000			03684ntm a22002537a 4500
008			201210b2010 a|||f bm|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a004
100	0		_aToka Sabry Mostafa _9194
245	1		_aAn intelligent Geographice Information System for Vehicle Routing _cToka Sabry Mostafa
260			_c2010
300			_a p. _bill. _c21 cm.
500			_3Supervisor: Hoda Talaat
502			_aThesis (M.A.)—Nile University, Egypt, 2010 .
504			_a"Includes bibliographical references"
505	0		_aContents: The Vehicle Routing Problem with Time Window (VRPTW) 2.2. Geographic Information Systems (GIS 2.3. Global Positioning Systems (GPS) 2.4. Reinforcement Learning (RL) 3. IGIS-CVR MODEL FRAMEWORK 3.1. Model description 3.2. System Workflow 3.3. Model Architecture 3.4. The GIS Model 3.4.1. System’s Functionalities 4. THE RIENFORCMENT LEARNING MODEL 4.1. RL Principles 4.2. RL Basic Elements mapped to IGIS-CVR 4.3. Solution Approach 4.4. Elements of Q-Learning 4.4.1. Exploration and Exploitation 4.4.2. Learning Rate 4.4.3. Discount Factor 4.5. RL Model Implementation 5. SIMULATION BASED EVALUATION MODEL 5.1. Simulation Environment 5.2. Case study and Prototype implementation 5.2.1. Network definition 5.2.2. Mapping case study to RL elements 5.3. RL Prototype Implementation 5.4. Preliminary Analysis 5.4.1. Learning rate (α) 5.4.2. Training life time estimation 5.5. Case Study Assessment 5.5.1. Case 1: Static Case Evaluation 5.5.2. Case 2: Dynamic Case Evaluation
520	3		_aAbstract: In Egypt, freight movement relies heavily on road transport. Commercial vehicles (CVs) constitute a major segment of the vehicle population that travels the country’s roads contributing to (and suffering from) daily congestion. Enhancing CVs operations by minimizing their en-route travel times benefits both traffic network users as well as CV's business owners. The absence of traffic data collection infrastructure, in many developing countries, impedes the usage of readily available vehicle routing systems. In this research, we introduce a full modeling framework of an Intelligent Geographical Information System for Commercial Vehicle Routing (IGIS-CVR). IGIS-CVR integrates a Geographic information system (GIS) and a Reinforcement learning (RL) system to address the Vehicle Routing Problems with Time Windows (VRPTW). The developed model uses CVs as probe vehicles for on-themove data collection. Collected data is manipulated through a self-adaptive learning environment to capture traffic network dynamics. The scope of this research is focused on developing and implementing a prototype for the system’s learning model. The Q-learning concepts of the Temporal Difference (TD) solution approach of RL are used in the model formulation. Different estimation procedures for the model main parameters are explored. A simulation based evaluation model is introduced to test the performance and effectiveness of the developed RL prototype in two cases; 1) a static case under typical traffic conditions, 2) a dynamic case where changes in network conditions are imposed. The evaluation results of the prototype highlight its potential in effectively enhancing CV routing operations. An average decrease in the percentage of late arrivals by; 77.5% for the static case, and 48% for the dynamic case are achieved through the model implementation.
546			_aText in English, abstracts in English .
650		4	_aITS _9195
655		7	_2NULIB _aDissertation, Academic _9187
690			_aITS _9195
942			_2ddc _cTH
999			_c8731 _d8731