Built environment determinants of pedestrians’ and bicyclists’ route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route

Sarjala, Satu

doi:10.1016/j.jtrangeo.2019.05.004

Cited by 27 publications

(19 citation statements)

References 26 publications

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“…Note that smoothing the ridership by applying the moving average technique can be a suitable method to remove additional variation of ridership because of day of week and annual patterns ( 30 ). As for built environment, researchers have primarily used land-use characteristics to investigate their effects on transit ridership ( 31 , 32 ). However, land-use data are usually acquired from costly land-use surveys that are not frequently updated (such as population, residential density, employment density, and so on), so the data rarely coincide with the current state of urban development in time or accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…The ordinary least squares (OLS) regression model was the most common method in previous studies ( 21 ). Moreover, other traditional statistical models like the structural equation model (SEM), zero-inflated negative binomial model, hierarchical Bayesian models, and Poisson regression model were also frequently used in the field of transit ridership analysis ( 2 , 31 , 35 , 36 ). In a word, these global statistical models assume that the relationships between independent variables and dependent variables are consistent over space, but they ignored the spatial characteristics of spatial data, like spatial auto-correlation and spatial non-stationarity.…”

Section: Related Workmentioning

confidence: 99%

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How Built Environment Impacts Online Car-Hailing Ridership

Wang

Chen

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Extensive research has shown that unilateral optimization of transit systems is not effective enough to significantly increase its transport efficiency. Considering that urban land-use characteristics, including residential, work, consumption, transit, and so forth, are significantly interrelated with travel demands and travel behaviors, this paper provides a way to optimize transit system by raising awareness of the relation between ridership and built environment. This paper adopted point of interest (POI) data to investigate the effect of physical built environment on online car-hailing ridership in Chengdu, China. The study area was tessellated with several Voronoi cells; these cells were further clustered into three ridership patterns based on the time-varying characteristic of ridership. Given that some differences existed in the three ridership patterns, a separate spatial ridership model was developed to understand the factors that influence ridership patterns using geographic weighted regression (GWR) analysis. The data and results verified that the built environment had various influences on online car-hailing alighting ridership in spatial and temporal dimensions, of which the significant POI factors for determining the ridership pattern during different periods were detected. Remarkably, this study took the ridership dataset from the online car-hailing transit system, mainly because the pick-up (PU) and drop-off (DO) locations generated by this service are closest to the origin and destination of the trip, except that it is more popular recently. Therefore, the analysis of the impact of built environment on travel based on the online car-hailing dataset can be captured in greater detail, with a higher degree of accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

How Built Environment Impacts Online Car-Hailing Ridership

Wang

Chen

et al. 2020

Transportation Research Record: Journal of the Transportation R

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“…Although the grid-based spatial representation in this study was used as an epitome of outdoor facility, a purely numerical and virtual experiment was conducted. In the only study of grid-based spatial representation based on actual GPS data, the impact of the built environment on pedestrian and bicycle commuting trips was examined using a grid-based spatial representation with a spatial resolution of 20 m [33], wherein the GPS points were assigned to a grid containing each point, and there was no provision for assigning the points in case the noise was included.…”

Section: Spatial Representation Of Gps Datamentioning

confidence: 99%

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Hidaka

Yamamoto

2021

Sustainability

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Understanding the decision-making behavior of pedestrians is essential for urban designers and developers in enhancing the commercial and aesthetic value of streets and other urban spaces. However, limited research has been conducted on the activity scheduling behavior of pedestrians. The majority of the studies conducted on outdoor facilities utilize spatial representations by links and are unable to sufficiently represent the highly flexible behavior of pedestrians. This study proposes a new method to discretize data from the global positioning system (GPS) into a two-dimensional grid-based spatial representation with a high spatial resolution. The information regarding the stay at the point of interests (POIs) is extracted from the discretized data, and the activity scheduling model is estimated. The estimation results indicate that the visitors’ attributes, such as the age of the representative and number of children, affect the probability of the activity choice and the time spent at the POI. The probability of choosing the main gate increases in the latter half of the stay, confirming the existence of time pressure. The information on the decision-making behavior of the visitors to a facility, obtained from the GPS data, can be applied to the data-oriented spatial design process to create attractive and lively spaces.

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“…), and time information. Based on the Chinese age division standard, we divide the age of passengers into 5 age groups: chil-Sustainability 2021, 13, 7419 4 of 19 dren (0-12), youths (13)(14)(15)(16)(17)(18), adults (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35), middle-aged individuals (36-59), and elderly individuals (60 and above). The data fields and descriptions are shown in Table 1.…”

Section: Intercity Car-hailing Datamentioning

confidence: 99%

“…Similar to intracity travel analysis, stage least squares (2SLS) regression [3]. Traditional methods of passenger travel demand analysis mainly include global regression methods [22,23], which assume that all variables are stationary and independent across the study area and ignore spatial heterogeneity. Geographically weighted regression (GWR) models overcome this shortcoming by allowing independent variables to alter spatially.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel

Wang

Mao

et al. 2021

Sustainability

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Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts and transportation facilities are the factors with the greatest influence on intercity online car-hailing travel. These results can provide practical help to city managers improve the management of intercity traffic and develop better transportation policies.

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Built environment determinants of pedestrians’ and bicyclists’ route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route

Cited by 27 publications

References 26 publications

How Built Environment Impacts Online Car-Hailing Ridership

How Built Environment Impacts Online Car-Hailing Ridership

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel

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