Oracle Corporation
Apr. 19, 11:00AM, Interschool Lab, 750 CEPSR
Abstract:
Traffic congestion is a serious world-wide problem. Drivers have little
knowledge of historical and real-time traffic congestion for the paths they
take and often tend to drive suboptimal routes. The availability of traffic
data provides a great opportunity for developing more intelligent solutions
to the transportation problem. However, the following questions need to be
addressed: How can we turn available traffic data into a useful form? How
can we develop a practical algorithm that use these data and find a good
path under uncertainty and congestion? How can we reduce city-scale
congestion?
In this talk, I will present practical algorithms for these questions and
their implementation in a city-scale environment.
First, I will present a stochastic route planning algorithm that finds the
best path for a group of mobile agents to achieve time-critical goals
guaranteeing the highest probability of task achievement while dealing with
uncertainty of travel time. Second, I will present a distributed
congestion-aware multi-agent path planning algorithm that minimizes
aggregate travel time of all the agents in the system. As the number of
agents grows, congestion created by agents' path choices should be
considered. Using a data-driven congestion model, we develop a practical
method for determining the optimal paths for all the agents in the system.
Third, I will demonstrate a path planning system using the proposed
algorithms and traffic sensor data. We predict the traffic speed and flow
for each location from a large set of sensor data collected from roving
taxis and inductive loop detectors. Our system uses a data-driven traffic
model that captures important traffic patterns and conditions using the two
sources of data. We evaluate the system using a rich set of GPS traces from
16,000 taxis in Singapore and show that the city-scale congestion can be
mitigated by planning drivers' routes, while incorporating the congestion
effects generated by their route choices.
Speaker Biography:
Sejoon Lim received a Ph.D. degree in Electrical Engineering and Computer
Science from MIT. His research interests lie in traffic data analysis,
traffic modeling and estimation, vehicle routing under uncertainty,
multi-agent systems, and intelligent transportation systems. He is currently
a Senior Member of Technical Staff at Oracle Corporation, focusing on
parallel server technology and cloud computing systems.