Abdullah Kurkcu will stand for the final examination for the degree of Doctor of Philosophy in Transportation Planning and Engineering on Friday, March 23 at 2:30 p.m. in the C2SMART Lab.

Dissertation Title:

Connected Transportation Systems: Next Generation Traffic Simulation and Data Collection Tools and Techniques

Abstract:

In a connected environment, users, applications, or nodes of the system are continuously connected to one another, enabling hundreds and even thousands of devices to communicate with each other without central communication hubs. This connectivity provides the whole system with capabilities that could not be achieved by any of the individual elements on their own. While connected environments have the potential to change the way we understand and design our relationship to natural and built systems, this concept is still in its early days. This dissertation studies several real-life and simulated connected environments, with the goal of developing and testing novel methods to evaluate connected transportation solutions. It evaluates the quality of data generated in several connected systems using big data analytics and attempts to understand their impacts on the reliability, safety, and mobility of transportation systems.

Earlier chapters in this dissertation explore opportunities to simulate connectivity within transportation networks and to build applications to improve traffic mobility and safety. To achieve this goal, microscopic traffic simulation tools coupled with customized plugins for simulating communication scenarios are used. To evaluate the potential impact of connectivity on transportation networks, it is also imperative to know the information transfer speed from one vehicle to all the others, especially in the presence of accidents where information dissemination time is a crucial issue. This dissertation proposes an analytical framework based on a disease-spread model to estimate the time it takes to transfer the critical information to the target network. The results show that the developed model can predict the information transfer time reasonably well for market penetration levels higher than 20% and the approach is practical for dense urban scenarios with high traffic densities.

In an effort to move from a simulated environment to real-world implementation, alternative open data collection procedures for transportation analysis are also introduced. The primary objective is to acquire and use data for segments in a transportation network where physical sensor infrastructure is limited. The results show that open data sources can deliver useful information for regular and breakdown traffic conditions.

The following chapters focus on the integration and use of the various ubiquitous Internet of Things (IoT) devices in a connected transportation environment. A pedestrian sensor was developed that can detect devices with wireless capabilities within a predefined area. Data collected with the sensor was used to demonstrate the possibilities for modeling passenger arrivals in a highly congested transportation facility. Initial tests revealed promising results in terms of employing this data for system performance evaluation. A doubly-stochastic arrival model was developed to accurately capture the time-invariant nature of passenger arrivals. Finally, this new model was used to demonstrate the importance of accurate modeling of stochastic time-variant arrivals in a capacitated queueing system where under certain conditions major deviations from the mean can cause system failures and/or significant delays.

The findings in this dissertation demonstrate the usefulness and reliability of connected systems for improving transportation operations, traffic mobility and safety. For future studies, comparisons of simulated results with field data from upcoming pilot studies will allow for an assessment of how well these simulated systems work when applied to the real world. Further analyses and calibration of simulation models can deliver a better representation of real-world systems which will lead to better informed decision-making.

Event Details:

March 23 at 2:30 p.m.

C2SMART Lab

6 Metrotech Center, 4th Floor