# Inferring transportation modes from GPS trajectories using a convolutional neural network

arXiv: https://arxiv.org/abs/1804.02386 · HTML: https://mlsanity.com/paper/1804.02386/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 26 of 26)

- [UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces](https://mlsanity.com/paper/2411.03859/) (2024)
- [Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification](https://mlsanity.com/paper/2308.14250/) (2023)
- [An Unsupervised Learning Method with Convolutional Auto-Encoder for Vessel Trajectory Similarity Computation](https://mlsanity.com/paper/2101.03169/) (2021)
- [FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder](https://mlsanity.com/paper/2407.09239/) (2024)
- [The Devil Is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection](https://mlsanity.com/paper/2109.09504/) (2021)
- [Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models](https://mlsanity.com/paper/2306.00016/) (2023)
- [Feature Pyramid biLSTM: Using Smartphone Sensors for Transportation Mode Detection](https://mlsanity.com/paper/2310.11087/) (2023)
- [Gaussian Process Latent Class Choice Models](https://mlsanity.com/paper/2101.12252/) (2021)
- [TraClets: Harnessing the power of computer vision for trajectory classification](https://mlsanity.com/paper/2205.13880/) (2022)
- [Classifying Spatial Trajectories](https://mlsanity.com/paper/2209.01322/) (2022)
- [A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data](https://mlsanity.com/paper/2209.08500/) (2022)
- [Fast discovery of multidimensional subsequences for robust trajectory classification](https://mlsanity.com/paper/2102.04781/) (2021)
- [Outdoor Position Recovery from HeterogeneousTelco Cellular Data](https://mlsanity.com/paper/2108.10613/) (2021)
- [Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories](https://mlsanity.com/paper/2110.12113/) (2021)
- ["Is not the truth the truth?": Analyzing the Impact of User Validations for Bus In/Out Detection in Smartphone-based Surveys](https://mlsanity.com/paper/2202.11961/) (2022)
- [Large Scale Passenger Detection with Smartphone/Bus Implicit Interaction and Multisensory Unsupervised Cause-effect Learning](https://mlsanity.com/paper/2202.11962/) (2022)
- [Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology](https://mlsanity.com/paper/2303.07107/) (2023)
- [Evaluating geospatial context information for travel mode detection](https://mlsanity.com/paper/2305.19428/) (2023)
- [Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems](https://mlsanity.com/paper/2306.01282/) (2023)
- [Unify Change Point Detection and Segment Classification in a Regression Task for Transportation Mode Identification](https://mlsanity.com/paper/2312.04821/) (2023)
- [Performance Evaluation of GPS Trajectory Rasterization Methods](https://mlsanity.com/paper/2401.01676/) (2024)
- [Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network](https://mlsanity.com/paper/2404.15323/) (2024)
- [TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis](https://mlsanity.com/paper/2506.16401/) (2025)
- [Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs](https://mlsanity.com/paper/2507.05507/) (2025)
- [Detecting Transportation Mode Using Dense Smartphone GPS Trajectories and Transformer Models](https://mlsanity.com/paper/2603.00340/) (2026)
- [From Mobile Data to Business Insights: An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support](https://mlsanity.com/paper/2607.03394/) (2026)

## Abstract (arXiv)

Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human's bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN's input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.