EXPERT SYSTEM MODELLING

FOR ROUTE NETWORK DESIGN ANDSCHEDULING FOR URBAN BUS

TRANSIT SYSTEM

BY

PROF.(DR.) S.L.DHINGRATRANSPORTATION SYSTEM ENGINEERING

CIVIL ENGINEERING DEPT.I.I.T., BOMBAY

INDIA

INTRODUCTION TO EXPERT SYSTEM

Al Technology

• Al imitates basic human learning and thought process

• Sub fields of Al domain

1. Robotics2. Expert Systems.3. Neural Networks4. Genetic Algorithm5. Natural Language Processing6. Speech recognition7. Speech Synthesis

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, rulesobjects, 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.

• Frame Based: a collection of objects used in a knowledge base of an expert system.

• Induction: a machine learning technique that derives its decision makingcapabilities 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 ofhuman experts.

APPLICATION OF EXPERT SYSTEM

FOR

ROUTING AND SCHEDULING

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

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 disallowrouting 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

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.

Different approaches fail to provide planners with a handy and powerful toolin 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.

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.

The MCKB can invoke any knowledge base and rules are formed for each knowledge base, like

i. Objective Knowledge baseii. Terminal Identification Knowledge baseiii.Route evaluation knowledge baseiv.Service evaluation knowledge basev. Headway Analysis knowledge basevi.Walk time wait time knowledge basevii.Financial Constraint Knowledge Base

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

Desired Number of Buses

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

Scheduling Analysis Sub-modal This sub-modal reveals the impact of scheduling carried out on thetransit 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 parametersare obtained. The various parameters used in scheduling sub-model are,

i. Route No.ii. Link No.iii. Average link flowiv. Maximum link flowv. Desired No of busesvi. Maximum bus Numbervii. Desired headwayviii. Minimum headwayix. Waiting timex. Operating distancexi. Walk time

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

Analysis and Selection of Routes

Information

R.

No.

Demand

Satisfied

Distance

Traveled

Passenger

X Km

Av. Link Density

SD of

Link D

SD of

LU

RUC

1 300 32 6000 12.38 5.42 0.07 0.94

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

CIDCO PROJECTS IN MAHARASHTRAON-GOING PROJECTS

NEW TOWN PROJECTS FUTURE NEW TOWN PROJECTS

ANALYSIS OF MODEL, RESULTS AND CONCLUSION

S. No. Parameters Existing parameters

Designed parameters

1. Total Numbers of Routes (Kms) 333 142

2. Total Route Length (Kms) 4624 4762

3. Fleet Size 3029 2290-3486

4. Operative Distance 638794 681193

5. Passenger Served 4506289 4476042

6. Total Earning (Rs.) 8266666 12601000

7. Total Cost (Rs.) 8708269 11866000

CONCLUSIONS

• Conventional transit network design process is user dependent and lacks of consistency and reliability.

• Knowledge Based Expert System captures the knowledge of an expert or experts and uses it to aid the solution of the problem

• An Expert System is developed for bus transit route network design and scheduling

Routing and Scheduling Problems and Solution Software

Two main short comings of most general purpose system to date:

• Inadequacy to handle particular problem settings and additional side constraints.

• The inadequacy of the database system especially in its interface with user.

Main Short comings of the design process till date

• Network Size: Constraint for Solution Techniques• No General Purpose Software available• Restricted to isolated route design and schedules

Experts System Approach

• Interaction of routes and effect of overlapping routes• Optimal allocation of fleet• Scheduling of different size of vehicle• Transfer demand analysis• Design of social service routes• Waiting Time and Walking Time analysis

Thank U