Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

Abstract

Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists’ route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In this paper, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists’ preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

Problems with quantitative categorization: An argument for qualitative approaches

“…Pritchard (2018) provides a detailed account of the methods available for surveilling bicycle route and looks at the ways in which understanding route preference might be determined, finding eventually that the best method to determine these was to conduct video interviews with cyclists. Magnana, Rivano, and Chiabaut, (2022) look at methods for clustering known cycling GPS traces, using a DBSCAN and LSTM approach. These clusters were used to build routes that better resembled the ways that cyclists choose to navigate, instead of the commonplace shortest path methods.…”

A Systematic Literature Review on Machine Learning in Shared Mobility

“…This enriched data proves especially valuable for studies on station locations, intermodal usage, and trip purposes. Moreover, comprehensive trip details, including trajectories combined with map data [274,275] or infrastructure points-of-interest [269], have been utilized for route choice and station location research. Beyond OD and trajectory data, one study employed a questionnaire to ascertain the trip purpose of customers [278].…”

Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

“…Cluster analysis, as the main research method of data mining, has become a focus of researchers' attention. Magnana et al [11] analyzed the path selection rules based on travel characteristics and used density clustering algorithm to analyze GPS data, obtaining the original candidate path set. Gao et al [12] used the traveler's job type as a basis, analyzed trajectory data and combined with hierarchical clustering and random forest algorithm to classify and predict travel purpose.…”