Trip Planning and Scheduling Queries in Spatial Databases: A Survey

  • Tanzima HashemEmail author
  • Mohammed Eunus Ali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10721)


Planning and scheduling trips in an optimized manner allow users to perform their daily activities with convenience. A trip planning query finds a trip for a single user or a group jointly visiting different types of points of interests (POIs) such as a restaurant, a pharmacy and a movie theater with the minimum travel cost, whereas a trip scheduling query distributes the tasks of visiting different POI types among the group members by computing individual trips for the group members. In recent years, researchers have proposed variants of location based trip queries that include single trip planning queries, group trip planning queries, group trip scheduling queries, obstructed trip planning queries, dynamic group trip planning queries, and privacy preserving trip planning queries. Processing trip planning and scheduling queries in real time is a computational challenge as trips may involve more than one user and POIs of multiple types, and more importantly, the query answer is evaluated from a huge POI database. In this survey, we give an overview of the state of the art approaches for processing trip planning and scheduling queries. We compare these approaches from different angles like the number of users involved in a query (i.e., single or group), the type of the data space (i.e., Euclidean space/road networks/obstructed space), the sequence of POI types (i.e., fixed/flexible), static or dynamic, optimization parameters (i.e., distance/popularity) and privacy.



This research has been done in the department of Computer Science and Engineering, Bangladesh University of Engineering and Technology (BUET). The work is supported by the research grant from BUET.


  1. 1.
    California road network data (2017).
  2. 2.
    Ahmadi, E., Nascimento, M.A.: A mixed breadth-depth first search strategy for sequenced group trip planning queries. In: MDM, pp. 24–33 (2015)Google Scholar
  3. 3.
    Ali, M.E., Tanin, E., Scheuermann, P., Nutanong, S., Kulik, L.: Spatial consensus queries in a collaborative environment. ACM Trans. Spatial Algorithms Syst. 2(1), 3:1–3:37 (2016)CrossRefGoogle Scholar
  4. 4.
    Aljubayrin, S., He, Z., Zhang, R.: Skyline trips of multiple POIs categories. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9050, pp. 189–206. Springer, Cham (2015). Google Scholar
  5. 5.
    Anwar, A., Hashem, T.: Optimal obstructed sequenced route queries in spatial databases. In: EDBT, pp. 522–525 (2017)Google Scholar
  6. 6.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-Tree: an efficient and robust access method for points and rectangles. In: SIGMOD, pp. 322–331 (1990)Google Scholar
  7. 7.
    Cao, X., Chen, L., Cong, G., Xiao, X.: Keyword-aware optimal route search. PVLDB 5(11), 1136–1147 (2012)Google Scholar
  8. 8.
    Chao, I.M., Golden, B.L., Wasil, E.A.: “Don’t trust anyone”: privacy protection for location-based services. PMC 7, 44–59 (2011)Google Scholar
  9. 9.
    Chen, H., Ku, W., Sun, M., Zimmermann, R.: The multi-rule partial sequenced route query. In: SIGSPATIAL, pp. 10:1–10:10 (2008)Google Scholar
  10. 10.
    Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Fan, L., Bonomi, L., Shahabi, C., Xiong, L.: Multi-user itinerary planning for optimal group preference. In: Gertz, M., et al. (eds.) SSTD 2017. LNCS, vol. 10411, pp. 3–23. Springer, Cham (2017). CrossRefGoogle Scholar
  12. 12.
    Ghinita, G., Kalnis, P., Khoshgozaran, A., Shahabi, C., Tan, K.: Private queries in location based services: anonymizers are not necessary. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, 10–12 June 2008, pp. 121–132 (2008)Google Scholar
  13. 13.
    Gutin, G., Karapetyan, D.: A memetic algorithm for the generalized traveling salesman problem. Nat. Comput. 9(1), 47–60 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Hashem, T., Kulik, L.: Safeguarding location privacy in wireless ad-hoc networks. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 372–390. Springer, Heidelberg (2007). CrossRefGoogle Scholar
  15. 15.
    Hashem, T., Kulik, L., Zhang, R.: Privacy preserving group nearest neighbor queries. In: EDBT, pp. 489–500 (2010)Google Scholar
  16. 16.
    Hashem, T., Ali, M.E., Kulik, L., Tanin, E., Quattrone, A.: Protecting privacy for group nearest neighbor queries with crowdsourced data and computing. In: UbiComp, pp. 559–562 (2013)Google Scholar
  17. 17.
    Hashem, T., Hashem, T., Ali, M.E., Kulik, L.: Group trip planning queries in spatial databases. In: Nascimento, M.A., Sellis, T., Cheng, R., Sander, J., Zheng, Y., Kriegel, H.-P., Renz, M., Sengstock, C. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 259–276. Springer, Heidelberg (2013). CrossRefGoogle Scholar
  18. 18.
    Hashem, T., Barua, S., Ali, M.E., Kulik, L., Tanin, E.: Efficient computation of trips with friends and families. In: CIKM, pp. 931–940 (2015)Google Scholar
  19. 19.
    Hashem, T., Hashem, T., Ali, M.E., Kulik, L., Tanin, E.: Trip planning queries for subgroups in spatial databases. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 110–122. Springer, Cham (2016). CrossRefGoogle Scholar
  20. 20.
    Hu, H., Lee, D.L.: Range nearest-neighbor query. IEEE Trans. Knowl. Data Eng. 18(1), 78–91 (2006)CrossRefGoogle Scholar
  21. 21.
    Jahan, R., Hashem, T., Barua, S.: Group trip scheduling (GTS) queries in spatial databases. In: EDBT, pp. 390–401 (2017)Google Scholar
  22. 22.
    Laporte, G.: A concise guide to the traveling salesman problem. JORS 61(1), 35–40 (2010)CrossRefzbMATHGoogle Scholar
  23. 23.
    Li, F., Cheng, D., Hadjieleftheriou, M., Kollios, G., Teng, S.-H.: On trip planning queries in spatial databases. In: Bauzer Medeiros, C., Egenhofer, M.J., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 273–290. Springer, Heidelberg (2005). CrossRefGoogle Scholar
  24. 24.
    Mahin, M.T., Hashem, T., Kabir, S.: A crowd enabled approach for processing nearest neighbor and range queries in incomplete databases with accuracy guarantee. Pervasive Mob. Comput. 39, 249–266 (2017)CrossRefGoogle Scholar
  25. 25.
    Mokbel, M.F., Chow, C., Aref, W.G.: The new casper: a privacy-aware location-based database server. In: ICDE, pp. 1499–1500 (2007)Google Scholar
  26. 26.
    Mouratidis, K., Yiu, M.L.: Shortest path computation with no information leakage. PVLDB 5(8), 692–703 (2012)Google Scholar
  27. 27.
    Ohsawa, Y., Htoo, H., Sonehara, N., Sakauchi, M.: Sequenced route query in road network distance based on incremental Euclidean restriction. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012. LNCS, vol. 7446, pp. 484–491. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  28. 28.
    Ohsawa, Y., Htoo, H., Win, T.N.: Continuous trip route planning queries. In: Pokorný, J., Ivanović, M., Thalheim, B., Šaloun, P. (eds.) ADBIS 2016. LNCS, vol. 9809, pp. 198–211. Springer, Cham (2016). CrossRefGoogle Scholar
  29. 29.
    Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: ICDE, pp. 301–310 (2004)Google Scholar
  30. 30.
    Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2), 529–576 (2005)CrossRefGoogle Scholar
  31. 31.
    Rego, C., Gamboa, D., Glover, F., Osterman, C.: Traveling salesman problem heuristics: leading methods, implementations and latest advances. Eur. J. Oper. Res. 211(3), 427–441 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  32. 32.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, pp. 71–79 (1995)Google Scholar
  33. 33.
    Samrose, S., Hashem, T., Barua, S., Ali, M.E., Uddin, M.H., Mahmud, M.I.: Efficient computation of group optimal sequenced routes in road networks. In: MDM, pp. 122–127 (2015)Google Scholar
  34. 34.
    Sharifzadeh, M., Kolahdouzan, M.R., Shahabi, C.: The optimal sequenced route query. VLDB J. 17(4), 765–787 (2008)CrossRefGoogle Scholar
  35. 35.
    Soma, S.C., Hashem, T., Cheema, M.A., Samrose, S.: Trip planning queries with location privacy in spatial databases. World Wide Web 20(2), 205–236 (2017)CrossRefGoogle Scholar
  36. 36.
    Tabassum, A., Barua, S., Hashem, T., Chowdhury, T.: Dynamic group trip planning queries in spatial databases. In: SSDBM, pp. 38:1–38:6 (2017)Google Scholar
  37. 37.
    Xu, Z., Rodrigues, B.: A 3/2-approximation algorithm for multiple depot multiple traveling salesman problem. In: Kaplan, H. (ed.) SWAT 2010. LNCS, vol. 6139, pp. 127–138. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  38. 38.
    Yiu, M.L., Mamoulis, N., Papadias, D.: Aggregate nearest neighbor queries in road networks. IEEE Trans. Knowl. Data Eng. 17(6), 820–833 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBangladesh University of Engineering and TechnologyDhakaBangladesh

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