Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression

Li, Bozhao; Cai, Zhongliang; Jiang, Lili; Su, Shiliang; Huang, Xue-Lin

doi:10.1016/j.cities.2018.12.033

Cited by 56 publications

(50 citation statements)

References 48 publications

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“…As a matter of fact, the big data by employing GPS tracking data of taxi has drawn more researchers to examine the pattern and influencing factors of taxi ridership [34][35][36][37][38]. But only a few studies have paid attention to the effects of taxi as feeder model for metros.…”

Section: Metro Access With Cycling and Taxismentioning

confidence: 99%

“…spatial heterogeneity or spatial autocorrelation. Geographically weighted regression (GWR) approach is advanced in capturing spatial heterogeneity of ridership and used in many studies [23,31,35,46]. But only a few studies considered the impacts of spatial autocorrelation on the relationship of the built environment and SBBS ridership, for example, the generalized additive mixed model (GAMM) proposed by Sun et al [47] and the spatial lag model (SLM) used by Zhang et al [48].…”

Section: Effects Of the Built Environment On Bike Sharing And Taxismentioning

confidence: 99%

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Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Chen

2020

Sustainability

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The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

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Section: Metro Access With Cycling and Taxismentioning

confidence: 99%

Section: Effects Of the Built Environment On Bike Sharing And Taxismentioning

confidence: 99%

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Chen

2020

Sustainability

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“…The hierarchical-clustering algorithm [36] is one of the most frequently used methods in unsupervised learning, providing a view of the data at different granularity levels, making it ideal for visualizing and explaining the characteristics of underlying data distribution. The key feature of the traditional method for hierarchical clustering is clustering several variables step by step, and the result of the algorithm is a pyramid structure [37]. According to the direction of the clustering process, hierarchical methods are divided into agglomerative and divisive [38].…”

Section: Hierarchical-clustering Methodsmentioning

confidence: 99%

Spatio-Temporal Change Characteristics of Spatial-Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China

Yang

Qiao

et al. 2019

IJGI

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Spatial-interaction networks are an important factor in geography that could help in the exploration of both human spatial-temporal behavior and the structure of urban areas. This paper analyzes changes in the spatio-temporal characteristics of the Spatial-Interaction Networks of Beijing (SINB) in three consecutive steps. To begin with, we constructed 24 sequential snapshots of spatial population interactions on the basis of points of interest (POIs) collected from Dianping.com and various taxi GPS data in Beijing. Then, we used Jensen–Shannon distance and hierarchical clustering to integrate the 24 sequential network snapshots into four clusters. Finally, we improved the weighted k-core decomposition method by combining the complex network method and weighted distance in a geographic space. The results showed: (1) There are three layers in the SINB: a core layer, a bridge layer, and a periphery layer. The number of places greatly varies, and the SINB show an obvious hierarchical structure at different periods. The core layer contains fewer places that are between the Second and Fifth Ring Road in Beijing. Moreover, spatial distribution of places in the bridge layer is always in the same location as that of the core layer, and the quantity in the bridge layer is always superior to that in the core layer. The distributions of places in the periphery layer, however, are much greater and wider than the other two layers. (2) The SINB connected compactly over time, bearing much resemblance to a small-world network. (3) Two patterns of connection, each with different connecting ratios between layers, appear on weekdays and weekends, respectively. Our research plays a vital role in understanding urban spatial heterogeneity, and helps to support decisions in urban planning and traffic management.

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“…As Table 5 shows, most of the approaches for taxi demand prediction have made use of several temporal (e.g., time of the day, hour, month, and the like), spatial (e.g., land use data), and meteorological (e.g., temperature) features of the urban environment. This is because such factors have proven to have a meaningful impact on the behavior of the taxi users [2].…”

Section: Related Workmentioning

confidence: 99%

“…Unlike other ridesharing services like Uber (), where users hire a ride in advance via Internet applications, taxicabs are usually requested by pedestrians in a more spontaneous manner, which makes taxi behavior much more unpredictable. Several solutions have been proposed from many different disciplines so as to improve the quality of service and the efficiency of urban taxi rides [2,3,4]. In that sense, a foremost course of action within the mobility data mining field has focused on predicting the taxi demand in different areas within a city [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

Terroso-Sáenz

Muñoz

Cecilia

2019

Sensors

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Road traffic pollution is one of the key factors affecting urban air quality. There is a consensus in the community that the efficient use of public transport is the most effective solution. In that sense, much effort has been made in the data mining discipline to come up with solutions able to anticipate taxi demands in a city. This helps to optimize the trips made by such an important urban means of transport. However, most of the existing solutions in the literature define the taxi demand prediction as a regression problem based on historical taxi records. This causes serious limitations with respect to the required data to operate and the interpretability of the prediction outcome. In this paper, we introduce QUADRIVEN (QUalitative tAxi Demand pRediction based on tIme-Variant onlinE social Network data analysis), a novel approach to deal with the taxi demand prediction problem based on human-generated data widely available on online social networks. The result of the prediction is defined on the basis of categorical labels that allow obtaining a semantically-enriched output. Finally, this proposal was tested with different models in a large urban area, showing quite promising results with an F1 score above 0.8.

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Exploring urban taxi ridership and local associated factors using GPS data and geographically weighted regression

Cited by 56 publications

References 48 publications

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Spatio-Temporal Change Characteristics of Spatial-Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China

QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data Analysis

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