Exploring Urban Mobility from Taxi Trajectories: A Case Study of Nanjing, China

Yuan, Yihong; Noc, Maël Le

doi:10.5220/0006891203020307

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Therefore, data from GPS systems provide a better understanding of the population movement behavior over time and space [17]. Yuan and Le Noc [18] examined urban movement using GPS data. The distance and the direction of travel are determined by the pick-ups and the drop-offs.…”

Section: Introductionmentioning

confidence: 99%

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

et al. 2021

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During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

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Section: Introductionmentioning

confidence: 99%

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

et al. 2021

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Discovering Intra-Urban Population Movement Pattern Using Taxis’ Origin and Destination Data and Modeling the Parameters Affecting Population Distribution

et al. 2021

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GPS-equipped vehicles are an effective approach for acquiring urban population movement patterns. Attempts have been made in the present study in order to identify the population displacement pattern of the study region using taxis’ origin and destination data, and then model the parameters affecting the population displacement pattern and provide an ultimate model in order to predict pick-up and drop-off locations. In this way, the passenger pick-up and drop-off locations have been identified in order to obtain the population movement pattern. In this study, Moran’s I index was used to measure the spatial autocorrelation, and hot spot analysis was used to analyze spatial patterns of pick-up and drop-off locations. Effective parameters modeling was performed using the Poisson regression. The results of the spatiotemporal distribution map for pick-up and drop-off locations indicated a similarity in patterns and equal results for some locations. Results also indicated different features of spatial distribution during different hours of the day. Spatial autocorrelation analysis results indicated a low probability of randomness in the general spatial distribution of the locations. The result of modeling the parameters shows the positive effect of the parameters on the pattern of population movement, and according to the p-value of 0.000, Poisson regression is significant for the pick-up and drop-off locations. The modeling results also highlighted the importance of movement patterns in recognizing urban hot spots, which is valuable for policymakers and urban planners.

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Exploring Urban Mobility from Taxi Trajectories: A Case Study of Nanjing, China

Cited by 2 publications

References 23 publications

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

Discovering Intra-Urban Population Movement Pattern Using Taxis’ Origin and Destination Data and Modeling the Parameters Affecting Population Distribution

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