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EXPERT SYSTEM MODELLING FOR ROUTE NETWORK DESIGN AND SCHEDULING FOR URBAN BUS TRANSIT SYSTEM BY

EXPERT SYSTEM MODELLING FOR ROUTE NETWORK DESIGN AND SCHEDULING FOR URBAN BUS TRANSIT SYSTEM BY PROF.(DR.) S.L.DHINGRA TRANSPORTATION SYSTEM ENGINEERING CIVIL ENGINEERING DEPT. I.I.T., BOMBAY INDIA. INTRODUCTION TO EXPERT SYSTEM. Al Technology.

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EXPERT SYSTEM MODELLING FOR ROUTE NETWORK DESIGN AND SCHEDULING FOR URBAN BUS TRANSIT SYSTEM BY

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  1. EXPERT SYSTEM MODELLING FOR ROUTE NETWORK DESIGN AND SCHEDULING FOR URBAN BUS TRANSIT SYSTEM BY PROF.(DR.) S.L.DHINGRA TRANSPORTATION SYSTEM ENGINEERING CIVIL ENGINEERING DEPT. I.I.T., BOMBAY INDIA

  2. INTRODUCTION TO EXPERT SYSTEM

  3. Al Technology • Al imitates basic human learning and thought process • Sub fields of Al domain 1. Robotics 2. Expert Systems. 3. Neural Networks 4. Genetic Algorithm 5. Natural Language Processing 6. Speech recognition 7. Speech Synthesis

  4. Characteristic features of expert system Architectural features of experts systems include: • Expert system shell:a development and software delivery environment for expert systems. It includes interfaces to one or more representations and associated inference engines. It allows ES development using natural language rather than computer programming languages. – Different KB can be used. • Knowledge Base:the collection of knowledge that includes the assertions, rules objects, assumptions and constraints used by an expert for solving difficult problems or tasks. • Rule Base:a collection of rules used in the knowledge base of an expert system. It has an IF-ANT/OR-THEN structure • Fact Base:a collection of facts used in the knowledge base rules to define faucal knowledge.

  5. Frame Based:a collection of objects used in a knowledge base of an expert system. • Induction:a machine learning technique that derives its decision making capabilities from case histories. • Inference:a process by which pieces of knowledge are combined to arrive at a conclusion (similar to logical thinking) • Forward Chaining:a search strategy that starts with a body of knowledge and attempts to make conclusions. • Backward Chaining:a search strategy that starts with the desired conclusion and tries to prove it with available information. • Heuristics:a “rule of thumb” a general rule based on experience or expertise of human experts.

  6. APPLICATION OF EXPERT SYSTEM FOR ROUTING AND SCHEDULING

  7. Characteristics of routing and scheduling problems Characteristics Possible options 1. Size of available Fleet - One Vehicle, Multiple vehicle 2. Type of Available Fleet - homogeneous (only one vehicle type) heterogeneous (multiple vehicle types special vehicle types (compartmentally etc.) 3. Housing of Vehicles - single depot (domicile) multiple depots. 4. Nature of Demands - Deterministic (know) demands stochastic demand requirements partial satisfaction of demand allowed. 5. Location of Demands - At nodes (not; necessarily all ) on arcs (not necessarily all) mixed

  8. 6. Underlying Network Undirected directed mixed Euclidean 7. Vehicle Capacity Imposed (all the same) Restrictions imposed (different vehicle capacities not imposed (unlimited capacity). 8. Maximum Route Times Imposed (same for all routes) imposed (different for different route not imposed) 9. Operations Pickups only drop—off (deliveries) Only mixed (pick ups and deliveries) ( or ) split deliveries (allowed or disallow routing costs fixed operating or vehicle acquisition cost's common carrier costs (for un-serviced demands) 10. Costs Variable 11. Objectives Minimize total routing costs minimize sum of fixed and variable minimize number of vehicles required maximize utility function based on service or convenience maximize utility function based on customer priorities

  9. Vehicle Scheduling The real world constraints commonly determine the complexity of the vehicle-scheduling problem. These restrictions are — • A constraint on the length of time a vehicle may be in service before it must reach the terminal and given a rest period. • The layover time i.e. the amount of time between two successive trips made by a vehicle. • The presence of variety of depots where vehicle may be housed. • Financial constraints, which put an upper limit to the number of vehicle used. • Service constraint, which put a layover limit to the number of vehicle, used.

  10. Different approaches fail to provide planners with a handy and powerful tool in the following aspect: 1. The mathematical programming approach is theoretically rigorous but fails to handle any network of realistic size. 2. The experienced-based approach is basically an intuitive approach and does not promise any solution in the optimal sense. 3. Simulation of the transit system, though a powerful tool, has been restricted mostly to individual routes or small size transit network. 4. Heuristic interactive graphic method that allows online interaction between the user and machine can be successfully employed in transit network design. Heuristic methods employ empirically derived rules for near optimal solutions. Interactive methods use human intuitive capabilities and knowledge to help. Search for the best solutions, but such methods depend on the knowledge and experience of very capable user. For this reason interactive graphics may not always produce high quality design.

  11. Scheduling Policy • Every route should be allocated minimum number of transit units depending on the demand served and level of services guided by knowledge base. • Every route should be allowed to have a maximum number of transit units, depending upon of demand served by the route and the level of service guided by the knowledge base. • Between minimum and maximum allocation every unit should be allocated to the best route. The factor called additional allocation factor defined as: Saving in waiting time / additional cost of operation The best route is one for which additional bus allocation factor is maximum • If the headway on the route becomes impractical to operate with standard type of vehicles then vehicle capacity should be increased. • Mixed type of vehicle should be allocated to a route to optimize between waiting time and operating cost.

  12. The MCKB can invoke any knowledge base and rules are formed for each knowledge base, like Objective Knowledge base Terminal Identification Knowledge base Route evaluation knowledge base Service evaluation knowledge base Headway Analysis knowledge base Walk time wait time knowledge base Financial Constraint Knowledge Base

  13. SCHEDULING SUBMODEL • To optimally allocate the buses on the various routes as per rules of service evaluation knowledge Base. • To carry out multiple type of vehicle scheduling as per rules of multiple vehicle type knowledge base • To carry out mixed type of vehicle scheduling as per rules of Headway Analysis knowledge base • To keep waiting time and walking time within limits as per rules of Wait Time - Walk Time analysis knowledge base • To satisfy financial constraints as per rules of financial constraints knowledge base. Scheduling Policy • Scheduling Criteria Sub-model • Scheduling Allocation Sub-model • Scheduling Analysis Sub-model

  14. Desired Number of Buses

  15. Scheduling allocation sub-modal • This sub-modal carries out the allocation as directed by the scheduling policy knowledgebase. Following steps are carried out in allocation sub-modal. i. Calculate the total minimum and total maximum number of buses required for each route at the given level of service and therefore total minimum and maximum number of buses required for system. ii. Allocate the minimum number of buses to each route and compare total minimum allocation to fleet size then, a) Either increase the fleet size to meet the required scheduling b) If, (a) is not possible then start removing buses from worst route till the entire given fleet size is allocated. c) If fleet size is between total minimum number and maximum number of buses required, then start allocating buses one by one to the best route until entire given fleet size is allocated. d) If fleet size is more than the total maximum no of buses required then, i. Either removes the extra buses and deploys them some Where else, ii. or if (a) is not the choice then continue to allocate buses to best route until entire fleet size is allocated

  16. Scheduling Analysis Sub-modal This sub-modal reveals the impact of scheduling carried out on the transit network. The scheduling is analyzed on the basis of operating distance, operating cost, waiting time and headway and the related facts and rules guided by MCKB. The scheduling is adjusted till the desired values of above parameters are obtained. The various parameters used in scheduling sub-model are, i. Route No. ii. Link No. iii. Average link flow iv. Maximum link flow v. Desired No of buses vi. Maximum bus Number vii. Desired headway viii. Minimum headway ix. Waiting time x. Operating distance xi. Walk time

  17. ROUTING SUBMODEL OBJECTIVE • Major Traffic Generation Should Be Linked • Appreciable Demand Should Be Satisfied • Major Part of Network Should Be Served TERMINAL IDENTIFICATION GENERATION OF ROUTES • Heuristic Route Generation Sub-model • Demand Deviated Route Generation Sub-model • Mathematical Route Generation Sub-model

  18. Analysis and Selection of RoutesInformation

  19. DESCRIPTION OF STUDY AEREA, TRIPS ANDDESIRE LINE DETAILS STUDY AREA Kanpur Bombay Population 2.1 Million 9.7 Million Bus Trips 55,000 4500,000 Fleet 152. Single Type 3029 Multiple Type Routes 21 333 Zones -- 86 Nodes 62 602 Links 158 11384 O—L Node wise Zone wise

  20. CIDCO PROJECTS IN MAHARASHTRAON-GOING PROJECTSNEW TOWN PROJECTS FUTURE NEW TOWN PROJECTS

  21. ANALYSIS OF MODEL, RESULTS AND CONCLUSION

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