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Urban Road Congestion Management - Capacity Investments and Pricing Policies

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Webinar Session presented by Hugo Silva (PUC), on August 30th, 2016.
BRT Centre of Excellence (www.brt.cl)

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Urban Road Congestion Management - Capacity Investments and Pricing Policies

  1. 1. Urban Road Congestion Management: Capacity Investments and Pricing Policies Hugo E. Silva Pontificia Universidad Católica de Chile Leonardo J. Basso and Ignacio Riquelme Universidad de Chile 1
  2. 2. Introduction • Congestion is a major issue. ▫ Santiago 2012: more than 30% of workers in Santiago spend more than 60 minutes in buses only getting to their workplace ▫ US 2013: each person spends 100 minutes per day in traffic on average, valued at $760 billion (Winston, 2013) ▫ London 2015: AM Peak speed in central London is 13,4 km/hr and in inner London is 17,9 km/hr 2
  3. 3. Introduction • Congestion is a major issue. • Urban transport prices do not reflect mg social costs. • What can we do? ▫ Build bigger (more) roads ▫ Manage current capacity – Public transport priority (e.g. subsidies, bus corridors) – Car congestion pricing – Driving restrictions 3
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  5. 5. 5 $ 50,000 $ 55,000 $ 60,000 $ 65,000 $ 70,000 $ 75,000 $ 80,000 $ 85,000 $ 90,000 $ 95,000 2002 2004 2006 2008 2010 2012 2014 2016 Millionsofdollars Source: US. Bureau of the Census US Total Construction Spending: Highway and street
  6. 6. Research questions • How efficient is a road investment policy? • How does it compare and interact with the other possible policies to reduce congestion? 6
  7. 7. Literature • Downs-Thomson paradox ▫ Downs (1962). Thomson (1977). Mogridge (1997) • Hypothesis: in equilibrium car and bus generalized cost will be equal (perfect substitutes) • Consequence: increasing road capacity makes congestion worse • Graphically.. 7
  8. 8. Downs-Thomson hypothesis Cost Total demand By car By public transport Car Public transport
  9. 9. Downs-Thomson paradox What happens with road capacity expansion for cars?
  10. 10. Literature • Basso and Jara-Díaz (2012, TR-A) ▫ If mode choice is more realistic (imperfect substitutes) the DT hypothesis does not hold and cars may be better off with road capacity expansion • Zhang, Lindsey, Yang (2016, TR-B) ▫ Relax the assumption of fixed demand, perfect subsitutability and include transit crowding. Different transit operation regimes • The paradox may not hold 10
  11. 11. Literature • Duranton & Turner (2011. AER): empirical ▫ Increasing lane kilometers leads to a proportional increase of veh-km travelled. ▫ Elasticity is 1 ▫ Holds for interstate travel • Hsu & Zhang (2014. JUrbanE) ▫ The elasticity for Japan is 1.2 – 1.3 11
  12. 12. Literature • Issues with this approach ▫ Aggregate (space and time of day). ▫ No distinction between increasing capacity of current roads or extending the length of the roads ▫ It does not allow for comparing policies and assessing the social benefit 12
  13. 13. Main features ▫ Comparison and interaction of a road investment policy with bus corridors, congestion pricing and transit subsidization ▫ Detailed demand model, MCPF, traffic engineering ▫ City effects: – Congestion and network size – Modal split – Income (GDP) ▫ Bus stop & payment (BS&P) technology 13
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  15. 15. Low vs high BS&P 15
  16. 16. Methodology 1. (Simplified) theoretical model for insights 2. More realistic and detailed model for simulations considering three city types: – European – American – Developing 16
  17. 17. The theory model • 𝑌" = 𝐻 𝑔", 𝑔' 𝑌' = 𝑌 − 𝑌" • 𝑔' = 𝐴𝐶𝐶 + 𝑃' + 𝑡' 𝑌", 𝑌', 𝑓, 𝐾 + 𝑡1 𝑌', 𝑓, 𝑘 + 𝑡3 𝑓 • 𝑔" = 𝐶𝑂" + 𝑃" + 𝑡" 𝑌", 𝑌', 𝑓, 𝐾 + 𝜀(𝑓) 8 𝑡1 𝑌', 𝑓, 𝑘 • 𝑊 = − ∫ ∑ 𝑦= 𝑑𝑔== + 𝑃" 8 𝑌" + 𝑃' 8 𝑌' − 𝑐 8 𝑓 − 𝜌 8 𝐾 • Optimize over 𝑃", 𝑃', 𝑓, bus size (k) and road capacity (𝐾). • Constraints to model different policies 17
  18. 18. The theory model 𝑃" − 𝑃' = 𝑌" 𝜕𝑡" 𝜕𝑌" + 𝑌' 𝜕𝑡' 𝜕𝑌" LMNOP − 𝑌' 𝜕𝑡' 𝜕𝑌' + 𝜕𝑡1 𝜕𝑌' MNOQR"NOSQ + 𝑌" 𝜕𝑡" 𝜕𝑌' + 𝜀 𝜕𝑡1 𝜕𝑌' LMNOQ • Only the price difference can be identified here, but separately in the simulation model • Interpretation as in separate models 18
  19. 19. The theory model 𝑃" − 𝑃' = 𝑌" 𝜕𝑡" 𝜕𝑌" + 𝑌' 𝜕𝑡' 𝜕𝑌" LMNOP − 𝑌' 𝜕𝑡' 𝜕𝑌' + 𝜕𝑡1 𝜕𝑌' MNOQR"NOSQ + 𝑌" 𝜕𝑡" 𝜕𝑌' + 𝜀 𝜕𝑡1 𝜕𝑌' LMNOQ • Only the price difference can be identified here, but separately in the simulation model • Interpretation as in separate models 19
  20. 20. The theory model 𝑃" − 𝑃' = 𝑌" 𝜕𝑡" 𝜕𝑌" + 𝑌' 𝜕𝑡' 𝜕𝑌" LMNOP − 𝑌' 𝜕𝑡' 𝜕𝑌' + 𝜕𝑡1 𝜕𝑌' MNOQR"NOSQ + 𝑌" 𝜕𝑡" 𝜕𝑌' + 𝜀 𝜕𝑡1 𝜕𝑌' LMNOQ • Only the price difference can be identified here, but separately in the simulation model • Cross-congestion effects. 20
  21. 21. The theory model 𝑃" − 𝑃' = 𝑌" 𝜕𝑡" 𝜕𝑌" + 𝑌' 𝜕𝑡' 𝜕𝑌" LMNOP − 𝑌' 𝜕𝑡' 𝜕𝑌' + 𝜕𝑡1 𝜕𝑌' MNOQR"NOSQ + 𝑌" 𝜕𝑡" 𝜕𝑌' + 𝜀 𝜕𝑡1 𝜕𝑌' LMNOQ • Only the price difference can be identified here, but separately in the simulation model • Bus stop and payment technology effects 21
  22. 22. The theory model • Bus corridors • 𝑃" − 𝑃' = 𝑌" TUP TVP + 𝑌' TUQ TVP LMNOP − 𝑌' TUQ TVQ + TUW TVQ MNOQR"NOSQ + 𝑌" TUP TVQ + 𝜖 TUW TVQ LMNOQ • Time functions 𝑡" and 𝑡' are different with bus lanes 22
  23. 23. The theory model • Increasing road capacity makes some terms less important. If road capacity is very large: • 𝑃" − 𝑃' = 𝑌" TUP TVP + 𝑌' TUQ TVP LMNOP − 𝑌' TUQ TVQ + TUW TVQ MNOQR"NOSQ + 𝑌" TUP TVQ + 𝜖 TUW TVQ LMNOQ • 𝑃" ≈ 𝑃' − TUW TVQ 𝑌' + 𝜖𝑌" • Hence, for any given PB, the optimal congestion toll may be very small. Even hit zero. It depends heavily on 𝑡1 • => importance BS&P technology 23
  24. 24. The simulation model • Based on Basso and Silva (2014, AEJ – Economic Policy) • Total elastic demand. Two periods (peak and off-peak) with intertemporal elasticity. Marginal cost of public funds • We use the best possible engineering functions available for 𝑡", 𝑡', 𝑡1, ε and bus costs. Two bus stop technologies (Tirachini, 2014 TR-A). ▫ Partial eq. (no housing. no labor) => fixed travel distance. shorter run (than e.g. Duranton and Turner). ▫ Possibly more favorable for road expansion as induced demand could be higher 24
  25. 25. • Nested logit demand model 25 The simulation model
  26. 26. • Real data • Congested baseline scenario: 13-18 km/hr • Two BS&P Tech: front door boarding only. Similar initial speeds. ▫ Inefficient: (A) payment in cash. 1 berth ▫ Efficient: (B) contactless card. 2 berths. • Bus corridor: at most one lane. 26 Data european city
  27. 27. 27 Efficiency Results Scenario REF SUBref100 CONref DLref CAP SUB100 CON DL Social Benefit 0 3550 6725 5179 5033 5998 6746 7376 CS change 0 37130 28672 33954 42207 44624 29808 42899 Bus fare peak 0.19 0.00 0.16 0.16 0.17 0.00 0.16 0.16 Bus fare off-peak 0.19 0.00 0.16 0.16 0.17 0.00 0.16 0.16 Car toll peak 0.00 0.00 0.62 0.00 0.00 0.00 0.58 0.00 Car toll off-peak 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Frequency peak 25 51 50 63 37 47 50 62 Frequency off-peak 25 30 24 44 24 28 24 39 Bus size 39 35 51 47 37 40 50 39 Car peak speed 13 15 22 13 21 21 22 18 Bus peak speed 11 12 15 19 16 15 15 21 Car off-peak speed 41 41 41 36 47 45 42 50 Bus off-peak speed 26 25 24 26 28 26 25 28 Peak share 41 43 41 43 45 45 42 45 Off-peak share 49 48 49 48 46 46 49 46 No Travel share 9 9 9 9 8 8 9 8 Car share | peak 85 74 61 57 81 74 63 66 Bus share | peak 15 26 39 43 19 26 37 34 Car share | offpeak 84 76 83 79 83 77 83 81 Bus share | offpeak 16 24 17 21 17 23 17 19 Bus stops per km 2 2 2 3 2 2 2 3 Number of bus lanes 0 0 0 1 0 0 0 1 Road Capacity 3600 3600 3600 3600 4662 4355 3706 4446 3 Lanes – BS&P Tech A
  28. 28. 28 Efficiency Results • Case A (low BS&P) and 3 lanes 0 1000 2000 3000 4000 5000 6000 7000 8000 SUBref100 CONref DLref CAP SUB100 CON DL Social Benefit Without capacity investment With capacity investment +0,9 lanes +0,6 lanes +0,1 lanes +0,7 lanes
  29. 29. 29 Efficiency Results • Investing in road capacity as a unique policy increases welfare • =>The Downs-Thomson paradox does not hold 0 1000 2000 3000 4000 5000 6000 7000 8000 SUBref100 CONref DLref CAP SUB100 CON DL Social Benefit Without capacity investment With capacity investment +0,9 lanes +0,6 lanes +0,1 lanes +0,7 lanes
  30. 30. 30 Efficiency Results • Case A (low BS&P) and 3 lanes 0 1000 2000 3000 4000 5000 6000 7000 8000 SUBref100 CONref DLref CAP SUB100 CON DL Social Benefit Without capacity investment With capacity investment +0,9 lanes +0,6 lanes +0,1 lanes +0,7 lanes
  31. 31. 31 0 2000 4000 6000 8000 10000 12000 14000 +0,9 +0,6 +0,1 +0,7 0 2000 4000 6000 8000 10000 12000 14000 +0,1+0,0 +0,6 +0,9 Case A Case B • What happens if BS&P is improved: Case B • In all cases, bus and car speeds are low and quite similar across reference cases Efficiency Results
  32. 32. 32 0 2000 4000 6000 8000 10000 12000 14000 +0,9 +0,6 +0,1 +0,7 0 2000 4000 6000 8000 10000 12000 14000 +0,1+0,0 +0,6 +0,9 3 Case A Case B • If BS&P tech is improved, all other policies work better (this, in addition to the direct benefits of improving BS&P tech) Efficiency Results
  33. 33. 33 0 2000 4000 6000 8000 10000 12000 14000 +0,9 +0,6 +0,1 +0,7 0 2000 4000 6000 8000 10000 12000 14000 +0,1+0,0 +0,6 +0,9 3 Case A Case B • If K is fixed: Order of policies does not change Efficiency Results
  34. 34. 34 0 2000 4000 6000 8000 10000 12000 14000 +0,9 +0,6 +0,1 +0,7 0 2000 4000 6000 8000 10000 12000 14000 +0,1+0,0 +0,6 +0,9 3 Case A Case B • Investing in road capacity as a unique policy always increases welfare => The Downs-Thomson paradox does not hold Efficiency Results
  35. 35. 35 0 2000 4000 6000 8000 10000 12000 14000 +0,9 +0,6 +0,1 +0,7 0 2000 4000 6000 8000 10000 12000 14000 +0,1+0,0 +0,6 +0,9 3 Case A Case B • Case A: Capacity investment is dominated. Build capacity but for buses (corridor). • Case B: improve bus stops, implement a policy and DO NOT expand road capacity Efficiency Results
  36. 36. Conclusions • Increasing road capacity is efficient by itself but it is not the best policy • BS&P tech is key: it improves the benefits of all other policies ▫ Low BS&P tech => incentives to invest in roads, but FOR BUSES do something else additionally ▫ High BS&P tech => better to implement congestion management policies, do not invest in capacity • What matters is not congestion but “congestionability” • BS&P is never seen as a strategic tool. But it affects strategic decisions 36
  37. 37. Conclusions • Results are robust with respect to network size • Build capacity for buses (corridor) • With optimal K, optimal “congestion” toll is zero. • “Optimal” subsidy is negative. 37 0 5000 10000 15000 20000 25000 30000 +1,5 +1,2+1,5+1,2 0 2000 4000 6000 8000 10000 12000 14000 +0,9 +0,6 +0,1 +0,7 Case A
  38. 38. Conclusions • Results are robust with respect to network size • Manage road capacity and do not build. • Before: congestion pricing. • After: bus corridors 0 2000 4000 6000 8000 10000 12000 14000 +0,1+0,0 +0,6 +0,9 0 5000 10000 15000 20000 25000 30000 +1,4 +1,0 +0,0 +0,0 Case B
  39. 39. Conclusions • Preliminary results for U.S. ▫ Building capacity improves as a stand-alone policy ▫ It can surpass management policies (at ref. capacity) ▫ Low BS&P: expand capacity less and build a corridor for buses ▫ High BS&P: same conclusion, implement congestion management 39
  40. 40. Thanks! Questions? Comments? 40

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