Using ALPR data to understand the vehicle use behaviour under TDM measures

Using ALPR data to understand the vehicle use behaviour under TDM measures

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

Buy article PDF
(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
Your details
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 Shanghai, China, two transportation demand management (TDM) measures, auctions of Shanghai vehicle licence plates and a narrow-time-based travel restriction policy, have been implemented to control the vehicle ownership and the use of vehicles registered outside Shanghai (VROS). To investigate the impact of these two TDM measures on VROS, vehicle use behaviour is analysed with automatic licence plate recognition (ALPR) data. A two-step k-means clustering algorithm is proposed to classify VROS' use behaviour from ALPR raw data. Moreover, the spatiotemporal patterns of each type of VROS and the structure of total transportation demand are analysed. The results show that VROS in Shanghai expressway network can be classified into five types. Type 1 vehicles are used for commuting (COM), and type 2 vehicles are used with high intensity during both on workdays and non-workdays (HHI). COM and HHI are mainly used by local Shanghai residents who cannot obtain a local licence plate under the auction policy. These two types of VROS only account for 3.6% of total vehicles but generate 14.2% of total traffic demand. If the users of COM and HHI transferred to public transit, the traffic congestion of expressway network would be greatly alleviated.


    1. 1)
      • 1. Meyer, M.D.: ‘Demand management as an element of transportation policy: using carrots and sticks to influence travel behaviour’, Transp. Res. A, Policy Pract., 1999, 33, (7), pp. 575599.
    2. 2)
      • 2. Loukopoulos, P.: ‘A classification of travel demand management measures. Threats from car traffic to the quality of urban life: problems, causes and solutions’ (Elsevier, Amsterdam, 2007), pp. 273292.
    3. 3)
      • 3. Steg, L.: ‘Factors influencing the acceptability and effectiveness of transport pricing’, in ‘Acceptability of transport pricing strategies’ (Elsevier, Amsterdam, 2003), pp. 187202.
    4. 4)
      • 4. ‘Auction results for the past years’. Available at
    5. 5)
      • 5. Gärling, T., Eek, D., Loukopoulos, P., et al: ‘A conceptual analysis of the impact of travel demand management on private car use’, Transp. Policy, 2002, 9, (1), pp. 5970.
    6. 6)
      • 6. Tertoolen, G., Van Kreveld, D., Verstraten, B: ‘Psychological resistance against attempts to reduce private car use’, Transp. Res. A, Policy Pract., 1988, 32, (3), pp. 171181.
    7. 7)
      • 7. Deslauriers, B.C., Everett, P.B.: ‘Effects of intermittent and continuous token reinforcement on bus ridership’, J. Appl. Psychol., 1977, 62, (4), p. 369.
    8. 8)
      • 8. Taylor, M.A.P.: ‘Voluntary travel behaviour change programs in Australia: the carrot rather than the stick in travel demand management’, Int. J. Sust. Transp., 2007, 1, (3), pp. 173192.
    9. 9)
      • 9. Cairns, S., Sloman, L., Newson, C., et al: ‘Smarter choices: assessing the potential to achieve traffic reduction using ‘soft measures’, Transp. Rev., 2008, 28, (5), pp. 593618.
    10. 10)
      • 10. Brög, W., Erl, E., Ker, I., et al: ‘Evaluation of voluntary travel behaviour change: experiences from three continents’, Transp. Policy, 2009, 6, (6), pp. 281292.
    11. 11)
      • 11. Santos, G., Rojey, L.: ‘Distributional impacts of road pricing: the truth behind the myth’, Transportation (Amst), 2004, 31, (1), pp. 2142.
    12. 12)
      • 12. Givoni, M.: ‘Re-assessing the results of the London congestion charging scheme’, Urban Stud., 2012, 49, (5), pp. 10891105.
    13. 13)
      • 13. Loukopoulos, P., Jakobsson, C., Gärling, T., et al: ‘Car-user responses to travel demand management measures: goal setting and choice of adaptation alternatives’, Transp. Res. D, Transp. Environ., 2004, 9, (4), pp. 263280.
    14. 14)
      • 14. Asakura, Y., Hato, E., Kashiwadani, M.: ‘Origin-destination matrices estimation model using automatic vehicle identification data and its application to the Han-Shin expressway network’, Transportation (Amst), 2000, 27, (4), pp. 419438.
    15. 15)
      • 15. Zhou, X., Mahmassani, H.S.: ‘Dynamic origin-destination demand estimation using automatic vehicle identification data’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (1), pp. 105114.
    16. 16)
      • 16. Dion, F., Rakha, H.: ‘Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates’, Transp. Res. B, Methodol., 2006, 40, (9), pp. 745766.
    17. 17)
      • 17. Ma, X., Koutsopoulos, H.: ‘A new online travel time estimation approach using distorted automatic vehicle identification data’. Proc. 11th Int. IEEE Conf. Intelligent Transportation Systems, Beijing, China, October 2008.
    18. 18)
      • 18. Castillo, E., Menéndez, J.M., Jiménez, P.: ‘Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations’, Transp. Res. B, Methodol., 2008, 42, (5), pp. 455481.
    19. 19)
      • 19. Sánchez-Cambronero, S., Castillo, E., Menéndez, J.M., et al: ‘Dealing with error recovery in traffic flow prediction using Bayesian networks based on license plate scanning data’, J. Transp. Eng., 2011, 137, (9), pp. 615629.
    20. 20)
      • 20. Chen, H., Xie, X., Feng, Y., et al: ‘A novel method of trip route estimation based on vehicle license plate recognition system’, Procedia-Soc. Behav. Sci., 2013, 96, pp. 643652.
    21. 21)
      • 21. Duda, R.O., Hart, P.E., Stork, D.G.: ‘Pattern classification’ (John Wiley and Sons, New York, NY, USA, 2012, 2nd edn.).
    22. 22)
      • 22. Wu, J.: ‘Advances in K-means clustering: a data mining thinking’ (Springer Science & Business Media, Berlin, Germany, 2012).
    23. 23)
      • 23. Maulik, U., Bandyopadhyay, S.: ‘Performance evaluation of some clustering algorithms and validity indices’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (12), pp. 16501654.

Related content

This is a required field
Please enter a valid email address