Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources

Saumya Bhatnagar; Mauro Vallati

doi:10.1109/MT-ITS68460.2025.11223556

Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The design and development of intelligent transport systems rely heavily on the availability of data, which allows for a more comprehensive understanding of current traffic conditions and their likely evolution. While merging all data sources together might be tempting, ensuring the consistency of data collected from different sources is crucial. Noise or large discrepancies can jeopardise the usefulness of merging data and hinder the potential benefits. This paper investigates the integration of data from different sources by analysing the consistency of data from two sensor sets deployed in a region of Manchester, United Kingdom. To perform this analysis, we identified suitable road segments for consideration and leveraged extended Kalman filters. Further, we exploit the opportunity to assess the sensitivity of the sensors to potentially critical circumstances.

Original language	English
Title of host publication	9th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems
Subtitle of host publication	(MT-ITS)
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9798331580636
ISBN (Print)	9798331580643
DOIs	https://doi.org/10.1109/MT-ITS68460.2025.11223556
Publication status	Published - 11 Nov 2025
Event	9th Conference on Models and Technologies for Intelligent Transportation Systems - University of Luxembourg, Luxembourg, Luxembourg Duration: 8 Sept 2025 → 10 Sept 2025 Conference number: 9 https://www.uni.lu/fstm-en/conferences/mt-its-2025/

Conference

Conference	9th Conference on Models and Technologies for Intelligent Transportation Systems
Abbreviated title	MT-ITS 2025
Country/Territory	Luxembourg
City	Luxembourg
Period	8/09/25 → 10/09/25
Internet address	https://www.uni.lu/fstm-en/conferences/mt-its-2025/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/MT-ITS68460.2025.11223556Licence: Unspecified

Cite this

@inproceedings{d4568e1ab098418e8b84503ed892fc68,

title = "Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources",

abstract = "The design and development of intelligent transport systems rely heavily on the availability of data, which allows for a more comprehensive understanding of current traffic conditions and their likely evolution. While merging all data sources together might be tempting, ensuring the consistency of data collected from different sources is crucial. Noise or large discrepancies can jeopardise the usefulness of merging data and hinder the potential benefits. This paper investigates the integration of data from different sources by analysing the consistency of data from two sensor sets deployed in a region of Manchester, United Kingdom. To perform this analysis, we identified suitable road segments for consideration and leveraged extended Kalman filters. Further, we exploit the opportunity to assess the sensitivity of the sensors to potentially critical circumstances.",

keywords = "Traffic, Sensors, Data sources, Kalman filters",

author = "Saumya Bhatnagar and Mauro Vallati",

year = "2025",

month = nov,

day = "11",

doi = "10.1109/MT-ITS68460.2025.11223556",

language = "English",

isbn = "9798331580643",

booktitle = "9th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems",

publisher = "IEEE",

address = "United States",

note = "9th Conference on Models and Technologies for Intelligent Transportation Systems, MT-ITS 2025 ; Conference date: 08-09-2025 Through 10-09-2025",

url = "https://www.uni.lu/fstm-en/conferences/mt-its-2025/",

}

Bhatnagar, S & Vallati, M 2025, Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources. in 9th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems: (MT-ITS)., 11223556, IEEE, 9th Conference on Models and Technologies for Intelligent Transportation Systems, Luxembourg, Luxembourg, 8/09/25. https://doi.org/10.1109/MT-ITS68460.2025.11223556

Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources. / Bhatnagar, Saumya ; Vallati, Mauro.
9th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems: (MT-ITS). IEEE, 2025. 11223556.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources

AU - Bhatnagar, Saumya

AU - Vallati, Mauro

N1 - Conference code: 9

PY - 2025/11/11

Y1 - 2025/11/11

N2 - The design and development of intelligent transport systems rely heavily on the availability of data, which allows for a more comprehensive understanding of current traffic conditions and their likely evolution. While merging all data sources together might be tempting, ensuring the consistency of data collected from different sources is crucial. Noise or large discrepancies can jeopardise the usefulness of merging data and hinder the potential benefits. This paper investigates the integration of data from different sources by analysing the consistency of data from two sensor sets deployed in a region of Manchester, United Kingdom. To perform this analysis, we identified suitable road segments for consideration and leveraged extended Kalman filters. Further, we exploit the opportunity to assess the sensitivity of the sensors to potentially critical circumstances.

AB - The design and development of intelligent transport systems rely heavily on the availability of data, which allows for a more comprehensive understanding of current traffic conditions and their likely evolution. While merging all data sources together might be tempting, ensuring the consistency of data collected from different sources is crucial. Noise or large discrepancies can jeopardise the usefulness of merging data and hinder the potential benefits. This paper investigates the integration of data from different sources by analysing the consistency of data from two sensor sets deployed in a region of Manchester, United Kingdom. To perform this analysis, we identified suitable road segments for consideration and leveraged extended Kalman filters. Further, we exploit the opportunity to assess the sensitivity of the sensors to potentially critical circumstances.

KW - Traffic

KW - Sensors

KW - Data sources

KW - Kalman filters

U2 - 10.1109/MT-ITS68460.2025.11223556

DO - 10.1109/MT-ITS68460.2025.11223556

M3 - Conference contribution

SN - 9798331580643

BT - 9th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems

PB - IEEE

T2 - 9th Conference on Models and Technologies for Intelligent Transportation Systems

Y2 - 8 September 2025 through 10 September 2025

ER -

Shall I Merge that Data? Assessing Consistency of Traffic Data Collected from Multiple Sources

Abstract

Conference

UN SDGs

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