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JIN Hai-tao, JIN Feng-jun, CHEN Zhuo, WANG Jiao-e, YANG Yu. Commute activity identification based on spatial and temporal information of transit chaining breaks[J]. Journal of Traffic and Transportation Engineering, 2018, 18(5): 176-184. doi: 10.19818/j.cnki.1671-1637.2018.05.017
Citation: JIN Hai-tao, JIN Feng-jun, CHEN Zhuo, WANG Jiao-e, YANG Yu. Commute activity identification based on spatial and temporal information of transit chaining breaks[J]. Journal of Traffic and Transportation Engineering, 2018, 18(5): 176-184. doi: 10.19818/j.cnki.1671-1637.2018.05.017
shu

Commute activity identification based on spatial and temporal information of transit chaining breaks

doi: 10.19818/j.cnki.1671-1637.2018.05.017
  • 1.

    Key Laboratory of Regional Sustainable Development Modeling,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences, Beijing 100101, China

  • 2.

    Beijing Transportation Information Center, Beijing 100161, China

  • 3.

    College of Resources and Environment,University of Chinese Academy of Sciences, Beijing 100049, China

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    Abstract
  • An approach to improve the recognition of transfers, working commutes, and nonworking commutes in smartcard data mining was introduced.The study focus was shifted from the information of transit processes to the durations and displacements between the transit chaining breaks.The probabilities of transit chaining breaks were calculated by two dimensions of the break durations and displacements, and a joint probability distribution matrix of spatial andtemporal variables for workdays and non-working days was made.The differences between the two types of distribution were compared.The stabilities of the break duration sequences and break displacement sequences were examined.The mutation points and turning points of the two curves were marked to infer the important threshold parameters for the transferring durations and displacements generated by the transfers.A moving average filter was utilized to smooth both workdays and non-working days curves of margin duration values.The relationship between the mutation and extremes of the curve was explained for the three types of commute activities relating to the transfers, working commutes, and non-working commutes.The approach was verified by a weeklong sample dataset of the Beijing bus and subway system.The threshold parameters of the common commute activities in the dataset were determined according to the time series and the displacement sequence curve.Analysis result shows that the spatial and temporal information at the breaks can provide more reasonable identification parameters for the commute activities.A tolerance distance of approximately 1.6 km between the transit connections is found among the cardholders.The threshold of transit break duration between the transferring and non-working commutes is 22-48 min.The threshold of working and non-working commutes is approximately 478 min, and the maximum probability of non-working duration is 140 min.The transit chaining break durations of working commutes fall into a normal distribution with an expected value of 601 and a standard deviation of 44.The parameters generated by the new approach lead to an improvement in commute activity recognition, the recognition rates of the transfers, working commutes and non-working commutes increse by 16.1%, 4.2%and 6.2%, respectively.So the spatial and temporal information of transit chaining breaks can not only provide the basis for the commute activity identification, but also achieve better recognition results.

     

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