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TARS: traffic-aware route search

Abstract

In a traffic-aware route search (TARS), the user provides start and target locations and sets of search terms. The goal is to find the fastest route from the start location to the target via geographic entities (points of interest) that correspond to the search terms, while taking into account variations in the travel speed due to changes in traffic conditions, and the possibility that some visited entities will not satisfy the search requirements. A TARS query may include temporal constraints and order constraints that restrict the order by which entities are visited. Since TARS generalizes the Traveling-Salesperson Problem, it is an NP-hard problem. Thus, it is unlikely to find a polynomial-time algorithm for evaluating TARS queries. Hence, we present in this paper three heuristics to answer TARS queries—a local greedy approach, a global greedy approach and an algorithm that computes a linear approximation to the travel speeds, formulates the problem as a Mixed Integer Linear Programming (MILP) problem and uses a solver to find a solution. We provide an experimental evaluation based on actual traffic data and show that using a MILP solver to find a solution is effective and can be done within a limited running time in many real-life scenarios. The local-greedy approach is the least effective in finding a fast route, however, it has the best running time and it is the most scalable.

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Notes

  1. In practice, the travel-time function depends on the date, e.g., the travel-time function for workdays may not be the same as the one for weekends, however, this is a technical issue, which we ignore to simplify the model. It can be handled by using different time functions according to the day of the travel.

  2. See http://msdn.microsoft.com/en-us/library/cc966826.aspx.

  3. http://developer.yahoo.com/search/local/V3/localSearch.html

  4. http://msdn.microsoft.com/en-us/library/cc966826.aspx

  5. http://www.gurobi.com/

References

  1. Abdalla A, Frank AU (2012) Combining trip and task planning: how to get from a to passport. In: Proceedings of the 7th international conference on geographic information science. Lecture notes in computer science, vol 7478. Springer, pp 1–14

  2. Balas E (1989) The prize collecting traveling salesman problem. Networks 19:621–636

    Article  Google Scholar 

  3. Bertsimas D, Simchi-Levi D (1996) A new generation of vehicle routing research: robust algorithms, addressing uncertainty. J Oper Res 44(2):286–304

    Article  Google Scholar 

  4. Booth J, Sistla P, Wolfson O, Cruz IF (2009) A data model for trip planning in multimodal transportation systems. In: Proceedings of the 12th international conference on extending database technology: advances in database technology, EDBT ’09. ACM, New York, NY, USA, pp 994–1005

    Chapter  Google Scholar 

  5. Caldwell T (1961) On finding minimum routes in a network with turn penalties. Commun ACM 4(2):107–108. doi:10.1145/366105.366184

    Article  Google Scholar 

  6. Chen H, Ku WS, Sun MT, Zimmermann R (2008) The multi-rule partial sequenced route query. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’08. ACM, New York, NY, USA, pp 10:1–10:10

    Google Scholar 

  7. Dechter R (2003) Constraint processing. Elsevier Morgan Kaufmann

  8. Ding B, Yu JX, Qin L (2008) Finding time-dependent shortest paths over large graphs. In: Proceedings of the 11th international conference on extending database technology: advances in database technology, EDBT ’08. ACM, New York, NY, USA, pp 205–216

    Chapter  Google Scholar 

  9. Dolev N, Kanza Y, Doytsher Y (2008) Efficient orienteering-route search over uncertain spatial datasets. In: FIG working week—integrating generations, Stockholm (Sweden)

  10. Doytsher Y, Galon B, Kanza Y (2011) Storing routes in socio-spatial networks and supporting social-based route recommendation. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks, LBSN ’11. ACM, New York, NY, USA, pp 49–56

    Google Scholar 

  11. Evangelos K, Yang D, Tian X, Donghui Z (2006) Finding fastest paths on a road network with speed patterns. In: Proceedings of the 22nd international conference on data engineering. IEEE

  12. Feiyue L, Bruce G, Edward W (2005) Solving the time dependent traveling salesman problem. In: The next wave in computing, optimization, and decision technologies. Operations research/computer science interfaces series, vol 29. Springer, pp 163–182

  13. Friedman R, Hefez I, Kanza Y, Levin R, Safra E, Sagiv Y (2012) Wiser: a web-based interactive route search system for smartphones. In: Proceedings of the 21st international conference companion on World Wide Web, WWW ’12 Companion. ACM, New York, NY, USA, pp 337–340

    Chapter  Google Scholar 

  14. Geisberger R, Sanders P, Schultes D, Delling D (2008) Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: McGeoch C (ed) Experimental algorithms (Lecture notes in computer science), vol 5038. Springer Berlin Heidelberg, pp 319–333

    Google Scholar 

  15. Gonzalez H, Han J, Li X, Myslinska M, Sondag JP (2007) Adaptive fastest path computation on a road network: a traffic mining approach. In: Proceedings of the 33rd international conference on very large data bases, VLDB ’07. VLDB Endowment, pp 794–805

  16. Gutin G, Punnen A, Barvinok A, Gimadi EK, Serdyukov AI (2002) The traveling salesman problem and its variations (Combinatorial Optimization), 1st edn. Springer. http://link.springer.com/book/10.1007/b101971/page/1

  17. Haiquan C, Wei-Shinn K, Min-Te S, Roger Z (2011) The partial sequenced route query with traveling rules in road networks. GeoInformatica 15:541–569

    Article  Google Scholar 

  18. Hefez I, Kanza Y, Levin R (2011) Tarsius: a system for traffic-aware route search under conditions of uncertainty. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’11. ACM, New York, NY, USA, pp 517–520

    Google Scholar 

  19. Hill AV, Benton WC (1992) Modelling intra-city time-dependent travel speeds for vehicle scheduling problems. J Oper Res Soc 43:343–351

    Article  Google Scholar 

  20. Hoel EG, Heng WL, Honeycutt D (2005) High performance multimodal networks. In: Proceedings of the 9th international conference on advances in spatial and temporal databases, SSTD’05. Springer, Berlin, Heidelberg, pp 308–327

    Chapter  Google Scholar 

  21. Irina D, Stefan R, Jean-Francois C, Gilbert L (2010) The traveling salesman problem with pickup and delivery: polyhedral results and a branch-and-cut algorithm. Math Program 121:269–305

    Article  Google Scholar 

  22. Jagadeesh G, Srikanthan T, Quek KH (2002) Heuristic techniques for accelerating hierarchical routing on road networks. IEEE Trans Intell Transp Syst 3(4):301–309. doi:10.1109/TITS.2002.806806

    Article  Google Scholar 

  23. Kanza Y, Levin R, Safra E, Sagiv Y (2009) An interactive approach to route search. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’09. ACM, New York, NY, USA, pp 408–411

    Google Scholar 

  24. Kanza Y, Levin R, Safra E, Sagiv Y (2010) Interactive route search in the presence of order constraints. Proc The International Journal on Very Large Data Bases Endow 3(1–2):117–128

    Google Scholar 

  25. Kanza Y, Safra E, Sagiv Y, Doytsher Y (2008) Heuristic algorithms for route-search queries over geographical data. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’08. ACM, New York, NY, USA, pp 11:1–11:10

    Google Scholar 

  26. Kim S, George B, Shekhar S (2007) Evacuation route planning: scalable heuristics. In: Proceedings of the 15th annual ACM international symposium on advances in geographic information systems, GIS ’07. ACM, New York, NY, USA, pp 20:1–20:8

    Chapter  Google Scholar 

  27. Kim S, Shekhar S, Min M (2008) Contraflow transportation network reconfiguration for evacuation route planning. IEEE Trans Knowl Data Eng 20(8):1115–1129

    Article  Google Scholar 

  28. Kirby RF, Potts RB (1969) The minimum route problem for networks with turn penalties and prohibitions. Transp Res 3(3):397–408. doi:10.1016/S0041-1647(69)80022-5

    Article  Google Scholar 

  29. Laporte G, Nobert Y (1983) Generalized traveling salesman problem through n-sets of nodes—an integer programming approach. Information Systems and Operational Research (INFOR) 21(1):61–75

    Google Scholar 

  30. Letchner J, Krumm J, Horvitz E (2006) Trip router with individualized preferences (trip): incorporating personalization into route planning. In: Proceedings of the 18th conference on innovative applications of artificial intelligence, IAAI’06, vol 2. AAAI Press, pp 1795–1800

  31. Levin R (2011) Web site. http://db64.cs.technion.ac.il/tars/. Accessed 24 June 2011

  32. Li F, Cheng D, Hadjieleftheriou M, Kollios G, Teng SH (2005) On trip planning queries in spatial databases. In: Proceedings of the 9th international conference on advances in spatial and temporal databases, SSTD’05. Springer, Berlin, Heidelberg, pp 273–290

    Chapter  Google Scholar 

  33. Lu Q, George B, Shekhar S (2005) Capacity constrained routing algorithms for evacuation planning: a summary of results. In: Proceedings of the 9th international symposium on advances in spatial and temporal databases. Springer, pp 291–307

  34. Lu Q, George B, Shekhar S (2007) Evacuation route planning: a case study in semantic computing. Int J Semant Comput 1(2):249–303

    Article  Google Scholar 

  35. Pahlavani P, Delavar MR, Frank AU (2012) Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem. Int J Appl Earth Obs Geoinf 18(0):313–328. doi:10.1016/j.jag.2012.03.004. http://www.sciencedirect.com/science/article/pii/S0303243412000487

    Article  Google Scholar 

  36. Pop PC (2007) New integer programming formulations of the generalized travelling salesman problem. Am J Appl Sci 4(11):932–937

    Article  Google Scholar 

  37. Safra E, Kanza Y, Dolev N, Sagiv Y, Doytsher Y (2007) Computing a k-route over uncertain geographical data. In: Proceedings of the 10th international conference on advances in spatial and temporal databases, SSTD’07. Springer, Berlin, Heidelberg, pp 276–293

    Chapter  Google Scholar 

  38. Srivastava SS, Kumar S, Garg RC, Sen P (1969) Generalized traveling salesman problem through n sets of nodes. Canadian Operational Research Society Journal 7:97–101

    Google Scholar 

  39. Sharifzadeh M, Kolahdouzan M, Shahabi C (2008) The optimal sequenced route query. The International Journal on Very Large Data Bases 17(4):765–787. doi:10.1007/s00778-006-0038-6

    Article  Google Scholar 

  40. Sharifzadeh M, Shahabi C (2008) Processing optimal sequenced route queries using voronoi diagrams. GeoInformatica 12:411–433

    Article  Google Scholar 

  41. Sung K, Bell MG, Seong M, Park S (2000) Shortest paths in a network with time-dependent flow speeds. Eur J Oper Res 121:32–39

    Article  Google Scholar 

  42. Tatomir B, Rothkrantz L (2006) Hierarchical routing in traffic using swarm-intelligence. In: International conference on intelligent transportation. IEEE, pp 230–235

  43. Terrovitis M, Bakiras S, Papadias D, Mouratidis K (2005) Constrained shortest path computation. In: Proceedings of the 9th international symposium on advances in spatial and temporal databases, pp 923–923

  44. Tian Y, Lee KCK, Lee WC (2009) Monitoring minimum cost paths on road networks. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, GIS ’09. ACM, New York, NY, USA, pp 217–226

    Google Scholar 

  45. Tsitsiklis JN (1992) Special cases of traveling salesman and repairman problems with time windows. Networks 22:263–282

    Article  Google Scholar 

  46. Winter S (2002) Modeling costs of turns in route planning. GeoInformatica 6(4):345–361. doi:10.1023/A:1020853410145

    Article  Google Scholar 

  47. Woensel T, Kerbache L, Peremans H, Vandaele N (2007) A queueing framework for routing problems with time-dependent travel times. Journal of Mathematical Modelling and Algorithms 6(1):151–173. doi:10.1007/s10852-006-9054-1

    Article  Google Scholar 

  48. Xu J, Guo L, Ding Z, Sun X, Liu C (2012) Traffic aware route planning in dynamic road networks. In: Proceedings of the 17th international conference on database systems for advanced applications, DASFAA’12, vol Part I. Springer, Berlin, Heidelberg, pp 576–591

    Google Scholar 

  49. Yang K, Gunturi VMV, Shekhar S (2012) A dartboard network cut based approach to evacuation route planning: A summary of results. In: Proceedings of the 7th international conference on geographic information science. Springer, pp 325–339

  50. Zhou X, George B, Kim S, Wolff JMR, Lu Q, Shekhar S (2010) Evacuation planning: a spatial network database approach. Knowledge and Data Engineering 33(2):26–31

    Google Scholar 

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Correspondence to Roy Levin.

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Levin, R., Kanza, Y. TARS: traffic-aware route search. Geoinformatica 18, 461–500 (2014). https://doi.org/10.1007/s10707-013-0185-z

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  • DOI: https://doi.org/10.1007/s10707-013-0185-z

Keywords

  • Geographic information systems
  • Route search
  • Temporal constraints
  • Probabilistic data
  • Heuristic algorithms
  • Traffic