Dynamic Graph Mining for Multi-weight Multi-destination Route Planning with Deadlines Constraints

Abstract

Route planning satisfied multiple requests is an emerging branch in the route planning field and has attracted significant attention from the research community in recent years. The prevailing studies focus only on seeking a route by minimizing a single kind of Travel Cost, such as trip time or distance, among others. In reality, most users would like to choose an appropriate route, neither fastest nor shortest route. Usually, a user may have multiple requirements, and an appropriate route would satisfy all requirements requested by the user. In fact, planning an appropriate route could be formulated as a problem of Multi-weight Multi-destination Route Planning with Deadlines Constraints (MWMDRP-DC). In this article, we propose a framework, namely, MWMD-Router, which addresses the MWMDRP-DC problem comprehensively. To consider the travel costs with time-variation, we propose not only four novel dynamic graph miner to extract travel costs that reveal users’ requirements but also two new algorithms, namely, Basic MWMD Route Planning and Advanced MWMD Route Planning , to plan a route that satisfies deadline requirements and optimizes another criterion like travel cost with time-variation efficiently. To the best of our knowledge, this is the first work on route planning that considers handling multiple deadlines for multi-destination planning as well as optimizing multiple travel costs with time-variation simultaneously. Experimental results demonstrate that our proposed algorithms deliver excellent performance with respect to efficiency and effectiveness.

Analysis and Study on Intelligent Tourism Route Planning Scheme Based on Weighted Mining Algorithm

“…For geophotos (photos with geographical labels), the scenic spot area is extracted by using DBSCAN (density-based spatial clustering of applications with noise) clustering algorithm, and the user's scenic spot interest matrix and scenic spot area heat vector are established. A BIPM (based on interest popularity and month) personalized scenic spot recommendation algorithm based on user preference, photo time context, and scenic spot area heat is proposed to build a personalized scenic spot recommendation model [7]. Peng et al point out that there are information construction problems in the application of big data mining in smart tourism.…”

“…In general, a multitask programming solution is to turn multiple goals into a single task problem with the appropriate tools. We consider setting the comprehensive objective as P and then solving the single objective; then min P M ×(1 − F), (7) where M refers to all the expenses of a trip, F is the satisfaction of a trip, and 1 − F is the dissatisfaction of a trip. erefore, P can be understood as the expenses spent by tourism enthusiasts on dissatis ed activities during a trip.…”

Route Discovery Development for Multiple Destination Using Artificial Ant Colony

“…Huang et al, [11] proposed an algorithm by implementing the dynamic graph miner for multi-destination route to plan the route and measure its cost and time according to user requirements and deadlines with efficient optimizing.…”

“…One of the challenges that face multiple destination route discovery protocol is how to represent a model for the protocol. In [11] they use graph model as a grid for each route, as shown in Figure 3, where the route is represented as 3*3 grids. Accordingly, this route is divided into five segments which are {p1, p2}, {p3, p4, p5, p6}, {p7, p8}, {p9, p10}, and {p11, p12, p13, p14}.…”

“…Here, there are two different routes: route (a), which goes to the plaza first and route (b), which goes to the office first, the best path is considered when satisfy the user requirements such as reduce travel cost and fuel consumption. [11] First, ant colony optimization (ACO) is used to solve optimization problems; then, it is developed to be used for artificial intelligence applications. ACO is based on behavior of ants for finding food.…”