An Artificial Intelligence Approach to Estimate Peak-Hour Travel Time

Average delays are an example of traffic network performance measures. They can be measured at intersections to estimate the average delay per vehicle at various levels, such as intersection, approach, or lane group. On the other hand, average delays at a given route are implicitly measured by estimating the difference between free-flow travel time to the observed ones. Different methods are used to estimate travel time for a given route, such as floating car, average speed, and vehicle tracking methods. This paper focuses on developing an artificial neural networks (ANN) model to predict travel time for specific routes based on field travel time measurements and other easy to measure characteristics, that are related to geometric layouts, peak-hour periods, posted speed limits, and route lengths. Travel time data for 75 different segments located in the State of Qatar were studied. Directional travel time data were measured in three different peak periods. A total of 450 travel time values were collected and analyzed. An ANN model was trained to estimate travel time. The results show that the ANN model was able to provide a reasonable estimation of travel time using limited information. The slopes of the regression plots between observed and predicted travel time values show a clear linear trend, with slopes around 0.85, and an intercept that is around 2.0.