scholarly journals Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation

2019 ◽  
Vol 34 (6) ◽  
pp. 1210-1234 ◽  
Author(s):  
Shuhui Gong ◽  
John Cartlidge ◽  
Ruibin Bai ◽  
Yang Yue ◽  
Qingquan Li ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Activity Patterns ◽  
Bayesian Probability ◽  
Trajectory Data ◽  
Temporal Clustering ◽  
Spatio Temporal ◽  
Taxi Trajectory

scholarly journals Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data

2020 ◽  
Vol 13 (1) ◽  
pp. 112
Author(s):  
Helai Huang ◽  
Jialing Wu ◽  
Fang Liu ◽  
Yiwei Wang
Keyword(s):  
Waiting Time ◽  
Urban Areas ◽  
Transportation Systems ◽  
Trajectory Data ◽  
Peak Period ◽  
Important Indicator ◽  
Destination Attractiveness ◽  
Urban Accessibility ◽  
Spatio Temporal ◽  
Taxi Trajectory

Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

scholarly journals Density Estimation of Spatio-temporal Point Patterns Using Moran's Statistic

2018 ◽  
Vol 7 (2) ◽  
pp. 80
Author(s):  
Jennifer L. Lorio ◽  
Norou Diawara ◽  
Lance A. Waller
Keyword(s):  
Infectious Disease ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Poisson Process ◽  
Common Factor ◽  
Disease Spread ◽  
Point Patterns ◽  
Rate Of Increase ◽  
Spatio Temporal ◽  
Over Time

Moran's Index is a statistic that measures spatial autocorrelation, quantifying the degree of dispersion (or spread) of objects in space. When investigating data in an area, a single Moran statistic may not give a sufficient summary of the autocorrelation spread. However, by partitioning the area and taking the Moran statistic of each subarea, we discover patterns of the local neighbors not otherwise apparent. In this paper, we consider the model of the spread of an infectious disease, incorporate time factor, and simulate a multilevel Poisson process where the dependence among the levels is captured by the rate of increase of the disease spread over time, steered by a common factor in the scale. The main consequence of our results is that our Moran statistic is calculated from an explicit algorithm in a Monte Carlo simulation setting. Results are compared to Geary's statistic and estimates of parameters under Poisson process are given.

scholarly journals VISUALIZATION OF SPATIO-TEMPORAL RELATIONS IN MOVEMENT EVENT USING MULTI-VIEW

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1469-1476
Author(s):  
K. Zheng ◽  
D. Gu ◽  
F. Fang ◽  
Y. Wang ◽  
H. Liu ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Multiple Scales ◽  
Temporal Relations ◽  
Discovery Process ◽  
Trajectory Data ◽  
Statistical Tools ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Visual Interface ◽  
Taxi Trajectory

Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.

scholarly journals SPATIO-TEMPORAL CHARACTERISTICS OF RESIDENT TRIP BASED ON POI AND OD DATA OF FLOAT CAR IN BEIJING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 99-105
Author(s):  
N. Mou ◽  
J. Li ◽  
L. Zhang ◽  
W. Liu ◽  
Y. Xu
Keyword(s):  
Research Unit ◽  
Concentric Circle ◽  
Relative Dispersion ◽  
Residential Areas ◽  
Trajectory Data ◽  
Periodic Patterns ◽  
Temporal Characteristics ◽  
The Road ◽  
Spatio Temporal ◽  
Taxi Trajectory

Due to the influence of the urban inherent regional functional distribution, the daily activities of the residents presented some spatio-temporal patterns (periodic patterns, gathering patterns, etc.). In order to further understand the spatial and temporal characteristics of urban residents, this paper research takes the taxi trajectory data of Beijing as a sample data and studies the spatio-temporal characteristics of the residents' activities on the weekdays. At first, according to the characteristics of the taxi trajectory data distributed along the road network, it takes the Voronoi generated by the road nodes as the research unit. This paper proposes a hybrid clustering method – based on grid density, which is used to cluster the OD (origin and destination) data of taxi at different times. Then，combining with the POI data of Beijing, this research calculated the density of the POI data in the clustering results, and analyzed the relationship between the activities of residents in different periods and the functional types of the region. The final results showed that the residents were mainly commuting on weekdays. And it found that the distribution of travel density showed a concentric circle of the characteristics, focusing on residential areas and work areas. The results of cluster analysis and POI analysis showed that the residents' travel had experienced the process of "spatial relative dispersion – spatial aggregation – spatial relative dispersion" in one day.

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