Abstract
Understanding passenger behaviors is of great importance in intelligent transportation and infrastructure planning. However, the passenger trajectories are actually complex temporal data, which consist of rich spatial and temporal information. What’s more, the observed passenger trajectories may be a mixture of different types of passengers with various travelling purposes. These difficulties make the prediction of passenger behaviors a challenging work. To address these problems, this paper improves the existing passenger behavior prediction methods from the following two aspects: 1) Encoding the travelling sequence with personalized semantic sensing, and 2) constructing multi-pattern prediction models to capture multiple travelling purposes and dynamics. Along this line, this paper provides a novel passenger behavior prediction model, namely, Semantic and multi-Pattern Long Short-Term Memory (SP-LSTM) model. Particularly, 1) a translation unit is designed, which is able to encode an observed travelling sequence into a structured sequence with consideration of individual travelling semantics; 2) a multi-pattern learning schematic is proposed, which first identifies the travelling patterns of passengers and then handles different patterns with different learning modules; 3) a unified learning framework is provided to integrate the semantic sensing module and multi-pattern learning module together, and present the final prediction results. To evaluate the proposed method, this paper conducts experiments on real-world passenger travelling data. Results demonstrate the superiority of SP-LSTM over both classical and the state-of-the-art methods.
Highlights
Public transportation plays an important role in daily life of residents, especially in metropolises such as New York, USA and Beijing, China
To fill these research gaps, we propose a novel passenger behavior prediction method named Semantic and multi-Pattern Long Short-Term Memory (SP-LSTM) model
We presents the passenger behavior prediction method proposed in this paper, namely Semantic and multi-Pattern Long Short-Term Memory (SP-LSTM) model
Summary
Public transportation plays an important role in daily life of residents, especially in metropolises such as New York, USA and Beijing, China. With the development of deep learning in recent years, Recurrent Neural Network (RNN) [6] was proposed for modeling sequences by recursive unit, and its variants have become popular state-of-the-art methods on this field. To fill these research gaps, we propose a novel passenger behavior prediction method named Semantic and multi-Pattern Long Short-Term Memory (SP-LSTM) model. This model contains two modules called Semantic Sensing module (SS-LSTM) and multi-Pattern Learning module (PL-LSTM) respectively. The main contributions of the paper can be summarized as follows: 1) A novel semantic sensing module is proposed for LSTM-based sequence prediction, which improves the prediction performance by uncovering semantics hidden in the observed sequences.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
