Automatic Route Setting and Dynamic Rescheduling Following Disturbance
| dc.contributor.advisor | Abebe, Teklu (Mr.) | |
| dc.contributor.author | Mouhiyadin, Said | |
| dc.date.accessioned | 2019-06-01T09:48:23Z | |
| dc.date.accessioned | 2023-11-04T15:16:46Z | |
| dc.date.available | 2019-06-01T09:48:23Z | |
| dc.date.available | 2023-11-04T15:16:46Z | |
| dc.date.issued | 2018-09 | |
| dc.description.abstract | Nowadays, development cannot be achieved without modern infrastructure capacity. The new railway project which links Addis Ababa to Djibouti aims to fulfill this task through safety and reliability as major goals. Railway operating near their theoretical capacity is particularly vulnerable to disruption by human behavior or engineering failures that lead to delays or to even cancellations of services. In order to prevent such incidents, the interlocking system is set to automatic or semi-automatic mode and rescheduling is planned by the system to set a new route for the remaining trains. This study aimed to analyze how the system reacts and respond to disruptive incidents in minimizing the impact of such disturbance. After reviewing relevant literatures regarding most occurring disruptive incidents in train operation and their consequences on the railway network reliability, we defined train rescheduling operation and more precisely the automatic route selection with their characteristics parameters after a disruption such as an accident on a level crossing or faults in electronics device. We introduce C++ programming language to model route setting using spanning tree configuration. A case study on rescheduling operation on a selected British railway area is carried on to evaluate delays cost after disruption. This data was processed to model how delays relevant to a specific disruption affect trains in a railway network using a neural network with a high performance. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/18383 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Automatic route setting | en_US |
| dc.subject | train scheduling | en_US |
| dc.subject | operation disruption | en_US |
| dc.subject | neural network | en_US |
| dc.subject | spanning tree problem | en_US |
| dc.title | Automatic Route Setting and Dynamic Rescheduling Following Disturbance | en_US |
| dc.type | Thesis | en_US |