Traffic measurement loops are a widely-used tool for monitoring vehicular traffic on roads and highways. These devices are typically installed in pairs, with two loops in each lane, and measure, for instance, the time it takes for a vehicle to travel between them. By analyzing the data collected by these loops, traffic experts can gain valuable insights into traffic patterns and make informed decisions about roadway design and traffic flow management. Additionally, digital speed signs are often controlled by the information obtained by these measurements. In this project, we aimed to develop a method for distinguishing pairs of traffic measurement loops (the two loops in a lane) and connecting them to their official coordinate (represented by a point). FME is well suited to solve this problem because of its ability to process large amounts of measurement loops in an automated and generic way. Geometrically, we started with a dataset of polylines, representing the loops, and a dataset of points, representing the location. Each polyline in that dataset represented multiple neighbouring loops. So in this workflow, we first created an individual polyline per loop. We split the loops based on their spatial composition, a rectangle connected to a line (a tail). In a second step, we tried to find pairs of loops based on their shapes and positions. Finally, we linked each pair to a nearby point. The spatial nature of this assignment played to the strengths of FME, making it an ideal tool for the task. This method proved to be highly effective, even in the case the dataset was incomplete or inaccurate by finding different geometrical operations that solved edge cases. In the end, we succeeded in linking 80% of the available points to a pair of traffic measurement loops. This FME implementation could also be adapted to similar problems, for example, datasets of infrastructure that miss a clear structure and could use some data validation and geometry manipulation.

1. Unraveling traffic measurement loops
using FME

2. Presenter
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Margot
D’haemer
BIM consultant
Nordend

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Agenda
1. Introduction
2. Solving the problem – FME workflow
3. Results
4. Conclusion

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Introduction

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Context
Monitoring traffic
• Copper wires 🡪 some physics
• In pairs

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Problem: unstructured datasets
● No relation between the 2 datasets
● Multiple loops are represented by 1 polyline
POLYLINES
POINTS

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Structurize datasets
1. Split polyline
2. Find pairs of loops
3. Connect each pair to a point
🡪 HOW?
Aim of the project

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Connect each pair to a point

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1
2
3
4
pair_id = X
pair_id =Y
A
B
point_id
A
B
loop_id point_id
1 A
2 B
3 A
4 B
pair_id
loop_id
X
1
Y
2
X
3
Y
4
1
2
3
4
A
B

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Workflow – perfect dataset
Neighborfinder
○ For every pair we find 1 closest point

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Workflow – imperfect dataset
Neighborfinder

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Workflow – imperfect dataset
1. Neighborfinder
2. Count # connections = x
○ A 🡪 x = 0
○ B 🡪 x = 2
3. If x > 1 🡪 search for (x-1) closest
points
○ B 🡪 search for 1 closest point
○ Neighborfinder
■ Base = B
■ Candidate = unused point A
A
B

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Workflow – imperfect dataset
4. Result:
o all desired points are found 🡪 aggregate
▪ B 🡪 closest point A is found
5. Center point of bounding box
o Around aggregates group of points
o Around each group of pairs that had the
same point found in step 1
A
B

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Workflow – imperfect dataset
6. Move points
A
B

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Workflow – imperfect dataset
7. Neighborfinder
A
B

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Results

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INPUT Step 1 Step 2 Step 3
OUTPUT
4104 polylines 8530 individual loops
122 not splitted
- 4226 pairs
- 78 individual loops
- 3842 pairs related to
a point
= 90%

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Conclusion

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Summary
Input data Output data
- Geometrically not usable
- No relations between different
datasets
- Structured and logical data
- Relations between two sets of
data
FME is well suited for this kind of problems

20. ThankYou!
margot@nordend.eu