The artificial neural network (ANN) approach has been recognized as a capable
technique to model the highly complex and nonlinear problem of travel time prediction.
In addition to the nonlinearity, a traffic system is also temporally and spatially dynamic.
Addressing the temporal-spatial relationships of a traffic system in the context of neural
networks, however, has not received much attention. Furthermore, many of the past
studies have not fully explored the inclusion of incident information into the ANN model
development, despite that incident might be a major source of prediction degradations.
Additionally, directly deriving corridor travel times in a one-step manner raises some
intractable problems, such as pairing input-target data, which have not yet been
adequately discussed.
In this study, the corridor travel time prediction problem has been divided into
two stages with the first stage on prediction of the segment travel time and the second
stage on corridor travel time aggregation methodologies of the predicted segmental
results. To address the dynamic nature of traffic system that are often under the influence
of incidents, time delay neural network (TDNN), state-space neural network (SSNN), and an extended state-space neural network (ExtSSNN) that incorporates incident inputs
are evaluated for travel time prediction along with a traditional back propagation neural
network (BP) and compared with baseline methods based on historical data. In the first
stage, the empirical results show that the SSNN and ExtSSNN, which are both trained
with Bayesian regulated Levenberg Marquardt algorithm, outperform other models. It is
also concluded that the incident information is redundant to the travel time prediction
problem with speed and volume data as inputs. In the second stage, the evaluations on
the applications of the SSNN model to predict snapshot travel times and experienced
travel times are made. The outcomes of these evaluations are satisfactory and the method
is found to be practically significant in that it (1) explicitly reconstructs the temporalspatial
traffic dynamics in the model, (2) is extendable to arbitrary O-D pairs without
complete retraining of the model, and (3) can be used to predict both traveler
experiences and system overall conditions.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-12-7602 |
| Date | 2009 December 1900 |
| Creators | Zeng, Xiaosi |
| Contributors | Zhang, Yunlong |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | application/pdf |
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