Impacts of Automated Vehicles on Traffic Flow Changes

정승원,; 문영준,; 이성렬,; 황기연,

doi:10.12815/kits.2017.16.6.244

Cited by 3 publications

(2 citation statements)

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“…However, this has caused various problems, such as traffic jams, parking difficulties, and traffic accidents. In addition, soot and noise emitted from vehicles cause environmental pollution and serious climate problems, such as global warming [1]. Accordingly, many countries worldwide are transitioning from the existing automobile-oriented traffic management policies to those centered on public and green transport to solve urban traffic and environmental problems [2].…”

Section: Introductionmentioning

confidence: 99%

“…Bicycles are an effective means of transportation that can simultaneously improve the health of users and the urban environment. Accordingly, public bicycle rental systems that promote the use of bicycles are being introduced worldwide to significantly reduce urban traffic and environmental problems [1]. Moreover, various policies are being promoted to revitalize the use of public bicycles.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Factors Affecting the Extra Journey Time of Public Bicycles

Jung,

Jung

2023

Sustainability

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Many countries worldwide are introducing public bicycle systems to reduce urban traffic and environmental problems. However, studies on the usage behavior of public bicycles have not considered the trip purpose of riders extensively due to data limitations. Therefore, this study defined the “extra journey time” from usage time and origin–destination (OD) expected time and clustered public bicycle usage behaviors. Subsequently, the effects of the location characteristics of the departure and arrival stations, road environmental factors, and weather conditions for each cluster were analyzed. Three clusters were obtained from the results. Riders in Cluster 1 were inferred to have used the bicycles to commute and for work purposes, and riders in Clusters 2 and 3 used the bicycles for leisure purposes. Moreover, the bike station location characteristics, road environmental factors, and weather conditions influenced the probability of classification into one cluster. In particular, bike lanes near the departure and arrival stations increased the probability of classification under Clusters 2 and 3. The trip patterns according to the extra journey time of public bicycles were classified under these clusters. Furthermore, the differences in the characteristics of the bicycle usage types were identified according to the location and meteorological factors affecting them.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%