The prediction of bus arrival time using automatic vehicle location systems data

Advanced Traveler Information System (ATIS) is one component of Intelligent \nTransportation Systems (ITS), and a major component of ATIS is travel time \ninformation. The provision of timely and accurate transit travel time information is \nimportant because it attracts additional ridership and increases the satisfaction of transit \nusers. The cost of electronics and components for ITS has been decreased, and ITS \ndeployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which \nis a part of ITS, have been adopted by many transit agencies. These allow them to track \ntheir transit vehicles in real-time. The need for the model or technique to predict transit \ntravel time using AVL data is increasing. While some research on this topic has been \nconducted, it has been shown that more research on this topic is required. \nThe objectives of this research were 1) to develop and apply a model to predict bus \narrival time using AVL data, 2) to identify the prediction interval of bus arrival time and \nthe probabilty of a bus being on time. In this research, the travel time prediction model \nexplicitly included dwell times, schedule adherence by time period, and traffic \ncongestion which were critical to predict accurate bus arrival times. The test bed was a \nbus route running in the downtown of Houston, Texas. A historical based model, \nregression models, and artificial neural network (ANN) models were developed to \npredict bus arrival time. It was found that the artificial neural network models performed \nconsiderably better than either historical data based models or multi linear regression \nmodels. It was hypothesized that the ANN was able to identify the complex non-linear \nrelationship between travel time and the independent variables and this led to superior \nresults. \nBecause variability in travel time (both waiting and on-board) is extremely important for \ntransit choices, it would also be useful to extend the model to provide not only estimates \nof travel time but also prediction intervals. With the ANN models, the prediction \nintervals of bus arrival time were calculated. Because the ANN models are non \nparametric models, conventional techniques for prediction intervals can not be used. \nConsequently, a newly developed computer-intensive method, the bootstrap technique \nwas used to obtain prediction intervals of bus arrival time. \nOn-time performance of a bus is very important to transit operators to provide quality \nservice to transit passengers. To measure the on-time performance, the probability of a \nbus being on time is required. In addition to the prediction interval of bus arrival time, \nthe probability that a given bus is on time was calculated. The probability density \nfunction of schedule adherence seemed to be the gamma distribution or the normal \ndistribution. To determine which distribution is the best fit for the schedule adherence, a \nchi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well \nthe schedule adherence. With the normal distribution, the probability of a bus being on \ntime, being ahead schedule, and being behind schedule can be estimated.