The aim of safety activities in Japanese railroad companies is to raise the customers’ safety. In order to raise it, it is very important that safety management is evaluated from customers. However, currently there is little information to evaluate safety management and customer survey. A lot of information is the point of view of customer service. In this study, we structured customers’ mental images on the safety in railroad services. Furthermore, we carried out the investigation among transportation bureau, and evaluated based on the structure of mental images for the safety that we built in this study. First, this investigation was item choice form. The 10 item; Regularity, Education of the customer manner, Broadcast in the train, Ride-Comfort quality, Opening and closing of the door, Safety control system, Persistence of the safe, Crime prevention, Cleanliness of facilities, Service of the employee, was provided by the preliminary survey for the person in charge of safety management of several railroad companies. Next, we interviewed 400 people using the transportation about impression point of “the safety” for a transportation bureau and importance for ten factors. Target railroad bureau has posture to adopt various safety management activities positively. It seems that the posture led to high trust of the overall customers for constant work. On the other hand, there is a difference of the level among employees in correspondence for an individual event such as “enlightenment the customer manner” or “service of the employee”. It showed that the difference led to anxiety for the customers. A problem surfaced reducing the difference of ability among employees including service in future. In this study, we structured customers’ mental images on the safety in railroad services. Moreover, based on the suggested structure, we evaluated the safety activities of transportation bureau from the viewpoint of customers. In the future, we expect to expand the operating range and improve their utility.
ASJC Scopus subject areas
- Artificial Intelligence
- Industrial and Manufacturing Engineering