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Providing real and accurate travel time information usually, assists road users to plan their trips and choose the appropriate mode of transport. However, accurate prediction of travel time is a challenging problem, especially in developing countries where heterogeneous flow conditions exist and there are no records of information about the travel time for travelers. Most of the dynamic travel-time prediction models developed emphasize on link travel time without taking into account delay time at the intersections and waiting time at the bus stops. The objective of this study was to compare Multi Linear Regression and Artificial Neural Network models to obtain a suitable model for developing a dynamic travel-time prediction model using waiting time at the bus stop, intersection delay time, link distance, traffic volume, link travel time, peak hours and off-peak hours as model inputs. Link travel time was modeled by a well trained Neural Network and Kalman filtering dynamic algorithm using field survey data collected by employing public buses in Dar es Salaam city. The model was validated by using data collected in five main routes in Dar es Salaam City. The Root Mean Square Error and Mean Absolute Percent Error were used to evaluate the performance of the model by comparing it with other prediction models. Findings indicate that the integration of the Artificial Neural Network and Kalman Filter algorithm model (ANN-KF) promised to be a reasonable model for predicting dynamic travel time in Dar es Salaam city.

COMPARATIVE ASSESSMENT OF DYNAMIC TRAVEL TIME PREDICTION MODELS IN THE DEVELOPING COUNTRIES CITIES