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Estimating Public Bicycle Trip Characteristics with Consideration of Built Environment Data
- Source :
- Sustainability, Vol 13, Iss 500, p 500 (2021), Sustainability, Volume 13, Issue 2
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- A reliable estimation of public bicycle trip characteristics, especially trip distribution and duration, can help decision-makers plan for the relevant transport infrastructures and assist operators in addressing issues related to bicycle imbalance. Past research studies have attempted to understand the relationship between public bicycle trip generation, trip attraction and factors such as built environment, weather, population density, etc. However, these studies typically did not include trip distribution, duration, and detailed information on the built environment. This paper aims to estimate public bicycle daily trip characteristics, i.e., trip generation, trip attraction, trip distribution, and duration using points of interest and smart card data from Nanjing, China. Negative binomial regression models were developed to examine the effect of built environment on public bicycle usage. Totally fifteen types of points of interest (POIs) data are investigated and factors such as residence, employment, entertainment, and metro station are found to be statistically significant. The results showed that 300 m buffer POIs of residence, employment, entertainment, restaurant, bus stop, metro station, amenity, and school have significantly positive effects on public bicycle generation and attraction, while, counterintuitively, 300 m buffer POIs of shopping, parks, attractions, sports, and hospital have significantly negative effects. Specifically, an increase of 1% in the trip distance leads to a 2.36% decrease in the origin-destination (OD) trips or a 0.54% increase of the trip duration. We also found that a 1% increase in the number of other nearby stations can help reduce 0.19% of the OD trips. The results from this paper can offer useful insights to operators in better estimating public bicycle usage and providing reliable services that can improve ridership.
- Subjects :
- negative binomial regression
Computer science
Geography, Planning and Development
lcsh:TJ807-830
0211 other engineering and technologies
Negative binomial distribution
lcsh:Renewable energy sources
smart card
02 engineering and technology
Management, Monitoring, Policy and Law
Transport engineering
0502 economics and business
Duration (project management)
Built environment
lcsh:Environmental sciences
lcsh:GE1-350
050210 logistics & transportation
public bicycle
Renewable Energy, Sustainability and the Environment
Amenity
lcsh:Environmental effects of industries and plants
05 social sciences
021107 urban & regional planning
Trip distribution
lcsh:TD194-195
TRIPS architecture
road traffic engineering
Residence
trip distribution
trip duration
Trip generation
Subjects
Details
- Language :
- English
- ISSN :
- 20711050
- Volume :
- 13
- Issue :
- 500
- Database :
- OpenAIRE
- Journal :
- Sustainability
- Accession number :
- edsair.doi.dedup.....28acf81912dfa9fcd2407629c2ca04dd