A Prediction Precision Inference Method for Passenger Alighting Station Based on the Condition Hypothesis

  • Fan Li
  • Qingquan Li
  • Zhao Huang
  • Jizhe XiaEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 617)


Smart IC-card has been widely used in fare payment systems of public transport, which produces a large number of ticket checking records and spatiotemporal trajectory information. Accurately predicting passengers’ travel stations based on IC-card data plays an important role in intelligent transportation. However, incomplete IC-Card transaction records are widely existing. The IC-card not only does not record the actual boarding stations but also lacks the information of alighting stations because passengers do not need to swipe card when they get off. Therefore, it is difficult to construct the actual passenger travel link, which makes it challenging to predict alighting stations accurately. Targeting on this challenge, we propose a “Boarding Cluster to Alighting Station” alighting station prediction model (BCTAS) by condition hypothesis. First, the model analyzes the travel characteristics of passengers’ public transport. Second, the smart IC-card transaction records and map-matching algorithm are used to construct the mixed boarding station link. Third, the model performs the station clustering and cluster expansion to merge the same name station and the nearest station into a cluster, and further constructs the mixed boarding cluster link. Fourth, a Variable Order Markov Model that named Prediction by Partial Match (PPM) is adopted to predict the mixed boarding cluster link and then predict the boarding station. Fifth, the model infers the prediction precision of the alighting cluster and alighting station based on the condition hypothesis. Finally, our approach was evaluated by using the public transport data obtained in Shenzhen city, China. The results show that (a) with the increase of training data, the precision of the model is gradually enhanced, (b) by using the mixed boarding cluster link, the prediction precision of the boarding cluster and boarding station could reach 88.05% and 84.52% respectively, (c) Based on the condition hypothesis, it can be inferred that the lower limit of the prediction precision of the alighting cluster and alighting station is 78.09% and 74.96%, respectively.


Alighting station prediction Smart IC-card transaction records Station clustering and cluster expansion Variable order Markov model Prediction by partial match model (PPM) Condition hypothesis 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shenzhen Key Laboratory of Spatial Smart Sensing and ServicesShenzhen UniversityShenzhenChina
  2. 2.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  3. 3.College of Information EngineeringShenzhen UniversityShenzhenChina

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