I have to admit driving in New York is downright boring compared to West Virginia. The roads are so flat, so straight, and so wide.
But then again, New York’s mountains have wide valleys, and it’s rare that a road has to traverse a narrow canyon or do any kind of significant climbing. Few major highways climb over mountains in New York.
County Route 47 in Catskills near Winnisook Lake at 2,680 elevation is the highest elevation all season highway in New York. There aren’t a lot of high elevation roads in New York for sure. The highest Interstate in New York is Southern Tier Expressway in Almond at 2,110 elevation
This shows the percentage of automobiles registered by the ten most popular makes of automobile in New York State. Chevrolet is by far the most popular in the state, although foreigns are more popular downstate.
Make
CHEVR
FORD
TOYOT
HONDA
JEEP
NISSA
SUBAR
DODGE
GMC
HYUND
County
ALBANY
11.3
11.1
10.1
12.7
4.3
5.6
5.3
2.7
2.0
3.1
ALLEGANY
20.4
17.8
5.5
3.4
5.5
3.4
3.0
7.6
5.4
1.4
BRONX
5.0
7.1
17.0
17.9
4.3
8.4
2.3
2.4
1.2
3.9
BROOME
14.1
10.7
15.1
8.7
3.8
5.8
4.3
3.6
3.1
4.6
CATTARAUGUS
20.4
16.5
6.4
3.6
6.3
3.0
3.3
6.1
5.5
1.7
CAYUGA
21.6
13.1
7.6
6.9
5.4
4.9
4.6
4.3
3.7
1.9
CHAUTAUQUA
17.8
17.0
7.6
6.0
5.9
3.6
4.9
5.6
3.4
2.6
CHEMUNG
15.3
13.1
8.9
6.8
4.6
9.5
4.0
5.1
4.2
3.8
CHENANGO
18.2
16.7
7.6
5.3
5.3
4.0
6.2
5.3
4.5
2.6
CLINTON
13.7
14.9
9.1
8.3
4.6
4.2
5.1
3.8
4.6
4.7
COLUMBIA
12.1
12.3
14.0
7.9
4.4
3.7
8.0
3.3
3.7
2.2
CORTLAND
19.3
13.8
6.5
5.5
5.2
6.5
6.5
4.9
3.3
2.7
DELAWARE
15.9
13.0
8.5
6.8
5.4
4.4
7.0
5.2
4.6
2.6
DUTCHESS
9.4
9.4
10.2
13.9
4.8
5.4
7.7
2.8
2.3
4.0
ERIE
17.4
14.5
9.1
6.4
6.1
4.2
4.1
3.3
3.1
3.1
ESSEX
15.5
15.9
9.3
6.5
5.8
3.0
6.2
4.0
4.3
2.2
FRANKLIN
17.2
16.5
7.5
5.3
5.0
2.6
4.8
5.1
5.8
2.6
FULTON
18.1
14.3
8.5
6.4
5.9
6.5
3.0
5.1
2.8
2.1
GENESEE
24.5
13.5
8.9
4.2
5.3
2.7
2.6
4.9
4.4
1.7
GREENE
14.0
13.6
7.4
7.1
4.9
3.9
8.8
3.7
4.8
2.7
HAMILTON
15.6
13.7
10.9
6.4
5.4
3.2
5.3
3.7
4.5
1.9
HERKIMER
16.9
15.0
7.6
7.0
5.4
4.0
4.5
4.4
3.8
2.7
JEFFERSON
14.2
16.5
8.4
7.2
5.8
3.6
3.9
5.1
3.2
2.5
KINGS
4.1
6.5
16.3
14.2
3.6
9.1
3.0
1.8
1.1
3.6
LEWIS
16.4
20.9
5.8
8.6
5.1
2.2
2.9
4.8
4.9
1.5
LIVINGSTON
21.0
15.7
7.2
6.2
5.6
3.1
3.7
5.5
3.7
2.0
MADISON
17.9
13.6
9.3
5.3
5.7
3.1
5.3
4.6
3.9
2.2
MONROE
16.6
10.2
10.5
10.3
4.3
5.7
4.7
2.8
3.1
3.2
MONTGOMERY
17.4
14.4
6.9
8.9
5.9
5.6
3.2
4.8
3.1
2.5
NASSAU
6.2
7.6
11.4
12.3
6.3
7.7
3.3
2.1
1.8
4.2
NEW YORK
3.9
7.4
11.9
11.3
4.3
4.6
4.1
1.6
1.0
2.3
NIAGARA
23.7
13.7
7.0
5.2
5.8
3.4
2.2
4.0
4.3
2.4
ONEIDA
13.5
13.3
10.1
8.4
5.2
4.7
4.8
3.6
3.8
2.9
ONONDAGA
14.9
10.1
10.3
8.0
5.9
4.8
6.0
3.6
2.5
3.2
ONTARIO
16.2
14.1
9.7
7.7
4.7
4.0
5.5
3.4
3.7
3.2
ORANGE
8.5
10.8
12.1
12.3
5.3
6.2
5.2
3.2
2.0
4.7
ORLEANS
27.5
15.5
5.3
4.6
4.7
2.4
2.4
5.3
5.2
1.7
OSWEGO
21.4
13.9
5.8
4.3
6.5
4.3
3.6
5.2
3.2
2.0
OTSEGO
13.9
14.6
9.6
8.9
4.9
5.2
7.2
4.9
3.7
2.5
OUT-OF-STATE
15.7
17.8
8.7
2.0
3.8
10.2
2.3
3.1
2.1
4.0
PUTNAM
8.4
8.6
10.1
13.9
5.9
4.2
9.3
2.5
2.5
3.9
QUEENS
4.8
7.3
16.6
15.0
4.1
9.8
2.7
1.9
1.2
3.7
RENSSELAER
12.9
12.5
9.2
12.0
4.9
5.1
6.0
3.0
2.9
2.9
RICHMOND
5.5
8.1
11.5
12.0
5.7
9.5
2.6
2.2
1.8
5.8
ROCKLAND
5.0
8.8
16.3
15.9
4.5
6.3
5.5
1.7
1.3
4.2
SARATOGA
10.2
11.0
11.1
13.8
5.0
4.9
5.4
2.6
2.8
3.0
SCHENECTADY
10.5
10.4
9.7
14.8
4.6
6.2
4.8
2.9
2.7
3.5
SCHOHARIE
16.8
13.8
7.1
8.1
5.6
3.8
5.7
4.8
4.2
2.4
SCHUYLER
14.8
15.8
8.1
5.5
5.6
5.6
6.0
6.3
3.6
2.3
SENECA
18.4
17.9
7.4
5.4
4.9
5.2
3.3
5.2
3.1
2.8
ST LAWRENCE
20.5
15.4
8.1
4.8
5.6
2.2
3.9
5.9
4.6
1.5
STEUBEN
17.0
15.4
6.7
4.7
6.1
5.5
4.2
6.6
4.3
2.5
SUFFOLK
8.9
10.3
10.8
11.2
6.9
7.0
3.2
2.9
2.0
4.7
SULLIVAN
11.7
12.6
9.9
7.8
5.9
5.0
5.2
4.6
3.0
4.1
TIOGA
15.3
14.3
10.7
7.6
4.6
5.8
4.8
5.2
4.2
3.1
TOMPKINS
11.5
9.8
14.0
12.1
3.7
5.2
9.1
3.2
2.1
2.8
ULSTER
8.9
10.0
11.1
10.4
5.3
6.2
7.9
3.8
3.1
3.8
WARREN
12.5
12.4
9.8
10.9
5.4
4.0
6.2
3.2
3.2
4.7
WASHINGTON
15.3
14.9
8.3
8.8
5.3
4.0
5.3
4.6
3.6
3.7
WAYNE
21.2
15.1
6.6
6.2
5.0
4.1
3.8
4.5
4.1
2.3
WESTCHESTER
6.0
7.2
11.1
14.7
5.7
5.0
6.5
1.6
1.6
2.9
WYOMING
22.8
17.4
6.2
3.5
6.3
2.4
2.9
5.4
4.1
1.5
YATES
19.3
16.3
7.7
5.1
5.3
3.4
4.7
5.3
4.7
2.3
import pandas as pd
import seaborn as sns
url='/media/hd2/auto/autoreg.csv.zip'
df=pd.read_csv(url)
sf=df[((df['Record Type']=='VEH'))].groupby(['County','Make']).count()['VIN'].unstack().T
sf=(sf/sf.sum()*100).fillna(0).T
tb=sf[ sf.sum().sort_values(ascending=False).index[:10]]
tb
cm =sns.color_palette("Spectral_r", as_cmap=True)
html=tb.style.background_gradient(cmap=cm,axis=1).render()
with open('/tmp/auto.html', 'w') as f:
f.write(html)
Electric cars and road tax – a sticky wicket ⛽
Eventually the automobile is going to become electric. Even if climate change wasn’t a serious threat, the need for more urban pollution control and the flexibility of fuel that electricity represents and the precise control of torque and speed of electric motors means that’s where the future of the automobile industry resides.
But how on earth do you tax automobiles to cover the cost of running electric cars on these roads? It’s easy to tax gasoline, as it’s primarily a motor vehicle fuel. You can’t tax electricity as a fuel because it has many different uses.
You could tax roads by installing cameras and sensors to create toll roads but the thing that is not a practical option except on the biggest thoroughfares and would lead many roads to be untaxed, unfairly burdening commuters on select routes. People might shun the major highways to save money on tolls, causing additional traffic problems.
The other way would be a mile tax but that’s harder than some think. You could tax based on vehicle weight and miles traveled but that opens a series of problems. For one, if it’s a yearly tax collected after safety inspections, than the big tax bill at the end of the year would be be difficult for families to afford. They could do budget plans, but even those might challenging to administer, especially for folks without bank accounts. Without paying for the tax up front, people also might over use the roads and hit with a surprise tax bill.
Smart vehicles could talk to a satellite or cell phone tower to report their mileage but I would imagine that would lead to all kinds of privacy concerns. Would the government use GPS coordinates to secretly track people? Plus in outlying areas there might be infrequent and poor cell and satellite service, making it difficult to get regular reports on vehicle milage. It also would be impossible to track legacy electric vehicles without these sensors – and risk abuse when people unplugged or tampered with these sensors.
