Exploring Human Activity Patterns Using Taxicab Static Points

Jia, Tao; Jiang, Bin

doi:10.3390/ijgi1010089

Cited by 21 publications

(8 citation statements)

References 20 publications

(20 reference statements)

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“…Second, only two-day pattern (an average weekday and an average weekend) was used to infer the urban land use (Toole et al, 2012). The difference between weekdays and that between weekends are neglected, despite the fact that the significant differences exist between weekdays and between weekends in terms of activities of residents (Jia and Jiang, 2012;Liu et al, 2012;Soto and Frias-Martinez, 2011a).…”

Section: Related Workmentioning

confidence: 99%

A new insight into land use classification based on aggregated mobile phone data

Pei

Sobolevsky

Ratti

et al. 2014

International Journal of Geographical Information Science

347

239

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43Land use classification is essential for urban planning. Urban land use types can be 44 differentiated either by their physical characteristics (such as reflectivity and texture) 45 or social functions. Remote sensing techniques have been recognized as a vital 46 method for urban land use classification because of their ability to capture the 47 physical characteristics of land use. Although significant progress has been achieved 48 in remote sensing methods designed for urban land use classification, most techniques 49 focus on physical characteristics, whereas knowledge of social functions is not 50 adequately used. Owing to the wide usage of mobile phones, the activities of residents, 51 which can be retrieved from the mobile phone data, can be determined in order to 52 indicate the social function of land use. This could bring about the opportunity to 53 derive land use information from mobile phone data. To verify the application of this 54 new data source to urban land use classification, we first construct a time series of 55 aggregated mobile phone data to characterize land use types. This time series is 56composed of two aspects: the hourly relative pattern, and the total call volume. A 57 semi-supervised fuzzy c-means clustering approach is then applied to infer the land 58 use types. The method is validated using mobile phone data collected in Singapore. 59Land use is determined with a detection rate of 58.03%. An analysis of the land use 60 classification results shows that the accuracy decreases as the heterogeneity of land 61 use increases, and increases as the density of cell phone towers increases. 62

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Section: Related Workmentioning

confidence: 99%

A new insight into land use classification based on aggregated mobile phone data

Pei

Sobolevsky

Ratti

et al. 2014

International Journal of Geographical Information Science

347

239

View full text Add to dashboard Cite

show abstract

“…Some studies estimated CO 2 emissions from trajectories and applied them to sustainable location planning [6] and market analysis for the retail sector [7]. A recent study explored the relationship between human activities and landscape patterns [8], and studies more relevant to our work focused on mining human activity patterns [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data

Jia

Zheng

2017

IJGI

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Abstract:Human activity hotspots are the clusters of activity locations in space and time, and a better understanding of their functionality would be useful for urban land use planning and transportation. In this article, using trajectory data, we aim to infer the functionality of human activity hotspots from their scaling pattern in a reliable way. Specifically, a large number of stopping locations are extracted from trajectory data, which are then aggregated into activity hotspots. Activity hotspots are found to display scaling patterns in terms of the sublinear scaling relationships between the number of stopping locations and the number of points of interest (POIs), which indicates economies of scale of human interactions with urban land use. Importantly, this scaling pattern remains stable over time. This finding inspires us to devise an allometric ruler to identify the activity hotspots, whose functionality could be reliably estimated using the stopping locations. Thereafter, a novel Bayesian inference model is proposed to infer their urban functionality, which examines the spatial and temporal information of stopping locations covering 75 days. Experimental results suggest that the functionality of identified activity hotspots are reliably inferred by stopping locations, such as the railway station.

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“…Our main contribution is that we extend the tools of time series analysis to the collective dynamics of road traffic rather than at individual level, by mapping the vehicle records from GPS into road usage so as to offer the predictability of traffic conditions at different locations. In contrast to the traditional way based on origin-destination analysis [25], our approach relies only on short-time historical records of traffic conditions without the need of a priori knowledge of drivers' origins and destinations and their associated navigation strategies. Our accessibility of such individual-level information is inherently limited by the diversity in population, job switching, moving and urbanization.…”

mentioning

confidence: 99%

Predictability of Road Traffic and Congestion in Urban Areas

Wang

Mao

et al. 2015

PLoS ONE

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Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any priori knowledge of drivers' origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers' behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion.

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Exploring Human Activity Patterns Using Taxicab Static Points

Cited by 21 publications

References 20 publications

A new insight into land use classification based on aggregated mobile phone data

A new insight into land use classification based on aggregated mobile phone data

Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data

Predictability of Road Traffic and Congestion in Urban Areas

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