a tool for periodic timetabling (for suburban...
TRANSCRIPT
A tool for periodic timetabling(for suburban railways)
Soumya Dutta, Narayan Rangaraj, K.N. Singh and Madhu N. Belur
Dept of Electrical Engineering,Industrial Engineering and Operations Research
Indian Institute of Technology Bombay (IITB)
This talk at: http://www.ee.iitb.ac.in/%7Ebelur/railways/
16th Feb, 2017
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 1 / 15
Outline
Objectives of the tool: ‘IITB-Suburban-Service-Timetabler’Types of constraintsData formats: input and outputMethods/techniquesFurther plans
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 2 / 15
Objective
Timetabling: Herculean task when done manuallyVarious complex constraintsOnly few experts can modify manuallyCan only ‘tweak’ existing timetablePeriodic constraints: need of the ‘hour’
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 3 / 15
Periodic timetable
Desired: hourly timetableOnly minute-value to remember (for each service)Need not be exactly evenly spacedRepeats each hour: for example:from CST to Vashi, say 3 services per hour:
8:13, 8:40, 8:52,9:13, 9:40, 9:52,
10:13, 10:40, 10:52Aim: ‘quite’ well-spaced within the hour
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 4 / 15
Constraints
Hard constraints:Headway constraintsFrequency of serviceMinimum traversal timesTurn-around constraints
Soft constraints:Spacing between consecutive ‘similar’ services
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 5 / 15
Inputs to the tool
Inputs offline (for now):Different infrastructural parameters:
Stations, trackstypes of services:
Inputs Online: web-version:
Passenger demands, traversal timesturnaround times at terminals
Timetable downloadable from the website in a few hours (or minutes)
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 6 / 15
Tool output
A feasible periodic timetableGuaranteed to satisfy:
headway, turnaround, traversal constraintsspecified frequency
Incorporated into timetable generationOutput-format: One up-file, and one down-file
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 7 / 15
More output
Rake linkage chart:
Rake cycleslist of services to be performed by each rakeNumber of rakes also calculated
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More output
A terminal occupancy chart:Occupancy of each major stationNumber of trains occupying terminal resources plotted time-wise
Future plan: A graphical way of viewing terminal occupancy
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 9 / 15
Planned improvements
Line planning can be incorporatedProper platform allocation at all stationsA graphical editing tool for viewing the network and changingany infrastructure
PRESENT PLAN
As of now inputs will be taken from the Central Railways officethrough a web-interface.The inputs asked will be frequency, turnaround time and traversaltimes.The outputs will be displayed on a web page within a day
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 10 / 15
Sample output timetable
Arrival and departure timings
Each column: arrival or departure at station
Each row: one serviceHour-value not required, minute-values45.5 ≡ 45 minutes, 30 seconds
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 11 / 15
Sample: rake linkage
Service-type
Servicenum-ber
Source-Dest. Dep-Arr. linkingservice
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Sample: platform occupancy
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Working time table (taken from Central Railway)
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Method/techniques used
Mixed Integer Linear Programming (MILP) formulationDue to soft/hard (and integer) constraintsComputationally difficult to solveGurobi for solving MILPFor pre-post processing: Python and Bash scriptingFinal output: xls-like file (csv file)Web-interface for input/output
Credits for Gurobi/Python implementation: Shashank Dangayach andSoumya Dutta
Thank you
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 15 / 15
Method/techniques used
Mixed Integer Linear Programming (MILP) formulationDue to soft/hard (and integer) constraintsComputationally difficult to solveGurobi for solving MILPFor pre-post processing: Python and Bash scriptingFinal output: xls-like file (csv file)Web-interface for input/output
Credits for Gurobi/Python implementation: Shashank Dangayach andSoumya Dutta
Thank you
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 15 / 15
Method/techniques used
Mixed Integer Linear Programming (MILP) formulationDue to soft/hard (and integer) constraintsComputationally difficult to solveGurobi for solving MILPFor pre-post processing: Python and Bash scriptingFinal output: xls-like file (csv file)Web-interface for input/output
Credits for Gurobi/Python implementation: Shashank Dangayach andSoumya Dutta
Thank you
Soumya, NR, KN, Madhu (EE and IE&OR, IIT Bombay) CST-railways meeting 15 / 15