble strips. Two types of devices were tested and compared with permanent rumble strips: portable plastic rumble strips and adhesive rubberized polymer rumble strips. These devices were tested for their ability to generate steering wheel vibrations and in-vehicle and roadside sound. Analysis revealed that the portable plastic rumble strips were more effective on cars than on trucks for generating in-vehicle vibration and increasing the in-vehicle sound level. Further, they were generally better than the adhesive rumble strips in matching the characteristics of the tested permanent rumble strip. This was also true for the configurations that contained fewer than six portable plastic rumble strips. If the vibration and sound generated by the permanent rumble strips is considered the standard performance, various configurations of the portable plastic rumble strips can be implemented in short-term work zones and provide results similar to those of permanent rumble strips.

As road safety audits (RSAs) have been implemented by agencies across the United States, transportation professionals have realized that RSAs are an effective tool to proactively improve the future safety performance of a roadway during the planning and design stages and to identify safety issues in existing transportation facilities. But limited study has been done of the implementation of RSA recommendations on existing roadways. The Utah Local Technical Assistance Program Center conducted an RSA of 1300 East in Salt Lake City, Utah. The rapid implementation of the recommendations was one of the major successes of the project. In addition, a public involvement process refined the recommendations and helped develop a consensus that the safety improvements implemented would be accepted. The result of the safety audit was the implementation of countermeasures that should increase the safety of the roadway. The ability to introduce a bike lane and signalized crosswalks had a significant impact toward the goal of making roads more pedestrian- and bicycle-friendly and implementing a “complete street” solution. The roadway is now much more accessible to pedestrians and bicyclists while maintaining much of the mobility already in place for motorized vehicles.

As the National Highway System reaches the end of its serviceable life, transportation agencies increasingly need to focus on the preservation, rehabilitation, and maintenance of these roads. In light of significant increases in work zone activity, transportation officials and contractors are challenged to find ways to reduce the negative impacts on driver mobility. The key to addressing this challenge is to recognize potential impacts well in advance. One major tool used for this purpose is computer simulation. Many simulation models exist, some designed specifically for work zone analysis, including QUEWZ, QuickZone, CORSIM, and CA4PRS. This purpose of this paper is to present case studies that illustrate and evaluate these models for ease of use, data requirements, and ability to simulate and assess work zone strategies, shedding light on the relative reliability and accuracy of these simulation models as well as their user-friendliness and data requirements. This paper compares simulation results with actual work zone conditions in eight locations across New England. The results of this evaluation will be of interest to state and local transportation engineers responsible for planning and designing work zone strategies. This research has shown that some simulation models provide a low-risk, low-cost environment in which to test and analyze a variety of work zone alternatives. For example, QUEWZ and QuickZone were able to provide reasonable order of magnitude queue length estimates on Interstate highways that were comparable with observations made in the field. In addition, such estimates required few data, including hourly volume and roadway geometry information.

Driver distraction and speeding are two contributors to crashes in construction and maintenance work zones. Rumble strips can be effective and have been used in some states to alert drivers to reduce speed in advance of some change in the driving situation, such as at intersections. Previous research has examined the potential of using temporary rumble strips in advance of work zones. The purpose of this research was to compare the attention-getting characteristics of several temporary rumble strips with permanent rumble strips. Two types of devices were tested and compared with permanent rumble strips: portable plastic rumble strips and adhesive rubberized polymer rumble strips. These devices were tested for their ability to generate steering wheel vibrations and in-vehicle and roadside sound. Analysis revealed that the portable plastic rumble strips were more effective on cars than on trucks for generating in-vehicle vibration and increasing the in-vehicle sound level. Further, they were generally better than the adhesive rumble strips in matching the characteristics of the tested permanent rumble strip. This was also true for the configurations that contained fewer than six portable plastic rumble strips. If the vibration and sound generated by the permanent rumble strips is considered the standard performance, various configurations of the portable plastic rumble strips can be implemented in short-term work zones and provide results similar to those of permanent rumble strips.

The objective of this research was to develop analytical models and procedures for estimating the capacity of a freeway work zone by considering various geometric-, traffic-, and work zone-related parameters. The study was conducted in two stages: simulation-based modeling and field data collection. In the first stage, CORSIM (Version 5.1) was used to develop a comprehensive database for various work zone scenarios. Analytical models were developed to predict work zone capacity on the basis of these simulated data and previous literature findings considering three work zone configurations: two-to-one, three-to-two, and three-to-one lane closures. In the second stage, field data were collected at a freeway work zone to evaluate and refine the analytical models developed. Data were collected at the freeway work zone site during 15 evening peak periods, which included left- and right-lane closures as well as rainy weather conditions. The observed capacities were compared with those predicted by the new analytical models as well as to those estimated by the Highway Capacity Manual 2000. It was concluded that the analytical models developed predicted within 1% the capacity of the study work zone.

The objective of this research was to develop analytical models and procedures for estimating the capacity of a freeway work zone by considering various geometric-, traffic-, and work zone–related parameters. The study was conducted in two stages: simulation-based modeling and field data collection. In the first stage, CORSIM (Version 5.1) was used to develop a comprehensive database for various work zone scenarios. Analytical models were developed to predict work zone capacity on the basis of these simulated data and previous literature findings considering three work zone configurations: two-to-one, three-to-two, and three-to-one lane closures. In the second stage, field data were collected at a freeway work zone to evaluate and refine the analytical models developed. Data were collected at the freeway work zone site during 15 evening peak periods, which included left- and right-lane closures as well as rainy weather conditions. The observed capacities were compared with those predicted by the new analytical models as well as to those estimated by the Highway Capacity Manual2000. It was concluded that the analytical models developed predicted within 1% the capacity of the study work zone.