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")

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

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 vehicle types we have:
matrix_vehicles.matrices

['SOV', 'TAXI']

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	227712.0	226048.0	453760.0	1420.0	1444.0	2864.0	64.0	64.0	128.0	4248.0	3220.0	7468.0	3280.0	4496.0	7776.0	19776.0	19436.0	39212.0	18956.0	19972.0	38928.0
10	48209	48453	88296.0	88236.0	176532.0	0.0	0.0	0.0	0.0	0.0	0.0	940.0	1300.0	2240.0	1516.0	664.0	2180.0	4588.0	4696.0	9284.0	12364.0	12440.0	24804.0
2	48021	48453	19460.0	19052.0	38512.0	8.0	4.0	12.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	1752.0	1704.0	3456.0	1800.0	1680.0	3480.0
7	48055	48209	11568.0	11684.0	23252.0	0.0	0.0	0.0	0.0	0.0	0.0	216.0	248.0	464.0	0.0	0.0	0.0	480.0	452.0	932.0	584.0	616.0	1200.0
6	48053	48491	8424.0	8624.0	17048.0	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	252.0	280.0	532.0
11	48209	48491	6292.0	6172.0	12464.0	0.0	0.0	0.0	0.0	0.0	0.0	12.0	4.0	16.0	0.0	0.0	0.0	76.0	120.0	196.0	60.0	72.0	132.0
8	48055	48453	5712.0	5760.0	11472.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	8.0	0.0	0.0	0.0	408.0	308.0	716.0	932.0	924.0	1856.0
5	48053	48453	5444.0	4912.0	10356.0	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	200.0	408.0
0	48021	48055	3884.0	3996.0	7880.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	32.0	72.0	104.0	52.0	40.0	92.0
3	48021	48491	2632.0	2624.0	5256.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	124.0	120.0	244.0	136.0	116.0	252.0
9	48055	48491	984.0	972.0	1956.0	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	0.0	0.0	0.0
1	48021	48209	852.0	1020.0	1872.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	40.0	36.0	76.0	48.0	40.0	88.0
4	48053	48209	172.0	192.0	364.0	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

Vehicle matrices sorted by largest amount of HD_TRUCK trips

(matrix_trips.to_df()).sort_values("HD_TRUCK_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	227712.0	226048.0	453760.0	1420.0	1444.0	2864.0	64.0	64.0	128.0	4248.0	3220.0	7468.0	3280.0	4496.0	7776.0	19776.0	19436.0	39212.0	18956.0	19972.0	38928.0
10	48209	48453	88296.0	88236.0	176532.0	0.0	0.0	0.0	0.0	0.0	0.0	940.0	1300.0	2240.0	1516.0	664.0	2180.0	4588.0	4696.0	9284.0	12364.0	12440.0	24804.0
2	48021	48453	19460.0	19052.0	38512.0	8.0	4.0	12.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	1752.0	1704.0	3456.0	1800.0	1680.0	3480.0
8	48055	48453	5712.0	5760.0	11472.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	4.0	8.0	0.0	0.0	0.0	408.0	308.0	716.0	932.0	924.0	1856.0
7	48055	48209	11568.0	11684.0	23252.0	0.0	0.0	0.0	0.0	0.0	0.0	216.0	248.0	464.0	0.0	0.0	0.0	480.0	452.0	932.0	584.0	616.0	1200.0
6	48053	48491	8424.0	8624.0	17048.0	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	252.0	280.0	532.0
5	48053	48453	5444.0	4912.0	10356.0	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	200.0	408.0
3	48021	48491	2632.0	2624.0	5256.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	124.0	120.0	244.0	136.0	116.0	252.0
11	48209	48491	6292.0	6172.0	12464.0	0.0	0.0	0.0	0.0	0.0	0.0	12.0	4.0	16.0	0.0	0.0	0.0	76.0	120.0	196.0	60.0	72.0	132.0
0	48021	48055	3884.0	3996.0	7880.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	32.0	72.0	104.0	52.0	40.0	92.0
1	48021	48209	852.0	1020.0	1872.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	40.0	36.0	76.0	48.0	40.0	88.0
4	48053	48209	172.0	192.0	364.0	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
9	48055	48491	984.0	972.0	1956.0	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	0.0	0.0	0.0