Multimode trip information detection using personal trajectory data

Fei Yang · Zhenxing Yao · Yang Cheng · Bin Ran · Da Yong Yang

33 citations0/0 files storedJournal of Intelligent Transportation Systems

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Human Mobility and Location-Based Analysis Urban Transport and Accessibility Data Management and Algorithms

Abstract

Handheld global positioning system (GPS) devices can serve as a new tool to collect an individual's trip information with advantages of low cost, accurate data, and intensive spatial coverage. Various machine learning algorithms have been explored to detected trip train information in previous studies; however, few of them focused on the evaluation and comparison of the performance and applicability of different models. Meanwhile, according to previous studies, car and bus mode detection is a thorny issue due to their similar travel characteristics, and algorithms still need to be well explored and improved to solve this problem. In this article, an innovative method is proposed to detect trip information, including trip modes, mode-changing time and location, and other attributes, from personal trajectory data. The method is a two-step process. A machine learning algorith-based module (including artificial neural network, support vector machine, random forests, and Bayesian network) is firstly used to identify walk, bicycle, and motorized trip modes (bus or car); we thoroughly compared the performance of these four algorithms. Then a second module, using critical points on the GPS trajectories, is further developed to distinguish car and bus mode, incorporated with GIS map information. Field test results show that the proposed machine learning models can all be applied for walk, bicycle, and motorized mode detection with high detection rates exceeding 90%; however, the algorithms work relatively poorly for bus and car mode detection, with results mostly below 75%. The proposed two-step method can greatly improve bus and car mode detection accuracy by 14–30%. As a result, the average mode detection rates for all the four modes are above 90%. Compared with mode detection results by using only the machine learning algorithm, the proposed two-step method has much better performance in both accuracy and consistency.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 33 citations exist upstream.

References

2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands 2011Transportation mode detection using mobile phones and GIS information 2011A GPS/GIS method for travel mode detection in New York City 2013Transportation mode recognition using GPS and accelerometer data 2012Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification 2003Impact of Underreporting on Mileage and Travel Time Estimates: Results from Global Positioning System-Enhanced Household Travel Survey 2012Transportation mode-based segmentation and classification of movement trajectories 2006Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems

Cited by

2021Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA 2016Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation 2021An App-Assisted Travel Survey in Official Statistics: Possibilities and Challenges 2021Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.