Amelia C. Regan - US grants

Dynamic Freight and Fleet Management: Modeling, Algorithm Development and Implementation

The proposed project involves the development of an integrated research and teaching program. Its educational aim is to define the goals of a graduate program in transportation systems engineering (systems analysis and applied operations research) which includes a significant logistics component, to create a graduate sequence in freight network analysis and logistics and to guide the development of a four course undergraduate sequence in computer and mathematical methods for Civil and Environmental Engineering.

The research focus is in the area of dynamic freight and fleet management. Commercial vehicle operations are increasingly dominated by operations which require that decisions be made in real-time, as changes in customer demands, traffic network conditions, and the availability of resources (vehicles, drivers, repair equipment, etc.) occur. The rapid development of advanced information technologies suitable for use in real-time operations has outpaced the development of algorithms and implementable systems to support dynamic fleet management. The proposed research involves algorithm development aimed specifically at time-constrained and stochastic fleet and freight management systems coupled with modeling efforts which focus on the representation of freight transportation networks with a sufficient degree of operational realism in order to draw reasonable inferences into the likelihood of successful implementation of the algorithms developed. Applications include trucking operations, (long haul truckload, less-than-truckload and intermodal drayage between maritime ports, railheads and air freight terminals), local package pickup and delivery operations and urban service fleets.

NSF/USDOT: Dynamic and Stochastic Vehicle Dispatching with Time Dependent Travel Times: The Next Generation of Algorithms
Researchers have long understood that stochastic and time-dependent travel times, stochastic service or handling times and stochastic demand arrival patterns render schedules developed using static assignment models sub-optimal and often infeasible. Nonetheless, the development of alternative models and solution methods has proceeded at a relatively slow pace. The costs associated with these infeasibilities can be very high and are borne by commercial vehicle operators, their customers and society at large.

This research represents a new generation of routing and scheduling models, ones that explicitly incorporate time-dependent and stochastic travel times, dynamic service requests and stochastic service times. The applications of interest are local truckload trucking operations, local less-than-truckload operations, local pickup and delivery operations and service fleets.

Advances in information technologies for transportation systems have led to the accumulation of large quantities of raw data on transportation system status. To fully leverage such data, new tools must be developed to combine and effectively analyze large databases. The proposed research involves the application of nonlinear multivariate analysis methods to analyze combined truck traffic, detailed traffic flow, accident, and environmental data in order to identify the influences of mixes of truck traffic on the likelihood of accidents by type under different traffic conditions and on different types of network links. Understanding the complex factors surrounding truck accidents, can provide opportunities for intervention to enhance safety. The main analysis method is nonlinear canonical correlation analysis with multiple sets of mixed categorical, ordinal, and numerical variables. This eigenvalue method, implemented through alternating least squares algorithms, is characterized by the optimal scaling of the nonlinear variables and graphical interpretation of results.

The project has four distinct phases: (1) establishing a comprehensive database of traffic flow and crash information that is appropriate for identifying truck safety issues on urban freeways (2) identifying, through a specific type of multivariate nonlinear model, freeway locations and time periods where the mix of truck traffic within particular traffic flow conditions has the most adverse safety effects, (3) identifying ways to improve safety in problematic time-space situations. (4) identifying ways to apply our research to data available in other states.

In summary, the work will develop tools to help identify unsafe traffic conditions so that accidents can be avoided. We focus on truck involved accidents because these tend to be more severe than those involving only passengers and because trucks are increasingly equipped with communication devices and their drivers can be easily warned that they are entering unsafe conditions.

The broader impacts of the proposed research include improvements in traffic safety, the technical training of graduate student researchers and outreach to local high schools with significant under-represented populations.

The University of California, Irvine proposes an American Indian Summer Institute in Computer Science (AISICS) that aims to broaden participation in the computer sciences by actively engaging American Indian and Alaskan Native (AI/AN) students in cross-disciplinary culturally relevant hands-on information and computer science educational and social activities. For each of three summers, AI/AN high school students will work with computer science professors, graduate students, undergraduates, and high school teachers to develop interactive story projects. These projects will utilize technologies and concepts used in computer game development, will draw on the significant interest that many high school students feel for computer games, and will provide a means to present culture and history shared by American Indian storytellers in a modern context. About 100 AI/AN high school students will experience a cross-disciplinary culturally relevant curriculum, and their work will become part of a repository that provides a resource for digital curricular content made available to K-12 educators. The students will also participate in an American Indian studies course, a communication skills course, and informational workshops about college and the college admissions process. UC Irvine has offered a previous model of AISICS for eleven summers since 1991. This new AISICS project will enhance the partnership among the University, Title VII American Indian high school education programs, and other American Indian organizations.

The iSTART program aims to increase the retention and graduation rates of engineering and computer science (CS) students at the University of California Irvine (UCI) as well as transfer of students from three local community colleges (Irvine Valley College, Saddleback College, and Santa Ana College). The implementation of evidence-based strategies to address the needs of engineering and computer science students at UCI and partner community colleges will increase the number and diversity of well-prepared graduates who enter the engineering and computer science workforce. Specifically, the iSTART program will establish a formal First Year Experience Program at UCI, improve the articulation and transfer process between community colleges and UCI, and establish specific innovation-based retention initiatives to provide a system of support and engagement for students.

The iSTART program will leverage existing infrastructure, activities, and partnerships, as well as integrate a coordinated set of evidence-based practices and interventions to support students' learning and engagement and transfer from community colleges. For UCI lower-division students, a new Introduction to Engineering sequence will engage freshmen in design projects with themes around Global Grand Challenges. Similarly, a hands-on and collaborative CS: Principles course will be developed and implemented to provide an entryway to students interested in pursuing information and computer science. To address challenges lower-division students encounter in mathematics courses, a new Analytical Formulation of Engineering seminar that incorporates collaborative and active learning will be developed for engineering students, which is modeled after the successful revisions to mathematics courses in the computer science curriculum. Moreover, multiple evidence-based interventions for students at the partner community colleges will be implemented through the recently established Corridor to Academic Success in Engineering and Computer Science (CASECS) program. These include cross-enrollment opportunities, articulating courses to improve the transfer process as well as modify engineering and computer science courses at the partner community colleges to integrate hands-on and applied learning activities, recruiting and building cohorts at the partner community colleges who will have access to co-advising, peer mentoring, and supplemental tutoring, and a summer bridge program for transfer students. Finally, the integration of a design and prototyping space for industry-driven student design projects will provide students co-curricular experiential learning opportunities to develop and support students' innovation and creativity skills. An evaluation plan that includes formative and summative evaluation will inform improvements during the project period as well as measure the outcomes of the project through surveys and student tracking. Knowledge gained from the evaluation will help provide a modular model for improving student retention that can be adapted at other institutions with similar needs.