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

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

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

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)

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

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 = + 1 (plus other aspects / impacts) Average annual Mobility growth = 3 % local variations Be aware of double counting

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

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

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)

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

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 = + 0.1 to + 0.4 (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 = *

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 = + 0.0x Relatively small portion of total car travel costs Other car costs normally increase in line with CPI (5-6 %)  No impact

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 = + 0.4 (varies widely: local circumstances) Peak Congestion might increase with > 4% annually  + 1 to + 2 % more Train use Depending on socio-economic developments

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 + 1 to + 2 % e-toll, parking costs + 0 Congestion + 1 to + 2 % Total annual average (+/– local circumstances) + 5 to 6 % ( – Capped)

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?: pieter.onderwater@smec.com