Intelligent transportation system real time traffic speed prediction with minimal data
Abstract: An Intelligent
Transportation System (ITS) must be able to predict traffic speed for short
time intervals into the future along the branches between the many nodes in a
traffic network in near real time using as few observed and stored speed values
as possible. Such predictions support
timely ITS reactions to changing traffic conditions such as accidents or
volume-induced slowdowns and include re-routing advice and time-to-destination
estimations.
Design/methodology/approach: Traffic sensors are embedded in the
interstate highway system in Detroit, Michigan, USA, and metropolitan
area. The set of sensors used in this
project is along interstate highway 75 (I-75) southbound from the intersection
with interstate highway 696 (I-696). Data from the sensors including speed,
volume, and percent of sensor occupancy, were supplied in one minute intervals
by the Michigan Intelligent Transportation Systems Center (MITSC). Hierarchical linear regression was used to
develop a speed prediction model that requires only the current and one
previous speed value to predict speed up to 30 minutes in the future. The model was validated by comparison to
collected data with the mean relative error and the median error as the primary
metrics.
Findings and Originality/value: The model was a better predicator of
speed than the minute by minute averages alone.
The relative error between the observed and predicted values was found
to range from 5.9% for 1 minute into the future predictions to 10.9% for 30
minutes into the future predictions for the 2006 data set. The corresponding median errors were 4.0% to
5.4%. Thus, the predictive capability of
the model was deemed sufficient for application.
Research limitations/implications: The model has not yet been embedded in
an ITS, so a final test of its effectiveness has not been accomplished.
Social implications: Travel delays due to traffic incidents, volume
induced congestion or other reasons are annoying to vehicle occupants as well
as costly in term of fuel waste and unneeded emissions among other items. One goal of an ITS is to improve the social
impact of transportation by reducing such negative consequences. Traffic speed prediction is one factor in
enabling an ITS to accomplish such goals.
Originality/value: Numerous data intensive and very sophisticated
approaches have been used to develop traffic flow models. As such, these models aren’t designed or well
suited for embedding in an ITS for near real-time computations. Such an application requires a model capable
of quickly forecasting traffic speed for numerous branches of a traffic network
using only a few data points captured and stored in real time per branch. The model developed and validated in this
study meets these requirements.
Author: Luana Georgescu, David
Zeitler, Charles Robert Standridge
Journal Code: jptindustrigg120027
