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| Queens Bus Redesign Exposed (349279) | |||
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Queens Bus Redesign Exposed |
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Posted by Stephen Bauman on Wed Jul 24 20:05:17 2024 One problem with the redesign data presentation is it encourages readers to nitpick rather than examine the broad picture.The broad picture should be: "how long will the trip take from origin to destination". I'm going to concentrate on any trip to the nearest subway station. The trip can start at any time and from anywhere in Queens. What's the population weighted trip time averaged over specific time periods and geographic areas? That's the question I attempted to answer. I assumed a person started a trip every 5 minutes throughout a 24 hour day. That person started that trip from any of the 14,059 Queens census blocks. I calculated walking time to the nearest subway station and all the bus stops that closer than the nearest subway station. For each of the bus stops I looked at the schedule and determined when the next bus would arrive at a subway station. I compared the arrival times. If the bus were quicker, it was a bus-to-subway trip. If a walk to the subway were quicker, it was either a walk-to-subway trip for walking distances less than 1/2 mile or hike-to-subway trip for walking distances greater than 1/2 mile. I took population weighted time averages for the standard service hours: Night; Early AM; AM Peak; Midday; PM Peak; Evening; Late Evening and 24 Hours. I took geographic weighted averages for all the elected official districts: congressional; state senate; state assembly; city council and Community Boards and neighborhoods, as well as all of Queens. I took these averages for a weekday, Saturday, and Sunday. It's a lot of data - 171 pages of tables. It does answer when and where question of who has good and who has bad service. The answer is very little has changed for all the 590 pages in the Final Draft. Here'a a link to the tables. https://public-transit-time-bucket.s3.amazonaws.com/share/PrintResults2.pdf Please save the tables on your local drive. The data is in Amazon Web Services (AWS) cold storage. Storage is cheap but data transfers get expensive. Thanks. |
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