Urban SDK provides data to stream-line and enhance traffic analysis, routing, and planning with live and historical data.
Contents:
Traffic Speed Schema
The dataset is available in JSON, Shapefile, GEOJSON, and CSV file types.
Column Name | Description | Type | Example |
| Unique and persistent ID tied to urban sdk road network. | String |
|
| 15 minute intervals of time data requested | String |
|
| 95th percentile mph speed in float value (precision two) | Float |
|
| Indicates the proportion of real time data included in the speed calculation. *See below for detailed calculation. | Float |
|
| Indicates classification of the roads depending on the speed, importance and connectivity. | Integer |
|
| Length of road segment in miles | Float |
|
| The associated county name. | String |
|
| The associated state name. | String |
|
| Geometry that includes latitude and longitudes of row. | String |
|
Detailed Calculations
Speed
The expected speed along the roadway; will not exceed the legal speed limit.
Confidence
The value of the confidence
field indicates the proportion of real time data included in the speed calculation. It is a normalized value between 5 and 40 with the following meaning:
confidence
= 5 indicates gap-filled speeds10 <
confidence
<= 15 indicates suggestive speeds20 <
confidence
<= 25 indicates highly suggestive speeds30 <
confidence
<= 35 indicates confident speedsconfidence
= 40 indicates highly confident speeds
This field can be used to identify whether the data for a location is derived from real time probe sources or historical information only.
The algorithm for calculating confidence factor is derived from the well-established statistical concept of confidence level, and uses the following general formula:
𝑧=Δ√𝑁−1max(𝜎𝑠𝑚𝑝, 𝜆)
Where N is the number of probe samples, σsmp is their standard deviation, and 𝜆 and Δ are some fixed constants based on how probe speed samples are obtained and processed. For example, probe speed samples are always integers, so their calculated standard deviation is higher than if they were more precise. The 𝜆 factor compensates for that. z is a confidence metric, which is then mapped into the ranges above.
Use this field in conjunction with your particular analytical case to control outliers. For example, if you are calculating an average behavior over a period of time, you could choose to omit records marked with the lowest confidence level, to reduce the effect of outliers.
funclass_id
The functional class is used to classify roads depending on the speed, importance and connectivity of the road.
Must satisfy: 1 ≤ value < 5
The value represents one of the five levels:
1: allowing for high volume, maximum speed traffic movement
2: allowing for high volume, high speed traffic movement
3: providing a high volume of traffic movement
4: providing for a high volume of traffic movement at moderate speeds between neighbourhoods
5: roads whose volume and traffic movement are below the level of any functional class