KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Link Label-Based Optimal Path Algorithm Considering Station Transfer Penalty - Focusing on A Smart Card Based Railway Network -

역사환승페널티를 고려한 링크표지기반 최적경로탐색 - 교통카드기반 철도네트워크를 중심으로 -

  • Received : 2018.10.08
  • Accepted : 2018.11.06
  • Published : 2018.12.01
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Abstract

Station transfers for smart card based railway networks refer to transfer pedestrian movements that occur at the origin and destination nodes rather than at a middle station. To calculate the optimum path for the railway network, a penalty for transfer pedestrian movement must be included in addition to the cost of within-car transit time. However, the existing link label-based path searching method is constructed so that the station transfer penalty between two links is detected. As such, station transfer penalties that appear at the origin and destination stations are not adequately reflected, limiting the effectiveness of the model. A ghost node may be introduced to expand the network, to make up for the station transfer penalty, but has a pitfall in that the link label-based path algorithm will not hold up effectively. This research proposes an optimal path search algorithm to reflect station transfer penalties without resorting to enlargement of the existing network. To achieve this, a method for applying a directline transfer penalty by comparing Ticket Gate ID and the line of the link is proposed.

교통카드기반 도시철도네트워크에서 역사환승은 중간환승역이 아닌 출발역과 도착역의 노드에서 발생하는 환승보행이동을 의미한다. 철도네트워크에서 최적의 경로를 탐색하기 위해서는 차내통행시간 이외에 환승보행이동에 대한 페널티를 반영하는 방안이 요구된다. 그러나 기존 링크표지기반 경로탐색기법은 링크와 링크의 사이에서 나타나는 환승페널티가 인식되도록 설계되었다. 따라서 출발역과 도착역에서 나타나는 역사환승페널티를 반영하지 못하는 한계가 발생한다. 역사환승페널티를 반영하기 위해 가상링크를 도입하여 네트워크를 확장하는 방안이 있으나 링크표지기반알고리즘을 효과적으로 유지하지 못하는 단점이 발생된다. 본 연구는 역사환승페널티를 반영하기 위하여 네트워크확장없이 최적경로를 탐색하는 알고리즘을 제안한다. 이를 위해 단말기ID와 링크의 노선을 비교하여 노선환승페널티를 직접 적용하는 방안을 제안한다.

Keywords

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Fig. 1. Three Lines’ Ticket Gate ID of ‘Express Bus Terminal’ Station

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Fig. 2. Link Expression Centering to ‘Express Bus Terminal’ Station

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Fig. 3. Link Label-Based Railway Station Network

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Fig. 4. Network Expansion of BigNode

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Fig. 5. Links Connected to Departure and Arrival BigNode

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Fig. 6. Seoul Metropolitan Railway Network

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Fig. 7. Case Study Process using Smart Card Data

Table 1. Smart Card Ticket Gate ID

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Table 2. Links Connected by Two BigNodes

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Table 3. Transfer Penalty Between Lines (Tab)

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Table 4. Headway of Railway Line (Hb)

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Table 5. Transfer Station Ration to Demand between r and s

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Table 6. Highest Frequency of Non Station Transfer (f-- )

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Table 7. Highest Frequency of Departure Station Transfer (fr-)

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Table 8. Highest Frequency of Arrival Station Transfer (f-s)

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Table 9. Highest Frequency of Departure & Arrival Station Transfer (frs)

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Table 10. Change of Number of Transfer Considering All r-s Pairs

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