Abstract
Human-flow pattern can reflect the urban population mobility and the urban operating state. Understanding the trajectory of urban-population moving patterns can improve the effectiveness of urban-management measures. While most of the existing studies on human moving have placed a huge emphasis on location forecasting through the types of activities humans take part in and urban land-use types, this type of forecasting research is limited to relying on specific activity types and land-use types. The urban-population moving pattern has spatial and temporal characteristics, and this feature greatly affects the prediction of where humans will visit. This study aimed to predict the possible places to visit by using the spatiotemporal model. We analyzed the itinerary characteristics of urban taxis and proposed a model based on the taxi itinerary characteristics to predict the drop-off locations. This model can be used to predict the possible arrival locations of urban taxis. We selected three grids of travel data from each period in another day to test the prediction accuracy of the proposed model. The results show that the model can predict the destination of urban taxis to a certain degree.
Highlights
The widespread deployment of sensors provides a way for the collection of big data, and massive amounts of big data provide basic data resources for the mining of deep information
(2) K-means clustering and kernel density are applied to analyze the temporal and spatial distribution characteristics of taxi data, and the study area is divided into hexagonal grid cells of suitable size with hexagonal grid according to the analysis results
We assumed that any two points between two adjacent polygons can be reached directly, and we found that, when a regular hexagon has a side length of 300 m, the maximum distance between any two points inside two adjacent polygons is 1960 m, which is basically in line with the minimum travel distance we chose to take a taxi (>1500 m)
Summary
The widespread deployment of sensors provides a way for the collection of big data, and massive amounts of big data provide basic data resources for the mining of deep information. Useful information mined from human-flow datasets covering all aspects of the city provides researchers with key information and decision support for scientifically planning urban functional areas, rationally dispatching urban resources, and effectively responding to emergencies. The acquisition of massive amounts of human movement data enables city managers to analyze historical activity information and use historical information to predict the direction of future urban activities, so as to rationally allocate urban resources and promote efficient urban operations. Applying different research models to a variety of activity trajectory datasets can dig out a variety of potential information about urban operations and provide scientific support from multiple perspectives for promoting urban development.
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