NSF – CAREER Project

Urban Transport Network Design with Privacy-Aware Agent Learning

Principle Investigator: Joseph Chow

ABSTRACT

Technological advances for transportation systems are quickly evolving from their roots in highway materials and traffic control 70 years ago to technologies that support “smart cities”: autonomous vehicles, on-demand mobility services, and traffic control with machine learning, among others. In 2016, the U.S. Department of Transportation proposed spending $4 billion on autonomous vehicles, and pledged $40 million in tackling smart cities as a grand challenge. However, successful operation of these technologies in a large scale, highly congested city remains prone to operational pitfalls and obstacles. For example, how should a service operator best deploy their vehicles or inform their travelers in real time to optimize service and learning potential simultaneously, while acknowledging their privacy? Some high profile failures include the on-demand transit service Kutsuplus in Helsinki and the Car2Go car share service in San Diego. These systems are highly dynamic, but methods in machine learning and dynamic optimization are not designed for the unique intricacies of urban transport networks. This Faculty Early Career Development (CAREER) Program project is for integrated research and advanced education to create new technologies for these dynamic systems, train students and professionals in these technologies, and engage with private operators and incubators in New York City to operationalize them. The project will use real data from industry partners in ridesharing and autonomous vehicle systems operations, and drive innovation and entrepreneurship by defining new functional roles that mix transportation, computer science, and economics. This will culminate in a test bed in New York City that is expected to shape a next-generation national interdisciplinary research center on “smart transit” over the next decade. The PI will recruit local high school students to work with his Ph.D. students each summer; the high school students will be identified through the university’s ARISE (Applied Research Innovations in Science and Engineering) program which creates STEM education experiences for women, minorities, and students from low-income backgrounds.

The research marries three theories together in order to address these new mobility problems in smart cities: dynamic resource allocation under uncertainty, agent-based machine learning, and privacy optimization in a network context. All three are necessary because transportation systems need to be optimized holistically, but data is typically obtained from multiple travelers or vehicles in operation. As such, agent learning and minimization of privacy concerns in the system optimization is needed. The methodology expands the science of inverse optimization by integrating it with dynamic network optimization with privacy control as constraints on the estimated parameters. The research benefits all types of dynamic mobility systems: it will make shared autonomous vehicle fleet operations viable and on-demand service fleets more sustainable and resilient. Data privacy and security in a transport system design context will also be advanced, allowing data from travelers to be more accessible. This research will benefit smart cities, sensor deployment, artificial intelligence, collective behavior, differential privacy, service systems, public policy, and network economics.