10.15140/VY9Z-6W53
Fitzgerald, Clark
0000-0003-3446-6389
UC Davis
Zhang, Michael
UC Davis
Caltrans PEMS highway sensor average flows by occupancy
UC Davis
2018
National Science Foundation
1650042
2018-02-08T01:10:41Z
en
dataset
https://github.com/clarkfitzg/pems_fd
http://pems.dot.ca.gov/
4477828 bytes
3
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
This data summarizes average vehicle flow as a function of occupancy for
traffic sensor data available from CalTrans Performance Management System
(PEMS). It's useful because it shows the behavior of traffic in
congested regimes, without requiring the preprocessing of several hundred
GB of the raw data. Open the pdf files to see what this data looks like.
First open the pdf files to see what this data looks like. Traffic
engineers model the flow of traffic (vehicles per hour) as a function of
traffic density (vehicles per mile). This model dictates how traffic will
flow in a given stretch of road, so it is known as the fundamental diagram
Daganzo (1997). Flow is the number of vehicles that pass over the detector
in a 30 second period, and occupancy is the fraction of time that a
vehicle is over the detector. We downloaded 10 months of 30 second loop
detector data in 2016 from the CalTrans Performance Measurement System
(PEMS) http://pems.dot.ca.gov/ website. We chose Caltrans district 3, the
San Francisco Bay Area, because this area contains many observations of
high traffic activity and it’s large enough to motivate the computational
techniques. We used a nonparametric method based on dynamically binning
the data using the values of the occupancy and then computing the mean
flow in each bin. We started out with a fixed minimum bin width of w =
0.01, which means that there will be no more than 1/w = 100 bins in total.
We chose 0.01 because it provides sufficient resolution for the
fundamental diagram in areas of low density. Furthermore, we required that
each bin has at least k observations in each bin. Some experimentation for
a few different stations showed that choosing k = 200 provided a visually
smooth fundamental diagram.
First open the pdf files to see what this data looks like. The following R
command will load the data: fd_shape =
read.table("fd_shape.tsv" , col.names =
c("station", "right_end_occ", "mean_flow",
"sd_flow", "number_observed") , colClasses =
c("integer", "numeric", "numeric",
"numeric", "integer") , na.strings =
"NULL" ) The columns are as follows: station: station ID
from PEMS right_end_occ: right end of the occupancy bin where the means
are observed. Ranges from 0 to 1 sd_flow: standard deviation of vehicle
flow in bin mean_flow: mean vehicle flow in bin number_observed: the
number of vehicles in bin
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