DC Field | Value | Language |
dc.contributor.author | Abebe, Yonas | - |
dc.date.accessioned | 2020-04-07T11:35:22Z | - |
dc.date.available | 2020-04-07T11:35:22Z | - |
dc.date.issued | 2019-08 | - |
dc.identifier.uri | . | - |
dc.identifier.uri | http://hdl.handle.net/123456789/5278 | - |
dc.description.abstract | Access to transport service is critical to the development of all aspects of a nation train arrival time
management including staff behavior, affordability, and ticket payment system and also somewhat
satisfied with reliability, comfort, safety and security accessibility and availability. However, this
transport services are not free from problems. Passenger loading is the main problems of all railway
services operators. This research therefore aims to design a predictive model that can determine Train
Arrival Time Management of Addis Ababa light transit operating control center data. To overcome the
drawback of simple statistical method, we proposed the use of data mining techniques, for the data
analysis for train arrival time management.
The study follows hybrid data mining process model. After experiment survey for problem understanding,
selected around 20,000 records of three years from OCC data. After eliminating irrelevant and
unnecessary data, a total of 15040 datasets with 12 attributes are used for the purpose of conducting this
study. Data preprocessing was done to clean the datasets. After data preprocessing, the collected data has
been prepared in arff format suitable for the DM tasks.
The study was conducted using WEKA software version 3.8 and three classification techniques;
namely, J48 algorithm from decision tree, Naïve Bayes and JRIP, algorithm from rule induction. As a
result, J48 decision tree algorithm with Percentage split (66%) registered better performance of
95.5612% accuracy.
As a result, the study showed that scoring high value in speed, headway time and passenger loading
attributes in train arrival time management are determinant factors for the arrival time success in the
AALRT. Besides, the study revealed that other regions train arrival time management is more
associated with success rate. | en_US |
dc.language.iso | en | en_US |
dc.publisher | St. Mary's University | en_US |
dc.subject | Data Mining, Knowledge discovery | en_US |
dc.subject | OCC, QoS, Classification, Hybrid | en_US |
dc.title | Designing a Predictive Model for Train Arrival Time Management, Using Data Mining Approach | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Master of computer science
|