The use of the empirical deck design method has increased its acceptance due to the economic advantages that it presents when compared to its counterpart, the traditional method. This can be attributed to the fact that the empirical method provides an appropriate design where the deck withstands stress not only due to the steel reinforcement but to an implicit arching membrane stress set-up as an effect of the lateral restraint surrounding the deck slab known as Compressive Membrane Action (CMA). It has been proved through research that most design codes underestimate the strength of laterally restrained slabs. However, there is still a lack of acceptance in practical bridge design codes. This thesis presents an analysis addressing the influence that the lateral stiffness of the support beams has on the overall bridge deck performance. The lateral stiffness behavior was assessed through a programmed electronic spreadsheet where a comparison with different current code requirements and an additional approach was made. Through this analysis it was determined that not only does the support beam lateral stiffness plays an important role in the overall bridge deck slab ultimate capacity, but mapping out this influence is a priority that may also be useful in setting the basis for future design criteria.

Freight deliveries on signalized urban streets are known to cause lane blockages during delivery. Traffic congestion associated with urban freight deliveries has gained increasing attention recently as traffic engineers and planners are tasked with finding solutions to manage increasing demand more sustainably with limited road capacity. The goal of this research is to evaluate two models for quantifying the capacity and signalized control delay effects of a lane-blocking freight delivery on an urban arterial. The two methods are: an All-or-Nothing model similar to methodology used in the Highway Capacity Manual 6th Edition, and a Detailed model consistent with kinematic wave theory. The purpose is to provide insight on the use of these tools for analysis of urban freight delivery. The signalized control delay results of the two models are compared with observed video data of urban deliveries from one city block of 8th Ave in New York City. Empirical confirmation of double-parked delivery impact on signalized controlled delay remains elusive due to an inability to isolate the effects of the deliveries from other traffic perturbations in the video sample. Instead, microscopic simulation using Aimsun is used for comparison to the theoretical models and the results lend credibility to the Detailed model. The simulation results show a similar trend of delay impact from double-parked deliveries located at a range distances from the intersection and more closely resembled the Detailed model. The All-or-Nothing model would provide only a coarse representation of the capacity and delay effects. The more detailed approach that accounts for the dynamics of queuing in front of the delivery vehicle provides closed form analytical formulas for capacity and signalized control delay that can account for varying locations of deliveries as well as analysis periods with some blocked cycles and others unblocked. Two policy implications are proposed: 1) that double-parked deliveries located mid-block likely result in less signalized control delay impact, and 2) freight receivers that attract double-parked deliveries near an intersection stop line should be prioritized in urban freight delivery mitigation policies such as off-hour delivery.

Since their introduction to the 2009 Edition of the Manual on Uniform Traffic Control Devices, flashing yellow arrows (FYA) have had significant success in communicating the permissive turn message. While widely used for the permissive left turn maneuver, agencies recently have been utilizing flashing yellow arrows for the use with right turn applications as drivers interact with crossing pedestrians. As pedestrian conflicts are a concern during the permissive green phase, there is additional worry for the potential interaction between a pedestrian and vehicle turning right on red. This research explores the existing driver comprehension of permissive right turns during both green and red phases through static evaluation and microsimulation. Proposed traffic devices including the FYA and the Dynamic No Turn on Red sign were evaluated in relation to the existing signal and sign conditions implemented in the field. In comparing the proposed FYA to the existing circular green signal, the survey evaluation determined a statistically significant increase in drivers’ yielding responses when interacting with the FYA as opposed to the circular green. Through application of the VISSIM program, it was determined that right turning speeds with the FYA present were significantly lower than when interacting with solely the circular green. Both the static evaluation and microsimulation determined a strong similarity between the existing circular red and R10-11 sign and the proposed dynamic no turn on red sign which verifies the strong understanding drivers have of the message and the sign itself.

Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying travelers’ behavior by providing rewards to enable shifts to more energy-efficient modes, (e.g., from auto to either bus or bicycles). Optimization conditions are formulated for the problem to understand solution properties, and numerical tests are carried out to study the effects of system parameters (e.g., token budget and coefficient of tokens) on the optimal solutions (i.e., energy savings). The multinomial logit model is used to formulate the full problem, comprised of an objective function and constraint of a token budget ranging from $5,000-$10,000. Comparably, the full problem is computationally reduced by various parameterization strategies, in that the number of tokens assigned to all travelers’ is parameterized and proportional to the expected energy savings. An optimization solution algorithm is applied with a global and local solver to solve a lagrangian sub-problem and a duo of heuristic solution algorithms of the original problem. These were determined necessary to attain an optimal and feasible solution. Input data necessary for this analysis is obtained for the Town of Amherst, MA from the Pioneer Valley Planning Commission (PVPC). The results demonstrated strong evidence to conclude a positive correlation between the system’s energy savings and the aforementioned system parameters. The local and global solvers solution algorithm reduced the average energy consumption by 11.48% - 19.91% and12.79% – 21.09% consecutively for the identified token budget range from a base case scenario with no tokens assigned. The duo of lagrangian heuristic algorithms improved the full problems solution i.e., higher energy savings, when optimized over a local solver, while the parameterized problem formulations resulted in higher energy savings when compared to the full problems’ formulation solution over local solver, but higher energy savings compared over the global solver. The Computational run-time for the global and local solvers solution algorithm for the full problem formulation required 43 hours and 24 minutes consecutively, while the local solver for the lagrangian heuristics and parameterized problem solution algorithm took 13 minutes and 7 minutes consecutively. Future research on this paper will be comprised of a bi-level optimization problem formulation where a high level optimization aims at maximizing system-wide energy savings, while a low-level consumer surplus maximization problem is solved for each system user.

The objective of the current study is to evaluate the use of virtual reality (VR) headsets to measure driving performance. This is desirable because they are several orders of magnitude less expensive and, if validated, could greatly extend the powers of simulation. Out of several possible measures of performance that could be considered for evaluating VR headsets, the current study specifically examines drivers’ latent hazard anticipation behavior both because it has been linked to crashes and because it has been shown to be significantly poorer in young drivers compared to their experienced counterparts in traditional driving simulators and in open road studies. The total time middle-aged drivers spend glancing at a latent hazard and the average duration of each glance was also compared to these same times for younger drivers using a VR headset and fixed-based driving simulator. In a between-subject design, forty-eight participants were equally and randomly assigned to one out of four experimental conditions – two young driver cohorts (18 – 21 years) and two middle-aged driver cohorts (30 – 55 years) navigating either a fixed-based driving simulator or a VR-headset-based simulator. All participants navigated six unique scenarios while their eyes were continually tracked. The proportion of latent hazards anticipated by participants which constituted the primary dependent measure was found to be greater for middle-aged drivers than young drivers across both platforms. Results also indicate that the middle-aged participants glanced longer than their younger counterparts on both platforms at latent hazards, as measured by the total glance duration but had no difference when measured by the average glance duration. Moreover, the difference in the magnitude of performance between middle-aged and younger drivers was the same across the two platforms. There were also no significant differences found for the severity of simulator sickness symptoms across the two platforms. The study provides some justification for the use of virtual reality headsets as a way of understanding drivers’ hazard anticipation behavior.

A before-and-after analysis was performed at eleven intersections where a bike box was installed in Portland, Oregon to explore the safety effects of the treatment. Video data were gathered prior to installation at 14 intersections where a bike box installation was planned by the Portland Bureau of Transportation. Cameras were set up to capture three full twenty-four hour days (72 hours) of data for each intersection from Tuesday through Thursday. Of the 14 original selected intersections, 11 intersections actually received the bike box treatment. Video data were again gathered for these intersections after the installation of the bike box for another three full twenty-four hour days (72 hours) between Tuesday and Thursday. One day of data (24 hours) was selected for observation from both the before and after periods in the analysis for each study intersection during midweek. Safety effects were evaluated by three metrics: 1) observed conflicts; 2) observed cyclist behavior for all conflicts as measured by head or shoulder checks; and 3) reported crash data. To develop the conflict data, a log was created of each motor vehicle and bicycle passing through the intersection for approximately 528 hours of video. All conflicts that were observed during the period were further reviewed by an expert panel that scored conflicts by severity. Following this review, a total of 18 conflicts were observed during the before period. The total exposure in the before period was 39,497 motor vehicles in the vehicle lane adjacent to the bike lane (10,454 of which were right-turning) and 7,849 bicycles. A total of 19 conflicts were observed during the after period. Total exposure was 42,381 motor vehicles in the vehicle lane adjacent to the bike lane (11,053 of which were right-turning) and 5,852 bicycles. The sample size of observed conflicts was insufficient to draw statistically significant conclusions for any of the specific intersections that were treated. When taking in account the total amount of conflicts, the limited data suggest a slight increase in the rate of conflicts when normalized against a product of right-turning vehicles and bicycles observed in the intersection. The data also suggest that the installation of a bike box at an intersection reduces the rate of conflicts per hundred motor vehicles and increases the rate of conflicts per hundred bicyclists. Data regarding head-checks from the bicyclist shows an increase in bicyclists observing the possibility of conflicts approaching from behind as they pass through the intersection. A review or crash data at each of the intersections shows an increase at three of the observed intersections and a decrease at the remaining five.

Long popular in northern Europe, protected bike lanes, also known as "cycle tracks" or "separated bike lanes," are seeing increased interest in the United States. One of the primary benefits of protected bike lanes is that they may provide a higher level of comfort than a standard bike lane that is only delineated by an inches-wide painted stripe. Several methods exist for quantifying the quality of service provided by a roadway for a bicyclist; however, many of these models do not consider protected bike lanes and of those that do, none are based on empirical data from the US. This is problematic as engineers, planners, and elected officials are increasingly looking to objective performance measures to help guide transportation project design and funding prioritization decisions. This thesis addresses this gap by presenting a cumulative logistic model to predict user comfort on protected bike lanes using surveys conducted in the United States. The model is for road segments only and not signalized intersections. It is developed from the results of in-person video surveys conducted in Portland, Oregon. The survey was completed by 221 individuals who viewed 20 video clips each. The model is validated using 3,230 responses to a survey of those who have ridden on protected bike lanes in multiple cities around the US. A cumulative logistic model is used because it predicts the distribution of ratings, providing a clearer picture of a facility's performance than a mean value produced by a simple linear model. The resulting model indicates that buffer type, one-way vs. two-way travel, motor vehicle speed, and motor vehicle average daily traffic volumes are all significant predictors of bicyclist comfort in protected bike lanes. Survey results also show that protected bike lanes are generally more comfortable than other types of on-street infrastructure, consistent with previous research findings.

This project examined cyclist red light running behavior using two data sets. Previous studies of cyclist compliance have investigated the tendencies of cyclists to run red lights on the whole by generalizing different maneuvers to their end outcome, running a red light. This project differentiates between the different types of red light running and focuses on the most egregious case, gap acceptance, which is when a cyclist runs a red light by accepting a gap in opposing traffic. Using video data, a mathematical model of cyclist red light running was developed for gap acceptance. Similar to other studies, this analysis utilized only information about the cyclist, intersection, and scenario that can be outwardly observed. This analysis found that the number of cyclists already waiting at the signal, the presence of a vehicle in the adjacent lane, and female sex were deterrents to red light running. Conversely, certain types of signal phasing, witnessing a violation, and lack of helmet increased the odds that a cyclist would run the red light. Interestingly, while women in general are less likely to run a red light, those who witnessed a violation were even more prone that men who had witnessed a violation to follow suit and run the red light themselves. It is likely that the differing socialization of women and men leads to different effects of witnessing a previous violator. The analysis also confirmed that a small subset of cyclists, similar to that found in the general population, are more prone to traffic violations. These cyclists are more willing to engage in multiple biking-related risk factors that include not wearing a helmet and running red lights. Although the model has definite explanatory power regarding decisions of cyclist compliance, much of the variance in the compliance choices of the sample is left unexplained. This points toward the influence of other, not outwardly observable variables on the decision to run a red light. Analysis of survey data from cyclists further confirms that individual characteristics not visible to the observer interact with intersection, scenario, and visible cyclist characteristics to result in a decision to comply (or not) with a traffic signal. Furthermore, cyclist characteristics, in general, and unobservable individual characteristics, specifically, play a larger role in compliance decisions as the number of compliance-inducing intersection traits (e.g. conflicting traffic volume) decrease. One such unobservable trait is the regard for the law by some cyclists, which becomes a more important determinant of compliance at simpler intersections. Cyclists were also shown to choose non-compliance if they questioned the validity of the red indication for them, as cyclists. The video and survey data have some comparable findings. For instance, the relationship of age to compliance was explored in both data analyses. Age was not found to be a significant predictor of non-compliance in the video data analysis while it was negatively correlated with stated non-compliance for two of the survey intersections. Gender, while having significant effects on non-compliance in the video dataset, did not emerge as an important factor in the stated non-compliance of survey takers. Helmet use had a consistent relationship with compliance between the video and survey datasets. Helmet use was positively associated with compliance in the video data and negatively associated with revealed non-compliance at two of the survey intersections. When coupled with the positive association between normlessness and stated willingness to run a red light, the relationship between helmet use and compliance solidifies the notion that a class of cyclists is more likely to consistently violate signals. It points towards a link between red light running and individuals who do not adhere to social norms and policies as strictly as others. Variables representing cyclists and motorists waiting at the signal were positively related to signal compliance in the video data. While an increased number of cyclists may be a physical deterrent to red light running, part of the influence on compliance that this variable and the variable representing the presence of a vehicle may be due to accountability of cyclists to other road users. This relationship, however, was not revealed in the stated non-compliance data from the survey. Efforts to increase cyclist compliance may not be worth a jurisdiction's resources since nearly 90% of cyclists in the video data were already compliant. If a problem intersection does warrant intervention, different methods of ensuring bicyclist compliance are warranted depending on the intersection characteristics. An alternative solution is to consider the applicability of traffic laws (originally designed for cars) to bicyclists. Creating separation in how laws affect motorists and cyclists might be a better solution for overly simple types of intersections where cyclists have fewer conflicts, better visibility, etc. than motorists. Education or other messaging aimed at cyclists about compliance is another strategy to increase compliance. Since cyclists appear to feel more justified in running red lights at low-volume, simple-looking intersections, it would probably be prudent to target messaging at these types of intersections. Many cyclists are deterred by high-volume and/or complicated looking intersections for safety reasons. Reminding cyclists of the potential dangers at other intersections may be a successful messaging strategy. Alternatively, reminding cyclists that it is still illegal to run a red light even if they feel safe doing so may be prudent. Additionally, messaging about the purpose of infrastructure such as bicycle-specific signals or lights that indicate detection at a signal may convince cyclists that stopping at the signal is in their best interest and that the wait will be minimal and/or warranted.

Pipeline map of east-central Tennessee published in 1983 by the State of Tennessee Department of Conservation, Division of Geology. Compiled by Robert A. Miller. Assisted by Frank McConnell, the Tennessee Gas Association, the Tennessee Public Service Commission, and the private and public utilities and carriers shown on the map. The legend denotes interstate and intrastate pipelines, gas utilities, and private gas company franchised areas. The scale is 1:250,000. The central-eastern sheet is part of a series of maps that cover the entire state. The coverage area on this map is from the Middle Tennessee Natural Gas Utility District to the Oak Ridge Utility District. Physical copy resides in the Government Information, Law and Maps Department of East Tennessee State University’s Sherrod Library. / https://dc.etsu.edu/rare-maps/1015/thumbnail.jpg

The Interactive Highway Safety Design Model (IHSDM) is a suite of software developed by the Federal Highway Administration (FHWA) for monitoring and analyzing two-lane rural highways in the United States. As IHSDM is a fairly "young" program a limited amount of research has been conducted to evaluate its practicability and reliability. To determine if IHSDM can be adopted into the engineering decision making process in Utah, a study was conducted under the supervision of the Utah Department of Transportation (UDOT) to evaluate its applicability to audit safety of two-lane rural highways in Utah. IHSDM consists of six modules: Policy Review Module (PRM), Crash Prediction Module (CPM), Design Consistency Module (DCM), Traffic Analysis Module (TAM), Intersection Review Module (IRM), and Driver/Vehicle Module (DVM) (still under construction). Among the six modules, two were chosen for evaluation because of their applicability to audit safety of the two-lane rural highways in Utah, namely CPM and IRM. For the evaluation of the CPM, three two-lane rural highway sections were selected. The results of this evaluation show that the CPM can produce reasonably reliable crash predictions if appropriate input data, especially alignment data, reflect the existing conditions at reasonable accuracy and engineering judgment is used. Using crash records available from UDOT's crash database and CPM's crash prediction capability, UDOT's traffic and safety engineers can locate "hot spots" for detailed safety audit, thus making the safety audit task more focused and effective. Unlike the CPM, the outputs of the IRM are qualitative and include primarily suggestions and recommendations. They will help the traffic and safety engineers identify what to look for as they visit the sites, such as a lack of stopping sight distance and a lack of passing sight distance. The interpretation of the IRM requires knowledge of various aspects of highway design, familiarity with A Policy on Geometric Design of Highways and Streets by the American Association of State Highway and Transportation Officials (AASHTO), and experience in traffic engineering. Based on the findings of the study, it is concluded that the CPM and IRM of IHSDM could be a useful tool for engineering decision-making during safety audits of two-lane rural highways. But the outputs from these modules demand knowledge and experience in highway design. It is recommended that the other modules of IHSDM be tested to fully appreciate the capability of IHSDM. The software can be a knowledgebased program that can help novice engineers to learn how to design safe two-lane rural highways.