The stay coefficient: a novel quantification of the relationship between stay time and travel time for urban shopping behavior analysis

ABSTRACT

Shoppers typically want to spend an amount of time at a destination that is proportional to the travel time required to arrive there; thus, the travel time can be considered the cost of their trip. This is likely to be the case across regions with different urban structures and cultures. The purpose of this study was therefore to analyze the shopping behaviors contained in travel survey data from three metropolitan areas in Japan to identify common patterns and indicators based on travel time and stay time, thereby obtaining an understanding to inform future trade area analyses. Both the travel time and stay time associated with shopping behavior were found to be log-normally distributed regardless of metropolitan area, and four shopping behavior patterns common among the metropolitan areas were identified. The “stay coefficient” was then defined to express the elasticity of stay time according to travel time, and its values were similar according to shopping behavior pattern regardless of metropolitan area. The stay coefficient proposed in this study can therefore be applied to identify shopping behavior patterns in any urban area based on the relationship between travel time and stay time, realizing a novel approach to the analysis of and marketing for trade areas when planning the construction or renovation of commercial facilities. This approach can help inform the decisions of urban policy makers, marketing advisors, and commercial facility operators, and should be of interest to researchers and practitioners working with geospatial, shopping, and other human behavioral characteristics.

KEYWORDS:

• Travel survey
• shopping behavior
• travel time
• stay time
• stay coefficient
• trade area analysis

Acknowledgements

This research was the result of the joint research with the Center for Spatial Information Science (CSIS) of the University of Tokyo (project number 690) and used the following data: People Flow 2008 Tokyo Metropolitan Area (spatially reallocated), People Flow 2011 Nagoya Metropolitan Area (spatially reallocated), and People Flow 2010 Kinki Metropolitan Area (spatially reallocated).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the CSIS of the University of Tokyo, but restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are however available from the author upon reasonable request and with permission of CSIS.

Additional information

Funding

This work was not supported by funding from any granting agency or financial source.

Notes on contributors

Takashi Yamada

Takashi Yamada received the PhD degree from Keio University, Japan. His research interests are human behavior modeling, simulation, and space design analysis in buildings and urban spaces.