Highlights Wi-Fi data from sensors at attractions and GPS traces are fused to obtain routes. GPS data are used to fill in gaps in Wi-Fi data to preserve key locations of a tour. Case study of Kyoto establishing daily routes of tourists. Recursive Logit model with fused data and crowdsourced ratings as explanatory variables.

    Abstract We establish a methodology that fuses point data with trajectory data leading to trip chains that reflect whether a person has visited key locations. In our study the point data are Wi-Fi data set from sensors at key tourist locations and the trajectories are a small sample of GPS footprints. The approach proves valuable in the context of tour estimation, placing a specific emphasis on determining whether major attractions have been visited. The data fusion approach evaluates the similarity of a sequence of Wi-Fi records with the GPS records which function as a “look-up database”. Based on this, likely missing observations in the Wi-Fi data sequence are estimated. With a Kyoto case study, we demonstrate that the fused data create reasonable tours. Through cross-validation it is verified that missing observations are fairly close to the true location. We then use the fused data with a Recursive Logit approach to estimate parameters that indicate the location attractiveness of touristic areas. The results demonstrate that we can estimate the attractiveness of restaurants and shops to predict tourist routes.


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    Title :

    Inferring travel patterns and the attractiveness of touristic areas based on fusing Wi-Fi sensing data and GPS traces with a Kyoto case study


    Contributors:


    Publication date :

    2024-02-13




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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