GPS-Based Trip Phase and Waiting Time Detection to and from Public Transport Stops Via Machine Learning Models

Aug 5, 2026

Recognizing passengers’ trip phases enables urban planners to better understand journey structure, but existing trip phase recognition methods often depend on GIS, surveys, or manual station observation. This paper proposes a machine-learning approach using raw GPS trajectories to determine access/egress time and distance and waiting time at bus stops. A random forest model is trained on two datasets (Geolife and Sussex-Huawei Locomotion) for transportation mode detection, followed by a dedicated algorithm for phase timing. Tests on Roma trips report 80.68% and 91.61% for access time/distance, 72.63% and 70.68% for egress time/distance, and 82.01% accuracy for waiting time.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Abstract

1. Introduction

2. Literature Review

2.1. Transport Mode Detection

25th Euro Working Group on Transportation Meeting (EWGT 2023) GPS-Based Trip Phase and Waiting Time Detection to and from Public Transport Stops Via Machine Learning Models Seyed Hassan Hosseinia*, Siavash Pourkhosroa, Guido Gentilea, Lory Michelle Bresciani Miristicea aSapienza University of Rome, Via Eudossiana, 18, 00184 Rome, Italy Abstract Recognizing passengers’ trip phases can provide valuable insights for urban planners in making decisions regarding urban planning, as it involves identifying the various stages of a passenger's journey. There is a significant research gap in current trip phase recognition research. Most existing works rely on traditional techniques such as GIS, surveys, and direct observation at stations to handle the task. The goal of this paper is to present a novel approach to determine the time and distance of access and egress trip phases and waiting time at bus stations, using a machine learning algorithm based on raw GPS trajectories. Specifically, we train a random forest model using two large datasets, Geolife GPS trajectory, and Sussex-Huawei Locomotion dataset, to detect the various transportation modes. Furthermore, a new algorithm is developed for detecting access time/distance, egress time/distance, and waiting time. Our approach is the first investigation in trip phase recognition that combines two large datasets and a machine learning model for trip phase detection. Our study yields the following accuracies on test trips saved in Roma: access time and distance predicted with 80.68% and 91.61%, while egress time and distance arrived at 72.63% and 70.68% accuracy. Passenger waiting time predicted from raw GPS data as a new feature with 82.01% accuracy at the bus station. These results underscore the effectiveness of our approach in predicting different phases. Keywords: Trip Phase Recognition; Public Transportation; Machine Learning; GPS trajectories, Access/Egress Phase 1. Introduction Passenger Travel patterns show significant variations in time and location due to urban expansion and functional division. Individuals in large cities often use a variety of modes for daily travel, and understanding urban trip phasesin public transport based trips can be essential to enhance urban planning. Such understanding enables us to identify * Corresponding author. Tel.: +39-3515765995 E-mail address: [seyedhassan.hosseini@uniroma1.it](seyedhassan.hosseini@uniroma1.it) areas of concern and devise practical solutions to address them. Mobile phones, as a capturing tool to record passenger movements via GPS and inertial sensors, offer a viable option for recognizing the behavioral patterns of individuals within urban areas. However, traditional methods like surveys and telephone and email interviews can be costly and time-consuming. Automating trip phase recognition is the key objective of this study. Passenger data collection and filtering are among the first steps. Our ultimate goal is to extract features from GPS points, train a machine learning model, and finally detect distinct journey stages. 2. Literature Review This section involves a detailed examination of two primary stages: the initial step involves determining the mode of transportation, whereas the second step explains the recognition of the passenger’s trip phase. 2.1. Transport Mode Detection The first step in trip phase identification is transport mode recognition for each chuck of GPS data and each study tries to follow a specific approach and use different sensor data. To categorize mobile sensor data in this study Feng et al. (2016) several techniques such as Naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, and support vector machine were applied with different final accuracies to categorize data into several modes of transport. Moreover, GPS data as a primary source of passenger data was used by Stenneth and Wolfson (2011) to identify various modes of transit. In this study

FAQs

What is the main goal of the proposed approach in the paper?

To detect access and egress trip phases and predict waiting time at bus stations using machine learning models trained on raw GPS trajectories.

Which machine learning model and datasets are used for trip-related detection?

A random forest model is trained using two large GPS trajectory datasets: Geolife and the Sussex-Huawei Locomotion dataset.

How accurate is the method on test trips saved in Roma?

Access time and distance achieve 80.68% and 91.61% accuracy, egress time and distance achieve 72.63% and 70.68%, and waiting time prediction reaches 82.01% accuracy.

Aug 5, 2026

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