What are the best methods for ensuring unbiased traffic data?

Historically traffic data collection was carried out manually with persons on shifts at roadside locations. Then other methods become available such as sensors/loop detection and video recording. For me between those methods, videos are important as we can double check the output to reduce human/machine error. Currently, AI data collection is getting more popular as it can learn to collect data based on our specific vehicle classification and time period. It goes without saying to validate the result with available census/toll data and always go back to the recording if you detect abnormalities.

-D'après mon expérience en matière de trafic, la fluidité est très souvent fonction de la bonne éducation des usagés de la route. Il faudra prendre aussi en compte les heures de pointe... En ce qui concerne les outils de collecte et d'analyse des données, les technologies telles que les cameras et radars sont mieux placées pour nous fournir les meilleurs chiffres.

I think that all data gathered from many sources are under bias and pollution that we call noise. It's not a problem because we cannot segregate data from neural networks that will process those inputs. We need to clean ?? noise.

All in all, quality data is of paramount importance before starting data analysis, therefore it is necessary to understand the background of the data (such as how the data is collected, cleaned or not, any potential with biasness etc.). The purpose is the avoid misunderstanding or misuse of data. Trusted data sources from official sites or databases would be a good choice.

Suitable traffic data sources requires considering reliability, accuracy, and relevance. Government agencies often provide comprehensive traffic data collected through various monitoring methods, such as traffic cameras, loop detectors, and GPS tracking systems. Additionally, private companies may offer specialized traffic data, including real-time traffic updates and historical traffic patterns derived from mobile apps or navigation systems. Evaluating the coverage area and frequency of data updates is crucial to ensure the data aligns with project needs. Furthermore, considering the data's accessibility and compatibility with analysis tools is essential for seamless integration into traffic management systems or research projects.

Human Beings and Machines are Creatures of Habit! Consistency is essential and Checking and double checking is essential As we can all make mistakes and malfunction! Only change a course when you have the Knowledge and Experience and Know the Need to Change It! Don’t just change something for the sake of Change!

Combining data from various sources can be a better way (e.g., traffic cameras, sensors, mobile apps, social media) to cross-verify and validate the accuracy of the information.

It is important to apply consistent and transparent methods for collecting and analyzing your data once you have selected the sources. This includes defining your data collection objectives, scope, and criteria, and documenting them in a plan. Standardized and validated tools, techniques, and procedures should be used for data collection and analysis, following the best practices and guidelines of your field. Additionally, training and supervision of data collectors and analysts is essential to ensure they follow the same protocols and quality standards. Quality checks and audits should be conducted regularly to identify any errors, gaps, or outliers.

S'il y a une chose cruciale, c'est la transparence du processus de collecte ou le dispositif de collecte si elle est faite par des intelligences artificielles. Un mauvais chiffre entra?nera un niveau d'information erroné et mettra en péril tout le trafic car les bonnes décisions ne seront pas prises.

To ensure consistency and transparency in gathering and analyzing traffic data, adhere to standardized collection protocols, implement rigorous quality control measures, and maintain detailed documentation. This includes traffic surveys such as traffic volume count, trip generation survey. Verify data accuracy through independent methods and make it openly accessible to stakeholders and the public. Foster stakeholder engagement to align with project goals and promote collaboration. These practices enhance the reliability and credibility of traffic data, facilitating informed decision-making and fostering trust among stakeholders.

It is important to divide the study area into strata based on relevant characteristics (e.g., time of day, day of the week, road types). Then samples can be collected proportionally from each stratum to ensure representation across different conditions.

La statistique descriptive ne sera jamais une science exacte où absolue. Ceci étant, peu importe la méthode utilisée pour collecter les données il y aura toujours une Marge d'erreur ou un intervalle de confiance. Il faut donc minimiser la marge d'erreur en collectant les données de trafic a répétition dans les mêmes endroits et en comparant les résultats pour trouver la moyenne.

To prevent or alleviate common biases in traffic data collection, utilize Mutiple methods, standardized protocols, and blinded data collection techniques. Regular calibration and validation of instruments, along with gathering data from multiple sources, help reduce measurement errors and confirmation biases. Subjecting methodologies and findings to peer review enhances credibility, while maintaining transparency in processes and documenting potential biases aids interpretation. These strategies collectively improve the reliability and accuracy of traffic data, enabling more informed decision-making in transportation planning and management.

The Traffic Data Quality Assessment ( #TDQA ) project by #FHWA and University of Maryland exemplifies successful bias mitigation, employing indicators, audits, and stakeholder collaboration. Amid the COVID-19 pandemic, challenges in traffic data analysis emerged, tackled through alternative data sources and advanced analytics. Future opportunities lie in technologies like #AI and #blockchain. #UAlbany 's #CTG focuses on enhancing e-government and public data management. Collaborative projects like the Connected Vehicle Technology Challenge with Cisco and the Traffic Incident Prediction and Management System with #IBM leverage AI and big data for improved traffic data collection and analysis, leading to enhanced safety and efficiency.

All I can say is, the text is covered the important considerations during traffic data collection. Additionally, it is also a good idea to take a consideration of time season of the data collection as this significantly affects the reliability of the data to be collected. Otherwise it is good reference! Thank you!

Les meilleures conditions pour le trafic routier changent d'un pays à un autre. Il serait judicieux d'adapter la méthode et les outils de collecte des données. Toutefois, associer la police aux enquêtes, constitue l'un des meilleurs indicateurs

We can learn how Google collects traffic data for Google Maps through various methods. One primary approach is by aggregating anonymized location data from mobile devices with Google Maps installed. This data includes real-time GPS signals from users' smartphones, which allows Google to determine the speed at which users are traveling along roads. Additionally, Google utilizes data from its Street View vehicles equipped with GPS and camera systems, which capture images and gather information about traffic conditions. Users also contribute to traffic data by reporting incidents, such as accidents or road closures, directly within the Google Maps app.