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An A2-Gurobi algorithm for route recommendation with big taxi trajectory data

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Abstract

To address the problems of high fuel consumption and severe traffic congestion caused by blindly cruising, we propose a Gurobi optimization algorithm combined with an A ngle and an A* algorithm (A2-Gurobi) to recommend the optimal taxi route based on big taxi trajectory data in the complex urban road network. Specifically, a r oad n etwork n ode e xtraction method (RNNE) based on the GPS direction of taxis is put forward to solve the difficulty of extracting road network nodes from the big Taxi GPS trajectory data. Next, an a ngle-based s harp p oint e limination approach (ASPE) is constructed to optimize the searching capability of the Gurobi algorithm to find the shortest path. Then, a Gurobi optimization algorithm (A-Gurobi) based on the A* algorithm is designed, which uses the heuristic function of the A* algorithm to enhance the ability of fast guidance from the origin to the destination, thereby improving the execution efficiency of the Gurobi algorithm. Finally, the A2-Gurobi algorithm is successfully applied to the optimal taxi route recommendation with real-world taxi trajectory big data. Compared with Gurobi, Dijkstra, Floyd, A*, Bi-A*-ACO, Bellman-Ford, BFS, Acyclic, and AC on the taxi trajectory data sets composed of 10, 20, 30, 40, and 50 nodes, the experimental results indicate that the distance of the A2-Gurobi algorithm for taxi route recommendation is 19.06%, 20.97%, 3.03%, 15.07%, 15.07%, and 3.85% shorter than that of Gurobi, A*, Bi-A*-ACO, BFS, Acyclic, and AC on average, respectively, and it also achieves the same distance as other algorithms. In terms of execution efficiency, our A2-Gurobi algorithm outperforms the baselines mentioned above by 38.56%, 96.63%, 66.66%, 66.66%, 90.87%, 97.60%, 97.27%, 97.21%, and 99.27%, respectively.

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Data Availability

The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Bouzid Y, Bestaoui Y, Siguerdidjane H (2019) Guidance-control system of a quadrotor for optimal coverage in cluttered environment with a limited onboard energy: complete software. J Intell Robot Syst 95:707–730

    Article  Google Scholar 

  2. Brito J, Martínez FJ, Moreno JA, Verdegay JL (2015) An ACO hybrid metaheuristic for close–open vehicle routing problems with time windows and fuzzy constraints. Appl Soft Comput 32:154–163

    Article  Google Scholar 

  3. Calabrò G, Torrisi V, Inturri G, Ignaccolo M (2020) Improving inbound logistic planning for large-scale real-world routing problems: a novel Ant-colony simulation-based optimization. Eur Trans Res Rev 12:1–11

    Google Scholar 

  4. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271

    Article  MathSciNet  MATH  Google Scholar 

  5. Feng L, Lv Z, Guo G, Song H (2016) Pheromone based alternative route planning. Digit Commun Netw 2:151–158

    Article  Google Scholar 

  6. Garrote L, Paulo J, Nunes UJ (2020) Reinforcement learning aided robot-assisted navigation: a utility and RRT two-stage approach. Int J Soc Robot 12:689–707

    Article  Google Scholar 

  7. Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4:100–107

    Article  Google Scholar 

  8. Hou P, Pan H, Guo C (2017) Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the asiasim. Int J Model Simul Sci Comput 8:1750046

    Article  Google Scholar 

  9. Hu W, Wu H, Cho H, Tseng F (2020) Optimal route planning system for logistics vehicles based on artificial intelligence. J Inter Technol 21:757–764

    Google Scholar 

  10. Huang R, Ning J, Mei Z, Fang X, Yi X, Gao Y, Hui G (2021) Study of delivery path optimization solution based on improved ant colony model. Multimed Tools Appl 80:1–13

    Article  Google Scholar 

  11. Kavraki LE, Svestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12:566–580

    Article  Google Scholar 

  12. Laarabi MH, Boulmakoul A, Mabrouk A, Sacile R, Garbolino E (2023) Real-time fastest path algorithm using bidirectional point-to-point search on a fuzzy time-dependent transportation network. In: 2014 international conference on advanced logistics and transport (ICALT). IEEE, pp 78–84

  13. LaValle SM (1998) Rapidly-exploring random trees: a new tool for path planning. Res Rep:98–11

  14. Lee DH, Lee SS, Ahn CK, Shi P, Lim CC (2021) Finite distribution estimation-based dynamic window approach to reliable obstacle avoidance of mobile robot. IEEE Trans Ind Electron 68:9998–10006

    Article  Google Scholar 

  15. Lengauer T, Tarjan RE (1979) A fast algorithm for finding dominators in a flowgraph. ACM Transa Program Langu Syst (TOPLAS) 1:121–141

    Article  MATH  Google Scholar 

  16. Liang C, Zhang X, Watanabe Y, Deng Y (2021) Autonomous collision avoidance of unmanned surface vehicles based on improved A-star and minimum course alteration algorithms. Appl Ocean Res 113:102755

    Article  Google Scholar 

  17. Noreen I, Khan A, Habib Z (2016) Optimal path planning using RRT* based approaches: a survey and future directions. Int J Adv Comput Sci Appl 7:97–107

    Google Scholar 

  18. Orozco-Rosas U, Montiel O, Sepúlveda R (2019) Mobile robot path planning using membrane evolutionary artificial potential field. Appl Soft Comput 77:236–251

    Article  Google Scholar 

  19. Pan G, Qi G, Zhang W, Li S, Wu Z, Yang LT (2013) Trace analysis and mining for smart cities: issues, methods, and applications. IEEE Commun Mag 51:120–126

    Article  Google Scholar 

  20. Qin G, Li T, Yu B, Wang Y, Huang Z, Sun J (2017) Mining factors affecting taxi drivers’ incomes using GPS trajectories. Transport Res C Emerg Technol 79:103–118

    Article  Google Scholar 

  21. Shao S, Guan W, Bi J (2018) Electric vehicle-routing problem with charging demands and energy consumption. IET Intell Trans Syst 12:202–212

    Article  Google Scholar 

  22. Sheng W, Li B, Zhong X (2021) Autonomous parking trajectory planning with tiny passages: a combination of multistage hybrid A-star algorithm and numerical optimal control. IEEE Access 9:102801–102810

    Article  Google Scholar 

  23. Wang P, Gao S, Li L, Sun B, Cheng S (2019) Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm. Energies 12:2342

    Article  Google Scholar 

  24. Xia D, Bai Y, Zheng Y, Hu Y, Li Y, Li H (2022) A parallel SP-DBSCAN algorithm on Spark for waiting spot recommendation. Multimed Tools Appl 81:4015–4038

    Article  Google Scholar 

  25. Xia D, Jiang S, Yang N, Hu Y, Li Y, Li H, Wang L (2021) Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data. Phys A Stat Mech Appl 578:126056

    Article  Google Scholar 

  26. Xia D, Wang B, Li H, Li Y, Zhang Z (2016) A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting. Neurocomputing 179:246–263

    Article  Google Scholar 

  27. Xia D, Zhang M, Yan X, Bai Y, Zheng Y, Li Y, Li H (2021) A distributed WND-LSTM model on MapReduce for short-term traffic flow prediction. Neural Comput Appl 33:2393–2410

    Article  Google Scholar 

  28. Xia D, Zheng Y, Bai Y, Yan X, Hu Y, Li Y, Li H (2022) A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot prediction. Multimed Tools Appl 81:27523–27549

    Article  Google Scholar 

  29. Xiang X, Qiu J, Xiao J, Zhang X (2020) Demand coverage diversity based ant colony optimization for dynamic vehicle routing problems. Eng Appl Artif Intell 91:103582

    Article  Google Scholar 

  30. Xu P (2019) Research on optimized model of travel route selection based on intelligent image information and ant colony algorithm. Multimed Tools Appl 78:1–17

    Google Scholar 

  31. Yang S, Gao T, Wang J, Deng B, Lansdell B, Linares-Barranco B (2021a) Efficient spike-driven learning with dendritic event-based processing. Front Neurosci 15:601109

  32. Yang S, Wang J, Deng B, Azghadi MR, Linares-Barranco B (2021b) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans Neural Netw Learn Syst

  33. Yu L, Jiang H, Hua L (2019) Anti-congestion route planning scheme based on Dijkstra algorithm for automatic valet parking system. Appl Sci 9:5016

    Article  Google Scholar 

  34. Yuan C, Weng S, Shen J, Chen L, He Y, Wang T (2020) Research on active collision avoidance algorithm for intelligent vehicle based on improved artificial potential field model. Int J Adv Robot Syst 17:1729881420911232

    Article  Google Scholar 

  35. Yuan Z, Yang Z, Lv L, Shi Y (2020) A Bi-Level path planning algorithm for Multi-AGV routing problem. Electronics 9:1351

    Article  Google Scholar 

  36. Zhang Y, Li L, Lin H, Ma Z, Zhao J (2023) Development of path planning approach based on improved A-star algorithm in AGV system. In: International conference on internet of things as a service. Springer, pp 276–279

  37. Zeng D, Yu Z, Xiong L, Zhao J, Zhang P, Li Y, Xia L, Wei Y, Li Z, Fu Z (2021) Driving-behavior-oriented trajectory planning for autonomous vehicle driving on urban structural road. Proc Instit Mech Eng D J Autom Eng 235:975–995

    Article  Google Scholar 

  38. Zhang J, Feng Y, Shi F, Wang G, Ma B, Li R, Jia X (2016) Vehicle routing in urban areas based on the oil consumption weight-Dijkstra algorithm. IET Intell Trans Syst 10:495–502

    Article  Google Scholar 

  39. Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol (TIST) 6:1–41

    Article  MathSciNet  Google Scholar 

  40. Zheng Y (2017) Urban computing: enabling urban intelligence with big data. Front Comput Sci 11:1–3

    Article  Google Scholar 

  41. Zhou Z, Wang J, Zhu Z, Yang D, Wu J (2018) Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik 158:639–651

    Article  Google Scholar 

Download references

Acknowledgements

This work described in this paper was supported in part by the National Natural Science Foundation of China (Grant nos. 62162012, 62173278, and 62072061), the Science and Technology Support Program of Guizhou Province (Grant no. QKHZC2021YB531), the Natural Science Research Project of Department of Education of Guizhou Province (Grant no. QJJ2022015), and the Scientific Research Platform Project of Guizhou Minzu University (Grant no. GZMUSYS[2021]04).

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Authors and Affiliations

  1. College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, China

    Dawen Xia, Jian Geng, Bingqi Shen, Dewei Bai & Wenyong Zhang

  2. Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College, Guiyang, 550008, China

    Yang Hu

  3. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Yantao Li

  4. College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Huaqing Li

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  1. Dawen Xia
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Correspondence to Dawen Xia.

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Xia, D., Geng, J., Shen, B. et al. An A2-Gurobi algorithm for route recommendation with big taxi trajectory data. Multimed Tools Appl 82, 46547–46575 (2023). https://doi.org/10.1007/s11042-023-15058-w

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