Developer

Report result

Service version: 1

Purpose

This document describes the results returned from the Area Analysis report, including the aggregated time series and geospatial data and properties of the analyzed region. Response format is GeoJSON.

Results overview

There are four results for each region geometry:

baseData

This section contains basic data about the geometry.

Data Key

Description

l

NETWORK_LENGTH - Sum of all road network segments where data was provided.

c

CONGESTION_LEVEL - Average congestion is calculated by collecting all travel times recorded by TomTom during a given period in a specific area and comparing them with the lowest travel times from when traffic is in a completely fluid state. Congestion is expressed as a percentage, indicating the increase in travel time due to excess traffic. For example, a congestion level of 40 means that, on average, journey times across that area's road network were 40% greater than when traffic is free-flowing.

fv

FREE_FLOW_SPEED - Average free-flow travel times are based on static factors in each city, making the score relevant to that city's infrastructure and environment.

t

TRAVEL_TIME - Average travel time per 10 km.

v

SPEED - Average speed on the road network.

timedData

This section contains time series-like data aggregated within the specified geometry. The data is categorized into various types, each represented by a specific shortcut. Below are the descriptions of each data type:

Data Key

Description

time

Time aggregation level. Possible values:

  • yearly - Annual aggregation.
  • monthly - Monthly aggregation.
  • weekly - Weekly aggregation.
  • daily - Daily aggregation.
  • hourly - Hourly aggregation.
  • average - Average week aggregation - aggregated by specific day and hour of the week (e.g., 'MONDAY-1').
l

NETWORK_LENGTH - Sum of all road network segments where data was provided.

c

CONGESTION_LEVEL - Average congestion is calculated by collecting all travel times recorded by TomTom during a given period in a specific area and comparing them with the lowest travel times from when traffic is in a completely fluid state. Congestion is expressed as a percentage, indicating the increase in travel time due to excess traffic. For example, a congestion level of 40 means that, on average, journey times across that area's road network were 40% greater than when traffic is free-flowing.

fv

FREE_FLOW_SPEED - Average free-flow travel times are based on static factors in each city, making the score relevant to that city's infrastructure and environment.

t

TRAVEL_TIME - Average travel time per 10 km.

v

SPEED - Average speed on the road network.

tiledData

This section contains data aggregated for the entire period and split for each Morton tile inside the geometry. The results are significantly simplified (rounded) for default access. Each tile represents a specific location within the analyzed geometry, providing insights based on localized data.

Data Key

Description

lat

Latitude - Latitude coordinate of the given aggregation tile.

lon

Longitude - Longitude coordinate of the given aggregation tile.

l

NETWORK_LENGTH - Sum of all road network segments where data was provided.

c

CONGESTION_LEVEL - Average congestion is calculated by collecting all travel times recorded by TomTom during a given period in a specific area and comparing them with the lowest travel times from when traffic is in a completely fluid state. Congestion is expressed as a percentage, indicating the increase in travel time due to excess traffic. For example, a congestion level of 40 means that, on average, journey times across that area's road network were 40% greater than when traffic is free-flowing.

fv

FREE_FLOW_SPEED - Average free-flow travel times are based on static factors in each city, making the score relevant to that city's infrastructure and environment.

t

TRAVEL_TIME - Average travel time per 10 km.

v

SPEED - Average speed on the road network.

anomalies

This section contains detected anomalies within the specified geometry and time frame. Anomalies are identified based on deviations from expected traffic patterns and can provide insights into unusual traffic conditions.

The anomalies object is a map where keys represent dataType (same as Data key above).

Data Key

Description

l

NETWORK_LENGTH

c

CONGESTION_LEVEL

fv

FREE_FLOW_SPEED

t

TRAVEL_TIME

v

SPEED

An anomaly object contains the start and end dates when the anomaly was detected, along with optional labels from HolidayAPI.

Field

Description

startDate

The start date and time when the anomaly was detected in YYYY-MM-DDTHH:MM:SS format according to ISO 8601.

endDate

The end date and time when the anomaly was detected in YYYY-MM-DDTHH:MM:SS format according to ISO 8601.

labels

An array of labels associated with the anomaly.

Example

Here is a representation of a typical response structure for the analysis:

1{
2  "features": [
3    {
4      "geometry": {
5        "type": "Polygon",
6        "coordinates": [
7          [
8            [
9              4.8757,
10              52.3685
11            ],
12            [
13              4.8961,
14              52.3824
15            ],
16            [
17              4.9236,
18              52.3634
19            ],
20            [
21              4.8757,
22              52.3685
23            ]
24          ]
25        ]
26      },
27      "properties": {
28        "name": "Amsterdam",
29        "timezone": "Europe/Amsterdam",
30        "level": 19,
31        "baseData": {
32          "v": 22.2,
33          "fv": 27.4,
34          "c": 23.4,
35          "t": 27.0,
36          "l": 15.74
37        },
38        "timedData": {
39          "yearly": [
40            {
41              "time": "2024-01-01T00:00",
42              "v": 22.2,
43              "fv": 27.4,
44              "c": 23.4,
45              "t": 27.0,
46              "l": 15.74
47            }
48          ],
49          "monthly": [
50            {
51              "time": "2024-08-01T00:00",
52              "v": 22.2,
53              "fv": 27.4,
54              "c": 23.4,
55              "t": 27.0,
56              "l": 15.74
57            }
58          ],
59          "weekly": [
60            {
61              "time": "2024-08-01T00:00",
62              "v": 22.2,
63              "fv": 27.4,
64              "c": 23.4,
65              "t": 27.0,
66              "l": 15.74
67            }
68          ],
69          "daily": [
70            {
71              "time": "2024-08-06T00:00",
72              "v": 22.2,
73              "fv": 27.4,
74              "c": 23.4,
75              "t": 27.0,
76              "l": 15.74
77            }
78          ],
79          "hourly": [
80            {
81              "time": "2024-08-06T07:00",
82              "v": 23.7,
83              "fv": 27.2,
84              "c": 14.5,
85              "t": 25.2,
86              "l": 15.74
87            }
88          ],
89          "average": [
90            {
91              "time": "MONDAY-1",
92              "v": 22.2,
93              "fv": 27.4,
94              "c": 23.4,
95              "t": 27.0,
96              "l": 15.74
97            }
98          ]
99        },
100        "tiledData": {
101          "tiles": [
102            {
103              "lat": 52.36822,
104              "lon": 4.87689,
105              "v": 20.0,
106              "fv": 20.0,
107              "c": 40.0,
108              "t": 50.0,
109              "l": 0.05
110            }
111          ]
112        },
113        "anomalies": {
114          "c": [
115            {
116              "startDate": "2024-08-06T07:00:00",
117              "endDate": "2024-08-06T08:00:00",
118              "labels": ["Local Event"]
119            }
120          ]
121        }
122      },
123      "id": "b78938a1-cdb0-4064-ac94-72b14ee9b4d9",
124      "type": "Feature"
125    }
126  ],
127  "properties": {
128    "startDate": "2024-08-06",
129    "endDate": "2024-08-06",
130    "dataTypes": [
131      "NETWORK_LENGTH",
132      "CONGESTION_LEVEL",
133      "FREE_FLOW_SPEED",
134      "TRAVEL_TIME",
135      "SPEED"
136    ],
137    "heatmap": false,
138    "frcs": [
139      0,
140      1,
141      2,
142      3,
143      4,
144      5
145    ]
146  },
147  "type": "FeatureCollection"
148}

Properties as input parameters

The properties section contains input parameters that were utilized in the report. These parameters are critical for understanding the context of the analysis:

  • startDate: The starting date for the analysis (e.g., "2024-08-06").
  • endDate: The ending date for the analysis (e.g., "2024-08-06").
  • dataTypes: An array of data types included in the report analysis, such as "NETWORK_LENGTH", "CONGESTION_LEVEL", etc.
  • heatmap: A boolean indicating whether the heatmap is simplified, depending on access.
  • frcs: An array representing the Functional Road Classes (e.g., [0, 1, 2, 3, 4, 5]).

These properties provide important context for the results and help to interpret the analysis effectively.