Static Traffic Assignment

We can also perform static traffic assignment using Open-Source libraries for back-of-the-envelop analysis during debugging efforts

Exporting matrices

sphinx_gallery_thumbnail_path = ‘../../examples/modelling_like_the_old_days/sta_results.png’

from pathlib import Path

from polaris.analyze.trip_metrics import TripMetrics
from polaris.runs.convergence.convergence_iteration import ConvergenceIteration
from polaris.runs.static_skimmer.static_assign import static_assignment
from polaris.runs.static_skimmer.static_graph import build_graph

Matrix

We get the demand matrices for the AM peak for one iteration

project_dir = Path("/tmp/Bloomington")
supply_pth = project_dir / "Bloomington-Supply.sqlite"

iteration_3 = ConvergenceIteration.from_dir(project_dir / "Bloomington_iteration_3")
tm3 = TripMetrics(supply_pth, iteration_3.files.demand_db)

# Let's say that one afternoon peak hour is from 16:45AM to 17:45AM, so we egt trips starting during that time
matrix = tm3.vehicle_trip_matrix(from_start_time=16.75 * 3600, to_start_time=17.75 * 3600)

# This matrix has multiple vehicle types, and we could separate them to make sure we observe
# link type constraints, but that shouldn't be needed in a back-of-the-envelope exercise
# Instead, we will just multiply the PCEs for each matrix to the matrices themselves

pces = {"SOV_0": 1.0, "TAXI_9": 1.0, "MD_TRUCK_17": 2.5, "HD_TRUCK_18": 4.0, "BPLATE_19": 2.0, "LD_TRUCK_20": 1.8}
for i, mat in enumerate(matrix.names):
    matrix.matrices[:, :, i] *= pces[mat]

Graph

We build an AequilibraE graph using the underlying Polaris supply model

This procedure asserts some things about the links so we can get everything we need for a static traffic assignment Assumptions are made about (HOURLY) capacities and centroid connector placements

graph = build_graph(supply_pth)

One can see the results of these assumptions in the graph object

graph.network.head()

	link_id	distance	a_node	b_node	capacity_ab	capacity_ba	time_ab	time_ba	direction	connector_penalty	source	id
0	4	118.540138	304	305	300	300	0.147315	0.147315	0	0.0	NaN	NaN
1	6	102.954767	305	307	300	300	0.127946	0.127946	0	0.0	NaN	NaN
2	10	76.423068	310	311	300	300	0.094974	0.094974	0	0.0	NaN	NaN
3	11	295.416116	312	310	300	300	0.367126	0.367126	0	0.0	NaN	NaN
4	12	187.871361	311	313	300	300	0.233476	0.233476	0	0.0	NaN	NaN

Assignment

We perform traffic assignment and skimming using the AequilibraE library

We can load the assignment parameters from the default values and change them

from polaris.runs.static_skimmer.static_skimmer_inputs import STAInputs

sta_pars = STAInputs()

# Not sure why somewhat would want msa over bi-conjugate Frank-Wolfe, but...
sta_pars.assignment_algorithm = "msa"
sta_pars.max_iterations = 10
sta_pars.rgap = 0.01

# By default assignment uses BPR, but we can change the parameters
sta_pars.bpr_alpha = 0.14
sta_pars.bpr_beta = 3.9

No turning constraints are observed in this assignment

assig = static_assignment(graph, matrix, sta_pars)

combined                                          :   0%|          | 0/185 [00:00<?, ?it/s]
Equilibrium Assignment                            :   0%|          | 0/10 [00:00<?, ?it/s]

Then we can see the results

link_loads = assig.results()
link_loads.head()

	SOV_0_ab	SOV_0_ba	SOV_0_tot	TAXI_9_ab	TAXI_9_ba	TAXI_9_tot	Preload_AB	Preload_BA	Preload_tot	Congested_Time_AB	Congested_Time_BA	Congested_Time_Max	Delay_factor_AB	Delay_factor_BA	Delay_factor_Max	VOC_AB	VOC_BA	VOC_max	PCE_AB	PCE_BA	PCE_tot
link_id
4	74.666667	69.333333	144.0	4.0	0.0	4.0	0.0	0.0	0.0	0.147426	0.147383	0.147426	1.000757	1.000462	1.000757	0.262222	0.231111	0.262222	78.666667	69.333333	148.0
6	74.666667	69.333333	144.0	4.0	0.0	4.0	0.0	0.0	0.0	0.128043	0.128005	0.128043	1.000757	1.000462	1.000757	0.262222	0.231111	0.262222	78.666667	69.333333	148.0
10	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.094974	0.094974	0.094974	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0
11	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.367126	0.367126	0.367126	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0
12	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.233476	0.233476	0.233476	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0

And get the skims

assig_class = assig.classes[0]
skim = assig_class.results.skims
# And show some elements
skim.time[:10, :10]

array([[ 0.        , 10.72760513, 11.20617489,  3.05766833,  9.56641341,
        12.06173296,  7.45214589,  8.55200264, 18.9720379 , 17.17400118],
       [ 9.90874479,  0.        ,  6.95467387,  7.99508093,  9.51255336,
         4.37168556,  3.95590967,  2.60239532, 10.99189049,  8.77673166],
       [ 6.13458828,  5.90068792,  0.        ,  4.22092442,  5.40889134,
         8.0112748 ,  5.20155633,  6.24198456, 12.61845729, 10.40329846],
       [ 3.0876974 ,  8.84397034,  9.3225401 ,  0.        ,  7.68277862,
        10.17809817,  5.5685111 ,  6.66836785, 17.0884031 , 15.29036639],
       [14.96908699, 13.62285316, 15.88315152, 13.05542312,  0.        ,
        15.73344004, 14.03605503, 13.9641498 , 20.34062254, 18.1254637 ],
       [ 9.14546826,  6.63200265,  3.58620029,  7.2318044 ,  8.41977131,
         0.        ,  8.21243631,  6.9732993 , 13.34977203, 11.1346132 ],
       [ 7.48213478,  4.80185237,  8.57545896,  5.56847092,  7.14113506,
         6.1359802 ,  0.        ,  2.47452003, 13.46340725, 11.24824842],
       [ 8.55973784,  3.44953778,  7.36665393,  6.64607398,  8.21873812,
         4.78366561,  2.47571979,  0.        , 12.11109266,  9.89593383],
       [12.67450177,  6.00695205,  8.61448033, 10.76083791, 12.27831034,
         6.03149201,  6.72166665,  5.3681523 ,  0.        ,  7.86616924],
       [18.74260674, 12.59716769, 15.20469597, 16.82894288, 15.04775945,
        12.62170766, 13.3118823 , 11.95836795, 10.08493359,  0.        ]])