Pareto-Improving Congestion Pricing on General Transportation Networks

Transcription

1 Transportation Seminar at University of South Florida, 02/06/2009 Pareto-Improving Congestion Pricing on General Transportation Netorks Yafeng Yin Transportation Research Center Department of Civil and Coastal Engineering University of Florida Joint ork ith Toi Laphongpanich, Ziqi Song and Di Wu 1

2 Congestion Pricing The basic concept has been around for over 80 years (e.g., Pigou, 1920 and Knight, 1924) Internalize the negative externality of a trip by charging up to the full cost that the trip imposes to the society Use tolls to provide incentives for users to change their travel behaviors to reduce traffic congestion, enhance system performance and improve social benefits 2

3 Congestion Pricing Practice Cordon Pricing Singapore, Oslo, London and Stockholm etc High-Occupancy/Toll Lanes I-15 in California I-394 in Minnesota I-95 in Florida Others U.S. Congress established Value Pricing Pilot Program The UK government has plans to introduce trial road-pricing schemes across England 3

4 Source:.stockholmsforsoket.se, the official ebpage for The Stockholm Congestion Pricing Trial 4

5 Public Acceptance Tolling is unappealing to the public Despite the success of the Stockholm Congestion Charging Trial in 2006, the results from the referendum are unclear Stockholm: 53% For and 47% Against 14 surrounding municipalities: 39.8% For and 60.2% Against. The possible loss of elfare associated ith, e.g., having to sitch to less desirable routes, departure times or transportation modes because the more desirable ones are no longer affordable 5

6 Pareto-Improving Pricing Approach When compared to the situation ithout any pricing intervention, Pareto-improving tolls increase the social benefit ithout increasing travel-related expense of every stakeholder that may include individual road users, transit passengers, transit operators, transportation authorities, etc. Our premise: Pareto-improving schemes are more appealing to the public. Neither rationing (e.g., Daganzo, 1995) nor revenue distribution (e.g., Yang and Guo, 2005) is required to achieve a Pareto improvement. 6

7 Example Cost function: t ij (v ij ) 3 10v v v v 12 10v 24 2 t ij (v ij ) = travel time for arc (i, j), here v ij is the flo on the arc There are 3.6 travelers for OD pair (1, 4) 7

8 Flo Distributions User Equilibrium (UE) Distribution Every user is a utility maximizer No user has any incentive to sitch travel routes Longer system delay System Optimal (SO) Distribution Minimum total travel time or system delay Some users have to use longer routes 8

9 Example (Cont d) User equilibrium solution ith total delay = Send units along path ith travel time Send units along path ith travel time Send 0 units along path ith travel time User Equilibrium Flos Resulting Travel Times 9

10 Example (Cont d) System optimal solution ith total delay = Send units along ith travel time Send units along ith travel time Send units along ith travel time System Optimal Flos Resulting Travel Times 10

11 Marginal Cost Pricing Set the toll on arc (i, j) equal to its marginal external cost at the system optimum v* 3 τ ij t ( v ) v = ij ij ij 10v v v v v

12 Marginal Cost Pricing: Example System optimum is in a tolled user equilibrium Send units along ith time = Send units along ith time = Send units along ith time = Tolled User Equilibrium Flos Resulting Travel Times 12

13 Marginal Cost Pricing: Comparison Without tolls , With MC tolls Without tolls: Time of = Time of = Total Delay = With MC tolls: Cost of 1 3 4= Cost of = Cost of 1 2 4= Total Delay = Toll Revenue =

14 Marginal Cost Pricing Marginal cost and other first-best pricing schemes are most likely doomed to be political failures because users ill find themselves orse off (Hau, 2005). 14

15 Pareto-Improving Tolls Consider the folloing set of tolls 3 10v 2+25v v v 12 10v

16 Pareto-Improving Tolls (Cont d) Resulting Flo Distribution has a total delay of Send 1.79 units along ith time = Send 0.45 units along ith time = Send 1.36 units along ith time Dominating Flo Dist. Resulting Travel Times 16

17 Pareto-Improving Tolls: Comparison Without tolls , With PI tolls Without tolls: Time of = Time of = Total Delay = With PI tolls: Cost of 1 3 4= Cost of = Cost of 1 2 4= Total Delay = Revenue =

18 Pareto-Improving Tolls Why is it possible? Is it alays possible? If not, ho do e kno hen it is? Ho do e find the tolls? 18

19 More on User Equilibrium User equilibrium is essentially a Nash equilibrium in Game Theory Game theory is concerned ith the general analysis of strategic interactions among multiple players Nash equilibrium in simultaneous non-cooperative games Each player s choice is optimal given others choice No player ould find it in his or her interest to deviate unilaterally from a Nash equilibrium strategy 19

20 Prisoner s Dilemma Nash equilibrium does not necessarily lead to Pareto efficiency. Confess Play B Deny Confess -3, -3 0, -6 Play A Deny -6, 0-1, -1 20

21 Dominating Flo Distribution Link Flos Link UE DF-F-1 DF-F-2 DF-F-3 (1, 3) (1, 2) (3, 2) (3, 4) (2, 5) Cost of the Longest Utilized Path System Cost or Total Delay SO Minimum Delay =

22 Pareto-Improving Tolls UE Pareto-Improving Link Flo Time Flo-3 Time Toll: PI-3 Gen Cost (1, 3) (1, 2) (3, 2) (3, 4) (2, 4) Path Total demand Cost to users Toll Revenue Total Delay

23 Summary Why is it possible? User equilibrium may not be Pareto efficient and dominated by another flo distribution A pricing scheme may be developed to evolve the system to the dominating flo distribution Is it alays possible? No Ho do e kno hether it is possible and ho to design a Pareto-improving pricing scheme? 23

24 24 Pareto-Improving Toll (PIT) Problem a τ c P r v t P r v t f V v s t v v t a UE a a a a ar a a a a ar r F T + = + 0,,, ) ) ( (, 0, ) ) ) ( ( (.. ) ( min λ λ τ δ λ τ δ = = = r P r r P r r ar a F f d f f v v V 0}, ; : { δ

25 Properties of the PIT Problem If (v*,τ*) solves the PIT problem and t(v*) T v* < t(v UE ) T v UE, then τ* is a vector of Pareto-improving tolls. If t(v*) T v* = t(v UE ) T v UE, then no Paretoimproving toll exists. The PIT problem belongs to a class of optimization problems difficult to solve (mathematical programs ith complementarity constraints) It violates a standard regularity condition. Commercial softare of nonlinear optimization problems are ineffective. 25

26 Our Solution Approach Solve a sequence of relaxed nonlinear optimization problems Commercial softare Use concepts from manifold suboptimization to improve the relaxation. Our algorithm Stops after a finite number of iterations. Produce a strongly regular solution. Local optimal solutions are strongly regular. 26

27 Extension: PIT-Elastic Demand min t( v) T v s.t. v = x 0 d D 1 ( z) dz Ax E d = 0,, t ( ) + β ( ρ ρ ), and ( i, j) L, ij v ij x ij ij i j ( t ( v ) + β ( ρ ρ )) = 0,, and ( i, j) L, ij ij ij i 1 ρ ρ D ( d ),, o( ) d ( ) 1 ( d) cue j D x 0, β 0, and ( i, j) L, ij ij 27

28 Extension: PIT-Heterogeneous Users k MCPI-P: min β tu ( ) s.t. v V T ( δ ( β τ ) λ ) f t ( u ) + = 0, k,and r P k, k k, r a ar a a a k λ a k, τ 0 F k ( ) δ β τ λ k, k ar ta ( ua ) + a, k,and r Pk c UE k, v k, k 28

29 Extension: Approximate PIT Pareto-improving tolls may not exist or lead to a significant reduction in congestion. Tolls are approximately Pareto-improving if Users are slightly orse-off Congestion decreases 29

30 30 Extension: Approximate PIT here 0 < α < 0.1 ( ) a τ c P r v t P r v t f V v s t v v t a UE a a a a ar a a a a ar r F T + + = + 0,, 1, ) ) ( (, 0, ) ) ) ( ( (.. ) ( min α λ λ τ δ λ τ δ

31 Numerical Results Netorks Nine Node: 18 links, 9 nodes, 4 O-D pairs Sioux Falls: 76 links, 24 nodes, 528 O-D pairs Hull: 798 links, 501 nodes, 158 O-D pairs Fixed Demands BPR Travel Time Functions 31

32 Netorks Nine-node Netork Sioux Falls 32

33 Results Nine-Node Sioux Falls Hull System Optimal Travel Delay (min.) User Equilibrium Travel Delay (min.) Dominating flo induced by exact PIT tolls Travel Delay (min.) Reduction (% of max.) 0.29% 0.12% 1.66% Dominating flo induced by approximate PIT tolls ith α = 0.05 Travel Delay (min) Reduction (% of max.) 46.90% 24.63% 25.25% Dominating flo induced by approximate PIT tolls ith α = 0.10 Travel Delay (min.) Reduction (% of max.) 46.90% 25.09% 32.73% 33

34 Increase of O-D Travel Time (Sioux Falls) Gini = SO UE c c Gini = API UE c c 34

35 Observations Pareto-improving tolls are relatively prevalent. Hoever, as second-best pricing schemes, they may not lead to a significant improvement in the system performance. When Pareto-improving tolls do not exist or achieve the desired improvement, the approximate version may be able to do so ithout making users severely orse off as in the case ith marginal cost pricing. 35

36 Multimodal Transportation Netorks To further generalize the theory and achieve higher levels of improvement through the synergy among different modes, e further consider multimodal transportation netorks that includes: Transit services High-occupancy/toll (HOT) General-purpose or regular lanes 36

37 Pareto-Improving Tolls In this setting, a pricing scheme refers to a strategy for tolling roads and highays as ell as adjusting fares on various transit lines. A Pareto improvement may be achieved through better distributing travel demands among the available modes of transportation revenue cross-subsidizing beteen transit and vehicle users 37

38 Pareto-Improving Tolls Problem max 1 ln exp( θ( ρ ρ ) β ) D τ st..,,, Ax m = E m d m, W, m M, m, m, m m, m j i l l θ m m l W x c, l L, T, T T l l x fω, i N, l L, W, T, + l l i i m M m, l m, d = D W,, x 0, l L, W, m M, x = 0, l L L, W, T, S H l m, l { } T x = 0, l L, W, m S, H. x 38

39 Pareto-Improving Tolls Problem (Cont d) ( x) ( t + τ ( ρ ρ )) x m, m, m, m l l j i l = 0,,,, ( ( ) τ μ γ ρ ρ ) xl S H { }, W, { } + l L L m S, H, l L, l L, T T T T T + t x ( ) = 0, l L, W, l L, l L, l l l l j i i j i j 1 (ln m, m ) m, m, 0,,, θ + i l L γ d + β + λ E ρ = W m M f μ = 1, i I, W, l l T, T T l( xl cl ) = 0, l L, W μ =, ( T ) 0, T l xl flωi l L, W, m, m, m ( ) τ ρ ρ, l L,, { } + t x + ( ) 0 W m S, H, l L, l L, l l j i i j ( ) t x l L W l L l L T T, T, + l + τl + μl + γl ( ρj ρi ) 0,,, i, j, T γ 0, l L, l T μ 0, l L, W, l 39

40 Pareto-Improving Tolls Problem (Cont d) τ τ τ τ τ = τ, l L, H S S l l S l T = 0, l L, { } { } = 0, l L L, H H T l = 0, l L L, T S H l H l S, τ 0, l L, l E ρ t, W, m M, m, m, m, UE m l m τ x 0, W, m M, l L. l, m l 40

41 Numerical Example (5, 12) 1 5 (5, 12) (6, 18) (3, 35) (9, 20) (9, 35) 2 6 (9, 12) (2, 11) (3, 25) 7 (2, 11) (3, 12) (8, 26) (4, 26) (4, 11) 9 (7, 32) (8, 30) (6, 33) (6, 11) (2, 19) (4, 36) 8 (6, 43) (6, 11) Regular link HOT link 1-3: : 30 O-D demand: 2-3: : 40 3 (8, 39) (6, 24) 4 Nine-node Netork 41

42 Results SO UE Pareto-improving Social benefits System travel time Revenue Demand Travel cost Travel cost increase (%) SOV HOV Transit SOV HOV Transit SOV HOV Transit

43 Tolls Toll SO Pareto-improving SOV HOV Transit SOV HOV Transit * * * * * * * * * *

44 Concluding Remarks Pareto-improving tolls A ne class of second-best tolls These tolls increase the social benefit ithout making any user orse off hen compared to the situation ithout any pricing intervention. Additionally, the Pareto-improving charging scheme requires neither rationing nor revenue distribution, but relies on a fact that the original Wardropian user equilibrium flo distribution may not be Pareto efficient. Pareto-improving tolls are relatively prevalent. Hoever, the tolls may not lead to a significant improvement in the system performance. 44

45 Concluding Remarks (Cont d) Pareto improvement can be further enhanced on multimodal transportation netorks by better distributing travel demands among the available modes of transportation and revenue crosssubsidizing beteen transit and vehicle users The Pareto-improving toll problems can be formulated as mathematical programs ith complementarity constraints, hich can be solved effectively using a manifold suboptimization technique. 45

46 Acknoledgement This research is support in parts by grants from the National Science Foundation (CMMI ) and the Center of Multimodal Solutions for Congestion Mitigation, University of Florida. 46