http://iet.metastore.ingenta.com
1887

Finding the ‘faster’ path in vehicle routing

Finding the ‘faster’ path in vehicle routing

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, the authors improve the faster criterion in vehicle routing by extending the bi-delta distribution to the bi-normal distribution, which is a reasonable assumption for travel time on each road link. Based on this assumption, theoretical models are built for an arbitrary path and subsequently adopted to evaluate two candidate paths through probabilistic comparison. Experimental results demonstrate the bi-normal behaviour of link travel time in practice, and verify the faster criterion's superiority in determining the optimal path either on an artificial network with bi-normal distribution modelling link travel time or on a real road network with real traffic data. This study also validates that when the link number of one path is large, the probability density function of the whole path can be simplified by a normal distribution which approximates the sum of bi-normal distributions for each link.

References

    1. 1)
      • G. Isaac , T. Lange .
        1. Isaac, G., Lange, T.: ‘Split routing as a part of the urban navigation’. 19th ITS World Congress, 2012, pp. 18.
        . 19th ITS World Congress , 1 - 8
    2. 2)
      • S. Li , M.-H. Meng , W. Chen .
        2. Li, S., Meng, M.-H., Chen, W., et al: ‘Sp-nn: a novel neural network approach for path planning’. IEEE Int. Conf. on Robotics and Biomimetics, 2007 (ROBIO 2007), 2007, pp. 13551360.
        . IEEE Int. Conf. on Robotics and Biomimetics, 2007 (ROBIO 2007) , 1355 - 1360
    3. 3)
      • M.K. Le , A. Bhaskar , E. Chung .
        3. Le, M.K., Bhaskar, A., Chung, E.: ‘Public transport travel-time variability definitions and monitoring’, J. Transp. Eng., 2015, 141, pp. 04014068-104014068-9.
        . J. Transp. Eng. , 04014068 - 04014061
    4. 4)
      • F. Montgromery , A. May .
        4. Montgromery, F., May, A.: ‘Factors affecting travel times on urban radial routes’, Traffic Eng. Control, 1987, 28, (9), pp. 452458.
        . Traffic Eng. Control , 9 , 452 - 458
    5. 5)
      • Y. Fan , Y. Nie .
        5. Fan, Y., Nie, Y.: ‘Optimal routing for maximizing the travel time reliability’, Netw. Spatial Econ., 2006, 6, (3–4), pp. 333344.
        . Netw. Spatial Econ. , 333 - 344
    6. 6)
      • S. Lim , H. Balakrishnan , D. Gifford .
        6. Lim, S., Balakrishnan, H., Gifford, D., et al: ‘Stochastic motion planning and applications to traffic’. Algorithmic Foundation of Robotics VIII, 2009, pp. 483500.
        . Algorithmic Foundation of Robotics VIII , 483 - 500
    7. 7)
      • E.D. Miller-Hooks , H.S. Mahmassani .
        7. Miller-Hooks, E.D., Mahmassani, H.S.: ‘Least expected time paths in stochastic, time-varying transportation networks’, Transp. Sci., 2000, 34, (2), pp. 198215.
        . Transp. Sci. , 2 , 198 - 215
    8. 8)
      • B.C. Dean .
        8. Dean, B.C.: ‘Algorithms for minimum-cost paths in time-dependent networks with waiting policies’, Networks, 2004, 44, (1), pp. 4146.
        . Networks , 1 , 41 - 46
    9. 9)
      • E. Mazloumi , G. Currie , G. Rose .
        9. Mazloumi, E., Currie, G., Rose, G.: ‘Using GPS data to gain insight into public transport travel time variability’, J. Transp. Eng., 2010, 136, (7), pp. 623631.
        . J. Transp. Eng. , 7 , 623 - 631
    10. 10)
      • Z. Cao , H. Guo , J. Zhang .
        10. Cao, Z., Guo, H., Zhang, J., et al: ‘Multiagent-based route guidance for increasing the chance of arrival on time’. Proc. 30th AAAI Conf. on Artificial Intelligence, 2016.
        . Proc. 30th AAAI Conf. on Artificial Intelligence
    11. 11)
      • Y. Fan , R. Kalaba , J. Moore .
        11. Fan, Y., Kalaba, R., Moore, J.II: ‘Arriving on time’, J. Optim. Theory Appl., 2005, 127, (3), pp. 497513.
        . J. Optim. Theory Appl. , 3 , 497 - 513
    12. 12)
      • Y.M. Nie , X. Wu , T. Homem-de Mello .
        12. Nie, Y.M., Wu, X., Homem-de Mello, T.: ‘Optimal path problems with second-order stochastic dominance constraints’, Netw. Spatial Econ., 2012, 12, (4), pp. 561587.
        . Netw. Spatial Econ. , 4 , 561 - 587
    13. 13)
      • S.D. Boyles , S.T. Waller .
        13. Boyles, S.D., Waller, S.T.: ‘A mean-variance model for the minimum cost flow problem with stochastic arc costs’, Networks, 2010, 56, (3), pp. 215227.
        . Networks , 3 , 215 - 227
    14. 14)
      • E. Nikolova .
        14. Nikolova, E.: ‘Approximation algorithms for reliable stochastic combinatorial optimization’. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2010, pp. 338351.
        . Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques , 338 - 351
    15. 15)
      • K.R. Hutson , D.R. Shier .
        15. Hutson, K.R., Shier, D.R.: ‘Extended dominance and a stochastic shortest path problem’, Comput. Oper. Res., 2009, 36, (2), pp. 584596.
        . Comput. Oper. Res. , 2 , 584 - 596
    16. 16)
      • H.B. Celikoglu .
        16. Celikoglu, H.B.: ‘A dynamic network loading model for traffic dynamics modeling’, IEEE Trans. Intell. Transp. Syst., 2007, 8, (4), pp. 575583.
        . IEEE Trans. Intell. Transp. Syst. , 4 , 575 - 583
    17. 17)
      • H.B. Celikoglu , E. Gedizlioglu , M. Dell'Orco .
        17. Celikoglu, H.B., Gedizlioglu, E., Dell'Orco, M.: ‘A node-based modeling approach for the continuous dynamic network loading problem’, IEEE Trans. Intell. Transp. Syst., 2009, 10, (1), pp. 165174.
        . IEEE Trans. Intell. Transp. Syst. , 1 , 165 - 174
    18. 18)
      • M. DellOrco , M. Marinelli , M.A. Silgu .
        18. DellOrco, M., Marinelli, M., Silgu, M.A.: ‘Bee colony optimization for innovative travel time estimation, based on a mesoscopic traffic assignment model’, Transp. Res. C, Emerg. Technol., 2016, 66, (1), pp. 4860.
        . Transp. Res. C, Emerg. Technol. , 1 , 48 - 60
    19. 19)
      • H.B. Celikoglu .
        19. Celikoglu, H.B.: ‘A dynamic network loading process with explicit delay modelling’, Transp. Res. Emerg. Technol., 2007, 15, (5), pp. 279299.
        . Transp. Res. Emerg. Technol. , 5 , 279 - 299
    20. 20)
      • H.B. Celikoglu .
        20. Celikoglu, H.B.: ‘Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram’, Eur. J. Oper. Res., 2013, 228, (2), pp. 457466.
        . Eur. J. Oper. Res. , 2 , 457 - 466
    21. 21)
      • H.B. Celikoglu .
        21. Celikoglu, H.B.: ‘Flow-based freeway travel-time estimation: a comparative evaluation within dynamic path loading’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 772781.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 772 - 781
    22. 22)
      • O. Deniz , G. Aksoy , H.B. Celikoglu .
        22. Deniz, O., Aksoy, G., Celikoglu, H.B.: ‘Analyzing freeway travel times within a case study: reliability of route traversal times’. Proc. 16th Int. IEEE Conf. on Intelligent Transportation Systems, 2013, pp. 195202.
        . Proc. 16th Int. IEEE Conf. on Intelligent Transportation Systems , 195 - 202
    23. 23)
      • H.B. Celikoglu , M.A. Silgu .
        23. Celikoglu, H.B., Silgu, M.A.: ‘Extension of traffic flow pattern dynamic classification by a macroscopic model using multivariate clustering’, Transp. Sci., 2016, 50, (3), pp. 966981.
        . Transp. Sci. , 3 , 966 - 981
    24. 24)
      • L. Sun , J. Yang , H. Mahmassani .
        24. Sun, L., Yang, J., Mahmassani, H.: ‘Travel time estimation based on piecewise truncated quadratic speed trajectory’, Transp. Res. A, Policy Pract., 2008, 42, (1), pp. 173186.
        . Transp. Res. A, Policy Pract. , 1 , 173 - 186
    25. 25)
      • U. Mori , A. Mendiburu , M. lvarez .
        25. Mori, U., Mendiburu, A., lvarez, M., et al: ‘A review of travel time estimation and forecasting for advanced traveller information systems’, Transportmetrica A, Transp. Sci., 2015, 11, (2), pp. 119157.
        . Transportmetrica A, Transp. Sci. , 2 , 119 - 157
    26. 26)
      • H.B. Celikoglu .
        26. Celikoglu, H.B.: ‘Dynamic classification of traffic flow patterns simulated by a switching multimode discrete cell transmission model’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (6), pp. 25392550.
        . IEEE Trans. Intell. Transp. Syst. , 6 , 2539 - 2550
    27. 27)
      • G. Laporte , F. Louveaux , H. Mercure .
        27. Laporte, G., Louveaux, F., Mercure, H.: ‘The vehicle routing problem with stochastic travel times’, Transp. Sci., 1992, 26, (3), pp. 161170.
        . Transp. Sci. , 3 , 161 - 170
    28. 28)
      • D. Ta , N. Dellaert , T. Van Woensel .
        28. Ta, D., Dellaert, N., Van Woensel, T., et al: ‘Vehicle routing problem with stochastic travel times including soft time windows and service costs’, Comput. Oper. Res., 2013, 40, (1), pp. 214224.
        . Comput. Oper. Res. , 1 , 214 - 224
    29. 29)
      • E. Miller Hooks .
        29. Miller Hooks, E.: ‘Adaptive least expected time paths in stochastic, time varying transportation and data networks’, Networks, 2015, 37, (1), pp. 3552.
        . Networks , 1 , 35 - 52
    30. 30)
      • Z. Cao , H. Guo , J. Zhang .
        30. Cao, Z., Guo, H., Zhang, J., et al: ‘Finding the shortest path in stochastic vehicle routing: a cardinality minimization approach’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (6), pp. 16881702.
        . IEEE Trans. Intell. Transp. Syst. , 6 , 1688 - 1702
    31. 31)
      • S.I. Chien , C.M. Kuchipudi .
        31. Chien, S.I., Kuchipudi, C.M.: ‘Dynamic travel time prediction with real-time and historic data’, J. Transp. Eng., 2003, 129, (6), pp. 608616.
        . J. Transp. Eng. , 6 , 608 - 616
    32. 32)
      • W. Dong , L.V. Hai , Y. Nazarathy .
        32. Dong, W., Hai, L.V., Nazarathy, Y., et al: ‘Shortest paths in stochastic time-dependent networks with link travel time correlation’, Transp. Res. Rec. J. Transp. Res. Board, 2013, 2338, (1), pp. 5866.
        . Transp. Res. Rec. J. Transp. Res. Board , 1 , 58 - 66
    33. 33)
      • Z. Cao , H. Guo , J. Zhang .
        33. Cao, Z., Guo, H., Zhang, J., et al: ‘Maximizing the probability of arriving on time: a practical q-learning method’. Proc. 31th AAAI Conf. on Artificial Intelligence, 2017, pp. 44814487.
        . Proc. 31th AAAI Conf. on Artificial Intelligence , 4481 - 4487
    34. 34)
      • U. Fastenrath , M. Becker .
        34. Fastenrath, U., Becker, M.: ‘Process for selection of a route for a dynamic navigation on private transport’. German Patent: EP 2071287A2, 2008.
        .
    35. 35)
      • C.E. Sigal , A.A.B. Pritsker , J.J. Solberg .
        35. Sigal, C.E., Pritsker, A.A.B., Solberg, J.J.: ‘The stochastic shortest route problem’, Oper. Res., 1980, 28, (5), pp. 11221129.
        . Oper. Res. , 5 , 1122 - 1129
    36. 36)
      • K. Davidson .
        36. Davidson, K.: ‘A flow travel time relationship for use in transportation planning’. Proc. 3rd Australian Road Research Board (ARRB) Conf., Sydney, 1966, vol. 3.
        . Proc. 3rd Australian Road Research Board (ARRB) Conf.
    37. 37)
      • R.E. Allsop .
        37. Allsop, R.E.: ‘Some possibilities for using traffic control to influence trip distribution and route choice’. Proc. Transportation and Traffic Theory, 1974, vol. 6.
        . Proc. Transportation and Traffic Theory
    38. 38)
      • C. Wenjie , G. Liqiang , C. Zhilei .
        38. Wenjie, C., Liqiang, G., Zhilei, C., et al: ‘An intelligent guiding and controlling system for transportation network based on wireless sensor network technology’. Proc. 5th Int. IEEE Conf. on Computer and Information Technology, 2005, pp. 810814.
        . Proc. 5th Int. IEEE Conf. on Computer and Information Technology , 810 - 814
    39. 39)
      • M. Mogridge , S. Fry .
        39. Mogridge, M., Fry, S.: ‘Variability of car journey times on a particular route in central London’, Traffic Eng. Control, 1984, 25, (HS-038 005), pp. 510511.
        . Traffic Eng. Control , 510 - 511
    40. 40)
      • H. Rakha , I. El-Shawarby , M. Arafeh .
        40. Rakha, H., El-Shawarby, I., Arafeh, M., et al: ‘Estimating path travel-time reliability’. Proc. Int. IEEE Conf. on Intelligent Transportation Systems, 2006, pp. 236241.
        . Proc. Int. IEEE Conf. on Intelligent Transportation Systems , 236 - 241
    41. 41)
      • G. Bennett .
        41. Bennett, G.: ‘Probability inequalities for the sum of independent random variables’, J. Am. Stat. Assoc., 1962, 57, (297), pp. 3345.
        . J. Am. Stat. Assoc. , 297 , 33 - 45
    42. 42)
      • L. Bondesson . (1997)
        42. Bondesson, L.: ‘Generalized gamma convolutions’ (Wiley Online Library, 1997).
        .
    43. 43)
      • N. Goodman .
        43. Goodman, N.: ‘Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction)’, Ann. Math. Stat., 1963, 34, (1), pp. 152177.
        . Ann. Math. Stat. , 1 , 152 - 177
    44. 44)
      • Y. Wang , Y. Zheng , Y. Xue .
        44. Wang, Y., Zheng, Y., Xue, Y.: ‘Travel time estimation of a path using sparse trajectories’. Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2014, pp. 2534.
        . Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining , 25 - 34
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0288
Loading

Related content

content/journals/10.1049/iet-its.2016.0288
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address