Adaptation patterns to high speed rail usage in Taiwan and China

Li, Yeun-Touh; Schmöcker, Jan-Dirk

doi:10.1007/s11116-016-9679-5

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…In this regard, it is necessary to grasp the dynamics and heterogeneity of public transportation users. Li and Schmöcker [2] and Lin et al [3] used questionnaires to have travellers indicate their reasons for public transport for descriptive analysis of behavioural changes. Tang et al [4] collected data on users' socioeconomic characteristics, vehicle ownership, public bicycle use, and user satisfaction using online questionnaires.…”

Section: Introductionmentioning

confidence: 99%

“…Gan et al [7] analyzed the daily mobility patterns concerning land use from a station ridership perspective. As interest in people movement and urban mobility has surged in recent years, studies have attempted to predict individual travel-mode choices using traditional random utility models and machine learning approaches [2,14,[22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

Chen

Liang

Chang

et al. 2022

Journal of Advanced Transportation

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Public transportation users increase as the population grows. In Taipei, Taiwan, this tendency is observed by analyzing historical data from the Mass Rapid Transit (MRT) and economy-shared bicycle (known as YouBike) riders. While this trend exists, the Taipei City government promotes green transportation by providing discounts to users who transfer from MRT or bus to YouBike within a particular period. Therefore, this study focuses on analyzing the patterns of users in order to identify possible clusters. Clusters of customers can be considered fundamental and competitive factors for the Ministry of Transportation to encourage the use of green transportation and promote a sustainable environment. Based on big data smart card information, this paper proposes using the RFM and K-means clustering algorithm to analyze and construct mode-switching traveller profiles on MRT and YouBike riders. As a result, three distinct clusters of MRT-YouBike riders have been identified: potential, vulnerable, and loyal. There are also suggestions regarding the most profitable groups, which customers to focus on, and to whom give special offers or promotions to foster loyalty among transit travellers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

Chen

Liang

Chang

et al. 2022

Journal of Advanced Transportation

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“…The results reveal the travel characteristics of passengers between Beijing and Shanghai and provided information for policy design and infrastructure management [13]. Scholars analyzed the use of the high-speed rail in Taiwan and China in the past 8 years, and they developed ten graphical long-term use patterns with detailed use descriptions to capture the behavioral dynamics of some samples and to explain them to some extent [14]. The above research studies mainly focused on time, cost, and external environmental factors to study the travel behavior of passengers or the choice behavior of passengers for a certain transportation mode.…”

Section: Introductionmentioning

confidence: 99%

Passenger Travel Path Selection Based on the Characteristic Value of Transport Services

et al. 2022

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In this paper, we establish a generalized cost function for passenger travel based on the characteristic value of transportation services, and we select high-speed rail, air, and air–rail as the selection branches in order to build a passenger travel decision-making model combined with a logit model to analyze the preference for passenger travel choices. The results show that, within the transportation network of the Chengdu–Chongqing economic circle, passengers are more likely to take the high-speed rail option directly, followed by air–rail and air options, and these results are concentrated within a transportation distance range of less than 1000 km, 1000–1200 km, and more than 1200 km, respectively. Among them, the OD travel routes comprised Chengdu and Yibin as the transit nodes of the combined travel account for more than 50%, which exhibits the high strategic development potential of air–rail combined transportation. Ridge regression analyses show that ticket price, quickness, convenience, and comfort influence the probability related to travelers’ travel choice at varying degrees. The elasticity values of the fatigue recovery time, travel time, and time value per capita for high-speed rail are much greater than the other two travel modes, indicating that these three factors have a high impact on the travel choice behavior of high-speed rail.

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“…With the HSR, the total travel time has drastically reduced to ninety minutes from Taipei to Kaohsiung and no more than 2 h from Taipei to Tainan. According to a survey on the long-term usage patterns of HSR in Taiwan and Shanghai, the improvement in accessibility is the prime reason accounting for the significant increase in HSR usage, with the speed advantage and travel time savings being the main motivation for commuters to choose to travel on the HSR [1]. While HSR brings about benefits, such as increased accessibility, time savings, and higher productivity, the resulting economic gains are also accompanied by a significant increase in land value and property prices along the HSR lines.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan

Low

Lee

2020

Applied Sciences

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High-speed rail (HSR) networks boost inter-city accessibility across a country and stimulate economic growth in inner cities. These economic gains, however, can often be accompanied by sharp increases in land and property prices along the lines that raise governmental concerns. This study examined the effect of the introduction of HSR on land prices in Taiwan and how the extent of such an effect varied with the stages of economic, societal, and infrastructural developments in different cities in Taiwan. Based on extensive published data, an empirical study was conducted using an integrated methodology comprising system dynamics, multivariate regression, and principal component analysis to examine the interacting relationships between the presence of HSR transportation and other important dimensions of city development in determining land prices. The study found that while land prices correlated with the greater locational accessibility brought about HSR, the extent of land price increases depended significantly on economic, societal, and infrastructural considerations such as the unemployment rate, risk-free interest rate, population density, and the existence of free trade zones, etc. This understanding of system behavior will be helpful for policy makers in devising ways to curb the escalation of property price while enjoying the benefits of HSR.

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Adaptation patterns to high speed rail usage in Taiwan and China

Cited by 9 publications

References 9 publications

RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study

Passenger Travel Path Selection Based on the Characteristic Value of Transport Services

A Data-Driven Analysis on the Impact of High-Speed Rails on Land Prices in Taiwan

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