Measuring Public Transit Accessibility Based On Google Direction API

Background: Accessibility is considered as an important indicator for the public transit service level. Transit accessibility is generally evaluated by its distance to transit access points such as bus stops and metro stations, and methods of measuring the access distances to transit points have bee...

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Vydáno v:The open transportation journal Ročník 13; číslo 1; s. 93 - 108
Hlavní autoři: Haitao, Jin, Fengjun, Jin, Qing, Hao, He, Zhu, Xue, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Sharjah Benham Science Publishers 2019
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Public transportation
ISSN:1874-4478, 1874-4478
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Abstract Background: Accessibility is considered as an important indicator for the public transit service level. Transit accessibility is generally evaluated by its distance to transit access points such as bus stops and metro stations, and methods of measuring the access distances to transit points have been relying heavily on geographic data of transit facilities, built environments and pedestrian routes. Data collection and analysis are tedious for researchers in conventional approaches. As the application of cloud computing is on the rise, open services provided by Google Cloud Platform may simplify the procedure of accessibility measurement if the outputs of the open computing services could be validated. Aims and Objectives: This paper aims to develop a method of measuring public transit accessibility based on Google Direction API rather than local data analysis. A mechanism of API (Application Program Interface) probing is introduced. In a case study, the metropolitan area of Beijing was sliced into gridded spaces, with transit access distance of each cell space calculated by Google Direction API. The access distances in the API feedbacks were compared with transit access numbers in each cell area in order to validate the method with their correlation coefficient. Results and Conclusion: It was found that Google Direction API generally gave shorter access distances in cell areas with more public access points. The conclusion is that open cloud services such as Google Direction API may serve as alternative solutions to public transit accessibility measurement. Transit researchers and agencies may take advantage of such open API services to avoid the tediousness of collecting and processing geographic data sets on transit facilities.
AbstractList Background: Accessibility is considered as an important indicator for the public transit service level. Transit accessibility is generally evaluated by its distance to transit access points such as bus stops and metro stations, and methods of measuring the access distances to transit points have been relying heavily on geographic data of transit facilities, built environments and pedestrian routes. Data collection and analysis are tedious for researchers in conventional approaches. As the application of cloud computing is on the rise, open services provided by Google Cloud Platform may simplify the procedure of accessibility measurement if the outputs of the open computing services could be validated. Aims and Objectives: This paper aims to develop a method of measuring public transit accessibility based on Google Direction API rather than local data analysis. A mechanism of API (Application Program Interface) probing is introduced. In a case study, the metropolitan area of Beijing was sliced into gridded spaces, with transit access distance of each cell space calculated by Google Direction API. The access distances in the API feedbacks were compared with transit access numbers in each cell area in order to validate the method with their correlation coefficient. Results and Conclusion: It was found that Google Direction API generally gave shorter access distances in cell areas with more public access points. The conclusion is that open cloud services such as Google Direction API may serve as alternative solutions to public transit accessibility measurement. Transit researchers and agencies may take advantage of such open API services to avoid the tediousness of collecting and processing geographic data sets on transit facilities.
Author He, Zhu
Xue, Yang
Haitao, Jin
Fengjun, Jin
Qing, Hao
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