County-to-county trip matrices

A common resource for a quick initial analysis of a model result is to look into county-to-county trip matrices, as that can shed light on the general validity of the demand model and help detect bigger issues with the model run.

from pathlib import Path

from polaris.analyze.trip_metrics import TripMetrics

Creating county-to-county matrices

%% sphinx_gallery_thumbnail_path = ‘../../examples/result_analysis/county_to_county.png’

Let’s work with the Austin model

project_dir = Path("/tmp/Austin")
last_iter = TripMetrics(project_dir / "Austin-Supply.sqlite", project_dir / "Austin-Demand.sqlite")

We can get the trip matrix for the last iteration from the trip table

matrix_trips = last_iter.trip_matrix(from_start_time=0, to_start_time=24 * 3600, aggregation="county")

# # Or we can make specify the particular modes we are interested in
# modes = ["SOV", "TAXI"]
# matrix_vehicles = last_iter.trip_matrix(from_start_time=0, to_start_time=24 * 3600, modes=modes, aggregation="county")

# And let's see what modes we have:
matrix_trips.names

Let’s look at some trips by the largest to smallest flow?

Trip matrices sorted by largest amount of SOV trips

matrix_trips.to_df().sort_values("SOV_tot", ascending=False)

	from_id	to_id	SOV_ab	SOV_ba	SOV_tot	BUS_ab	BUS_ba	BUS_tot	RAIL_ab	RAIL_ba	RAIL_tot	TAXI_ab	TAXI_ba	TAXI_tot	TNC_AND_RIDE_ab	TNC_AND_RIDE_ba	TNC_AND_RIDE_tot	MD_TRUCK_ab	MD_TRUCK_ba	MD_TRUCK_tot	HD_TRUCK_ab	HD_TRUCK_ba	HD_TRUCK_tot
12	48453	48491	288156.0	289128.0	577284.0	336.0	436.0	772.0	0.0	0.0	0.0	3256.0	2772.0	6028.0	1092.0	1184.0	2276.0	19856.0	19580.0	39436.0	19708.0	19404.0	39112.0
10	48209	48453	103092.0	102104.0	205196.0	0.0	0.0	0.0	0.0	0.0	0.0	788.0	964.0	1752.0	624.0	552.0	1176.0	4708.0	4928.0	9636.0	12404.0	12148.0	24552.0
2	48021	48453	21828.0	21676.0	43504.0	4.0	32.0	36.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1704.0	1632.0	3336.0	1644.0	1720.0	3364.0
7	48055	48209	9316.0	9216.0	18532.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	8.0	8.0	0.0	0.0	0.0	448.0	440.0	888.0	564.0	688.0	1252.0
6	48053	48491	8124.0	8144.0	16268.0	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	300.0	228.0	528.0
8	48055	48453	6968.0	6936.0	13904.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	8.0	16.0	24.0	360.0	380.0	740.0	804.0	884.0	1688.0
11	48209	48491	6864.0	6736.0	13600.0	0.0	0.0	0.0	0.0	0.0	0.0	76.0	76.0	152.0	28.0	20.0	48.0	52.0	88.0	140.0	88.0	80.0	168.0
5	48053	48453	4536.0	4336.0	8872.0	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	208.0	160.0	368.0
0	48021	48055	3120.0	3076.0	6196.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	28.0	64.0	92.0	52.0	68.0	120.0
3	48021	48491	2692.0	2592.0	5284.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	148.0	148.0	296.0	180.0	136.0	316.0
9	48055	48491	1124.0	1052.0	2176.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	8.0	0.0	0.0	0.0	4.0	0.0	4.0
1	48021	48209	1060.0	1048.0	2108.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	44.0	24.0	68.0	68.0	32.0	100.0
4	48053	48209	140.0	128.0	268.0	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	4.0	0.0	4.0