• Journal of Geo-information Science
  • Vol. 22, Issue 6, 1268 (2020)
Xiaolin ZHENG, Qiliang LIU*, Wenkai LIU, and Zhihui WU
Author Affiliations
  • Department of Geo-informatics, Central South University, Changsha 410083, China
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    DOI: 10.12082.dqxxkx.2020.190312 Cite this Article
    Xiaolin ZHENG, Qiliang LIU, Wenkai LIU, Zhihui WU. Discovery of Urban Human Mobility Patterns from Smart Card Transactions and Taxi GPS Trajectories: A Comparative Study[J]. Journal of Geo-information Science, 2020, 22(6): 1268 Copy Citation Text show less
    Hourly variation of origin and destination points extracted from smart card transactions and taxi GPS trajectories
    Fig. 1. Hourly variation of origin and destination points extracted from smart card transactions and taxi GPS trajectories
    The correlation between origin and destination time series oftwo traffic flowson weekdays and weekends
    Fig. 2. The correlation between origin and destination time series oftwo traffic flowson weekdays and weekends
    Spatial distributions of average daily trips extracted from smart card transactions and taxi GPS trajectories
    Fig. 3. Spatial distributions of average daily trips extracted from smart card transactions and taxi GPS trajectories
    The hot and cold spots of average daily trips extracted from smart card transactions and taxi GPS trajectories
    Fig. 4. The hot and cold spots of average daily trips extracted from smart card transactions and taxi GPS trajectories
    Histograms of log odds ratio based on each combination of trip category (outgoing vs. incoming) and type of day (weekdays vs. weekends)
    Fig. 5. Histograms of log odds ratio based on each combination of trip category (outgoing vs. incoming) and type of day (weekdays vs. weekends)
    Spatial patterns of log odds ratio based on each combination of trip category (outgoing vs. incoming) and type of day (weekdays vs. weekends)
    Fig. 6. Spatial patterns of log odds ratio based on each combination of trip category (outgoing vs. incoming) and type of day (weekdays vs. weekends)
    Spatial distributions of the regression coefficients calculated for three independent variables
    Fig. 7. Spatial distributions of the regression coefficients calculated for three independent variables
    Average travel distances of the trips extracted from smart card transactions and taxi GPS trajectories
    Fig. 8. Average travel distances of the trips extracted from smart card transactions and taxi GPS trajectories
    Histogram of the average travel distance extracted from the two traffic flows
    Fig. 9. Histogram of the average travel distance extracted from the two traffic flows
    Distance decay of trips extracted from smart card transactions and taxi GPS trajectories on weekdays and weekends
    Fig. 10. Distance decay of trips extracted from smart card transactions and taxi GPS trajectories on weekdays and weekends
    Spatial communities discovered from smart card and taxi data on weekdays and weekends
    Fig. 11. Spatial communities discovered from smart card and taxi data on weekdays and weekends
    相关性交通分析小区数量/个
    工作日行程起点周末行程起点工作日行程终点周末行程终点
    -1.0~ -0.80021
    -0.8~ -0.6932812
    -0.6~ -0.430138963
    -0.4~ -0.28065170171
    -0.2~0.0354409480481
    0.0~0.2246248264243
    0.2~0.4324293231224
    0.4~0.6289266138162
    0.6~0.81091364676
    0.8~1.01018318
    Table 1. Classification of traffic analysis zones based on the temporal correlation between two kinds of traffic flow
    Xiaolin ZHENG, Qiliang LIU, Wenkai LIU, Zhihui WU. Discovery of Urban Human Mobility Patterns from Smart Card Transactions and Taxi GPS Trajectories: A Comparative Study[J]. Journal of Geo-information Science, 2020, 22(6): 1268
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