Method for calculating the encounter probability in network space
Probabilistic time geography (PTG) can be used to measure the probability of random encounters between two moving objects. Available PTG methods are based on homogeneous space, while the network space is typically heterogeneous, which constrains individuals to the network. Therefore, the PTG of the...
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Veröffentlicht in: | Transactions in GIS 2020-04, Vol.24 (2), p.402-422 |
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creator | Yin, ZhangCai Li, Sanjuan Ying, Shen Jin, ZhangHaoNan Liu, Hui Xiao, JiaQiang |
description | Probabilistic time geography (PTG) can be used to measure the probability of random encounters between two moving objects. Available PTG methods are based on homogeneous space, while the network space is typically heterogeneous, which constrains individuals to the network. Therefore, the PTG of the network must consider the geographic network. Based on this, we put forward a method for calculating the encounter probability of objects that are moving in a network by considering network constraints on movement. These constraints not only restrict the space‐time accessibility of the moving objects, but also affect the probability of them visiting locations or encountering other objects in space and time. Therefore, in network space, our method improves measures of the likelihood of encounters between moving objects, thereby enabling more accurate predictions of encounter events and their probabilities. Finally, the performance of the method is experimentally evaluated. In the experiment, campus road network data and simulated trip data are used to calculate the encounter probabilities of two moving objects and to determine when and where two moving objects meet with the highest probability. |
doi_str_mv | 10.1111/tgis.12605 |
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Available PTG methods are based on homogeneous space, while the network space is typically heterogeneous, which constrains individuals to the network. Therefore, the PTG of the network must consider the geographic network. Based on this, we put forward a method for calculating the encounter probability of objects that are moving in a network by considering network constraints on movement. These constraints not only restrict the space‐time accessibility of the moving objects, but also affect the probability of them visiting locations or encountering other objects in space and time. Therefore, in network space, our method improves measures of the likelihood of encounters between moving objects, thereby enabling more accurate predictions of encounter events and their probabilities. Finally, the performance of the method is experimentally evaluated. 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Available PTG methods are based on homogeneous space, while the network space is typically heterogeneous, which constrains individuals to the network. Therefore, the PTG of the network must consider the geographic network. Based on this, we put forward a method for calculating the encounter probability of objects that are moving in a network by considering network constraints on movement. These constraints not only restrict the space‐time accessibility of the moving objects, but also affect the probability of them visiting locations or encountering other objects in space and time. Therefore, in network space, our method improves measures of the likelihood of encounters between moving objects, thereby enabling more accurate predictions of encounter events and their probabilities. Finally, the performance of the method is experimentally evaluated. 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Available PTG methods are based on homogeneous space, while the network space is typically heterogeneous, which constrains individuals to the network. Therefore, the PTG of the network must consider the geographic network. Based on this, we put forward a method for calculating the encounter probability of objects that are moving in a network by considering network constraints on movement. These constraints not only restrict the space‐time accessibility of the moving objects, but also affect the probability of them visiting locations or encountering other objects in space and time. Therefore, in network space, our method improves measures of the likelihood of encounters between moving objects, thereby enabling more accurate predictions of encounter events and their probabilities. Finally, the performance of the method is experimentally evaluated. 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subjects | Geography Mathematical analysis Object motion Probability Probability theory Roads Statistical analysis |
title | Method for calculating the encounter probability in network space |
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