1. SATC 2017 Influence Factors for Passenger Train Use
12 July 2017
Pieter Onderwater
Avisha Kishoon
Check logo: SMEC, UCT, DoT, UNAM, etc.

2. Presentation Outline Introduction Background Methodology Elasticities
Influence parameters
Results
Socio-economic
Train system
Other transportation systems
Summary and conclusion

3. Background Influence Factors for Passenger Train use.
PhD study on PT/Rail Planning (at UCT) by Pieter Onderwater
PT passenger demand planning parameters, for:
2 types of passengers: Captives, and Choice Users
2 types of PT Train systems: PRASA Metrorail, and Gautrain
This paper has the first (qualitative) result of:
Concept frameworks and analyses
Scientific (international) literature study  (draft) parameters
Future developments, impacting on Train use
To be continued

4. Methodology
Elasticity = % change in demand, in response to 1 % change in a variable
Price, Time
Mostly Negative = increase in price  decrease in demand
Mainly Inelastic = elasticity < (+/-) 1
Wide variety… = personal, trip purpose, etc.  range / average
Cross-Elasticity: impact by variables of other Transportation systems
Elasticity of other mode * substitution (mode share, diversion)
Mostly Positive, very Inelastic, location specific  no set value
Trip ‘budgets’:  Elasticity
Money Price
Time Time (Value of Time  Generalised Costs)
Effort (physical, mental) … (Willingness to Pay, Subjective time)

5. Influence parameters Public Transport / Train demand depends on:
Socio-Economic aspects
Population, jobs, economic growth, car-ownership
Train system aspects
Fare price, travel time, comfort, capacity constraints
Other Transportation systems’ aspects
Fuel price toll and parking costs, congestion, other PT’s LoS

6. 1. Socio-Economic aspects Population and Jobs
Study area average: Determinative aspect:
Socio growth  Population
Economic growth  (real) GDP / capita
Study area specific: Determinative row/column in OD:
Variation per station area
Train Peak travel = Commuters  Jobs (= GDP)
Off-Peak travel = Social, Leisure, etc.  Population
Per group: Captives, Choice Users  Car-ownership (=GDP)
Airport train service (e.g. Gautrain)  Airport pax (= GDP)
Elasticity = (plus other aspects / impacts)
Average annual Mobility growth = 3 % local variations
Be aware of double counting

7. 2. Train system Fare price
Captives are more price sensitive, compared to Choice Users
However, Captives have less alternatives  walk, or not travel
Low Price-Elasticity for making a PT trip
High Price-Elasticity between PT modes (train, bus, minibus-taxi)
Off-Peak, Weekend / Social, Leisure travellers are more price sensitive, compared to Peak / Commuters
Generally factor 1½ - 2 higher
Airport trips are less price sensitive
Price Elasticity = between – 0.1 and – 1.0 (generally: – 0.3 to – 0.6)
PT fares normally increase in line with CPI (5-6 %)  No impact

8. Train system Trip Time and Frequency
Choice Users are more time sensitive, compared to Captives
“Time is Money”
Peak / Commuters are more time sensitive, compared to Off-Peak, Weekend / Social, Leisure travellers
Airport trips are more time sensitive (stress, delays)
Time Elasticity = between – 0.4 and – 0.9
Trip time of train service is mostly constant  No impact
Unless serious improvements (e.g. PRASA Modernisation)
Frequency increase  reducing waiting time  small changes (few minutes)
 Little impact  + 1 to 2 % more Train use, once-off
Unless crowding  higher freq = more capacity = more comfort

9. Train system Effort: convenience, comfort
Choice Users are more effort sensitive, compared to Captives
They have a (convenient / comfortable) alternative  car
But very difficult to quantify…
Safety (subjective)
‘Traditional’ PT is deemed not to be safe  hardly used by Choice Users…
Discomfort due to crowding little extreme
Standing passengers: subjective time = * 1.5, increasing to * 2.0
Seated passengers: subjective time = * 1.0, increasing to * 1.6
Insufficient capacity: people left behind on the platform
This will reduce / cap the potential growth
Comfortable access, waiting time at station
Subjective time = * 1.2 to 3.0 (average: * 2)

10. Train system Capacity Constraints
Station Accessibility
Quality of public realm: impacts on walking, PT ranks
Local congestion: impacts on drop-off/pick-up, Parking, PT feeders, etc.
Station Parking (Choice Users only)
At specific stations only
Capacity Restrictions at the end of AM peak  and in off-peak
No constraints in weekends
This will reduce / cap the potential growth

11. 3. Car/Road system Fuel Price
Off-Peak, Weekend / Social, Leisure travellers are more price sensitive, compared to Peak / Commuters  similar for cross-elasticity
Generally factor 1½ - 2 higher
Impact on:
Choice Users  car costs
Captives  PT costs (relatively smaller impact)
Price Cross Elasticity = to (varies widely: local circumstances)
Fuel Price determined by Crude Oil and Dollar Exchange  highly volatile:
Expected annual increase = CPI + 5 %  + 1 to + 2 % more Train use = *

12. Car/Road system Toll and Parking
For Choice Users mainly:
E-toll in Peak has limited impact:  Off-Peak: more impact
Applicable on limited roads
Capped at R225/month
= R5 per trip for regular motorists  Full price
Non-Compliance…
Parking costs has limited impact:
Commuters (Peak)  parking on employers premises
Social (Off-Peak)  limited destinations: full payment, but limited time
Price Cross Elasticity = x
Relatively small portion of total car travel costs
Other car costs normally increase in line with CPI (5-6 %)  No impact

13. Car/Road system Congestion
Peak traffic is congested
Off-Peak traffic is hardly congested (yet)
Impact on:
Choice Users  car travel time  shift to Gautrain
not really to other PT
Captives  road based PT time  shift to Metrorail
Time Cross Elasticity = (varies widely: local circumstances)
Peak Congestion might increase with > 4% annually
 + 1 to + 2 % more Train use
Depending on socio-economic developments

14. Summary PT / Train patronage depends on: Rail patronage Annual growth:
Socio-Economic Population / Jobs growth + 3 % average
Train system Fare price 0
Trip time, frequency + 1 to + 2 % (once-off)
Other quality improvements + Growth
Capacity constraints – Capped growth
Other Transp. systems Fuel price to + 2 %
e-toll, parking costs + 0
Congestion to + 2 %
Total annual average (+/– local circumstances) to 6 % ( – Capped)

15. Conclusions
So far, the elasticity parameters are based on international scientific literature:
My PhD study will further investigate the SA context
Revealed and Stated Preference studies for Gautrain and PRASA Metrorail
Train demand can potentially grow with some 5% annually:
Half the impact by socio-economic growth (population, jobs, GDP)
Half the impact by road system (fuel price, congestion)
Once-off impacts by Train system improvements (e.g. PRASA Modernisation)
However, current Train systems are restricted by their capacity  reduced growth
Rolling Stock capacity = crowding, low freq.
Parking capacity, station accessibility
Programmes in place to increase capacity:
New Rolling stock Gautrain, PRASA
PRASA Modernisation
Questions?: