Public Traffic
About
Using public data about congestion and traffic in New York City, this program attempts to give directions based on traffic densities at given times. A congestion map and geolocation map were constructed seperately and can be used in finding directions.
Example Run
Data Into
The two main types of data were traffic densities overtime on roadways and the geolocation of each intersection. Since the goal of this project was to only use public and free data, many details were left out and had to be interpolated. Intersections were determined from matching patterns between concurrent street intersections. With these intersections identified the geolocations can be applied.
The following code goes over the data used.
import pandas as pd
congestion_path = 'https://data.cityofnewyork.us/resource/ry4b-kref.json'
congestion_data = pd.read_json(congestion_path)
congestion_data.describe()| _10_00_11_00am | _10_00_11_00pm | _11_00_12_00am | _11_00_12_00pm | _12_00_1_00_am | _12_00_1_00pm | _1_00_2_00am | _1_00_2_00pm | _2_00_3_00am | _2_00_3_00pm | ... | _6_00_7_00am | _6_00_7_00pm | _7_00_8_00am | _7_00_8_00pm | _8_00_9_00am | _8_00_9_00pm | _9_00_10_00am | _9_00_10_00pm | id | segment_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5945.00000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | ... | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 | 5945.000000 |
| mean | 487.28545 | 362.049790 | 318.573759 | 500.595795 | 268.121110 | 518.770227 | 199.495542 | 533.232464 | 158.692710 | 567.876198 | ... | 298.851135 | 578.716232 | 449.667142 | 521.869638 | 516.786123 | 459.429268 | 495.612616 | 403.548360 | 194.338099 | 72266.300757 |
| std | 543.55638 | 488.597643 | 455.668074 | 542.300931 | 397.695541 | 559.326384 | 321.381335 | 571.878622 | 283.657075 | 608.448369 | ... | 496.852700 | 632.129919 | 615.741179 | 597.114398 | 618.118818 | 549.351007 | 569.209612 | 510.630172 | 112.834724 | 52561.851711 |
| min | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 2153.000000 |
| 25% | 195.00000 | 110.000000 | 87.000000 | 208.000000 | 64.000000 | 219.000000 | 41.000000 | 228.000000 | 28.000000 | 247.000000 | ... | 71.000000 | 234.000000 | 123.000000 | 197.000000 | 170.000000 | 164.000000 | 184.000000 | 134.000000 | 87.000000 | 28934.000000 |
| 50% | 323.00000 | 202.000000 | 170.000000 | 337.000000 | 136.000000 | 353.000000 | 92.000000 | 366.000000 | 66.000000 | 395.000000 | ... | 148.000000 | 391.000000 | 264.000000 | 339.000000 | 340.000000 | 289.000000 | 326.000000 | 238.000000 | 209.000000 | 51933.000000 |
| 75% | 579.00000 | 394.000000 | 335.000000 | 598.000000 | 277.000000 | 621.000000 | 207.000000 | 645.000000 | 161.000000 | 691.000000 | ... | 319.000000 | 702.000000 | 530.000000 | 620.000000 | 627.000000 | 533.000000 | 584.000000 | 454.000000 | 292.000000 | 110445.000000 |
| max | 5577.00000 | 4468.000000 | 4815.000000 | 5592.000000 | 4463.000000 | 5766.000000 | 4489.000000 | 5247.000000 | 4818.000000 | 6171.000000 | ... | 5166.000000 | 5810.000000 | 9226.330000 | 5249.000000 | 7302.000000 | 5102.000000 | 5788.000000 | 4986.000000 | 377.000000 | 192292.000000 |
8 rows × 26 columns
geo_path = 'geolocations.json'
geo_data = pd.read_json(geo_path)
print geo_data.head() coordinates
1 AVE , EAST 120 ST [40.7986749, -73.9334714]
1 AVE , EAST 124 ST [40.8009838, -73.9317793]
1 AVE , EAST 97 ST [40.7839555, -73.9442204]
1 AVE , EAST 99 ST [40.7851892, -73.9433108]
1 AVENUE , EAST 60 STREET [40.7602427, -73.9615112]
Running the example
In order to use the program, just set your destination and starting location, setup the traveller and run.
An important note: within the nycdrive file are booleans specific for using the congestion map and the geolocation map. Altering these will change directions given.
from nycdrive import *
inter1 = ['STAFFORD AVE', 'HUGUENOT AVE']
inter2 = ['AVE T', 'CONEY ISLAND AVE']
inter_node1 = nyc_map.get_intersection_node(inter1)
inter_node2 = nyc_map.get_intersection_node(inter2)
max_nodes = len(nyc_map.nodes)
car = Traveller(inter_node1, inter_node2, 'NA')
car.dfs_travel()Now we can print the route.
catch_path = []
if True:
for i in range(max_nodes):
for n in range(max_nodes):
if i == n:
continue
node_i = nyc_map.nodes[i]
node_n = nyc_map.nodes[n]
car = Traveller(node_i, node_n, '_9_00_10_00pm')
if geolocation_map and not congestion_map:
car.greedy_travel()
else:
car.dfs_travel()
if len(car.path) > 7:# printed:
printed = True
catch_path = car.path
for pth in catch_path:
fspk1, fspk2 = pth.spks
sname1 = nyc_map.get_street_name(fspk1)
sname2 = nyc_map.get_street_name(fspk2)
print (sname1, sname2)('SPRING ST', 'CROSBY ST')
('BROADWAY', 'SPRING ST')
('BROADWAY', 'PRINCE ST')
('BROADWAY', 'E 12 ST')
('BROADWAY', 'E 11 ST')
('BROADWAY', 'DUANE ST')
('BROADWAY', 'MORRIS ST')
('BROADWAY', 'BEAVER ST')
The output is a list of intersections that are visited from start to destination.
Concept
The idea behind this project was to see how well a public and free database could do in comparison with production quality data. Although additional details could be fleshed out through processing the data even more, overall the data does not provide enough information to construct a working directional map of NYC. Certain attributes of streets such as one-way directions were eliminated to see if the results improved, but there was little improvement.
Contribute
Feel free to use this code for your personal amusement or even add to it. Contact me with any questions you might have.