Alternative intersections and interchanges, such as the diverging diamond interchange (DDI), the restricted crossing u-turn (RCUT), and the displaced left-turn intersection (DLT), have the potential to both improve safety and reduce delay. However, partially due to lingering questions about analysis methods and service measures for these designs, their rate of implementation remains low. This research attempts to answer three key questions. Can alternative intersections and interchanges be incorporated into the existing level of service and service measure schema, or is a new service measure with an updated level of service model required? Is the behavior of drivers at alternative intersections fundamentally similar to those at conventional intersections, such that traffic microsimulation applications can accurately model the behaviors observed in the field? Finally, is the planning level tool made available through FHWA an accurate predictor of the relative performance of various alternatives, or is an updated tool necessary? Discussion and case study analysis are used to explore the existing level of service and service measure schema. The existing control delay measure is recommended to be replaced with a proposed junction delay measure that incorporates geometric delay, with the existing level of service schema based on control type recommended to be replaced by a proposed schema using demand volume. A case study validation of micro- and macroscopic analysis methods is conducted, finding the two microscopic methods investigated to match field observed vehicle delays within 3 to 7 seconds for all designs tested, and macroscopic HCM method matching within 3 seconds for the DDI, 35 seconds for the RCUT, and 130 seconds for the DLT design. Taking the critical lane analysis method to be a valid measure of operations, the demand-volume limitations of each alternative design is explored using eighteen geometric configurations and approximately three thousand volume scenarios, with the DLT design predicted to accommodate the highest demand volumes before failure is reached. Finally, six geometries are examined using both the planning-level tool and the validated microsimulation tool, finding that the curve of the capacity-to-delay relationship varies for each alternative design, invalidating the use of critical lane analysis as a comparative tool. / Ph. D.

The TMA Traffic Operations Survey was developed, which focused on traffic operations services and transportation system management practices of TMOs. The survey had the following structure: background, membership, services, and follow-up. The survey was emailed to 157 TMOs around the nation and 35 responses were received, a 22.2% response rate. Of the participating organizations only five (17%) organizations provided traffic services including traffic control improvements, signal timing, and collection of traffic data. These findings determined that a minority of organizations are involved in traffic operations. The multiple organizations that are involved in traffic operations provided data on their websites including incident alerts, live traffic views, and real-time traffic or shuttle information. Very few of the organizations that used data to provide traffic operations services generated the data themselves. The research results determined that few organizations are actively attempting to assume a role in traffic operations.

With the advancement of implementation for a novel traffic control device, the Flashing Yellow Arrow (FYA), agencies across the country have continually sought strategies to improve intersection operations and safety, specifically with respect to the left-turn application. More so, permissive left-turn intervals have been communicated to drivers using several traffic signal indications; however, most frequently these phases are represented through the circular green (CG) ball and more recently, the FYA. Previous research in this area determined that the FYA indication produced the most effective communication of permissive left-turns. Further, this previous research led to the inclusion of the FYA in the 2009 edition of the Manual on Uniform Traffic Control Devices (MUTCD). In recent years, agencies across the country have embraced the implementation of the FYA for permissive left-turns. However, there remains a lack of national guidance on the definition of change and clearance intervals for transitioning between protected and permissive left-turns. Complicating the matter is the connection between traditional signal phasing/design and human factors. Investigation through driver comprehension and real-world operations will allow us to not only evaluate current conditions, but also experimental and future conditions. Recommendations provided from this research will ultimately offer agencies with the strategies for the most effective transition from a protected left-turn to a permissive left-turn phase.

This dissertation is based on the analysis of causes of traffic accidents in traffic operations and on portion of driver, self-propelled motor vehicle and terrestrial communications on it. On the basis of these analyses there are finished general conclusions for decreasing of traffic accident frequencies.

Automated Electric Transportation (AET) is a concept of an emerging cooperative transportation system that combines recent advances in vehicle automation and electric power transfer. It is a network of vehicles that control themselves as they traverse from an origin to a destination while being electrically powered in motion – all without the use of connected wires. AET's realization may provide unparalleled returns in the form of dramatic reductions in traffic-related air pollution, our nation’s dependence on foreign oil, traffic congestion, and roadway inefficiency. More importantly, it may also significantly improve transportation safety by dramatically reducing the number of transportation-related deaths and injuries each year as it directly addresses major current issues such as human error and adverse environmental conditions related to vehicle emissions. In this thesis, a logical strategy in transitioning from today’s current transportation system to a future automated and electric transportation system is identified. However, the chief purpose of this research is to evaluate the operational parameters where AET will be feasible from a transportation operations perspective. This evaluation was accomplished by performing lane capacity analyses for the mainline, as well as focusing on the merging logic employed at freeway interchange locations. In the past, merging operations have been known to degrade traffic flow due to the interruptions that merging vehicles introduce to the system. However, by analyzing gaps in the mainline traffic flow and coordinating vehicle movements through the use of the logic described in this thesis, mainline traffic operations can remain uninterrupted while still allowing acceptable volumes of merging vehicles to enter the freeway. A "release-to-gap" merging algorithm was developed and utilized in order to maximize the automated flow of traffic at or directly downstream of a freeway merge point by maximizing ramp flows without causing delay to mainline vehicles. Through these tasks, it is the hope of this research to aid in identifying the requirements and impending impacts of the implementation of this potentially life-altering technology.

Automatic Incident Detection Algorithms (AIDA) have been part of freeway management system software from the beginnings of ITS deployment. These algorithms introduce the capability of detecting incidents on freeways using traffic operations data. Over the years, several approaches to incident detection have been studied and tested. However, the size and scope of the urban transportation networks under direct monitoring by transportation management centers are growing at a faster rate than are staffing levels and center resources. This has entailed a renewed emphasis on the need for reliability and accuracy of AIDA functionality. This study investigates a new approach to incident detection that promises a significant improvement in operational performance. This algorithm is formulated on the premise that the current conditions facilitate the prediction of future traffic conditions, and deviations of observations from the predictions beyond a calibrated level of tolerance indicate the occurrence of incidents. This algorithm is specifically designed for easy implementation and calibration at any site. Offline tests with data from the Georgia-Navigator system indicate that this algorithm realizes a substantial improvement over the conventional incident detection algorithms. This algorithm not only achieves a low rate of false alarms but also ensures a high detection rate.

Among the most critical elements at signalized intersections are the design of vehicle detection equipment and the timing of change and clearance intervals. Improperly timed clearance intervals or improperly placed detection equipment can potentially place drivers in a Type I dilemma zone, where approaching motorists can neither proceed through the intersection before opposing traffic is released nor safely stop in advance of the stop bar. Type II dilemma zones are not necessarily tied to failures in design, but are more readily tied to difficulties in driver decision making associated with comprehension and behavior. The Type II dilemma zone issues become even more prevalent at high-speed intersections where there is greater potential for serious crashes and more variability in vehicle operating speeds. This research initiative attempts to further describe the impact of driver behavior and comprehension on dilemma zones. To address this notion several experiments are proposed. First, a large empirical observation of high-speed signalized intersections is undertaken at 10 intersection approaches in Vermont. This resulted in the collection of video and speed data as well as full intersection inventories and signal timings. These observations are reduced and analyzed for the purpose of reexamining the boundaries of a Type II dilemma zone. Second, a comparison of point and space sensors for the purpose of dilemma zone mitigation was conducted. This experiment provides evidence supporting the notion that space sensors have the potential for providing superior dilemma zone protection. Third, a computer based survey is conducted to identify if drivers comprehend the correct meaning of the solid yellow indication and how this relates to their predicted behavior. Lastly, a regression model is developed drawing on the data collected from the field observation as well as the static survey to determine how characteristics such as the speed and position of the vehicle as well as driver age and experience influence driver behavior in the Type II dilemma zone. Cumulatively, these experiments will shed additional light on the influence of driver behavior and comprehension on the Type II dilemma zone.

As the United States continues to build and repair the ageing highway infrastructure, the bearing of freeway work zones will continue to impact the capacity. To predict the capacity of a freeway work zone, there are several tools available for engineers to evaluate these work zones but only microsimulation has the ability to simulate the driver behavior. One of the limitations of current car-following models is that they only account for one overall behavioral condition. This dissertation hypothesizes that drivers change their driving behavior as they drive through a freeway work zone compared to normal freeway conditions which has the potential to impact traffic operations and capacity of work zones. Psycho-physical car-following models are widely used in practice for simulating car-following. However, current simulation models may not fully capture car-following driver behavior specific to freeway work zones. This dissertation presents a new multidimensional psycho-physical framework for modeling car-following based on statistical evaluation of work zone and non-work zone driver behavior. This new framework is close in character to the Wiedemann model used in popular traffic simulation software such as VISSIM. This dissertation used two methodologies for collecting data: (1) a questionnaire to collect demographics and work zone behavior data and (2) a real-time vehicle data from a field experiment involving human participants. It is hypothesized that the parameters needed to calibrate the multidimensional framework for work zone driver behavior can be derived statistically by using data collected from runs of an Instrumented Research Vehicle (IRV) in a Living Laboratory (LL) along a roadway. The design of this LL included the development of an Instrumented Research Vehicle (IRV) to capture the natural car-following response of a driver when entering and passing through a freeway work zone. The development of a Connected Mobile Traffic Sensing (CMTS) system, which included state-of-the-art ITS technologies, supports the LL environment by providing the connectivity, interoperability and data processing of the natural, real-life setting. The IRV and CMTS system are tools designed to support the concept of a LL which facilitates the experimental environment to capture and calibrate natural driver behavior. The objective is to have these participants drive the instrumented vehicle and collect the relative distance and the relative velocity between the instrumented vehicle and the vehicle in the front of the instrumented vehicle. A Phase I pilot test was conducted with 10 participants to evaluate the experiment and make any adjustments prior to the full Phase II driver test. The Phase II driver test recruited a group of 64 participants to drive the IRV through an LL set up along a work zone on I-95 near Washington D.C. in order to validate this hypothesis In this dissertation, a new framework was applied and it demonstrated that there are four different categories of car-following behavior models each with different parameter distributions. The four categories are divided by traffic condition (congested vs. uncongested) and by roadway condition (work zone vs. non-work zone). The calibrated threshold values are presented for each of these four categories. By applying this new multidimensional framework, modeling of car-following behavior can enhance vehicle behavior in microsimulation modeling. This dissertation also explored driver behavior through combining vehicle data and survey techniques to augment the model calibrations to improve the understanding of car-following behavior in freeway work zones. The results identify a set of survey questions that can potentially guide the selection of parameters for car-fallowing models. The findings presented in this dissertation can be used to improve the performance of driver behavior models specific to work zones. This in return will more acutely forecast the impact a work zone design has on capacity during congestion.

No description available.

This thesis analyzes the human as an element of accident system in the sphere of motor-vehicle traffic. Then interprets the risks, damages and injuries in this sphere which are caused by the driver. On this basis, the measures to reduce the number of injuries in the Czech Republic are drawn up. In this thesis is taken account of existing measures that were proceeded in this area. It focuses particularly on the subject "driver" and its role in traffic accidents.