Travel time estimation on signalized urban arterials has been one of the biggest
challenges in transportation engineering. This thesis focuses on the characterization of
arterial travel times by estimating the travel time distributions and collecting GPS data
from probe vehicles to predict travel times.
The main factors that affect travel time patterns on an arterial link roughly fall into four
categories: geometric structure, driving behavior, signal control and traffic demand. Four
states of travel time for through-through vehicles are defined. State 1 (non-stopped) and
State 3 (stopped) can be approximated using mixture normal densities while State 2 (nonstopped
with delay) and State 4 (stopped with delay) can be approximated with uniform
distributions. When prior travel time data is available, travel time distributions could be
estimated by EM algorithm. Otherwise, they can be estimated based on signal control and
geometric structure of the arterial.
Link travel times are then extended to route travel times. A method based on Markov
Chain is proposed to estimate mean route travel time. Results suggest that the proposed
method can capture the relationship of link travel times well and provide an accurate
estimation of mean route travel time.
Combined with travel time data collected from GPS probe vehicles, a real-time traffic condition identification approach based on Bayes theorem is proposed. Numerical
examples show a single GPS probe is able to identify real-time traffic condition
successfully in most cases. In addition, GPS travel times can also be used to refine the
existing travel time distributions using Bayesian update.
Finally, a comprehensive case study based on the NGSIM Peachtree Street Dataset is
demonstrated. Travel time distributions estimated from signal timing and the geometry iii
are considered as prior distributions. Traffic condition identification process is performed
and the probability one travel time sequence belongs to each traffic condition is
calculated. Data are then classified according to the posterior probabilities. Finally, a
Bayesian update is run to calculate posterior distributions under each traffic condition
combining with classified data. This update process can be repeated iteratively when new
GPS data are available. The results obtained from Bayesian update are also compared to
those estimated from EM algorithm. Overall the EM algorithm fits the data better than
Bayesian update. However, sometime Bayesian approach could reflect the real world
situation when some data is missing while EM does not.