UNIVERSITY OF PRETORIA SATC 10 July 2019 A

UNIVERSITY OF PRETORIA SATC 10 July 2019 A CASE STUDY ON THE FEASIBILITY OF BRT-ASSISTED TRANSPORT AT THE UNIVERSITY OF PRETORIA RONALD AROPET SUPERVISED BY: CHRISTO VENTER 1

A CASE STUDY ON THE FEASIBILITY OF BRT-ASSISTED TRANSPORT AT THE UNIVERSITY OF PRETORIA 1 Background Objectives 2 5 6 3 Methodology 4 Scenario Analysis Strategic Issues Conclusions 2

1 Background Collaborations between public transport operators and institutional transport services are becoming common worldwide. Typical arrangements include: Ø Unlimited, fare-free access Ø Seasonal access for specific periods Ø Partially subsidised access *specialised access cards and IDs are generally required 3

1 Background Tukkies Hatfield Hatfi Campus eld C amp us loof k n e o Gr us Camp Tuks Groenkloof Figure 1: UP Campuses & A Re Yeng Station positioning (Google Maps, 2019) 4

1 Background From 62, 000 UP students (as of 2017)… Other. Motorbike 5% 1% NMT 22% Uber Taxi Meter 2%Taxi 1% Car 39% UP bus service 14% Public transport 16% 25% park outside campus A Re Yeng - 3% Figure 2: Modal split of UP Student trips 5

1 Background Table 1: Benefits of integrating services Public Transport Service Provider Ø Assured ridership Ø Increased net revenue Ø Encourage wider public transport usage Institution Ø Reduced Operation Costs Ø Reduced Responsibilities Ø Reduce Traffic Students Ø Integrated options for their unique travel behaviour 6

2 Objectives 1. To investigate the feasibility of BRT-assisted transport along a selected route of UP’s Transport network. 2. To explore strategic issues and implications of implementing such a system. 7

3 Methodology 1. To investigate the feasibility of BRT-assisted transport along a selected route of UP’s Transport network. • • Route Properties Service Design - (Frequency, travel time & waiting time) Passenger Patterns - (Capacity computation) Cost Analysis - (Unit cost allocation method) 2. To explore strategic issues and implications of implementing such a system. • External Factors 8

3 Methodology Scenarios 1. Existing University Transport Solution (ES) 2. A Re Yeng offering (AO) 3. Modified Route (MR) 4. Route Assimilation (RA) 9

4 Scenario Analysis 4. 1 Routes Hatfield Campus oof l k n e Gro us Camp Figure 3: UP Campuses & A Re Yeng Station Positioning (Google 10 Maps, 2019)

4 Scenario Analysis 4. 1 Routes Table 2: Route Information 11

4 Scenario Analysis 4. 2 Service Design Assumptions Students would take the first available bus that passes a station Access and egress times were ignored ES travel times were manually recorded AO travel times, including waiting times, simulated from available bus schedule information. MR and RA → 25 km/h (off-peak) and (15 km/h) peak. Station dwell times varied by station popularity and station type. (Enclosed – pre-boarding fair enabled stations had less dwell times. ) 12

4 Scenario Analysis 4. 2 Service Design ES - Hatfield to Groenkloof AO - Tuks Groenkloof to Tukkies RA - Hatfield to Groenkloof 60 ES - Groenkloof to Hatfield MR - Hatfield to Groenkloof RA - Groenkloof to Hatfield AO - Tukkies to Tuks Groenkloof MR - Groenkloof to Hatfield Travel Time (mins) 50 40 AO 30 RA 20 MR ES 10 0 5: 00 6: 00 7: 00 8: 00 9: 00 10: 00 11: 00 12: 00 13: 00 14: 00 15: 00 16: 00 17: 00 18: 00 19: 00 20: 00 21: 00 22: 00 Time of Day Figure 4: Calculated scenario travel times. 13

4 Scenario Analysis Passenger Demand 80000 Passengers Trips 4. 3 Groenkloof - Hatfield - Groenkloof Total 70000 60000 50000 40000 30000 20000 10000 0 February March April May June July Months (2017) Figure 5: Trips between Hatfield and Groenkloof Campuses (UP Facilities, 2017) 14

4 Scenario Analysis 4. 3 Passenger Demand 15

4 Scenario Analysis 4. 3 Passenger Demand 16

4 Scenario Analysis 4. 3 Passenger Demand Table 2: Demand capacity computation Demand Capacity A Re Yeng demand without students (SABOA, 2016) Demand with students ridership included Standard bus (peak & off peak) Articulated bus (peak) AO (T 1) 78 143 146 970 118 665 158 760 AO (F 4) 4 512 AO (F 7) 4 425 MR *5 642 74 469 62 100 79 920 RA *39 072 107 899 109 350 - 77 764 37 125 35 640 - 72 765 - *induced ridership from trunk and feeder routes 17

4 Scenario Analysis 4. 4 Cost Analysis Existing Solution: R 10 770 per day Table 5: A Re Yeng Rates (Tshwane. gov. za, 2017) Distance-based fares Distance bands range Fare for single trip for connector covered cash value (R) (km) 0 -3 R 7. 00 *3 -8 R 8. 00 8 -14 R 10. 00 14 -21 R 12. 00 21 -29 R 14. 00 29 -38 R 16. 00 38 -48 R 18. 00 48 -59 R 20. 00 59 -71 R 22. 00 Discounted point system Travel Points Price (R) Travel points awarded Discount % Connector 20 Connector 60 Connector 80 Connector 100 Connector 150 Connector 200 Connector 350 Connector 500 R 20 R 60 R 80 R 100 R 150 R 200 R 350 R 500 20 60 96 120 180 240 440 640 0% 0% 17% 18% 19% 20% 21% 22% A Re Yeng Service Offering: R 21 216 per day 18

4 Scenario Analysis 4. 4 Cost Analysis Table 1: Daily Operating Costs Variable Daily use of vehicle Type of fuel Fuel consumption per vehicle Fuel price Student demographic Amount 287. 7 km (MR) 614. 4 km (RA) Diesel 1. 40 km/l 12. 13 Route specific Cost allocated to students Variable Drivers Per vehicle Driver rate Maintenance costs/hr/veh Vehicle km rate/day Vehicle hours/ day MR R 11 068. 35 RA R 14 913. 33 19 Amount 2 (MR) 3 (RA) R 712/day R 26. 65 R 13. 06 15

5 Strategic Issues Scheduling Conflicts Demand demographic and variation Student buy-in Safety & NMT Infrastructure 20

6 Conclusion The existing solution works A Re Yeng is currently capable, but not efficient Modified Route & Assumed Route show promise in service design, although costs are slightly higher. Other factors such as security and infrastructure should be carefully considered. 21

THANK YOU 22