一种基于动态情境感知的旅游路径规划方法

doi:10.15960/j.cnki.issn.1007-6093.2026.02.005

Abstract

Abstract: Tourist route planning is a challenging task that requires considering both temporal and spatial dimensions of tourism data. On one hand, it involves obtaining the spatial distribution of tourist attractions within the scenic area, and on the other hand, it takes into account the tourists' visiting behaviors during their exploration of the area. Therefore, in addition to collecting attribute information of various attractions in the scenic area, tourism route planning also requires a significant amount of data on tourists' visiting behaviors. This paper collects the aforementioned data and extracts the travel behavior information of tourists between attractions, proposing important indicators for measuring tourist travel behavior. Based on a comprehensive consideration of these indicators, the Travel Route Planning algorithm (TRP) is introduced with the aim of significantly reducing travel time between attractions. The experiments yield distinct results and corresponding travel times between scenarios without travel route planning and scenarios with travel route planning. Furthermore, by further optimizing the route planning, a comparison of travel times before and after optimization is obtained. The results indicate that the Travel Route Planning algorithm not only greatly reduces travel time but also provides valuable insights for determining optimal locations for tourist buses to make stops. Compared to three representative path planning algorithms currently available, the algorithm proposed in this paper exhibits significant advantages in response time and average solution quality.

Key words: spatio-temporal awareness, touring route planning, tourist points-of-interest

CLC Number:

O221.2

WANG Feng, HANG Bo, HUANG Jinzhou, XU Degang, ZHANG Zeyu, LIU Jiamou. Research of touring route planning based on spatio-temporal awareness[J]. Operations Research Transactions, 2026, 30(2): 69-78.

References

[1] Taylor K, Lim K H, Chan J. Travel itinerary recommendations with must-see points-of-interest [C]//Companion Proceedings of the Web Conference, 2018: 1198-1205.
[2] Bart T, David A S, Gerald F, et al. YFCC100M: The new data in multimedia research [J]. Communications of the ACM, 2016, 59(2): 64-73.
[3] Bochar L, Radi B. A new approach to treat the selective travelling salesman problem [J]. International Mathematical Forum, 2016, 11: 757-768.
[4] Feillet D, Dejax P, Gendreau M. Traveling salesman problems with profits [J]. Transportation Science, 2005, 39(2): 188-205.
[5] Gunawan A, Lau H C, Vansteenwegen P. Orienteering problem: A survey of recent variants, solution approaches and applications [J]. European Journal of Operational Research, 2016, 255(2): 315-332.
[6] Theodore T. Heuristic methods applied to orienteering [J]. Journal of the Operational Research Society, 1984, 35(9): 797-809.
[7] Pieter V, Wouter S, Dirk V O. The orienteering problem: A survey [J]. European Journal of Operational Research, 2011, 209(1): 1-10.
[8] Laporte G, Martello S. The selective travelling salesman problem [J]. Discrete Applied Mathematics, 1990, 26(2-3): 193-207.
[9] Brilhante I, Macedo J A, Nardini F M, et al. Where shall we go today? Planning touristic tours with tripbuilder [C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, 2013: 757-762.
[10] Brilhante I R, Macedo J A, Nardini F M, et al. On planning sightseeing tours with trip builder [J]. Information Processing and Management, 2015, 51(2): 1-15.
[11] De Choudhury M D, Feldman M, Amer-Yahia S, et al. Automatic construction of travel itineraries using social breadcrumbs [C]//Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, 2010: 35-44.
[12] Lim K H, Chan J, Leckie C, et al. Towards next generation touring: Personalized group tours [C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017: 325-334.
[13] Lim K H, Chan J, Leckie C, et al. Next generation touring: Personalized group tours [C]//Proceedings of the 26th International Conference on Automated Planning and Scheduling, 2016: 412-420.
[14] Lim K H, Chan J, Leckie C, et al. Personalized trip recommendation for tourists based on user interests, points of interest visit durations and visit recency [J]. Knowledge and Information Systems, 2018, 54(2): 375-406.
[15] Mukhina K D, Rakitin S V, Visheratin A A. Detection of tourists attraction points using Instagram profiles [J]. Procedia Computer Science, 2017, 108: 315-332.
[16] Quercia D, Schifanella R, Aiello L M. The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city [C]//Proceedings of the 25th ACM Conference on Hypertext and Social Media, 2014: 116-125.
[17] Wang X, Leckie C A, Chan J, et al. Improving personalized trip recommendation by avoiding crowds [C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management, 2016: 25-34.
[18] Brilhante I, Macedo J A, Nardini F M, et al. TripBuilder: A tool for recommending sightseeing tours [C]//Proceedings of the 36th European Conference on Information Retrieval, 2014: 771- 774.
[19] Woerndl W, Hefele A. Through discovered places-of-interests for city trip planning [C]//Information and Communication Technologies in Tourism, 2016: 441-453.
[20] Leung K W T, Lee D L, Lee W C. CLR: A collaborative location recommendation framework based on co-clustering [C]//Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011: 305-314.
[21] Shi Y, Serdyukov P, Hanjalic A, et al. Personalized landmark recommendation based on geotags from photo sharing sites [C]//Proceedings of the Fifth International Conference on Weblogs and Social Media, 2011: 622-625.
[22] Yao L, Sheng Q Z, Qin Y, et al. Context-aware point-of-interest recommendation using tensor factorization with social regularization [C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015: 1007-1010.
[23] Yuan Q, Cong G, Ma Z Y, et al. Time-aware point-of-interest recommendation [C]//Proceedings of the 36th International ACM SIGIR Conference on Research and development in Information Retrieval, 2013: 363-372.
[24] Benouaret I, Lenne D. A composite recommendation system for planning tourist visits [C]//IEEE/WIC/ACM International Conference on Web Intelligence, 2016: 626-631.
[25] Kofler C, Caballero L, Menendez M, et al. Near2me: An authentic and personalized social media-based recommender for travel destinations [C]//Proceedings of the 3rd ACM SIGMM International Workshop on Social Media, 2011: 47-52.
[26] Marinescu R, Lee J, Ihler A, et al. Anytime best+depth-first search for bounding marginal MAP [C]//Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017: 3775-3782.
[27] Lou Q, Dechter R, Ihler A. Anytime anyspace AND/OR best-first search for bounding marginal MAP [C]//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018: 6392-6400.
[28] Wang J Y, Wu N, Zhao W X, et al. Empowering A^* search algorithms with neural networks for personalized route recommendation [C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019: 539-547.
[29] Yang C X, Zhang J L, Gao X F, et al. MatTrip: Multi-functional attention based neural network for semantic travel route recommendation [C]//2021 IEEE International Conference on Web Services, 2021: 145-154.

Research of touring route planning based on spatio-temporal awareness

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	WEI Jiazhen, BIAN Wei. A survey on research advances in consensus-based optimization algorithm [J]. Operations Research Transactions, 2026, 30(1): 1-23.
[2]	ZHOU Yunxu, YAO Fanjun, GAO Hongwei. Blockchain empowering the digital transformation: Study on the coordination strategies of supply chain [J]. Operations Research Transactions, 2026, 30(1): 61-74.
[3]	ZHAO Hongxin, KONG Lingchen. Research on diversification portfolio optimization model and method [J]. Operations Research Transactions, 2026, 30(1): 75-92.
[4]	SUN Ke, WANG Jinting, WANG Zhongbin. The economics of waiting-area entertainment [J]. Operations Research Transactions, 2026, 30(1): 93-107.
[5]	XU Ke, JI Lanping, GONG Hua, LIU Peng, SUN Wenjuan. A coordinated multi-agent production and transportation scheduling on parallel machines based on auction algorithm [J]. Operations Research Transactions, 2026, 30(1): 121-136.
[6]	WANG Ziqi, WANG Junlin, XU Zi. A variance reduced gradient descent ascent algorithm for a class of nonconvex-nonconcave minimax problems [J]. Operations Research Transactions, 2026, 30(1): 197-206.
[7]	YANG Jinji, SHEN Chungen, YU Zhensheng. Convergence analysis of an adaptive proximal gradient-subgradient algorithm for square-root-loss regression problems [J]. Operations Research Transactions, 2026, 30(1): 217-234.
[8]	Ruiqing SUN, Rui ZHANG, Yan LAN, Weidong LI. LPT algorithm for early work maximization problem [J]. Operations Research Transactions, 2025, 29(4): 249-254.
[9]	Rui SONG, Yiran WANG, Zhongming WU. Convergence analysis of the inexact generalized alternating direction method of multipliers with indefinite proximal term [J]. Operations Research Transactions, 2025, 29(4): 175-190.
[10]	Bo ZHANG, Hongyu WANG, Yuelin GAO. A D.C. relaxation based branch-and-bound algorithm for sum-of-linear-products programming problems [J]. Operations Research Transactions, 2025, 29(4): 159-174.
[11]	Xin SUN, Dongdong GE, Desheng FU, Zhiwei WEI, Fenglian DONG, Shichang PAN. A hybrid distribution recursion and branch and bound algorithm for Petroluem refinery optimization problem [J]. Operations Research Transactions, 2025, 29(4): 141-158.
[12]	Zilin TAN, Honglin LUO. A second-order splitting method with its application [J]. Operations Research Transactions, 2025, 29(4): 121-140.
[13]	Xiaofang WANG, Zhian LIANG, Caixia GAO. Optimality conditions of weakly semi-E-convex programming [J]. Operations Research Transactions, 2025, 29(4): 72-82.
[14]	Qianqian CHI, Yuying ZHOU. An exact penalty approach to a cone constrained optimization problem [J]. Operations Research Transactions, 2025, 29(4): 61-71.
[15]	Chenglong BAO, Chang CHEN. A survey on the Bregman iteration in computing Landau's free functional minimization problems [J]. Operations Research Transactions, 2025, 29(3): 243-266.