Rail Planning Course Part A - Pieter Onderwater

2017/02/28

A.5.5
Pg 11

Example: densities in Cape Town

Mostly informal
Mostly high-income

A.5.5
Pg 12

Rail  Transit Oriented Development

Definition (3) of Public Transport (Metro Rule):
• It doesn’t really matter where you develop a (high quality) railway line,

in 20 years time it is functioning…!
– Provided it is really high quality !!!  Rail ! (and maybe BRT ?)
• = Transit Oriented Development
– Partly planned  Partly unplanned / anticipated
• Urban Passenger Rail upgrade (by Railway Company, Government):
– New rolling stock, modernised infrastructure, station upgrade
– Improved train service
• The next step is area development (partly by others):
– Local government  station area upgrade (public)
– Railway Property  precinct development (private and public)
– Local government  precinct development (public facilities)
– Private partners  more urban development (private)

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A.5.5
Pg 13

TOD and Private Developers

Private developers will step into the TOD market, once they believe in it
 When they can make money:
• First Railway Company and Local Municipality have to show they are

serious about rail transport / urban re-generation
– Long term vision, plans, budgets, implementation
• Then Private Developers will step in…!
– Short term profits / long term return on investment

‘Proof’ of TOD:
• Land Value increase near stations

– Office Rents / House Prices
– Compared to similar areas >1 km
–  10 to 30 % higher…! (50 % ?)

A.5.5
Pg 14

TOD Example: Amsterdam Bijlmer

1970’s:
• Amsterdam Bijlmer residential area

–  Amsterdam’s ‘township’
• New Urban Metrorail: Bijlmer  CBD

1990’s / now:
• Area next to Bijlmer developed:

– Hospital, Offices, Sport, Leisure
– >1 million m2
–  Attractive area
• Now Metrorail’s role:  Bijlmer
• Peak direction reversed / more balanced

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2017/02/28

A.5.5
Pg 15

TOD  also in Africa

Johannesburg Sandton
• Sandton is Jo’burg’s 2nd CBD
• (Partly) because of Gautrain…

All Gautrain stations: developments of R2bn/yr

Durban Bridge City
• New rail line in Durban North
• For a residential area of > ¼ mil population
• 1 station only, with feeder services
• Interchange station, combined with:

– Shopping Mall
– Court, Hospital
– Future office development

A.5.5
Pg 16

Place – Node Theory

• Theory by Prof. Bertolini
(University of Amsterdam NL)

• ‘Place’ and ‘Node’ should be in
balance:
– Quality  hierarchy of
network
– Quantity  number of
activities / connections

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A.5.5
Pg 17

Mixed-use Developments

Traditionally: CBD – Industrial area – Residential area

Mixed-use: Housing in CBD Offices, Shopping in Residential
University, Hospital at ‘the end of the line’

Generates: Contra-peak and Off-peak travel demand

A.5
Pg 2

Wrap Up: TOD Benefits

• Passenger  better PT service, urban facilities
• Railway Company  more trips, better spread peaks
• Railway Property  revenue from land use
• National government  better use of rail investments
• Municipalities  mobility, urban quality, city marketing
• Population  economic, social development
• Private developers  return on investment
• Entrepreneurs  job creation, sales
• Environment  reduce car traffic
• Motorists  ease of congestion
• Etc.  etc.

But….:

• TOD is not a general policy  cannot be applied everywhere

• Private market will dictate where government can only facilitate

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A.6
Pg 1

Passenger demand calculations

• Influence factors
• Traffic & Transport model
• PT / Train Trip Generation
• Ring Theory
• Effect of improvements
• Elasticity parameters

Goals:
• Different methodologies for Passenger demand
• Effect of influence factors

A.6
Pg 2

How to calculate demand / effects

Top-down  Integrated Traffic & Transport model
Bottom-up  Trip Generation, Elasticity Parameters
Experience  compare with previous improvements (surveys ?)

• Effect of costs / travel time is ‘relatively easy’ to calculate  economic
theory: elasticity parameters (T&T-model is more complicated)

• Effect of ‘softer’ quality improvements is difficult…: experience

• Effect of improvements  slow positive effect
– Full effect only after several months/years, due to adaptation behaviour

• Effect of deterioration  sudden high negative effect
– Recover only slowly after regaining confidence...

• Effect of marketing  temporally high, but low in medium-term  repeat
the marketing effort…! (but only start Marketing when basis quality is OK)

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A.6.1
Pg 1

Terminology

• Origin = home-side of a journey In Trip Out–In Trip Out
• Destination = activity-side of a journey

– As per AM peak Origin Journey Destination
– In PM peak reverse  same terminology

• In = boarding a train

• Out = alighting a train

– Total in+out = passengers per station  be aware of double counting

• Trip = trip between 2 stations in one train (boarding – alighting)

– 2 trip-ends  be aware of potential double counting (in+out)

• Journey (passenger) = one or more trips (possible change of trains)

between origin and destination (AM) or destination and origin (PM)

– Often the term ‘Trip ‘is used…

• Person  normally makes 2 journeys a day (e.g. in AM and PM)

A.6.1
Pg 2

Terminology

• Trips per day on corridor (2 directions)
– Determines operation revenue
– Socio-economic, environmental impact

• Busiest screen line: trips per peak hour (1 direction)
– Determines operation costs  required capacity
– ‘Roof Tile’ transportation pattern

• Trips per day  peak hr  direction  busiest section

– Example: 10  7 4

• Dynamic Occupancy ratio: = Pass*km
Sum of (pass/section * section length) Train*km
train capacity * corridor length

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A.6.1
Pg 3

Example Gautrain

Gautrain:

• AM-Peak Pretoria – Sandton, busiest section near Midrand (full capacity

= 100%)

– End section less occupied 50%

– Average in peak direction = 75%

• Contra-peak trip less occupied (average 35%)

– Max < 50% towards Pretoria Dynamic
Occupancy Ratio:

Contra = 35%

Peak = 75%
(max = 100%)

Total = 55%

A.6.2
Pg 1

Traffic & Transport model

4 Step model:

• Trip Generation  Origins, Destinations (time)  Land use

– Trip Gen parameters = based on population (and income), jobs

• But does it includes activities: shoppers, scholars, visitors?

• Trip Distribution  OD-matrix  Operations

– Distribution function = based on trip patterns, distance/time, budget

• But it should also be based on the quality per mode (next step)

• Modal Choice  % Car / PT modes  Quality

– Modal Split function = based on generalised costs per mode (time, costs)

• Route Assignment  Volumes (time)  Operations

– Route Choice model = based on shortest route (or multiple routes)

Calibration…!  Model of base year

Model for future years:

• Adjust: Socio data, T&T-Network, gen.costs factors, policy, etc.

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A.6.3.1
Pg 1

PT Trip-Generation for Residential area

1. Generation

– Per 1 unit = 1 household = 3.5 – 4 people

– Trip Generation No/Low/Mid-Income = 1-1½ trip /pers. /day

– Trip Generation High-Income = 2-2½

– Day * 40% = peak period * 50% = max peak hour

2. Modal Split

– PT Modal Split Captives (Lower-Income) = 40-50%, rest = walk

– PT Modal Split Choice Users (Higher-Income) = <10% rest = car

• High-Income areas are a Destination for Low-Income workers

3. Distribution, and Route choice

– External vs Internal trips (shop on the corner, prim. school, etc.)

– Inwards / outwards = 20/80% in AM

– Distribution per destination = gravity model

• If there is Rail (within 1-2 km) = rail is one of main routes

* Values are rough indications

A.6.3.1
Pg 2

Example: Trip-Gen for Residential area

• 1000 housing units = 4.000 people (Low-Income)

gen * 1-1½ = 5.000 trips per day

mod% * 40% = 2.000 external PT trips per day

peak * 40% = 800 PT trips per peak period

p.hour * 50% = 400 PT trips per peak hour

dir * 80% = 300 PT trips per peak direction

route / 15 = 20 taxis

route / 60 = 5 buses

distribution  working areas, nodes = x % (gravity model)

= 1-2 buses/hr per route = poor quality

 combine destinations to 1 rank – feeder

* Values are rough indications (and rounded)

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A.6.3.2
Pg 1

PT Trip-Generation Working area

1. Generation

– Mostly there is no information on workers…  derive from land use

– Gross km2 / ha of development area

• minus slopes, watercourses, green, roads, services, etc. - 50%

– = Net ha developable land: sites / plots

• minus access, parking, green, storage, land reserve, etc. - 20-50%

– = Build-up area: ‘platform’

• minus entrance, stairs, services Floor Area Ratio 0.7

• times multiple office floors (excl. parking) FAR of 1.5-2

– = m2 Gross Leasable Area (GLA)

* Values are rough indications

A.6.3.2
Pg 2

PT Trip-Generation Working area

Working area

1. Generation

– Gross ha of development  GLA *workers / 100 m2 GLA *

• Heavy industry 1 ha *15% *1-2 workers = 20 w / ha

• Light industry 1 ha *20% *2-3 workers = 50 w / ha

• Retail 1 ha *30% *3-4 workers = 100 w/ ha

• Office 1 ha *50% *4-5 workers = 200 w/ ha

– Trip Generation workers = 2 trips/day

– Trip Generation visitors (e.g. shoppers Retail) = ½ - 6 visitors trips/worker

– Day * 40-50% = peak period * 60% = max peak hour

* Values are rough indications

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A.6.3.2
Pg 3

PT Trip-Generation Working area

Working area = 40-50% rest = walk, car pass
= >0% rest = car
2. Modal Split = 30%
– PT Modal Split Low/Mid-Income
– PT Modal Split High-Income = walking
– Average = 80%
= gravity
3. Distribution, and Route choice = rail is one of main routes
– Internal trips, residential close by = feeder / distribution
– Inwards / outwards (shift work!)
– Distribution per destination
– If there is Rail (within 1-2 km)
– Link with major ranks only

A.6.3.2
Pg 4

Example: Trip-Gen for Working area

• 100 ha of industry = 4.000 workers (mainly Low-Income, partly High)

gen * 2 = 8.000 trips per day (few visitors)

mod% * 30% = 2.500 PT trips per day

peak * 40% = 1.000 PT trips per peak period

p.hour * 60% = 600 PT trips per peak hour

dir * 80% = 500 PT trips per peak direction

route / 15 = 30 taxis

route / 60 = 8 buses

distribution  residential areas = x % (gravity model)

= 1-3 buses/hr per route = poor quality

 combine destinations / rank – feeder

* Values are rough indications (and rounded)

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A.6.3.3
Pg 1

Calculate passenger numbers at stations

Passenger Demand / Trip generation:
• Population, number of jobs around station

– In SA  different for income groups!
– Different for urban / rural areas ?
• The closer to a station  the more train use
– Rings of (½) 1 and 2 km
• The better the quality of service  the more train use
– Connectivity, speed, high frequencies
• Additional: PT feeder lines, car P&R, bicycle
– Or competing parallel service…
– In SA  little bicycle use and Park & Ride

A.6.3.3
Pg 2

Ring Theory

(Dutch Railways, 1970’s):
• Every 500m: trip generation halves

– But every next ring is bigger
• Majority passengers < 2½km
• Also passengers > 2½km (bicycle), or

> 5-20km (PT)
– But relatively few

Differences in SA (compared to NL)
• Lower-Incomes  higher train use
• No cycling  max 2km

– Reduction is greater than half
 Calibration…!

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2017/02/28

A.6.4
Pg 1

Effect of improvements

• Shorter travel time / improved quality  will attract new passengers:
– Captives can travel more frequent or longer distances (short term)
– Choice Users might find PT/Train more attractive than Car (med term)
– New developments (TOD) will attract new passengers (long term)

A.6.4
Pg 2

Passenger demand influencing aspects

Socio-Economic factors: effect on PT trips:
• Demographic: size of population +++
• Economic: employment / number of jobs +++
• Economic: GDP, income, car ownership +/- !
• Spatial: density and mix +++/- !

Elasticity = + 1
With 1% more population, mobility (and PT patronage) will grow 1%

Beware of double counts:
• To assess a station: determine Pop-growth and Job-growth (per station)
• To assess a corridor: in Peak: determine Job-growth (= economic growth)

in off-peak: determine Pop-growth

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2017/02/28

A.6.4
Pg 3

Elasticity

Elasticity = impact on (PT) demand, by 1% change of an aspect

Quality of PT product: Captives / Choice Users

• Price / fares --/-

• Travel time: walking, waiting (frequency), trip time - /--

– Relative to In-vehicle Time

• Safety / security, convenience, comfort, experience + / ++ ?

– PT Supply elasticity (frequency, crowding/comfort) + / + ?

Cross elasticity = impact on (PT) demand, by 1% change of an aspect for

another Transportation mode (car, other PT):

• Fuel price, toll, parking fees o /+

• Time: traffic congestion + / ++

Assess costs / time, as portion of the total travel costs / trip time

A.6.4.1
Pg 1

Elasticity parameters

Cost elasticity *:

• Every 1 % cost decrease = x % passenger increase

– Inflation / CPI correction: real prices

– Scientific literature: -0.4 (wide range: -0.1 – -0.6)

• for: Captives < Choice Users

(although Captives are more receptive to price, they don’t have
much alternatives)

• for: Airport < Work < Leisure

• In NL:

– -0.3 – -0.7 (?)  little cost awareness (Higher-Income) ?

– Free PT  > 100 % passenger growth

• Few examples (local bus, off-peak, shopping trips, pensioners, etc.)

* Needs additional calibration / verification for SA..!

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2017/02/28

A.6.4.1
Pg 2

Elasticity parameters

Time elasticity *:

• Every 1 % travel time decrease = x % passenger increase

(travel time includes walking and waiting)

– Scientific literature: -0.5 wide range: -0.4 – -0.9

• for: Captives < Choice Users

• for: Leisure < Work < Airport

– Access time = 1.5 - 2 * in-vehicle time

– Waiting time = 2 - 3 * in vehicle time

– Comfort: Standing = 1.5 * in-vehicle time (and Crowding = * 1.5)

• In NL:

– Elasticity was: -0.6 for Captives !

– Now: -1 for Choice Users

(1.5 on long term)

* Needs additional calibration / verification for SA..!

A.6.4.1
Pg 3

Effect of higher frequency

• Higher frequency = less waiting time (and better perception of time…)

• Experience in NL of various case studies:

freq.: veh / hr Pass. growth *

½1 + 60 % Quality: generate new market
12 + 40 %

24 + 20 %

46 + 10 % Quantity: follow the market
68 +5%

• Half of the effect in peak period, other half in off-peak
– So increase of frequency in peak only  half the growth result

• SA: first calibrations show: higher effect of frequency increase…? *

* Needs additional calibration / verification for SA..!

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A.6.4.1
Pg 4

Supply Elasticity

• In some Scientific Literature:
• Supply Elasticity = x% more patronage, by 1% more vehicle trips/km (or

higher frequency)
– Values of +0.5 to +1

• Meaning: double the frequency is almost double the passengers…
• This seems to be in contradiction with the previous elasticities ?
– This can’t be explained by reduction of Waiting Time only,
– Also by reduction of Crowding  less people standing, more
comfortable seating
– It is attracting ‘latent demand’
– But this is only applicable for heavy overcrowded PT corridors

A.6.4.1
Pg 5

‘Soft’ quality improvements

• BRT, Light Rail, Metro: improved quality compared to standard bus 
more reliable, higher comfort quality of rolling stock and stations, etc.

• NL: Corrected for improved travel time (and examples without improved
travel time), show:
– Additional, on top of Transportation Quality

bus BRT Light Rail Metro

0 + 10 % + 20 % + 30 % Passengers *

* Needs additional calibration / verification for SA..!

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A.6.4.2
Pg 1

Cross-Elasticity parameters

Cross-Elasticity *:
• Every x % car travel costs/time increase = y % PT passenger increase
• = Own Elasticity * substitution (diversion, modal share)

– In general: half of the effect is caused by diversion, half the effect is
newly generated (or reduced) demand

Costs = fuel price, tolls, parking fees

– Scientific literature: +0.2 wide range: +0.1 – +0.4

– Toll: lower  only applicable on certain roads, capped to max, evasion…

Time = congestion

– Scientific literature: +0.4 wide range (Airport = +1.0 ?)

NL: +0.5 – +1

* Needs additional calibration / verification for SA..!

A.6.4.3
Pg 1

Effect on Choice Users

• Quality of PT/Train in relation to Car travel time * NL SA
30 % 30 %
% PT Car Captives Car
use ‘Captives’ 60 % 10 %
NL
Choice Users SA Choice 10 % 60 %
Users
PT/Train Captives
PT
1 234 1 23 4 Captives
Travel time in relation to car Travel time in relation to car

* Needs additional calibration / verification for SA..!

• At present: hardly any Choice Users in SA; and poor quality PT/Train

• In future  Higher incomes / car ownership  more Car ‘Captives’

 And better quality PT/Train:  Choice Users

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2017/02/28

A.6
Pg 3

Wrap Up: PT/Train Planning theory

• Little SA knowledge on PT/Train Planning parameters
– How to calculate PT demand, effect of improvements, etc.
– Different approaches by different consultants / Authorities
– Rely on overseas experience, but SA parameters might be different
– Little basis for decision making

•  Needs additional study: calibration / verification..!

• My ambition: develop a study program to obtain this local knowledge

– Me (PhD research) = overseas knowledge  SA context

– UCT (+ CSIR ?) = scientific research by other students

– Gautrain / PRASA (+ TA) = issue owners, decision makers

A.6
Pg 4

Questions ?

Any questions ?

[email protected]

Time for a brief test…

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