Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

An Entropy-Based Model for Recommendation of Taxis’Cruising Route

Cruising route recommendation based on trajectory mining can improve taxi-drivers'income and reduce energy consumption.However,existing methods mostly recommend pick-up points for taxis only.Moreover,their performance is not good enough since there lacks a good evaluation model for the pick-up points.Therefore,we propose an entropy-based model for recommendation of taxis'cruising route.Firstly,we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal features.Secondly,the information entropy of spatial-temporal features is integrated in the evaluation model.Then it is applied for getting the next pick-up points and further recommending a series of successive points.These points are constructed a cruising route for taxi-drivers.Experimental results show that our method is able to obviously improve the recommendation accuracy of pick-up points,and help taxi-drivers make profitable benefits more than before.

A Support System of Sightseeing Tour Planning Using Public Transportation in Japanese Rural Areas

Though most of tourists tend to visit multiple sightseeing spots during their sightseeing tours,it is difficult for them to efficiently acquire information necessary for their sightseeing tour planning.Additionally,in rural area,many people hope to use public transportation which has not developed as much as in urban areas.The present study aims to design and develop a support system of sightseeing tour planning in Japanese rural areas,adopting the information related to real timetables of public transportation on both the sea and the ground,and genetic algorism(GA).The system was developed integrating moving route recommendation system,web-geographic information systems(Web-GIS),and augmented reality(AR)application.Furthermore,Kagawa Prefecture in the western part was selected as the operation target area.The operation of the system was conducted for two months,targeting those inside and outside the operation target area,and web questionnaire surveys were conducted.From the evaluation results based on the web questionnaire surveys,the usefulness of all the original functions as well as of the entire system was analyzed.Additionally,though some users could not easily use the system,it is expected that they will get used to utilizing it by their continuous use.

Seeking for Passenger under Dynamic Prices: A Markov Decision Process Approach

In recent years, ride-on-demand (RoD) services such as Uber and Didi are becoming increasingly popular. Different from traditional taxi services, RoD services adopt dynamic pricing mechanisms to manipulate the supply and demand on the road, and such mechanisms improve service capacity and quality. Seeking route recommendation has been widely studied in taxi service. In RoD services, the dynamic price is a new and accurate indicator that represents the supply and demand condition, but it is yet rarely studied in providing clues for drivers to seek for passengers. In this paper, we proposed to incorporate the impacts of dynamic prices as a key factor in recommending seeking routes to drivers. We first showed the importance and need to do that by analyzing real service data. We then designed a Markov Decision Process (MDP) model based on passenger order and car GPS trajectories datasets, and took into account dynamic prices in designing rewards. Results show that our model not only guides drivers to locations with higher prices, but also significantly improves driver revenue. Compared with things with the drivers before using the model, the maximum yield after using it can be increased to 28%.

Personalized travel route recommendation using collaborative filtering based on GPS trajectories

Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations,based on the road networks and users’travel preferences.In this paper,we define users’travel behaviours from their historical Global Positioning System(GPS)trajectories and propose two personalized travel route recommendation methods–collaborative travel route recommendation(CTRR)and an extended version of CTRR(CTRR+).Both methods consider users’personal travel preferences based on their historical GPS trajectories.In this paper,we first estimate users’travel behaviour frequencies by using collaborative filtering technique.A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model.The CTRR+method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability.This paper also conducts some case studies based on a real GPS trajectory data set from Beijing,China.The experimental results show that the proposed CTRR and CTRR+methods achieve better results for travel route recommendations compared with the shortest distance path method.

Multiple Routes Recommendation System on Massive Taxi Trajectories

This paper presents a cloud-based multiple-route recommendation system, xGo, that enables smartphone users to choose suitable routes based on knowledge discovered in real taxi trajectories. In modern cities, GPS-equipped taxicabs report their locations regularly, which generates a huge volume of trajectory data every day. The optimized routes can be learned by mining these massive repositories of spatio-temporal information. We propose a system that can store and manage GPS log files in a cloud-based platform, probe traffic conditions, take advantage of taxi driver route-selection intelligence, and recommend an optimal path or multiple candidates to meet customized requirements. Specifically, we leverage a Hadoop-based distributed route clustering algorithm to distinguish different routes and predict traffic conditions through the latent traffic rhythm. We evaluate our system using a real-world dataset（〉100 GB） generated by about 20 000 taxis over a 2-month period in Shenzhen, China. Our experiments reveal that our service can provide appropriate routes in real time and estimate traffic conditions accurately.

APFD:an effective approach to taxi route recommendation with mobile trajectory big data

With the rapid development of data-driven intelligent transportation systems,an efficient route recommendation method for taxis has become a hot topic in smart cities.We present an effective taxi route recommendation approach(called APFD)based on the artificial potential field(APF)method and Dijkstra method with mobile trajectory big data.Specifically,to improve the efficiency of route recommendation,we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates.Then,based on the APF method,we put forward an effective approach for removing redundant nodes.Finally,we employ the Dijkstra method to determine the optimal route recommendation.In particular,the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing.On the map,we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony(AC)algorithm,greedy algorithm(A*),APF,rapid-exploration random tree(RRT),non-dominated sorting genetic algorithm-II(NSGA-II),particle swarm optimization(PSO),and Dijkstra for the shortest route recommendation.Compared with AC,A*,APF,RRT,NSGA-II,and PSO,concerning shortest route planning,APFD improves route planning capability by 1.45%–39.56%,4.64%–54.75%,8.59%–37.25%,5.06%–45.34%,0.94%–20.40%,and 2.43%–38.31%,respectively.Compared with Dijkstra,the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency.In addition,in the real-world road network,on the Fourth Ring Road in Beijing,the ability of APFD to recommend the shortest route is better than those of AC,A*,APF,RRT,NSGA-II,and PSO,and the execution efficiency of APFD is higher than that of the Dijkstra method.