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Route planning tools used by foreign passengers in the Tokyo Metropolitan Area. (Source: Survey at Tokyo Station.)
Source publication
Well known for its extensive network and state-of-the-art technology, the Japanese railway system has become a must-see attraction for foreign tourists and yet has not ceased to perplex them. Despite the growing use of multilingual signs, real-time train information, and information centers equipped with English-speaking staff, visitors from abroad...
Contexts in source publication
Context 1
... tools such as Google Maps. Figure 6 shows the result of the survey conducted in the Tokyo 7 ...
Context 2
... to our surveys, foreign passengers rely almost equally on traditional route plan- ning tools such as paper maps and Internet-based tools such as Google Maps. Figure 6 shows the result of the survey conducted in the Tokyo Station. We found that foreign passengers are much more likely to use route planning apps that they already know, for example, Google Maps, rather than apps that are more popular in Japan and are known to be more accurate. ...
Citations
... In recent years, the aging of the domestic population is progressing in Japan [11], and it has been increasingly noted that people are having trouble obtaining information on how to use and where to board public transportation. Currently, various efforts are being made to strengthen the communication of information concerning public transportation [12][13][14][15][16][17][18]. For example, in 2020, JR East adopted Toshiba's ToSpeak G3 text-to-speech software as a tablet terminal to be carried by train crews and station employees [19] and installed HOYA's ReadSpeaker text-to-speech software (hereinafter referred to as HOYA Broadcasting) in the concourses and platform of the Shinkansen [20]. ...
An experimental study on the effect of the speech characteristics of the signal-to-noise ratio (SNR) and speech rate on the intelligibility of announcements at railway stations was conducted using an artificial synthetic voice. Synthesized speech has recently been used in noisy environments both indoors and outdoors, but unlike its use in quiet environments, when the environment is noisy, the intelligibility of announcements may be reduced. For railway station announcements, while natural spoken voices are currently used for multilingual announcements and disaster response broadcasts, deep neural network synthesized voices, which use deep learning, have also been adopted. However, the effect of the acoustic characteristics such as the SNR and speech rate on the intelligibility of reproduced announcements in noisy public spaces such as railway stations has not yet been clarified from a practical viewpoint. In this paper, in order to determine the appropriate SNR and speech rate for synthetic voice announcements in railway stations, auditory impressions of announcements with varying SNR and speech rate were evaluated by participants using a five-point scale. Based on the evaluations, the appropriate conditions for the broadcast of synthetic voice announcements at the ticket gate and on the platform of a station are discussed.
User-centred passenger information design is a critical to overall rail passenger experience. One factor that can shape travel information use is passenger frequency of travel and trip knowledge, or expertise. Knowledge may potentially influence information seeking and perceived usefulness, and thus provide a basis to prioritise and personalise information. 293 survey participants rated their frequency of rail travel and self-reported travel knowledge, and rated 36 rail information functions for usefulness. Results confirmed trip frequency and self-reported expertise are strongly linked. Factor analysis identified most information functions fall into distinct six groups, with differential effects of travel frequency and expertise on information function preferences, though this only accounted for limited variance. Differential effects were also found for critical information functions that could not be factored. Overall, there is partial support for personalisation by trip frequency and expertise, particularly for disruption information or where unfamiliar passengers need support with basic trip activities. Practitioner Summary It may be valuable to personalise travel information based on expertise. A survey found distinct factors of information functions for rail travellers, and different preferences according to travel frequency and self-reported expertise. The results identify the most important information functions for rail travellers, and personalisation based on expertise and frequency. 3
Purpose
The purpose of this study is to integrate wayfinding strategies and open innovation to examine the factors of mobile application (M-App) design.
Design/methodology/approach
The study formulated an M-App model from the wayfinding sense-making and open innovation perspective. Samples were collected from the M-Apps users of Airbnb.com in a survey based on the principle of snowball sampling, and 416 samples were returned in total. The hypothesis testing of the model was conducted using structural equation modeling with Linear Structural Relations.
Findings
The operators of the lodging industry should design the recommended route on the M-Apps for tourists to reach the accommodation on the map function conveniently.
Practical implications
The orientation of wayfinding sense-making integrates the accommodation host to mark the direction of the accommodation on the map function of M-Apps to indicate the direction of the location.
Social implications
The operators of the lodging industry should design the recommended route on the M-Apps for tourists to conveniently reach the accommodation on the map function.
Originality/value
For the M-Apps designer of the lodging industry, devising the function with an arrow point on the map would enable tourists to check their current location handily. Moreover, the M-Apps of lodging businesses can reduce marketing expenses from the high recommendations of tourists.
Using the findings from visual cognitive psychology and computer vision, this study proposes a method that can predict how passenger visual attention to indoor visual guidance elements will affect the visual guidance quality inside passenger terminals. A saliency model is used to simulate the human visual attention, so as to understand how the visual guidance elements and visual noise are cognitively perceived by passengers. For every possible origin and destination node combination, the length and probability of the path that passengers are most likely to take (LP) are compared with the respective shortest path (SP). The overall evaluation of the terminal’s visual guidance quality can be expressed by the Extra Walking Index. The validity of the developed evaluation method is verified and it is then applied in a case study. The method can be used as a supporting tool for architects to identify relevant architectural features in the design phase and optimize them accordingly; in addition, it can also provide existing passenger terminals with specific improvement suggestions.
