Inferring land use from spatialtemporal taxi ride data

Choi, Junyong; No, Wonjun; Park, Minju; Kim, Young Chul

doi:10.1016/j.apgeog.2022.102688

Cited by 10 publications

(5 citation statements)

References 53 publications

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“…The high accuracy of CNNs underscores their suitability as a featured-extraction mechanism for land use analysis based on Twitter data. Our proposed model attains an accuracy enhancement of more than 7% when contrasted with prior models introduced by [37], [38], even in light of certain discrepancies in input parameters. While both our model and theirs employ Twitter data as a proxy for human mobility, our model integrates a novel movement behavior index.…”

Section: Discussion and Limitationsmentioning

confidence: 89%

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Optimizing Land Use Identification With Social Networks: Comparative Evaluation of Machine Learning Algorithms

Aljeri

2023

IEEE Access

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This paper presents a comprehensive comparison of various Machine Learning (ML) classifiers for urban land use identification using social media data. Two analysis cycles were conducted, with the second cycle introducing the "popularity index" parameter. The results demonstrate that incorporating the popularity index significantly improved the accuracy rates of all classifiers. The Convolutional Neural Network (CNN) consistently outperformed other classifiers with a 0.9 accuracy, but the key highlight was the popularity index parameter, which optimized urban land use identification. By providing crucial contextual information and normalizing raw tweet counts, the popularity index proved vital for leveraging social media data in urban land use classification. Our findings support the value of social media data for analyzing urban land use dynamics and highlight ML classifiers with the popularity index as a promising approach to monitor and understand ever-changing patterns. This research makes a significant contribution to data-driven urban planning by highlighting the capacity of social media data to provide real-time and detailed insights into urban activities and land use patterns. These insights, in turn, can inform and enhance strategies for sustainable urban development and resource allocation, making them more informed and effective.

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Section: Discussion and Limitationsmentioning

confidence: 89%

“…The study by [37] aims to infer land use patterns using spatial-temporal taxi ride data. The researchers utilize machine learning techniques to analyze the patterns and characteristics of taxi trips and correlate them with land use categories into the behaviors and preferences of individuals within urban areas.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Land Use Identification With Social Networks: Comparative Evaluation of Machine Learning Algorithms

Aljeri

2023

IEEE Access

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“…The K-Means method is an efficient unsupervised classification method and is widely used in related research [42,48]. In this study, the POI semantic sequences of TAZs were mapped onto a high-dimensional latent semantic feature vector space using the above spatial feature extraction model.…”

Section: Feature Clustering Analysismentioning

confidence: 99%

Spatial and Temporal Evolution of the Characteristics of Spatially Aggregated Elements in an Urban Area: A Case Study of Wuhan, China

Sun,

Kang,

Jiao

et al. 2023

IJGI

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Urban spatial elements present agglomeration and dispersion geographic processes in the urban development. Identifying the characteristics of their distribution changes and accurately capturing the evolution of the urban spatial structure is of great significance to urban construction and management. This study takes Wuhan as a case study and focuses on the spatial agglomeration distribution of urban elements. Point of Interest (POI) data from 2017 to 2021 were collected, and the Block2Vec model was employed to extract the comprehensive geographic information from various elements within the traffic analysis zones (TAZs). Subsequently, identification and division were carried out to access the level of urban spatial element agglomeration. Finally, the spatial–temporal evolution characteristics of urban aggregated elements in the Wuhan metropolitan development area over five years were compared and analyzed. The results indicate the following: (1) urban elements present an obvious circle structure in their spatial agglomeration, with distinct differences observed among different element types; (2) from 2017 to 2021, the Wuhan urban development zone experienced obvious expansion in urban space; (3) increased agglomeration of spatial elements mainly occurred in the surrounding areas of the city, while some areas in the city center displayed weaker element agglomeration and a reduction in various service facilities. The results demonstrate that the method used in this study could effectively identify the spatial agglomeration distribution of urban elements, as well as accurately distinguishing regions with distinct development characteristics. This approach could provide robust support for optimizing land use and urban spatial planning.

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“…Presently, taxis are equipped with GPS recording devices that promptly collect more precise spatiotemporal information, including passenger boarding and alighting locations, during journeys [11]. Difering from buses and rail transit, taxis operate without constraints of routes and schedules, ofering the most fexible and extensive trajectory data based on passenger preferences [18,19]. Taxi trajectory data exhibit higher accuracy and involves fewer privacy concerns compared to other modes of transportation.…”

Section: Introductionmentioning

confidence: 99%

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Chen,

Liu,

Gao

2024

Journal of Advanced Transportation

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Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

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Inferring land use from spatialtemporal taxi ride data

Cited by 10 publications

References 53 publications

Optimizing Land Use Identification With Social Networks: Comparative Evaluation of Machine Learning Algorithms

Optimizing Land Use Identification With Social Networks: Comparative Evaluation of Machine Learning Algorithms

Spatial and Temporal Evolution of the Characteristics of Spatially Aggregated Elements in an Urban Area: A Case Study of Wuhan, China

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

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