Workarounds in Public Transit Modelling with EMME and Perl Matt Carlson & John Armstrong

12th October 2007

21st International EMME Conference, Toronto

Contents

• Introduction to need for workarounds

• Case Studies

• EMME Wish Lists

Introduction

• EMME – Established in the UK in large public transport models:• Railplan (Transport for London)• Docklands Public Transport Model (Docklands Light Railway,

London)• PLANET (Department for Transport)

• However, there are cases where additional tools are needed in addition to EMME:• Processing electronic timetable data for rail services input to

EMME• ‘Cleaning up’ EMME outputs of transit lines• Reversing of EMME transit lines

• Hence scripting languages used

Scripting Languages

• AWK used with EMME/2 for decades

• Python used increasingly with Emme 3

• Perl used recently in Arup

• Doesn’t matter too much: just need to get the job done

Case Study 1 – Exporting EMME Transit Lines

• No matter how ‘readable’ the data is when imported to EMME, it is exported like this…

• Awkward spacing• No ‘clues’ about nodes

(multiple 6-digit nodes at stations don’t easily convert to unique and readable 4 character labels)

Step 1

• Remove carriage returns where us3 values are ‘orphaned’ on to new lines

Step 2

• Read all elements from the tidier file…

• …and re-export desired data with Names

Wish List 1

• To be able to choose which data is exported by EMME, including Database attributes

Case Study 2 – Reversing Transit Lines

INRO has included a facility in the network editor for reversing lines interactively, but what if:

1. An entire network needs to be transposed (such as to create a PM network from an AM)?

2. The reverse journeys required different nodes?

Use a script to reverse the lines

Example Network – DLR

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Example Network (Inset)

• Note separate nodes by direction• To enable line-to-line interchange movements at

complex stations

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Reverse Lines

• Use a script to read a ‘tidied’ output:• Input all values into an array• Export in reverse order• Look up opposite node

Wish List 2

• Splitting of stations into so many nodes would be un-necessary if line-to-line transfer information was more easily extracted• Currently there is a need to re-code the network with one node

per line and extract the line-to-line data (as transfer.mac* and transfer.awk*)• Perhaps if something similar to auto turn movements for transit

were output at the end of an assignment…

* See transfer.zip by Heinz Spiess at http://www.inro.ca/en/download/macros.php

Case Study 3 – Processing Transit Line Data

• Railplan Model (Rail, Metro, Tram, Bus)

• Rail is more complicated:• Stopping patterns vary,• Trains join and split,• Timetables change more

frequently

• Other modes simple by comparison• Usually headways only difference

• Need an automated approach

Data Sources 1 – Network Rail CIF

• Network Rail use a ‘Common Interface Format’ (CIF)

• This contains all rail movements in a given timetable period including• Freight Trains• Empty stock movements

• 3 Million+ Lines

• Cryptic Format

• Example: Manchester-London

Data Sources 2 – Issues

• Very little vehicle information• E.g. EMU 125mph – data suited to train pathing not passenger

use

• No capacity information (4/8/12 car???)

• Need to convert to multiple station nodes

• Need to convert to transit line codes

Methods - Software

• Perl is used

• Perl = Practical Extraction and Reporting Language

• Open Source

• Cross Platform

www.perl.org

Methods - Overview

1. Extract subset of ‘relevant’ trains from CIF;

2. For ‘relevant’ trains extract the subset of ‘relevant’ nodes required;

3. Look up ‘relevant’ nodes dependent on direction and TOC;

4. Subtract journey times between ‘relevant’ nodes;

5. Aggregation of identical lines;

6. Allocate a Railplan service code to each line;

7. Assign Vehicle Types;

8. Export in Railplan format;

9. Import to EMME

10. Harmonise times & Fix Join-Split

1 - Extract subset of ‘relevant’ trains from CIF

• Is a passenger service• Not freight or empty stock

• Is in a relevant TOC• Runs in the south east of the UK

• Is within the correct time period• Passes most important station between 7-10am / 10am-4pm

TOC TIPLOC Hierarchy Name

SW WATRLMN 1 London Waterloo

SW SOTON 2 Southampton Cent

SW RDNGSTN 3 Reading

SW RDNG4AB 3 Reading 4A/B

SW PSEA 4 Portsmouth & S

ES WATRINT 1 Waterloo International

ES ASHFKI 2 Ashford (Kent) International

SN VICTRIC 1 London Victoria

SN CHRX 2 London Charing Cross

SN LNDNBDE 3 London Bridge

SN KENOLYM 4 Kensington Olympia

SN ECROYDN 5 East Croydon

SN WCROYDN 6 West Croydon

SN BRGHTN 7 Brighton

SE CHRX 1 London Charing Cross

SE CANONST 2 London Cannon Street

SE LNDNBDE 3 London Bridge

2 - For ‘relevant’ trains extract the subset of ‘relevant’ nodes required

• Skeletal nature of model means stops near edge of model are ignored

3 - Look up ‘relevant’ nodes dependent on direction and TOC

• Stations are often split into separate nodes by direction

• Note:• 4 nodes at Raynes Park• 7 nodes at Waterloo

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4 - Subtract journey times between ‘relevant’ nodes

• The journey time is stored in us3

• This avoids problems with times for ‘irrelevant’ nodes• i.e. subtract the times between modelled nodes after discarding

‘irrelevant’ nodes

5 - Aggregation of identical lines

• Lines with identical stopping patterns are aggregated

• This includes:• noboa• noali

6 - Allocate a Railplan service code to each line

•Lines are named according to TOC, O-D Pair, Direction

TOC Origin Destination Updated Identifier

Direction

CC FENCHRS FN D CC GRAYS UPMNSTR KO U CC UPMNSTR GRAYS KO D CC UPMNSTR PITSEA KO D CC FENCHRS FN U CH MARYLBN MR U CH HWYCOMB BHAMSNH TO D CH MARYLBN MR D CH PADTON XX U CH PADTON XX D ES WATRINT ER D ES WATRINT ER U

7 - Assign Vehicle Types

•Vehicles are assigned according to:•TOC

•Timetabled Type

•Speed

TOC TrainType Speed RailplanTrainAM RailplanTrainIP CC EMU 326 325 CC EMU 75 326 325 CC EMU357 326 325 CC EMU357 100 326 325 CH DMUE 226 225 CH DMUE 100 226 225 CH DMUN 212 210 CH DMUN 75 212 210 CH DMUS 75 212 210 CH DMUT 212 210

8 - Export in Railplan format

• Note: Non-interpolated times

9 - Import to EMME

• Interpolate us3 times with splitime.mac

• Reset noboa and noali flags from us1 and us2• This allows splitime to work with ‘timing points’ as well as stops

10 – Modify in EMME

• Harmonise times for common stop-stop sections

• Fix join-split times

10a – Harmonise Times

• All Stop-to-Stop pairs are consistent and rounded to an integer us3

• Including common Stop-to-Stop pairs on different routes

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10b – Fix Join-Split Times

• Sections ‘x’ minutes long where trains stop at a dummy node are:• x-0.01 minutes on main leg• 0.01 minutes on dummy leg

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Transit Lines are Output (as Case Study 1)

• Transit Lines exported:• Node Names

shown for clarity• us3 times

interpolated and ‘bucket-rounded’ to 2 d.p.• Un-necessary info

(us1, us2) removed from file.

Wish List 3

• To model join-split trains as a single line, e.g. Y-shape

• To be able to view transit lines in terms of stop-stop times, not node-node times:• Similar to configurable attributes• Underlying segment data could still

be stored as now

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2

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4

5

6

7

8

9

1 0

1 1

1 2

1 3

1 41 5

1 6

1 7

1 8

1 9

2 0

2 1

2 2

2 3

2 4

2 5

2 6

2 7

2 8

2 9

3 0

3 1

3 2

3 3

3 4

3 5

Conclusions

• Various tedious or tricky operations are made possible by use of a scripting language

• Learning a scripting language pays off very quickly

Matt CarlsonArup

13 Fitzroy Street

LondonW1T 4BQ

UK+44 20 7755 4114

matt.carlson@arup.comwww.arup.com

linkedin.com/in/mattcarlson