TrafficAssign is a Julia package for…
- Load Transportation Networks data or construct traffic data from data frames.
- Traffic assignment with User Equilibrium.
load_tntp() loads Transportation
Networks data
(TNTP format).
Traffic() creates traffic data from trip and network data frames.
using TrafficAssign
traffic = load_tntp("Anaheim")Number of nodes: 416
Trips:
1406×3 DataFrame
Row │ orig dest trips
│ Int64 Int64 Float64
──────┼───────────────────────
1 │ 1 2 1365.9
2 │ 1 3 407.4
3 │ 1 4 861.4
4 │ 1 5 354.4
5 │ 1 6 545.1
6 │ 1 7 431.5
7 │ 1 8 1.0
8 │ 1 9 56.8
⋮ │ ⋮ ⋮ ⋮
1400 │ 38 31 84.2
1401 │ 38 32 25.1
1402 │ 38 33 24.6
1403 │ 38 34 31.1
1404 │ 38 35 21.4
1405 │ 38 36 19.1
1406 │ 38 37 2.3
1391 rows omitted
Network:
914×8 DataFrame
Row │ from to free_flow_time capacity alpha beta toll leng ⋯
│ Int64 Int64 Float64 Float64 Float64 Float64 Float64 Floa ⋯
─────┼──────────────────────────────────────────────────────────────────────────
1 │ 1 117 1.09046 9000.0 0.15 4.0 0.0 528 ⋯
2 │ 2 87 1.09046 9000.0 0.15 4.0 0.0 528
3 │ 3 74 1.09046 9000.0 0.15 4.0 0.0 528
4 │ 4 233 1.09046 9000.0 0.15 4.0 0.0 528
5 │ 5 165 1.09046 9000.0 0.15 4.0 0.0 528 ⋯
6 │ 6 213 1.09046 9000.0 0.15 4.0 0.0 528
7 │ 7 253 1.09046 9000.0 0.15 4.0 0.0 528
8 │ 8 411 1.0 5400.0 0.15 4.0 0.0 264
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
908 │ 413 404 2.0 5400.0 0.15 4.0 0.0 528 ⋯
909 │ 414 22 1.0 5400.0 0.15 4.0 0.0 264
910 │ 414 405 2.0 5400.0 0.15 4.0 0.0 528
911 │ 415 22 1.0 5400.0 0.15 4.0 0.0 264
912 │ 415 406 2.0 5400.0 0.15 4.0 0.0 528 ⋯
913 │ 416 23 1.0 5400.0 0.15 4.0 0.0 264
914 │ 416 407 2.0 5400.0 0.15 4.0 0.0 528
1 column and 899 rows omitted
assign_traffic() solves traffic assignment problems. By default, the
BiconjugateFrankWolfe() algorithm is used.
res = assign_traffic(traffic)Start Execution
Iteration: 1, Objective: 1291920.701, Relative-Gap: 0.025317752, Execution-Time: 0.281
Iteration: 2, Objective: 1287896.059, Relative-Gap: 0.002939095, Execution-Time: 0.813
Iteration: 3, Objective: 1287174.480, Relative-Gap: 0.002377171, Execution-Time: 0.899
Iteration: 4, Objective: 1286261.622, Relative-Gap: 0.001157943, Execution-Time: 0.946
Iteration: 5, Objective: 1286219.627, Relative-Gap: 0.000541186, Execution-Time: 1.221
Iteration: 6, Objective: 1286196.674, Relative-Gap: 0.000523331, Execution-Time: 1.252
Iteration: 7, Objective: 1286137.001, Relative-Gap: 0.000476912, Execution-Time: 1.315
Iteration: 8, Objective: 1286082.018, Relative-Gap: 0.000161641, Execution-Time: 1.365
Iteration: 9, Objective: 1286073.753, Relative-Gap: 0.000144621, Execution-Time: 1.417
Iteration: 10, Objective: 1286053.650, Relative-Gap: 0.000091432, Execution-Time: 1.449
TrafficAssign.TrafficAssignResults(TrafficImpl(416, [1, 2, 3, 4, 5, 6, 7, 8, 9, 9 … 412, 412, 413, 413, 414, 414, 415, 415, 416, 416], [117, 87, 74, 233, 165, 213, 253, 411, 379, 395 … 21, 402, 21, 404, 22, 405, 22, 406, 23, 407], sparse([2, 3, 4, 5, 6, 7, 8, 9, 10, 11 … 28, 29, 30, 31, 32, 33, 34, 35, 36, 37], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1 … 38, 38, 38, 38, 38, 38, 38, 38, 38, 38], [1171.2, 721.1, 1222.5, 243.4, 637.4, 682.3, 44.3, 198.6, 12.3, 35.8 … 42.1, 16.6, 128.1, 87.1, 54.0, 47.9, 234.6, 63.8, 41.3, 6.0], 416, 416), Graphs.SimpleGraphs.SimpleDiGraph{Int64}(914, [[117], [87], [74], [233], [165], [213], [253], [411], [379, 395], [338, 362] … [38, 53, 390, 408, 416], [211, 407, 409], [167, 408, 410], [396, 409, 411], [8, 410], [21, 402], [21, 404], [22, 405], [22, 406], [23, 407]], [[88], [62], [75], [234], [118], [166], [214], [411], [379, 395], [338, 362] … [38, 53, 390, 408, 416], [212, 407, 409], [168, 408, 410], [396, 409, 411], [8, 410], [21, 402], [21, 404], [22, 405], [22, 406], [23, 407]]), BPRImpl([1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.0, 1.0, 1.0 … 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0], [1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.090458488, 1.0, 1.0, 1.0 … Inf, 2.0, Inf, 2.0, Inf, 2.0, Inf, 2.0, Inf, 2.0], [9000.0, 9000.0, 9000.0, 9000.0, 9000.0, 9000.0, 9000.0, 5400.0, 5400.0, 5400.0 … 5400.0, 5400.0, 5400.0, 5400.0, 5400.0, 5400.0, 5400.0, 5400.0, 5400.0, 5400.0], [0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15 … 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15], [4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0 … 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 … 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [5280.0, 5280.0, 5280.0, 5280.0, 5280.0, 5280.0, 5280.0, 2640.0, 2640.0, 2640.0 … 2640.0, 5280.0, 2640.0, 5280.0, 2640.0, 5280.0, 2640.0, 5280.0, 2640.0, 5280.0])), [7074.9, 9662.5, 7669.000000000003, 12173.800000000003, 2586.7999999999984, 6576.600000000001, 7137.0999999999985, 722.0999999999997, 1317.8580762568981, 919.641923743101 … 564.0461041884911, 1396.4999999999998, 1495.8538958115087, 1245.3000000000002, 509.3999999999999, 619.8, 934.2, 904.5999999999998, 387.90000000000015, 1522.5], TrafficAssignLogs(1.2859360746767877e6, 1.2860536502344077e6, [1.2919207006261928e6, 1.2878960591928903e6, 1.2871744801439045e6, 1.2862616220919997e6, 1.2862196265185215e6, 1.286196673702687e6, 1.2861370010556476e6, 1.2860820184638724e6, 1.286073753071975e6, 1.2860536502344077e6], [0.025317752227496097, 0.002939095375328338, 0.002377171271697498, 0.0011579431276952786, 0.0005411859152905796, 0.0005233310815579923, 0.00047691214982615546, 0.00016164148263084357, 0.00014462108363500186, 9.14318836957612e-5], 1.659854130228e9, [0.28100013732910156, 0.812999963760376, 0.8990001678466797, 0.9460000991821289, 1.2209999561309814, 1.252000093460083, 1.315000057220459, 1.3650000095367432, 1.4170000553131104, 1.4490001201629639]))
# Get flow from traffic assignment results
res.flow
Now, the following algorithms are available.
FrankWolfe()ConjugateFrankWolfe()BiconjugateFrankWolfe()RestrictedSimplicialDecomposition()
# Requires long execution time.
traffic = load_tntp("GoldCoast")
res_FW = assign_traffic(traffic, algorithm=FrankWolfe())
res_CFW = assign_traffic(traffic, algorithm=ConjugateFrankWolfe())
res_BFW = assign_traffic(traffic, algorithm=BiconjugateFrankWolfe())
res_RSD_5 = assign_traffic(traffic, algorithm=RestrictedSimplicialDecomposition(max_points=5))
# res_RSD_10 = assign_traffic(traffic, algorithm=RestrictedSimplicialDecomposition(max_points=10))
- Maria Mitradjieva, Per Olov Lindberg, (2012) The Stiff Is Moving—Conjugate Direction Frank-Wolfe Methods with Applications to Traffic Assignment*. Transportation Science 47(2):280-293.
- Hearn, D.W., Lawphongpanich, S., Ventura, J. (1987). Restricted simplicial decomposition: Computation and extensions. In: Hoffman, K.L., Jackson, R.H.F., Telgen, J. (eds) Computation Mathematical Programming. Mathematical Programming Studies, vol 31. Springer, Berlin, Heidelberg.