Title: Understanding and predicting travel time with spatio-temporal features of network traffic flow, weather and incidents,
Abstract: Travel time on a route varies substantially by time of day and from day to
day. It is critical to understand to what extent this variation is correlated
with various factors, such as weather, incidents, events or travel demand level
in the context of dynamic networks. This helps a better decision making for
infrastructure planning and real-time traffic operation. We propose a
data-driven approach to understand and predict highway travel time using
spatio-temporal features of those factors, all of which are acquired from
multiple data sources. The prediction model holistically selects the most
related features from a high-dimensional feature space by correlation analysis,
principle component analysis and LASSO. We test and compare the performance of
several regression models in predicting travel time 30 min in advance via two
case studies: (1) a 6-mile highway corridor of I-270N in D.C. region, and (2) a
2.3-mile corridor of I-376E in Pittsburgh region. We found that some
bottlenecks scattered in the network can imply congestion on those corridors at
least 30 minutes in advance, including those on the alternative route to the
corridors of study. In addition, real-time travel time is statistically related
to incidents on some specific locations, morning/afternoon travel demand,
visibility, precipitation, wind speed/gust and the weather type. All those
spatio-temporal information together help improve prediction accuracy,
comparing to using only speed data. In both case studies, random forest shows
the most promise, reaching a root-mean-squared error of 16.6\% and 17.0\%
respectively in afternoon peak hours for the entire year of 2014.