Abstract
Commuters are the stable travel group for the public transportation (PT) service system. Accurately identifying the PT commuters is conducive to promoting PT service quality and development of urban sustainable transportation. This paper extracts individual PT travel chain information and constructs individual travel knowledge graphs of PT passengers based on the association matching algorithm and the theory of multilayer planning. A mixed dataset is formed by associating individual travel chains with travel survey data. Seven travel characteristic indicators regarding travel performance and spatiotemporal travel characteristics are extracted. The identification model of PT commuters is developed based on a three-layer backpropagation neural network (BPNN). The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. The evaluation indexes of overall accuracy and kappa coefficient of the constructed model are 94.5% and 87.9% separately. The results indicate that the model identification accuracy is acceptable, and the proposed characteristic indicators and systematic modelling procedure are effective. Then, the model performance is compared with the other five machine learning models further. The results confirm that the proposed model has a better identification accuracy and viability, and the model performance will improve with the increase of the sample size.
Highlights
With the continuous penetration of the concept of sustainable transport and green traveling, especially, the Chinese government put forward the goal of “carbon peak” and “carbon neutral” in 2021, and public transportation (PT) has become an increasingly important transportation option for the residents
E model performance depends on whether the heterogeneity in the attributes accurately indicates the difference in the passenger categories. erefore, to evaluate the predicted classification accuracy and validity of the PT commuter identification model and data fusion approach proposed in this paper, we adopted the evaluation indicators of overall accuracy (OA) and kappa coefficient (Kappa) which have been successfully applied in the previous studies [43,44,45]
The model validation method is relatively simple, it is very effective and clear, and easy to compare with other model results. en, these two indicators were applied in evaluating the PT commuter identification model proposed in this paper, and the OA and Kappa are estimated by equations (10) and (11): Training dataset
Summary
With the continuous penetration of the concept of sustainable transport and green traveling, especially, the Chinese government put forward the goal of “carbon peak” and “carbon neutral” in 2021, and public transportation (PT) has become an increasingly important transportation option for the residents. PT has occupied the largest share in the urban transportation market in the Chinese context. With a better understanding of the travel patterns of transit riders, transit authorities will be able to evaluate their current services to reveal how best to adjust their marketing strategies to attract higher PT usage [2]. Erefore, it is of great significance to effectively grasp the travel demands and mobility characteristics of the PT commuters, which is conducive to improving urban sustainable transport service. For this purpose, realizing accurate identification of the PT passenger category is the premise of revealing the travel demand and characteristics of heterogeneous passengers
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