Advanced Traveler Information System (ATIS) is one component of Intelligent
Transportation Systems (ITS), and a major component of ATIS is travel time
information. The provision of timely and accurate transit travel time information is
important because it attracts additional ridership and increases the satisfaction of transit
users. The cost of electronics and components for ITS has been decreased, and ITS
deployment is growing nationwide. Automatic Vehicle Location (AVL) Systems, which
is a part of ITS, have been adopted by many transit agencies. These allow them to track
their transit vehicles in real-time. The need for the model or technique to predict transit
travel time using AVL data is increasing. While some research on this topic has been
conducted, it has been shown that more research on this topic is required.
The objectives of this research were 1) to develop and apply a model to predict bus
arrival time using AVL data, 2) to identify the prediction interval of bus arrival time and
the probabilty of a bus being on time. In this research, the travel time prediction model
explicitly included dwell times, schedule adherence by time period, and traffic
congestion which were critical to predict accurate bus arrival times. The test bed was a
bus route running in the downtown of Houston, Texas. A historical based model,
regression models, and artificial neural network (ANN) models were developed to
predict bus arrival time. It was found that the artificial neural network models performed
considerably better than either historical data based models or multi linear regression
models. It was hypothesized that the ANN was able to identify the complex non-linear
relationship between travel time and the independent variables and this led to superior
results.
Because variability in travel time (both waiting and on-board) is extremely important for
transit choices, it would also be useful to extend the model to provide not only estimates
of travel time but also prediction intervals. With the ANN models, the prediction
intervals of bus arrival time were calculated. Because the ANN models are non
parametric models, conventional techniques for prediction intervals can not be used.
Consequently, a newly developed computer-intensive method, the bootstrap technique
was used to obtain prediction intervals of bus arrival time.
On-time performance of a bus is very important to transit operators to provide quality
service to transit passengers. To measure the on-time performance, the probability of a
bus being on time is required. In addition to the prediction interval of bus arrival time,
the probability that a given bus is on time was calculated. The probability density
function of schedule adherence seemed to be the gamma distribution or the normal
distribution. To determine which distribution is the best fit for the schedule adherence, a
chi-squared goodness-of-fit test was used. In brief, the normal distribution estimates well
the schedule adherence. With the normal distribution, the probability of a bus being on
time, being ahead schedule, and being behind schedule can be estimated.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/1458 |
| Date | 17 February 2005 |
| Creators | Jeong, Ran Hee |
| Contributors | Martin, Amy Epps, Rilett, Laurence R. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 2104256 bytes, electronic, application/pdf, born digital |
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