Stop Analytics allows users to solve for different use cases by applying different types of geographic aggregation, providing a comprehensive understanding of where and why vehicles are stopping and how to appropriately plan for these stops.
Learn what is considered a stop and have different scenarios determine if a vehicle is stopped.
A stop is defined as the end of a trip, and occurs when a vehicle has met any of the following conditions:
Idled for at least 200 seconds.
Shifted into park for at least 30 seconds.
The ignition has turned off.
If a vehicle idles for less than 200 seconds, then moves along without shifting into park at some point, the same trip continues, and the idling duration is not counted towards a stop. If a vehicle idles for more than 200 seconds, a new trip is created and only the idling time greater than the 200 seconds is reported as idling.
In most cases, when the ignition turns off, it is considered a stop.
However, this is not the case in the following scenarios:
If the next stop is found to be less than five meters from the start. It is assumed that this is the result of GPS drift or a quick car shuffle, and it is ignored as a stop.
If there are less than 30 seconds between when the ignition turns on/off. It is assumed that the ignition signals are faulty and are ignored.
If the distance and time between when an ignition turns on/off would result in a speed greater than five meters per second. It is assumed that the ignition signals are faulty and are ignored.
When the ignition turns on/off with no movement between. These are also ignored.
Events defined with a gear shift into the park position, are only identified after January 1, 2022. Data prior to this date only use movement, idling, and ignition status to define the start and end of trips.
Learn about Aggregation Mode in Stop Analytics.
| API Identifier | Name | Description | Use Cases |
|---|---|---|---|
| 0 | Road Segment | Stop data for an individual road segment (with or without the last direction of travel). |
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| 1 | Related Roads | Stop data for related roads. Related roads are defined as a continuously connected region (neighborhood) composed of road segments of similar road types (for example, residential, tertiary, and service roads in a suburb). This allows the user to get results back by “general area” such as a suburb/development or a stretch of highway. |
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| 2 | Zone | Stop data for standard or custom zones. |
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| 3 | H3 | Stop data for H3 cells (a hexagonal geocoding system). Allows you to see stops in broad or granular regions without specifying a particular zone or shape, or relying on roads. |
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| 4 | Geohash | Stop data Geohash (a geocoding system that uses alphanumeric strings). Allows you to see stops in broad or granular regions without specifying a particular zone or shape, or relying on roads. |
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| 5 | S2 | Stop data for S2 cells (a quadrilateral geocoding system). Allows you to see stops in broad or granular regions without specifying a particular zone or shape, or relying on roads. |
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The following are road type connections:
Primary, Secondary, and Motorway road types can only be connected to roads of a similar type.
Service roads are sometimes re-classified as Primary or Secondary in cases where they have only one parent type, and they either share a name with their connected parent type or both have no name.
Motorways can only be connected to Motorways. Motorways have a maximum length of 15 km after which they are recursively split into smaller sections, until each section is less than 15 km, or cannot be further reduced.
Residential road types can be joined with anything below them in the hierarchy (for example, Tertiary, Service, Living Street, etc.), presuming some conditions are met.
Tertiary and Living Street road types are reclassified as Residential. Service and Unclassified road types can be part of a residential neighborhood if they:
Connect to and only connected to residential road types (after all other non-service reclassification).
They are completely contained within a land-use area designated as residential only.
The following table outlines how large the Geohash aggregation mode is based on the resolution.
| Resolution | Average Geohash Area (m2) | Average Geohash Area (ft2) | Equivalent Circle Radius (m) |
|---|---|---|---|
| 1 | 25,000,000,000,00 | 269,097,500,000,000 | 2,820,947 |
| 2 | 781,250,000,000 | 8,409,296,875,000 | 498,677 |
| 3 | 24,336,000,000 | 261,950,270,400 | 88,013 |
| 4 | 762,450,000 | 8,206,935,555 | 15,578 |
| 5 | 23,912,100 | 257,387,453 | 2,758 |
| 6 | 744,200 | 8,010,494 | 486 |
| 7 | 23,409 | 251,972 | 86 |
| 8 | 729 | 7,853 | 15.24 |
| 9 | 22.75 | 244 | 2.69 |
| 10 | 0.709 | 7.630 | 0.475 |
| 11 | 0.022 | 0.239 | 0.084 |
| 12 | 0.001 | 0.007 | 0.015 |
The following table outlines how large the H3 aggregation mode is based on the resolution.
| Resolution | Average Hexagon Area (m2) | Pentagon Area (m2) | Average Hexagon Area (ft2) | Equivalent Circle Radius (m) |
|---|---|---|---|---|
| 0 | 4,357,449,416,078 | 2,562,182,162,955 | 46,903,195,160,011 | 1,177,717 |
| 1 | 609,788,441,794 | 328,434,586,246 | 6,563,708,160,617 | 440,569 |
| 2 | 86,801,780,398 | 44,930,898,497 | 934,326,588,225 | 166,222 |
| 3 | 12,393,434,655 | 6,315,472,267 | 133,401,820,382 | 62,808 |
| 4 | 1,770,347,654 | 896,582,383 | 19,055,863,559 | 23,738 |
| 5 | 252,903,858 | 127,785,583 | 2,722,234,473 | 8,972 |
| 6 | 36,129,062 | 18,238,749 | 388,889,988 | 3,391 |
| 7 | 5,161,293 | 2,604,669 | 55,555,699 | 1,281 |
| 8 | 737,327 | 372,048 | 7,936,528 | 484 |
| 9 | 105,332 | 53,147 | 1,133,789 | 183 |
| 10 | 15,047 | 7,592 | 161,969 | 69.20 |
| 11 | 2,149 | 1,084 | 23,138 | 26.15 |
| 12 | 307 | 154 | 3,305 | 9.88 |
| 13 | 43.87 | 22.13 | 472 | 3.73 |
| 14 | 6.26 | 3.16 | 67.45 | 1.41 |
| 15 | 0.895 | 0.452 | 9.633 | 0.533 |
The following table outlines how large the S2 aggregation mode is based on the resolution.
| Resolution | Average S2 Area (m2) | Average S2 Area (ft2) | Equivalent Circle Radius (m) |
|---|---|---|---|
| 0 | 85,011,012,190,000 | 915,050,034,111,941 | 5,201,907 |
| 1 | 21,252,753,050,000 | 228,762,508,554,895 | 2,600,953 |
| 2 | 5,313,188,260,000 | 57,190,627,111,814 | 1,300,476 |
| 3 | 1,328,297,070,000 | 14,297,656,831,773 | 650,238 |
| 4 | 332,074,270,000 | 3,574,414,234,853 | 325,119 |
| 5 | 83,018,570,000 | 893,603,585,623 | 162,559 |
| 6 | 20,754,640,000 | 223,400,869,496 | 81,279 |
| 7 | 5,188,660,000 | 55,850,217,374 | 40,639 |
| 8 | 1,297,170,000 | 13,962,608,163 | 20,319 |
| 9 | 324,290,000 | 3,490,625,131 | 10,159 |
| 10 | 81,070,000 | 872,629,373 | 5,079 |
| 11 | 20,270,000 | 218,184,253 | 2,540 |
| 12 | 5,070,000 | 54,572,973 | 1,270 |
| 13 | 1,270,000 | 13,670,153 | 635 |
| 14 | 320,000 | 3,444,448 | 319 |
| 15 | 79,172 | 852,206 | 158 |
| 16 | 19,793 | 213,051 | 79.37 |
| 17 | 4,948 | 53,262 | 39.68 |
| 18 | 1,237 | 13,315 | 19.84 |
| 19 | 309 | 3,328 | 9.92 |
| 20 | 77.32 | 832 | 4.96 |
| 21 | 19.33 | 208 | 2.48 |
| 22 | 4.83 | 51.99 | 1.24 |
| 23 | 1.21 | 13.02 | 0.621 |
| 24 | 0.300 | 3.22 | 0.309 |
| 25 | 0.076 | 0.813 | 0.155 |
| 26 | 0.019 | 0.203 | 0.078 |
| 27 | 0.005 | 0.051 | 0.039 |
| 28 | 0.001 | 0.013 | 0.019 |
| 29 | 0.000 | 0.003 | 0.010 |
| 30 | 0.000 | 0.001 | 0.005 |
Regional Domicile Analytics identifies the primary geographical base (domicile) for a vehicle based on its movement and dwell patterns, and analyzes the domicile duty cycles to understand regional fleet operations.
A vehicle’s domicile refers to the area, either a generalized geography or more localized bounded location, that a vehicle is said to originate from and/or belong to.
A vehicle’s domicile can be defined based on any of the following, measured across a set time-frame:
The location the vehicle makes the most stops
The location the vehicle is stopped the longest (as in the location of longest dwell time)
The location the vehicle spends the most time traveling
In the context of Altitude, we define a vehicle’s domicile as the generalized geography where the vehicle dwells the longest over the analysis period. The domicile geography is determined by aggregating dwell times of all vehicle stops in a cluster to determine the cluster with the greatest dwell time.
In Altitude, the domicile is typically determined over the full analysis period, with a set minimum of one month, and the maximum analysis period of one year.
Should the input analysis date range fall below one month, the period for determining the domicile is adjusted to be one month prior to the input end date.
The movement of a vehicle from when it leaves its domicile until it returns to its domicile is called the domicile duty cycle. Based on the operational pattern of a vehicle, it may have a single domicile duty cycle every few days to multiple domicile duty cycles each day.
A domicile duty cycle is typically characterized as a series of consecutive trips, which when chained together form the full domicile duty cycle: a trip that starts in the domicile and travels away from the domicile, zero, one or more trips that stops at non domicile locations, and a final trip that travels to and stops in the domicile. A domicile duty cycle only has a single domicile stop relating to the final trip in the series of trips that make up the domicile duty cycle.
In the ITS regional domicile analytics API, we allow the user two inputs to control how domicile duty cycles are created and/or filter out domicile duty cycles of lesser significance:
In regional domicile analytics, metrics are processed across all vehicles that meet eligibility criteria. Entering eligibility criteria is optional. If you do not provide eligibility criteria, we will process all vehicles that have valid domiciles in the input region.
A vehicle needs to have at least two domicile duty cycles in the analysis period to be considered, regardless of eligibility inputs.
A vehicle is considered eligible when its domicile duty cycle distances meet the user input eligibility criteria during the analysis period, and who has at least the minimum number of stops at the domicile.
The following percentile, distance, and stop input parameters allow users to define when a vehicle is considered to be eligible:
For example, if the eligibleMaxDistanceThreshold is set to 300 miles, the eligiblePercentileThreshold to 0.85 (85%) and an eligibleMinDomicileStops value of 2, the user is indicating that of all the domicile duty cycles for the vehicle, 85% (interpolated) of those domicile duty cycles should have a distance less than 300 miles, and the vehicle needs to have stopped at the domicile at least 2 times, for that vehicle to be considered eligible. And if eligible, the vehicle’s domicile duty cycles are included in the Regional Domicile Analytics analysis outputs. Note, that domicile duty cycle distances are interpolated to reasonably estimate the requested percentile in instances where the vehicle has less than 100 domicile duty cycles.
