Zeng, Xiaosi (2009-12). Dynamically Predicting Corridor Travel Time Under Incident Conditions Using a Neural Network Approach. Master's Thesis. Thesis uri icon

abstract

  • 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.
  • 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.

publication date

  • December 2009