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

Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data

Support vector machine and back propagation neutral network approaches for trip mode prediction using mobile phone data

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 Title Publication 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.

This study provides a methodology to identify travellers’ transportation modes by tracking the mobile phone data, which aims to obtain the accurate mode split rate for providing decision support in urban traffic planning. First, the effective mobile phone singling data and GPS data are collected from the communication operators and a mobile phone app, respectively. Considering the differences in velocity and acceleration of different trip modes, a trip mode characteristic description model is built based on wave characteristics and moving average method. Compared with the wave characteristics, the moving average method shows a better accuracy of 90%. Then training samples are drawn by two data selection methods including probability proportional to size sampling and equal amount sampling. Furthermore, the classifier method for mode choice prediction is developed by support vector machines (SVMs) and back propagation neutral network. Finally, the results of the case study show that using a 30-point moving average training data set can improve the prediction accuracy largely, and the SVM method gets a better accuracy of 82%. The potential of using the mobile phone data to build a new mode choice prediction method in the field of transportation is shown.

References

    1. 1)
      • 1. China Internet Network Information Center: ‘China statistical report on internet development (2017)’. 2018, pp. 15.
    2. 2)
      • 2. Zhong, G., Wan, X., Zhang, J., et al: ‘Characterizing passenger flow for a transportation hub based on mobile phone data’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (6), pp. 15071518.
    3. 3)
      • 3. Iqbal, M.S., Choudhury, C.F., Wang, P., et al: ‘Development of origin-destination matrices using mobile phone call data’, Transp. Res. C, Emerg. Technol., 2014, 40, (1), pp. 6374.
    4. 4)
      • 4. Alexander, L., Jiang, S., Murga, M., et al: ‘Origin-destination trips by purpose and time of day inferred from mobile phone data’, Transp. Res. C, Emerg. Technol., 2015, 58, (B), pp. 240250.
    5. 5)
      • 5. Dong, H., Wu, M., Ding, X., et al: ‘Traffic zone division based on big data from mobile phone base stations’, Transp. Res. C, Emerg. Technol., 2015, 58, (B), pp. 278291.
    6. 6)
      • 6. Demissie, M.G., Phithakkitnukoon, S., Sukhvibul, T., et al: ‘Inferring passenger travel demand to improve urban mobility in developing countries using cell phone data: a case study of Senegal’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (9), pp. 24662478.
    7. 7)
      • 7. He, K., Xu, Z., Wang, P., et al: ‘Congestion avoidance routing based on large-scale social signals’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (9), pp. 26132626.
    8. 8)
      • 8. Xu, Y., Shaw, S.L., Fang, Z., et al: ‘Estimating potential demand of bicycle trips from mobile phone data-an anchor-point based approach’, Int. J. Geo-Inf., 2016, 5, (8), pp. 18.
    9. 9)
      • 9. Lindsey, G., Hankey, S., Wang, X., et al: ‘Feasibility of using GPS to track bicycle lane positioning’, Center Transp. Stud., 2013, pp. 120.
    10. 10)
      • 10. Çolak, S., Alexander, L.P., Alvim, B.G., et al: ‘Analyzing cell phone location data for urban travel’, Transp. Res. Rec. J. Transp. Res. Board, 2015, 2526, (3), pp. 126135.
    11. 11)
      • 11. Sadeghvaziri, E., Rojas, M.B., Jin, X.: ‘Exploring the potential of Mobile phone data in travel pattern analysis’, Transp. Res. Rec. J. Transp. Res. Board, 2016, 2594, (1), pp. 2734.
    12. 12)
      • 12. Gonzalez, P.A., Weinstein, J.S., Barbeau, S.J., et al: ‘Automating mode detection for travel behaviour analysis by using global positioning systemsenabled mobile phones and neural networks’, IET Intell. Transp. Syst., 2010, 4, (1), pp. 3749.
    13. 13)
      • 13. Gong, H., Chen, C., Bialostozky, E., et al: ‘A GPS/GIS method for travel mode detection in New York city’, Comput. Environ. Urban Syst., 2012, 36, (2), pp. 131139.
    14. 14)
      • 14. Bohte, W., Maat, K.: ‘Deriving and validating trip destinations and modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands’, Transp. Res. C, Emerg. Technol., 2009, 17, (3), pp. 285297.
    15. 15)
      • 15. Pereira, F., Carrion, C., Zhao, F., et al: ‘The future mobility survey: overview and preliminary evaluation’. Proc. 10th Int. Conf. Eastern Asia Society for Transportation Studies, Taipei, Taiwan, 2013.
    16. 16)
      • 16. Rokib, S.A., Karim, M.A., Qiu, T.Z., et al: ‘Origin-destination trip estimation from anonymous cell phone and foursquare data’. Transportation Research Board Annual Meeting, 2015.
    17. 17)
      • 17. Hao, P., Boriboonsomsin, K., Wu, G., et al: ‘Modal activity-based stochastic model for estimating vehicle trajectories from sparse mobile sensor data’, IEEE Trans. Intell. Transp. Syst., 2016, 18, (3), pp. 701711.
    18. 18)
      • 18. Rojas, M.B., Sadeghvaziri, E., Jin, X.: ‘Comprehensive review of travel behavior and mobility pattern studies that used mobile phone data’, Transp. Res. Rec. J. Transp. Res. Board, 2016, 2563, (1), pp. 7179.
    19. 19)
      • 19. Mishalani, R.G., Mccord, M.R., Reinhold, T.: ‘Use of mobile device wireless signals to determine transit route-level passenger origin-destination flows: methodology and empirical evaluation’, Transp. Res. Rec. J. Transp. Res. Board, 2016, 2544, pp. 123130.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5203
Loading

Related content

content/journals/10.1049/iet-its.2018.5203
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address