Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Yang, Hongtai; Lu, Xiaozhao; Cherry, Christopher R.; Liu, Xiaohan; Li, Yanlai

doi:10.1016/j.jtrangeo.2017.09.007

Cited by 69 publications

(22 citation statements)

References 29 publications

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“…Additionally, the corrected Akaike information criterion (AICc)/Akaike's information criterion (AIC) was frequently adopted to indicate the goodness-of-fit of a spatial regression model (9 out of 11). R 2 is a suitable goodness-of-fit index for OLS (ordinary least square) regressions, whereas log-likelihood is often used for maximum likelihood estimations [18,[43][44][45][46][47][48][49][50][51][52].…”

Section: Determinants Of Bicycle-transit Integrationmentioning

confidence: 99%

Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach

Yang

et al. 2018

Sustainability

105

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Abstract:The primary objective of this study was to explore the factors that influence metro-bikeshare ridership from a spatial perspective. First, a reproducible method of identifying metro-bikeshare transfer trips was derived using two types of smart-card data (metro and bikeshare). Next, a geographically weighted Poisson regression (GWPR) model was established to explore the relationships between metro-bikeshare transfer volume and several types of independent variables, including sociodemographic, travel-related, and built-environment variables. Moran's I statistic was applied to examine the spatial autocorrelation of each explanatory variable. The modeling and spatial visualization results show that riding distance is negatively correlated with metro-bikeshare transfer demand, and the coefficient values are generally lower at the edge of the city, especially in underdeveloped areas. Moreover, the density of bus, bikeshare, and other metro stations within 2 km of a metro station has different impacts on metro-bikeshare transfer volume. Travelers whose origin or destination is entertainment related tend to choose bikeshare as a feeder mode to metro if this trip mode is available to them. These results improve our understanding of metro-bikeshare transfer spatial patterns, and several suggestions are provided for improving the integration between metro and bikeshare.

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Section: Determinants Of Bicycle-transit Integrationmentioning

confidence: 99%

Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach

Yang

et al. 2018

Sustainability

105

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“…Many studies concentrated on the mismatch between the potential passengers and empty taxi in spatial, then put forward the route choice model [8][9][10]. In addition, some scholars combine the distribution characteristics of travel demand with the urban spatial structure to consider the factors that affect residents' taxi travel [6,11,12]. e concept of built environment was proposed by scholars to discuss the influencing factors of residents' travel [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…erefore, some scholars proposed to use geographical weighted regression (GWR) model to overcome this problem [25]. is method considers the spatial nonfixed effect of independent variables due to different research areas, and it introduces the positional features of each sample point in space and uses the distance between these sample points as an important factor in defining regression weights [12,26].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Liu

Sun

et al. 2020

Journal of Advanced Transportation

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Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.

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“…Spatial correlation exists in many transportation phenomena, and the geographically weighted regression model framework has been used in various research to explore the spatial variation in association between trip demand for various trip purposes or traveler groups, traffic events, crashes and other explanatory variables such as the built-environment attributes, social-economical attributes, population, etc. For instance, one study used a geographically weighted Poisson regression model to examine the effects of the built-environment on students' metro ridership in Nanjing [23]; one study uses geographically weighted regressions to examine the implications of location and attitudinal characteristics for travel behavior in Chengdu [24]; another research developed zonal crash prediction models within the geographically weighted generalized linear model framework in order to explore the spatial variations in association with the number of crashes causing injuries and other explanatory variables in Belgium [25]; another study utilized GWR models to identify whether there are spatially varying relationships between walking, bicycling, traffic counts and ambient built-environment attributes including socioeconomic characteristics, transit accessibility indices, land use attributes and characteristics of intersections and roadway networks [26].…”

Section: Methodsmentioning

confidence: 99%

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Yang

Ding

2019

Sustainability

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Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

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Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Cited by 69 publications

References 29 publications

Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach

Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach

Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

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