Journey planning system and method based on big data

Abstract

The invention relates to a travel planning system and method based on big data, and the system comprises an information input module which is used for collecting the inputted demand information; an analysis module used for generating main scenic spot schemes and alternative scenic spot schemes, calculating the predicted travel time of each scenic spot scheme and sending the main scenic spot schemes to the scheme output module; calculating the current travel time of the main scenic spot scheme and the alternative scenic spot scheme according to the real-time travel time of each scenic spot acquired by the data statistics module, comparing the estimated travel time with the current travel time, and when the current travel time of the main scenic spot scheme and the alternative scenic spot scheme meets a preset condition, sending alternative scenic spot schemes meeting preset conditions to a scheme output module; a data statistics module used for obtaining the real-time travel time of each scenic spot in the main scenic spot scheme and the alternative scenic spot scheme, and sending the real-time travel time of each scenic spot to the analysis module; and a scheme output module used for receiving and outputting the generated main scenic spot scheme or the alternative scenic spot scheme meeting the preset condition.

Description

technical field [0001] The invention relates to the field of travel route planning, in particular to a big data-based itinerary planning system and method. Background technique [0002] Tourism is an important part of modern service industry. With the upgrading of consumption level and the adjustment of industrial structure, people's travel methods have gradually developed from following tour groups in the early days to self-guided travel, and the travel methods are gradually diversified and personalized. [0003] While allowing tourists to design their itinerary more flexibly, self-guided tours directly present tourist information such as scenic spot locations, scenic spot information, traffic information, and accommodation information to tourists. Tourists have to deal with a large amount of travel information to design their own trip planning. However, this kind of design often neglects the other. For example, the full number of people in each scenic spot greatly prolon...

AI Technical Summary

Problems solved by technology

For the above-mentioned scenic spots, when the itinerary plan is generated, tourists may automatically avoid or choose one of the scenic spots preferred by tourists due to the problem of the total visiting time, resulting in difficult choices for tourists in the itinerary planning and being forced to give up plans that include more scenic spots.

However, on the day of the tour, the actual number of queues in the scenic spot may be quite different from the estimated number of queues, allowing tourists to visit more scenic spots on the day, but tourists are guided by the itinerary planning and miss it, leaving regrets during the trip

In addition, it is also very unfavorable for the guidance and drainage of local tourism. For example, after a large number of tourists adopt the same itinerary plan, some scenic spots are too congested on the day of travel, which significantly increases the carrying capacity of some scenic spots, resulting in their actual play time being significantly longer than expected. time, and some scenic spots outside the plan have relatively few people on the day of the tour, and the actual play time is lower than the expected play time

Not only that, adding too many impact indicators for prediction and correction under the premise of taking historical play time as the main reference factor is an act of putting the cart before the horse. Not only is the calculation result inaccurate due to the above problems, but it is also easy to significantly increase the amount of system computation

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