Trip Purpose Identification of Docked Bike-Sharing From IC Card Data Using a Continuous Hidden Markov Model

Abstract

It is different from the previous supervised learning algorithm based on personal travel questionnaire, the aim of this study is to develop an unsupervised learning methodology to estimate the docked bike-sharing users' trip purposes using IC card data, which trip purposes were unknown from the dataset. The present study is able to extract the trip-chains, which is used to understand the complete individual trip process. A rigorous method is then proposed to interpret the purpose of each leg of the tripchain using a continuous hidden Markov model (CHMM). This method effectively combines the Gaussian mixture model and the hidden Markov model, and realizes the inference based on trip-chains. It is intended to enhance the understanding of docked bike-sharing users' transfer intention, which is different from most trip motivation recognition methods. The Gaussian mixture layer uses the feature space constructed by the spatial and temporal information on trip-chains from the IC card data, as well as the land-use characteristics of the docked bike-sharing docking stations to complete the transfer of the trip-chains to the trip modes. The hidden Markov structure can realize the process from the trip modes to the trip purposes. The IC card data of docked bike-sharing usage in Nanjing, China is used to interpret the specific steps of the proposed model. A questionnaire survey is conducted to obtain the real trip purposes, which is compared with the estimated results from the model to verify the effectiveness of the model.. The results show that the accuracies of single trip recognition and chain trip recognition are 0.770 and 0.756, respectively. Compared with the baseline algorithm, the model also shows good performance. Therefore, the proposed approach can be used to discover and interpret the trip purpose using the IC card data. INDEX TERMS Continuous hidden Markov model, IC card, docked bike-sharing, trip-chain, trip purpose

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

“…It is developed on the basis of the Markov chain, which is a discrete memoryless random process responsible for describing the relationship between the sequence of states of the next moment with the current one [83][84][85]. An HMM is an evolution of a Markov chain that requires two stochastic processes, adding a random relationship between the sequence of states and the observation vector, and where the sequence of states cannot be directly observed [83,84,[86][87][88][89]. Then, an HMM is a probabilistic time series model, doubly stochastic, which includes the transition of hidden states and emitting observations [90].…”