Autonomous Vehicles (AVs) have the potential to significantly evolve transportation. AVs are expected to make transportation safer by avoiding accidents that happen due to human errors. When AVs become connected, they can exchange information with the infrastructure or other Connected Autonomous Vehicles (CAVs) to efficiently plan their future motion and therefore, increase the road throughput and reduce energy consumption. Cooperative algorithms for CAVs will not be deployed in real life unless they are proved to be safe, robust, and resilient to different failure models. Since intersections are crucial areas where most accidents happen, this dissertation first focuses on making existing intersection management algorithms safe and resilient against network and computation time, bounded model mismatches and external disturbances, and the existence of a rogue vehicle. Then, a generic algorithm for conflict resolution and cooperation of CAVs is proposed that ensures the safety of vehicles even when other vehicles suddenly change their plan. The proposed approach can also detect deadlock situations among CAVs and resolve them through a negotiation process. A testbed consisting of 1/10th scale model CAVs is built to evaluate the proposed algorithms. In addition, a simulator is developed to perform tests at a large scale. Results from the conducted experiments indicate the robustness and resilience of proposed approaches.

Travel time is the main transportation system performance measure used by the planning community to evaluate the impacts of traffic congestion on infrastructure investment projects and policy development plans. Planners rely on the travel demand model tool estimates for the selection and prioritization of critical and sensitive projects to meet the fiscally constraint requirements imposed by the Federal Highway Administration (FHWA) on their transportation improvement programs (TIP). While travel demand model estimates have been successfully implemented in the evaluation of project scenarios or alternatives, the application of the methods used in the travel demand model to generate these estimates continues to present a critical challenge, particularly to modelers who have to produce a validated model upon which traffic predictions can be made. The various volume-delay functions (VDFs) including the Bureau of Public Roads (BPR) function, used in the travel demand model to relate traffic volume to travel time, are developed based on system-wide attributes. BPR function in its polynomial form is computationally efficient and simple for implementation in a transport planning software. The planning community has long recognized that the BPR function cannot capture traffic flow dynamics and queue evolution processes. Besides, it has difficulties in using the average travel time measure to describe an oversaturated bottleneck with high density but low throughput. This dissertation aims to propose a simplified and yet effective point-queue based modeling approach built on the cumulative vehicle arrival concept, and the polynomial equation formula, based on Newell’s method, to estimate travel time at a corridor level using real-world speed and count measurements. A traffic state estimation (TSE) method is also proposed to characterize data into various states, such as congested state and uncongested state, using Markov Chain to capture current traffic pattern and Bayesian Classifier to infer congestion effects. As the testbed for the case study, the research selects the Phoenix freeway corridor with year-round traffic data collected from embedded traffic loop detectors. The results and effectiveness of the proposed methods are discussed to shed light on the calibration of link performance function, which is an analytical building block for system-wide performance evaluation.

This dissertation investigates congestion mitigation during the ingress of a planned special event (PSE). PSEs would impact the regular operation of the transportation system within certain time periods due to increased travel demand or reduced capacities on certain road segments. For individual attendees, cruising for parking during a PSE could be a struggle given the severe congestion and scarcity of parking spaces in the network. With the development of smartphones-based ridesharing services such as Uber/Lyft, more and more attendees are turning to ridesharing rather than driving by themselves. This study explores congestion mitigation during a planned special event considering parking, ridesharing and network configuration from both attendees and planner’s perspectives.

Parking availability (occupancy of parking facility) information is the fundamental building block for both travelers and planners to make parking-related decisions. It is highly valued by travelers and is one of the most important inputs to many parking models. This dissertation proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility.

From an attendee’s perspective, the probability of finding parking at a particular parking facility is more treasured than occupancy information for parking search. However, it is hard to estimate parking probabilities even with accurate occupancy data in a dynamic environment. In the second part of this dissertation, taking one step further, the idea of introducing learning algorithms into parking guidance and information systems that employ a central server is investigated, in order to provide estimated optimal parking searching strategies to travelers. With the help of the Markov Decision Process (MDP), the parking searching process on a network with uncertain parking availabilities can be modeled and analyzed.

Finally, from a planner’s perspective, a bi-level model is proposed to generate a comprehensive PSE traffic management plan considering parking, ridesharing and route recommendations at the same time. The upper level is an optimization model aiming to minimize total travel time experienced by travelers. In the lower level, a link transmission model incorporating parking and ridesharing is used to evaluate decisions from and provide feedback to the upper level. A congestion relief algorithm is proposed and tested on a real-world network.

Priced Managed Lanes (MLs) have been increasingly advocated as one of the effective ways to mitigating congestion in recent years. This study explores a new and innovative pricing strategy for MLs called Travel Time Refund (TTR). The proposed TTR provides an additional option to paying drivers that insures their travel time by issuing a refund to the toll cost if they do not reach their destination within specified travel times due to accidents or other unforeseen circumstances. Perceived benefits of TTR include raised public acceptance towards priced MLs, utilization increase of HOV/HOT lanes, overall congestion mitigation, and additional funding for relevant transportation agencies. To gauge travelers’ interests of TTR and to analyse its possible impacts, a stated preference (SP) survey was performed. An exploratory and statistical analysis of the survey responses revealed negative interest towards HOT and TTR option in accordance with common wisdom and previous studies. However, it is found that travelers are less negative about TTR than HOT alone; supporting the idea, that TTR could make HOT facilities more appealing. The impact of travel time reliability and latent variables representing psychological constructs on travelers’ choices in response to this new pricing strategy was also analysed. The results indicate that along with travel time and reliability, the decision maker’s attitudes and the level of comprehension of the concept of HOT and TTR play a significant role in their choice making. While the refund option may be theoretically and analytically feasible, the practical implementation issues cannot be ignored. This study also provides a discussion of the potential implementation considerations that include information provision to connected and non-connected vehicles, distinction between toll-only and refund customers, measurement of actual travel time, refund calculation and processing and safety and human factors issues. As the market availability of Connected and Automated Vehicles (CAVs) is prognosticated by 2020, the potential impact of such technologies on effective demand management, especially on MLs is worth investigating. Simulation analysis was performed to evaluate the system performance of a hypothetical road network at varying market penetration of CAVs. The results indicate that Connected Vehicles (CVs) could potentially encourage and enhance the use of MLs.

Recently, automation, shared use, and electrification are proposed and viewed as the “three revolutions” in the future transportation sector to significantly relieve traffic congestion, reduce pollutant emissions, and increase transportation system sustainability. Motivated by the three revolutions, this research targets on the passenger-focused scheduled transportation systems, where (1) the public transit systems provide high-quality ridesharing schedules/services and (2) the upcoming optimal activity planning systems offer the best vehicle routing and assignment for household daily scheduled activities.

The high quality of system observability is the fundamental guarantee for accurately predicting and controlling the system. The rich information from the emerging heterogeneous data sources is making it possible. This research proposes a modeling framework to systemically account for the multi-source sensor information in urban transit systems to quantify the estimated state uncertainty. A system of linear equations and inequalities is proposed to generate the information space. Also, the observation errors are further considered by a least square model. Then, a number of projection functions are introduced to match the relation between the unique information space and different system states, and its corresponding state estimate uncertainties are further quantified by calculating its maximum state range.

In addition to optimizing daily operations, the continuing advances in information technology provide precious individual travel behavior data and trip information for operational planning in transit systems. This research also proposes a new alternative modeling framework to systemically account for boundedly rational decision rules of travelers in a dynamic transit service network with tight capacity constraints. An agent-based single-level integer linear formulation is proposed and can be effectively by the Lagrangian decomposition.

The recently emerging trend of self-driving vehicles and information sharing technologies starts creating a revolutionary paradigm shift for traveler mobility applications. By considering a deterministic traveler decision making framework, this research addresses the challenges of how to optimally schedule household members’ daily scheduled activities under the complex household-level activity constraints by proposing a set of integer linear programming models. Meanwhile, in the microscopic car-following level, the trajectory optimization of autonomous vehicles is also studied by proposing a binary integer programming model.

This study explores an innovative framework for a self-sustained traffic operations system using vehicle-to-vehicle (V2V) communications alone. The proposed framework is envisioned as the foundation to an alternative or supplemental traffic operation and management system, which could be particularly helpful under abnormal traffic conditions caused by unforeseen disasters and special events. Its two major components, a distributed traffic monitoring and platoon information aggregation system and a platoon-based automated intersection control system, are investigated in this study.



The distributed traffic monitoring and platoon information aggregation system serves as the foundation. Specifically, each equipped vehicle, through the distributed protocols developed, keeps track of the average traffic density and speed within a certain range, flags itself as micro-discontinuity in traffic if appropriate, and cross-checks its flag status with its immediate up- and down-stream vehicles. The micro-discontinuity flags define vehicle groups with similar traffic states, for initiating and terminating traffic information aggregation. The impact of market penetration rate (MPR) is also investigated with a new methodology for performance evaluation under multiple traffic scenarios.

In addition to MPR, the performance of the distributed traffic monitoring and platoon information aggregation system depends on the spatial distribution of equipped vehicles in the road network as well. The latter is affected by traffic dynamics. Traffic signal controls at intersections play a significant role in governing traffic dynamics and will in turn impact the distributed monitoring system. The performance of the monitoring framework is investigated with different g/C ratios under multiple traffic scenarios.

With the distributed traffic monitoring and platoon information aggregation system, platoons can be dynamically identified on the network in real time. This enables a platoon-based automated intersection control system for connected and autonomous vehicles. An exploratory study on such a control system with two control stages are proposed. At Stage I, vehicles of each platoon will synchronize into a target speed through cooperative speed harmonization. Then, a platoon of vehicles with the same speed can be treated as a single vehicle for speed profile planning at Stage II. Its speed profile will be immediately determined given speed profiles of other platoons and the control goal.

Women have evolved in the engineering profession over the decades. However, there is still a lot more room for female presence in the industry as women currently make up about 12-15% of working engineers. Based on many studies and surveys, it is clear that female confidence in their own performance and a feeling of belonging in the industry has evolved for the better. The studies and surveys also show that women still lack a certain confidence to get their engineering degree and then to pursue a career in engineering once they receive their degree. Research shows that the main cause for this is due to the stereotype that engineering is a masculine profession. Men and women both have this mindset because it has become a societal norm that most people go along with and do not even realize it. Unfortunately, it is very hard to overcome and change a societal norm, therefore, something needs to be done in order to fix this mindset. (Crawford). Based on studies and research, there are many ways the stereotype is being combatted. Social media has become a huge component in advocating for female engineers. Men and women are helping to fight the status quo by supporting female engineers and lobbying against people who think women do not belong in the industry. Industry professionals are teaming up with schools to figure out ways to make STEM programs more exciting for all young kids, but especially girls. They are also working to provide more mentors and role models for young girls in order to cheer them on and make them more confident in their abilities when learning and applying the STEM curriculum, as studies have proven that providing young girls with mentors can really help foster more female engineers in the long run. (Crawford). With all of the positive support and promotions of female engineers in the past few years, it is evident that women can certainly progress at a much faster pace than in previous decades.

The purpose of this project was to design a new railroad crossing for pedestrians and bicyclists in mid-block or urban areas. In order to develop a successful design, the needs of the railroad, the end-users, and the city governments were researched and converted into measurable engineering requirements. For the railroad companies, the most important need was a crossing that presents an effective barrier to users while a train is in the area. For bicyclists and pedestrians (the end-users), the most important need was for the crossing to be both reliable and easily accessible. For the city governments, the most important need was a crossing that is inexpensive yet sturdy. The approach to this project was similar to the approach used in many engineering design processes. First is the Introduction, which provides an overview of the issue and presents the full problem statement. Next is the Research of Prior Art, which details the past solutions to railroad crossings as well as the 3 E's of railroad crossing safety. After this, the customer needs are discussed in the Needs to Requirements section and the process of converting these into measurable engineering requirements is shown. Next, various conceptual design options are shown in the Conceptual Design section and a final conceptual design is chosen based on adherence to the stated requirements. This final conceptual design is then taken into the preliminary design phase and refined until it becomes the final preliminary design. After the Final Preliminary Design Description, the Project Conclusions and Recommendations are presented. Due to time and monetary constraints, this project ends after the preliminary design stage. Despite this, the conclusion of this project is that the final design presented here will be successful if additional resources are obtained to move it forward into the detailed design phase. For now, this project has come to a halt due to UP's reluctance to allow any additional railroad crossings in the Phoenix and Tempe, Arizona areas. It is recommended that city officials and bicyclist/pedestrian action groups continue talks with UP until they agree to allow additional crossings to be built that are geared towards non-motorized users.