mlsanityData to 7 Oct 2026

Mobile Sensing for Multipurpose Applications in Transportation

arXiv 2106.10733 ↗First posted 20 Jun 2021cs.CVcited by 12

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Abstract (arXiv)

Routine and consistent data collection is required to address contemporary transportation issues.The cost of data collection increases significantly when sophisticated machines are used to collect data. Due to this constraint, State Departments of Transportation struggles to collect consistent data for analyzing and resolving transportation problems in a timely manner. Recent advancements in the sensors integrated into smartphones have resulted in a more affordable method of data collection.The primary objective of this study is to develop and implement a smartphone application for data collection.The currently designed app consists of three major modules: a frontend graphical user interface (GUI), a sensor module, and a backend module. While the frontend user interface enables interaction with the app, the sensor modules collect relevant data such as video and accelerometer readings while the app is in use. The backend, on the other hand, is made up of firebase storage, which is used to store the gathered data.In comparison to other developed apps for collecting pavement information, this current app is not overly reliant on the internet enabling the app to be used in areas of restricted internet access.The developed application was evaluated by collecting data on the i70W highway connecting Columbia, Missouri, and Kansas City, Missouri.The data was analyzed for a variety of purposes, including calculating the International Roughness Index (IRI), identifying pavement distresses, and understanding driver's behaviour and environment .The results of the application indicate that the data collected by the app is of high quality.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 2 of its 22 references that are in the map. First posted on arXiv 20 Jun 2021.

Builds on

2 of 22 references.

References · 2

  1. 2020Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysiscited by 6
  2. 2020Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNetscited by 2

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 4; 2023: 5.

12 papers.

  1. 2023Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8cited by 13
  2. 2022A Region-Based Deep Learning Approach to Automated Retail Checkoutcited by 9
  3. 2022Driver Maneuver Detection and Analysis using Time Series Segmentation and Classificationcited by 8
  4. 2022Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Reviewcited by 7
  5. 2023DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videoscited by 6
  6. 2021Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particlescited by 4
  7. 2023AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environmentcited by 3
  8. 2021Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseasescited by 2
  9. 2021Classification of Anuran Frog Species Using Machine Learningcited by 1
  10. 2022Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Networkcited by 1
  11. 2023Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learningcited by 1
  12. 2023SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videoscited by 0