On December 6th, 2017, the Pacific Southwest Region 9 University Transportation Center (PSR) sponsored students from member universities California State University Long Beach (CSULB), the University of California, Irvine (UCI), and the University of California, Los Angeles (UCLA) to attend the Intelligent Transportation Society of California (ITS California) Southern California Workshop, "Harnessing Big Data & Analytics to Improve Transportation."  I was one of these fortunate students. As more and more data is made available, transportation agencies and planners need to acknowledge a potentiality of data analytics and consider how to use new technology to improve urban transportation. This lunch session aimed to provide participants with an overview of data-driven analysis, challenges the transit agencies will face, and the latest research initiatives in LA areas.  The following is a summary of the presentations.

The first presenter, James Lou of IBM, introduced IBM's solutions and project cases using big data analytics and an artificial intelligence technology (Watson). He emphasized a potentiality of the combination of IoT and AI to improve the traffic flows and reduce the infrastructure maintenance cost. For example, data collected through sensors and connected vehicles will enable data platform powered by AI to analyze real-time traffic, detect problems (accidents, etc.), predict the accident's impacts on traffic, and optimize the vehicle flows by proposing an alternative route to each vehicle.

The next presentation, given by Bob McQueen, talked about a practical approach to transportation analysis in a smart city. Based on his considerable experiences in transportation data analytics, he described not only a potentiality of data-driven analysis but also challenges of data collection. Currently, traffic-related data (GPS data, public transit data, vehicle generating data, etc.) is fragmented and owned by individuals and different enterprises. Because of issues of data privacy and business confidentiality, it is not easy to create cross-enterprise data gathering platform even through the system is indispensable to make urban transportation smarter.

Photo by Tomoko Kanda

The third presentation was about the analysis of arterial performance in LA county and an introduction of the excel based analysis tool. Tom Choe from System Metrics Group shared a project in which his team analyzed public data and compared the major arterial performance to attack the congestion problem in LA county. What makes his study remarkable was that it made the traffic congestion level of each arterial visible by using excel spreadsheets so that the governments could make priority setting (which arterials should be treated first?), funding decision making, investment benefit analysis, etc. Moreover, because his team's analysis tool is Excel-based, public officers can use the tool without any special training or technical expertise. This practical tool may help local governments to start to adopt data-driven analysis in a feasible way at the low cost.

The final presenter was Joe Butler from UC Berkeley, introducing a research initiative about freight movement modeling sponsored by UC Berkeley and LADOT. The study initiative is constructing freight demand model and evaluate energy consumption impacts of the freight volume/movement in LA areas utilizing the latest big data analytics technology developed at UC Berkley. Jow showed us the mapping with freight movements in LA county and energy consumption estimation of each scenario. Once completed, this demand modeling may help the government to grasp the freight flows in the hope of making smart investment decisions to reduce energy consumption at the county level.

During the session, the participants showed a high interest in the data-driven analysis and practical applications of new technologies to the daily transportation planning. Even though there are issues to be solved such as developing a common data gathering platform/system, it is obvious that data-driven analysis powered by big data enables urban planners and transportation agencies to correctly understand how people and vehicles move around in the city and implement more effective solutions more quickly. 

About the Author: Tomoko Kanda

Tomoko Kanda is a first-year Master of Urban and Regional Planning student at the UCLA Luskin School of Public Affairs, focusing on effective public-private partnership building in transportation field. Originally from Tokyo, Japan, Tomoko previously worked as a management consultant at Accenture, where she analyzed business model and successful factors of newly emerging mobility service providers (Ride sharing, Car sharing, itinerary planning, bike sharing, etc.) and evaluated how these new services influence customer experience and urban forms in Asian and North American markets. She can be reached at [email protected].