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README.Rmd
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README.Rmd
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[](https://github.com/UrbanAnalyst/ttcalib/actions?query=workflow%3AR-CMD-check)
[](https://www.repostatus.org/#concept)
# ttcalib: Calibration of travel times to empirical data
One metric used throughout this organisation is travel times relative to
equivalent times taken by motorcars. This repository documents procedures used
to calibrate estimates of motorcar travel times to empirical data.
## Empirical Data
### Uber movement data
The empirical data are from [Uber movement](https://movement.uber.com/), with
these analyses calibrating against [data from Santiago,
Chile](https://movement.uber.com/explore/santiago/travel-times?lang=en-US).
The data used are the "All Data" version for the first quarter of 2020, grouped
by "Hour of Day". The download tab on the website linked to above also includes
a link to the "Geo Boundaries", which are also required. Both of these data
should be saved to a local directory.
### OSM Network data
The Uber movement data extend over a far greater boundary than the "Santiago"
boundary returned by Nominatim. The OSM network data were therefore obtained
here from the complete Chile `pbf` file downloaded from Geofabrik, and then
processed with `osmium-tools` by:
1. Trimming to bbox of (-71.363,-33.851,-70.377,-33.113)
2. Constructing separate keyword-filtered subsets with keywords of: "highway",
"restriction", "access", "bicycle", "foot", "motorcar", "motor_vehicle",
"vehicle", "toll".
3. Converting all of these single `pbf` files to `osm` (XML) format.
4. Reading in each via
[`osmdata::osmdata_sc()`](https://docs.ropensci.org/osmdata/reference/osmdata_sc.html),
and combining all data into single `osmdata_sc` object.
## Calibration
The calibration proceeds in two steps:
1. Calibration of waiting times both at traffic lights, and to turn across
oncoming traffic. The effects of these parameters was examined in [a 2020
*Scientific Data* paper, "*Longitudinal spatial dataset on travel times and
distances by different travel modes in Helsinki
Region*](https://www.nature.com/articles/s41597-020-0413-y), which
implemented a complicated parametrisation of waiting times at various types
of intersections "based on previous research."
2. Calibration of estimated times to measures of network centrality. These
effects were examined in [a 2014 *Nature Communications* paper, "*Predicting
commuter flows in spatial networks using a radiation model based on temporal
ranges*](https://www.nature.com/articles/ncomms6347), which started with a
"base" model able to predict observed travel times with an r-squared
correlation coefficient of 0.639. This was then increased through inclusion
of the effects of centrality, using a simple threshold model, to 0.752.
These two types of calibration are successively applied here.
### Calibration to waiting times
Waiting times were examined through two parameters:
1. The effective waiting time at traffic lights; and
2. The effective waiting time to turn across oncoming traffic.
Street networks were weighted for time-based routing using specific values of
these two parameters, and travel times estimated for all 320,666 observed
origins and destinations in the Uber Movement data. The minimal-error model
corresponded to an R-squared correlation of 0.782 for an effective waiting time
at traffic lights of 8 seconds in morning peak hour traffic (7-10 am), or 9
seconds in afternoon traffic (3-7 pm). Corresponding effective waiting times to
turn across oncoming traffic were only 2 or 1 seconds, respectively, although
these made very little difference to model results compared with the effects of
traffic lights.
### Calibration to network centrality
The preceding waiting times were then used to calculate time-based metrics of
centrality, and to adjust observed travel times by centrality. These
adjustments made, however, very little difference, and increasing travel times
along more central portions of the network increased agreement with observed
values at most by only a few hundredths of a percent or less. The best model
was to logarithmically transform centrality, divide by the maximum value, and
increase travel times for the upper 30% of the centrality distribution by the
corresponding values. Even this, however, only increased resultant r-squared
values by just over 1%.
## Conclusion
This repository documents and justifies the general procedure pursued here, to
estimate vehicular travel times through using the following time penalties:
1. Wait at traffic lights = 9 seconds
2. Wait to turn across oncoming traffic = 1 second
No additional adjustments for network centrality are implemented. The estimated
times are then slightly faster than the observed times, with a median ratio of
log-times of 0.94.
