ActivityNET: Neural networks to predict public transport trip purposes from individual smart card data and POIs

Abstract

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility. Here, we propose a framework, ActivityNET, using Machine Learning (ML) algorithms to predict passengers' trip purpose from Smart Card (SC) data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals' daily travel patterns from smart card data and combining them with POIs using the proposed "activity-POIs consolidation algorithm". Phase II feeds the extracted features into an Artificial Neural Network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile

“…Subsequently, using a random forest model, the authors estimate the purpose of the trips within four categories: trips to work, business, leisure, and return home. Finally, Sari Aslam et al (2021) estimate the trip purposes for the city of London using data from smart cards and information on points of interest. Through a neural network model, the authors classify trip purposes into two categories, namely, primary (work and home) and secondary (leisure, food, shopping, recreational, and travel).…”

Differences in Urban Development in China from the Perspective of Point of Interest Spatial Co-Occurrence Patterns

“…POIs serve as a proxy for sensing fine-granular urban functions [16][17][18]. POIs provide a clear representation of the types of urban activities and the specific locations where these activities occur [10].…”

Conflating point of interest (POI) data: A systematic review of matching methods

“…Based on POI locations and human mobility data, researchers conducted studies to infer travel purposes (Y. Liu et al, 2015;Gong et al, 2016;Sari Aslam et al, 2021) and estimate housing values (Fu et al, 2014;Kang et al, 2021). Based on the time when people visit POIs, researchers studied temporal dynamics of cities and regional variability across different urban areas (McKenzie, Janowicz, Gao, & Gong, 2015;McKenzie, Janowicz, Gao, Yang, et al, 2015;Sparks et al, 2020).…”