Cluster Analysis of Intercity Rail Passengers in Emerging High-Speed Rail Corridor

Abstract

Recent pledges by the U.S. federal government to increase investments in high-speed passenger rail have highlighted the importance of identifying the potential impacts of these investments on current rail passengers. Identification of these impacts depends largely on the demographics, travel habits, and attitudes of these passengers—in essence, who these passengers are. This investigation applied cluster analysis, a method typically used to determine potential consumer groups for marketing research, to identify similar groups of rail passengers riding Amtrak's Hiawatha service between Chicago, Illinois, and Milwaukee, Wisconsin. These groups were then analyzed for their potential responses to future high-speed rail service. Analysis results showed that clustering was an effective method to determine differences between segments of a market, in this case, current passenger rail users. Further analysis indicated that these segments exhibited potentially different responses to future high-speed rail service. It is recommended that attention be paid to the potential needs of these current passenger segments to increase the likelihood of success for future high-speed rail service.

The numerical example for evaluating the criteria describing the quality of the trip by international train

“…The economic impacts of intercity passenger rail service (evidence from passenger surveys) [23] and optimal train operation for minimum energy consumption, considering track alignment speed limit, and schedule adherence were analysed [7]. Cluster analysis of intercity rail passengers in emerging high-speed rail corridor was performed [24] and rail transit travel time reliability and estimation of passenger route choice behaviour (the analysis using automatic fare collection data) were described [25].…”

Identification and Assessment of Potential High-Speed Rail Routes from Megaregion Perspective

“…The element of the MFPT matrix corresponds to a nonspatial measure of proximity or functional distance between regions (20). Cluster analysis, recently used by Sperry et al (21) to identify groups of Amtrak (National Rail Passenger Corporation) passengers, is used to group regions by similar patterns of functional distances, forming functional regions.…”

Clustering of the inland waterway navigational environment and its effects on ship energy consumption

“…NA IV includes legs 12,13,14,15,16,17,18,19,20, and 21; all 10 of the legs are located in the lower reach of the Yangtze River, which is usually called the natural waterway. The depth in this area is larger than that in the middle reach (Figure 1).…”

“…More specifically, the similarity among cases within a group (known as cluster) is maximized while the similarity among the groups is minimized. 19 Cluster analysis is extensively used in many research areas, such as transportation planning, passenger flow forecast, 20 identification of abnormal daily building energy consumption profiles, 21 and the study of the spatial effects of carbon dioxide emissions from residential energy consumption. 22 In this study, the partition of the Yangtze River waterway using the cluster analysis method is realized.…”