Strategy for Introducing Autonomous Driving Mobility Service in Transportation Vulnerable Areas: A Case Study of Gyeonggi Province in Republic of Korea

Local governments in Republic of Korea provide local mobility services (e.g., taxis and buses) for transportation vulnerable areas with limited access to mass public transit. However, the losses incurred by the operation of these local mobility services have led to the fiscal burden for subsidizing...

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Bibliographic Details
Published inKSCE journal of civil engineering Vol. 26; no. 11; pp. 4793 - 4804
Main Authors Seo, Younghoon, Lim, Donghyun, Kim, Junghwa, Kim, Daejin, Kim, Hyungjoo
Format Journal Article
LanguageEnglish
Published Seoul Korean Society of Civil Engineers 01.11.2022
Springer Nature B.V
Subjects
Autonomous vehicles
Case studies
Civil Engineering
Engineering
Geotechnical Engineering & Applied Earth Sciences
Industrial Pollution Prevention
Infrastructure
Labour
Local government
Losses
Mobility
Operating costs
Probabilistic models
Public transportation
Taxis
Traffic
Transportation Engineering
Urban areas
South Korea
Gyeonggi province (Gyeonggi-do)
Autonomous driving
Vulnerable area
Gaussian mixture model
Mobility service
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Summary:Local governments in Republic of Korea provide local mobility services (e.g., taxis and buses) for transportation vulnerable areas with limited access to mass public transit. However, the losses incurred by the operation of these local mobility services have led to the fiscal burden for subsidizing the local mobility services. To address this problem, autonomous mobility service is emerging as an alternative mode of transportation that can reduce the operating costs for the local mobility services (e.g., labor and fuel costs). This study proposes a step-by-step strategy for introducing autonomous mobility services in Gyeonggi Province, where there are a number of transportation vulnerable areas mainly due to geographical disparities. First, this study applied the Gaussian mixture model (GMM) to classify the transportation vulnerable areas, and the areas were clustered into four classes. Then, the strategy for introducing the autonomous mobility service target to each of the transportation vulnerable areas classified. Finally, the strategy was evaluated by checking them against the actual status of mobility service. The result derived from our proposed methodology contain 4 clusters with the order of introducing autonomous mobility services and is verified with the current status of welfare public transportation.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-022-0248-5