# Mobile Sensing for Multipurpose Applications in Transportation

arXiv: https://arxiv.org/abs/2106.10733 · HTML: https://mlsanity.com/paper/2106.10733/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis](https://mlsanity.com/paper/2010.10681/) (2020)
- [Driver Behavior Extraction from Videos in Naturalistic Driving Datasets with 3D ConvNets](https://mlsanity.com/paper/2011.14922/) (2020)

## Built on by (most cited first, 12 of 12)

- [Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8](https://mlsanity.com/paper/2304.08256/) (2023)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos](https://mlsanity.com/paper/2304.08261/) (2023)
- [Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particles](https://mlsanity.com/paper/2111.09496/) (2021)
- [AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment](https://mlsanity.com/paper/2301.09315/) (2023)
- [Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases](https://mlsanity.com/paper/2112.01503/) (2021)
- [Classification of Anuran Frog Species Using Machine Learning](https://mlsanity.com/paper/2112.05148/) (2021)
- [Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network](https://mlsanity.com/paper/2205.10507/) (2022)
- [Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning](https://mlsanity.com/paper/2304.09246/) (2023)
- [SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos](https://mlsanity.com/paper/2304.09247/) (2023)

## 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.