考量轉乘特性之捷運系統旅客路徑選擇研究

Abstract

都市內的捷運路網在近年來快速擴張，使得乘客在捷運系統內有更多的路徑選擇。當乘客有多條合理路徑可供選擇時，轉乘路徑特性便成為其選擇路徑時所考慮的關鍵因素。為了能更準確的預估出捷運系統的路段流量，一個良好的大眾運輸系統旅次指派模式對於大眾運輸規劃是至關重要的。在過往文獻及現今實務所使用之大眾運輸系統旅次指派模式多未考量到旅客在系統當中考慮轉乘路徑特性之路徑選擇行為，然而實際上於不同車站之轉乘對旅客所造成之負效用並非相同且與轉乘站特性相關，因此了解捷運系統之旅客是如何看待與感知轉乘站特性並做出其路徑選擇，此一行為機制值得深入探討。 本研究的目標為探討捷運系統旅客是如何看待轉乘路徑特性並做出路徑選擇。在許多情況下，旅客個人的選擇過程中包含了非補償性選擇策略，因此，本研究採用了限制多項羅吉特模式 (constrained multinomial logit model)以考量旅客對於關鍵轉乘路徑特性的非補償性選擇策略，以更加真實的反應出旅客對於每條可行路徑之態度，並結合多項羅吉特模式 (multinomial logit model) 建立一混合模式作為路徑選擇模式。此外，本研究亦根據所提出的路徑選擇模式估計結果進一步發展一歸屬模式 (membership model)，以探討各種旅次特性與其所對應之非補償性行為之關聯。本透過綜合敘述性及顯示性偏好問卷調查旅客之路徑選擇以及對各使用轉乘站的感知，其調查結果為後續模式發展所使用。 本研究係針對臺北捷運系統及其乘客進行研究，經分析後證實使用本研究所提出之模型可以更好地描繪乘客實際路經選擇行為，提出之混合模式之適配度也優於傳統多項羅吉特模式。本研究也得到不同旅次分群下的非補償性行為參數，並對每一分群之特性進行探討。 本研究結果可用於捷運系統興建前之運量預測及營運時之人流掌控，尤其是針對轉乘設施規劃更可利用本研究提出之路徑選擇模式對該轉乘站之轉乘流量進行估計。更加理解旅客對轉乘特性之感知和相關態度後，本研究之發現可以作為設計及營運部門在制定及規劃更有效率及具吸引力的轉乘環境時之參考基準。

Transfer stations connect two or more lines in a metro system which enables passengers to switch across lines to reach their destination. Passengers’ path choice behavior is highly affected by the design of transfer stations in a complex metro network that provides passengers with multiple path alternatives. Hence, the layout of transfer stations is gaining impact on passengers’ path choices in an expanding metro network. To better design transfer stations and network operation, a comprehensive understanding of passenger behavior is critical and essential for transportation planning and management. Accordingly, this study investigates metro passengers’ perception of transfer stations through an online questionnaire survey and further develops a path choice model that can reflect the effects of different types of transfers and the attributes of transfer paths. Also, a membership model is constructed to identify four groups with different thresholds of transfer walking time. To model the path choice behavior with thresholds on the critical attributes of transfers, this study adopts the Constrained Multinomial Logit Model (CMNL) to capture both compensatory and non-compensatory behavior. A case study on Taipei Metro, which services two million passengers a day over Taipei Metropolitan Area, is conducted to validate the proposed model. The result shows that the prediction accuracy of the proposed model is significantly higher than a Pure-MNL model and allows a more convincing explanation of passengers’ behavior with the groups of passengers specified. Several novel findings are derived from this study, including that in-vehicle travel time may not significantly affect path choice of passengers with non-compensatory behavior. Moreover, transfer walking time is more negatively perceived by passengers without non-compensatory behavior. Based on the improved understanding of passengers’ path choice behavior and attitudes toward attributes of transfer stations, the relevant authorities may be able to have a precise prediction of traffic volume of both new lines and new transfer stations and further provide more quality service with more effective operating strategies.