SATC 2019 Patronage Time Distribution Ratios for Train (and PT) Services 8 July 2019 Pieter Onderwater Pieter.onderwater@hatch.com

Pieter Onderwater (1962) Education: 1980 – 1988 Technical University Delft: MSc Civil Engineering Traffic & Transport: PT/Rail Planning 2017 – now University of Cape Town: PhD on Rail Planning Work: 1988 – 1989 Swiss Railway Company, University Delft 1989 – 2001 Goudappel Coffeng T&T Consultancy 1992 – 2006 Municipalities of The Hague and Rotterdam 2006 – 2011 DHV Consultants Rail, the Netherlands 2011 – 2013 SSI / Royal HaskoningDHV, South Africa 2013 – 2018 SMEC, South Africa 2018 – now Hatch Africa Lecturing: 2000 – 2010 Deventer, Utrecht techikons, Twente University, the Netherlands 2012 – now University of Cape Town 2017 – now University of Namibia

1. Objective and Methodology Objective of this paper: Determine the Time Distribution of train (and other PT) trips The patronage ratio per hour, compared to an average workday To substantiate planning parameters To test some pre-conceptions Methodology: Analyses of passengers’ activities, and travel behaviour Review data sets Results: ratio per hour compared to average workday; also weekends Determine required peak, off-peak service frequency Passenger Demand  PT Supply Impact of off-peak service frequency on time ratios PT Supply  Passenger Demand Conclusions and Recommendations

2. Activities lead to Transport peaks Transportation is a derived activity, a means to get people involved in economic and social activities People’s activities, Trip purposes PT % Economic activities: fixed 80 %  Mostly in Peak periods Commute to Work, Business some in Weekends Education Supportive activities: 10 %  Often in Off-Peak Shopping and Weekends Visit Facilities and Services Social activities: 10 %  Mostly in Off-Peak Visit Family and Friends and Weekends Leisure (> 30 % in Europe)

3. Data Household Travel Surveys (2008, 2013) eThekwini, Johannesburg, Cape Town NHTS and other HTS did not have specific info PRASA Rail Census (2008, 2012) eThekwini, Cape Town Gauteng was not made available, yet Gautrain patronage data (2015 – 2018) European train systems (2017) Switzerland, the Netherlands BRT and Feeder services (2015, 2018) Johannesburg, Cape Town

3. Peak Periods Peak period = 3-hour period with the highest patronage Time to enter the system: station / train Top Peak hour is (often) the middle hour in this peak period Household Travel Surveys and ITP’s do not have the correct peak times ! Stated peak times: 6:00 – 9:00 16:00 – 19:00 Peak times differ per region, mostly due to sun-rise and sun-set times: eThekwini: 5:00 – 8:00 15:00 – 18:00 Gauteng: 5:30 – 8:30 15:00 – 18:00 Cape Town: 5:30 – 8:30 15:30 – 18:30 Europe (average): 6:30 – 9:30 16:00 – 19:00 Hardly any differences between ‘traditional’ PT, BRT, Metrorail and Gautrain Although data of Metrorail Gauteng was not available…

4. Demand spread over the day

4. Demand spread over the day SA PT trips: AM = 35-40 % off-peak = 20-30 % PM = 30-40 % Peak direction = 70 – 80 % Contra-peak direction = 20 – 30 % Top Peak hour = 50% of peak = 15 – 20 % of day total  year = 270 - 300 workdays In NL: AM = 25-30 % off-peak = 40-50 % PM = 25-30 % Peak direction = 60 – 70 % Contra-peak = 30 – 40 % (or 50 / 50 %) Top Peak hour = wider peaks = 10 – 12 (max 15) % of day total  year = 300 - 330 days (weekend trips) Because: Multiple node metro area  higher contra-peak  more balanced More social activities in PT  more off-peak / weekend trips Better off-peak service  more off-peak trips / peak spreading SATC 2019

4. Demand spread over the day Period # hrs/day hr% of day In peak dir Contra peak Frequency AM top peak 1 hr 15-20 15 in-bound 5 out-bound 100 % AM shoulders 1+1 hr 10 7 3 80-100 % Off-peak 7 hr 3-5 2 25-50 % PM shoulders 7 out-bound 3 in-bound PM top peak 15 5 Evening >2 hr 1-3 1 20-30 % 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 In-bound Out-bound Early – AM Peak – Mid-day Off-Peak – PM Peak – Evening

4. Weekend patronage Weekend’s patronage is lower than workdays And a bit more evenly spread / lower peaks Sat Sun Reason ? Metrorail 50 % 30 % Gautrain 30 % 25 % low freq, more car use Europe 60 % 50 % more social trips, higher freq BRT 40 % 25 %

5. Passenger Demand  PT Supply High peak demand  high peak service frequency / supply Seated and standing capacity in train, BRT (n/a in minibus-taxi) Low off-peak demand  low (or no) off-peak service frequency Seated capacity mostly Take into account: minimum Level of Service frequency E.g. > 2 trains / hr in off-peak; clock-face = consistent frequencies Run shorter trains in off-peak (n/a with bus and minibus-taxi) PT System Off-Peak / Peak ratio Pass Demand  PT freq Supply Metrorail 2 % / 20 % = 10 % 20-25 % Gautrain 3 % / 15 % = 20 % 50 % Europe 5 % / 12 % = 40 % 80 % BRT 3 % / 15 % = 20 % 20 % Minibus-taxi 2 % / 20 % = 10 % 10 %

6. PT Supply  Demand Ratios Higher off-peak ratios are found at corridors with a decent off-peak service: Train system Service freq. Patronage ratio in off-peak Metrorail ½-1 tr/hr 10-15 % Metrorail 1-2 tr/hr 20-25 % Gautrain 3 tr/hr 25 % Europe 2-4 tr/hr 35 % Similar in evenings and weekends Poor off-peak service  passengers will try to find other PT mode, or travel in peaks (also for non-peak-reliant activities)  higher peaks… Or not travel at all… Relatively higher off-peaks / lower top peaks = Lower capital costs, Lower operational costs = Similar revenue  better cost efficiency Peak spreading  shift from peak to off-peak Attract new off-peak market

7. Conclusions and Recommendations Apply correct peak times Not 6:00 – 9:00 and 16:00 – 19:00 as stated  actually ½ to 1 hour earlier Different per region (due to daylight times)  Conduct travel surveys accordingly: not 6:00 – 18:00, but 5:00 – 19:00 Demand-driven PT systems: supply is determined 1-on-1 by demand Supply-driven PT systems: apply minimum QLoS Peak = seated and standing capacity; off-peak = seated only Minimum QLoS frequency, clock-face timetable Train system = run with shorter trains, to provide higher off-peak freq. Improved off-peak service  higher off-peak patronage / lower peak ratio  Improved cost efficiency PRASA Modernisation  check impact (Mamelodi – Pretoria) !

Discussion Any questions ? Pieter.onderwater@hatch.com .