An intelligent Geographice Information System for Vehicle Routing (Record no. 8731)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03684ntm a22002537a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 201210b2010 a|||f bm|| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | EG-CaNU |
| Transcribing agency | EG-CaNU |
| 041 0# - Language Code | |
| Language code of text | eng |
| Language code of abstract | eng |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 004 |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Toka Sabry Mostafa |
| 245 1# - TITLE STATEMENT | |
| Title | An intelligent Geographice Information System for Vehicle Routing |
| Statement of responsibility, etc. | Toka Sabry Mostafa |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Date of publication, distribution, etc. | 2010 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| Other physical details | ill. |
| Dimensions | 21 cm. |
| 500 ## - GENERAL NOTE | |
| Materials specified | Supervisor: Hoda Talaat |
| 502 ## - Dissertation Note | |
| Dissertation type | Thesis (M.A.)—Nile University, Egypt, 2010 . |
| 504 ## - Bibliography | |
| Bibliography | "Includes bibliographical references" |
| 505 0# - Contents | |
| Formatted contents note | Contents:<br/>The Vehicle Routing Problem with Time Window (VRPTW) <br/>2.2. Geographic Information Systems (GIS<br/>2.3. Global Positioning Systems (GPS) <br/>2.4. Reinforcement Learning (RL) <br/>3. IGIS-CVR MODEL FRAMEWORK <br/>3.1. Model description <br/>3.2. System Workflow <br/>3.3. Model Architecture <br/>3.4. The GIS Model <br/>3.4.1. System’s Functionalities <br/>4. THE RIENFORCMENT LEARNING MODEL <br/>4.1. RL Principles <br/>4.2. RL Basic Elements mapped to IGIS-CVR <br/>4.3. Solution Approach <br/>4.4. Elements of Q-Learning <br/>4.4.1. Exploration and Exploitation <br/>4.4.2. Learning Rate <br/>4.4.3. Discount Factor <br/>4.5. RL Model Implementation <br/>5. SIMULATION BASED EVALUATION MODEL <br/>5.1. Simulation Environment <br/>5.2. Case study and Prototype implementation <br/>5.2.1. Network definition <br/>5.2.2. Mapping case study to RL elements <br/>5.3. RL Prototype Implementation <br/>5.4. Preliminary Analysis <br/>5.4.1. Learning rate (α) <br/>5.4.2. Training life time estimation <br/>5.5. Case Study Assessment <br/>5.5.1. Case 1: Static Case Evaluation <br/>5.5.2. Case 2: Dynamic Case Evaluation |
| 520 3# - Abstract | |
| Abstract | Abstract:<br/>In Egypt, freight movement relies heavily on road transport. Commercial vehicles (CVs) constitute a<br/>major segment of the vehicle population that travels the country’s roads contributing to (and suffering<br/>from) daily congestion. Enhancing CVs operations by minimizing their en-route travel times benefits both<br/>traffic network users as well as CV's business owners. The absence of traffic data collection<br/>infrastructure, in many developing countries, impedes the usage of readily available vehicle routing<br/>systems. In this research, we introduce a full modeling framework of an Intelligent Geographical<br/>Information System for Commercial Vehicle Routing (IGIS-CVR). IGIS-CVR integrates a Geographic<br/>information system (GIS) and a Reinforcement learning (RL) system to address the Vehicle Routing<br/>Problems with Time Windows (VRPTW). The developed model uses CVs as probe vehicles for on-themove<br/>data collection. Collected data is manipulated through a self-adaptive learning environment to<br/>capture traffic network dynamics.<br/>The scope of this research is focused on developing and implementing a prototype for the<br/>system’s learning model. The Q-learning concepts of the Temporal Difference (TD) solution approach of<br/>RL are used in the model formulation. Different estimation procedures for the model main parameters are<br/>explored. A simulation based evaluation model is introduced to test the performance and effectiveness of<br/>the developed RL prototype in two cases; 1) a static case under typical traffic conditions, 2) a dynamic<br/>case where changes in network conditions are imposed. The evaluation results of the prototype highlight<br/>its potential in effectively enhancing CV routing operations. An average decrease in the percentage of late<br/>arrivals by; 77.5% for the static case, and 48% for the dynamic case are achieved through the model<br/>implementation. |
| 546 ## - Language Note | |
| Language Note | Text in English, abstracts in English . |
| 650 #4 - Subject | |
| Subject | ITS |
| 655 #7 - Index Term-Genre/Form | |
| Source of term | NULIB |
| focus term | Dissertation, Academic |
| 690 ## - Subject | |
| School | ITS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| 650 #4 - Subject | |
| -- | 195 |
| 655 #7 - Index Term-Genre/Form | |
| -- | 187 |
| 690 ## - Subject | |
| -- | 195 |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Total Checkouts | Full call number | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Main library | Main library | 12/10/2020 | 004/T.M.I/2010 | 12/10/2020 | 12/10/2020 | Thesis |