IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3822-d247908.html
   My bibliography  Save this article

Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage

Author

Listed:
  • Fahad Alrukaibi

    (Department of Civil Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait)

  • Rushdi Alsaleh

    (Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada)

  • Tarek Sayed

    (Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada)

Abstract

The objective of this study is to estimate the real time travel times on urban networks that are partially covered by moving sensors. The study proposes two machine learning approaches; the random forest (RF) model and the multi-layer feed forward neural network (MFFN) to estimate travel times on urban networks which are partially covered by moving sensors. A MFFN network with three hidden layers was developed and trained using the back-propagation learning algorithm, and the neural weights were optimized using the Levenberg–Marquardt optimization technique. A case study of an urban network with 100 links is considered in this study. The performance of the proposed models was compared to a statistical model, which uses the empirical Bayes (EB) method and the spatial correlation between travel times. The models’ performances were evaluated using data generated from VISSIM microsimulation model. Results show that the machine learning algorithms, e.g., RF and ANN, achieve average improvements of about 4.1% and 2.9% compared with the statistical approach. The RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracies reaching 90.7%, 89.5%, and 86.6% respectively. Moreover, results show that at low moving sensor penetration rate, the RF and MFFN achieve higher estimation accuracy compared with the statistical approach. At probe penetration rate of 1%, the RF, MFFN, and the statistical approach models correctly predict real time travel times with estimation accuracy of 85.6%, 84.4%, and 80.9% respectively. Furthermore, the study investigated the impact of the probe penetration rate on real time neighbor links coverage. Results show that at probe penetration rates of 1%, 3%, and 5%, the models cover the estimation of real time travel times on 73.8%, 94.8%, and 97.2% of the estimation intervals.

Suggested Citation

  • Fahad Alrukaibi & Rushdi Alsaleh & Tarek Sayed, 2019. "Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3822-:d:247908
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3822/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/14/3822/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    2. Jenelius, Erik & Koutsopoulos, Haris N., 2013. "Travel time estimation for urban road networks using low frequency probe vehicle data," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 64-81.
    3. Coifman, Benjamin, 2002. "Estimating travel times and vehicle trajectories on freeways using dual loop detectors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(4), pages 351-364, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Junzhuo Li & Wenyong Li & Guan Lian, 2022. "Optimal Aggregate Size of Traffic Sequence Data Based on Fuzzy Entropy and Mutual Information," Sustainability, MDPI, vol. 14(22), pages 1-17, November.
    2. Binglei Xie & Yu Sun & Xiaolong Huang & Le Yu & Gangyan Xu, 2020. "Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays," Sustainability, MDPI, vol. 12(18), pages 1-23, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitris Bertsimas & Arthur Delarue & Patrick Jaillet & Sébastien Martin, 2019. "Travel Time Estimation in the Age of Big Data," Operations Research, INFORMS, vol. 67(2), pages 498-515, March.
    2. Luo, Xiaoqian & Wang, Dianhai & Ma, Dongfang & Jin, Sheng, 2019. "Grouped travel time estimation in signalized arterials using point-to-point detectors," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 130-151.
    3. Nantes, Alfredo & Ngoduy, Dong & Miska, Marc & Chung, Edward, 2015. "Probabilistic travel time progression and its application to automatic vehicle identification data," Transportation Research Part B: Methodological, Elsevier, vol. 81(P1), pages 131-145.
    4. Peer, Stefanie & Knockaert, Jasper & Koster, Paul & Tseng, Yin-Yen & Verhoef, Erik T., 2013. "Door-to-door travel times in RP departure time choice models: An approximation method using GPS data," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 134-150.
    5. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    6. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    7. Treiber, Martin & Kesting, Arne & Helbing, Dirk, 2010. "Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 983-1000, September.
    8. Chen, Chao, 2004. "Travel Times on Changeable Message Signs: Pilot Project," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2wp2p0m6, Institute of Transportation Studies, UC Berkeley.
    9. Martínez-Díaz, Margarita & Pérez, Ignacio, 2015. "A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 19-35.
    10. Liang Shen & Feiran Wang & Yueyuan Chen & Xinyi Lv & Zongliang Wen, 2022. "A Reliability-Based Stochastic Traffic Assignment Model for Signalized Traffic Network with Consideration of Link Travel Time Correlations," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    11. Coogan, Samuel & Flores, Christopher & Varaiya, Pravin, 2017. "Traffic predictive control from low-rank structure," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 1-22.
    12. Cui, JianXun & Liu, Feng & Janssens, Davy & An, Shi & Wets, Geert & Cools, Mario, 2016. "Detecting urban road network accessibility problems using taxi GPS data," Journal of Transport Geography, Elsevier, vol. 51(C), pages 147-157.
    13. Saif Eddin Jabari & Nikolaos M. Freris & Deepthi Mary Dilip, 2020. "Sparse Travel Time Estimation from Streaming Data," Transportation Science, INFORMS, vol. 54(1), pages 1-20, January.
    14. Büchel, Beda & Corman, Francesco, 2022. "Modeling conditional dependencies for bus travel time estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    15. Deboosere, Robbin & El-Geneidy, Ahmed M. & Levinson, David, 2018. "Accessibility-oriented development," Journal of Transport Geography, Elsevier, vol. 70(C), pages 11-20.
    16. Catarina N. S. Silva & Justas Dainys & Sean Simmons & Vincentas Vienožinskis & Asta Audzijonyte, 2022. "A Scalable Open-Source Framework for Machine Learning-Based Image Collection, Annotation and Classification: A Case Study for Automatic Fish Species Identification," Sustainability, MDPI, vol. 14(21), pages 1-13, November.
    17. Ghasri, Milad & Ardeshiri, Ali & Zhang, Xiang & Waller, S. Travis, 2024. "Analysing preferences for integrated micromobility and public transport systems: A hierarchical latent class approach considering taste heterogeneity and attribute non-attendance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    18. Wu, Min & Wang, Nanxi & Yuen, Kum Fai, 2023. "Can autonomy level and anthropomorphic characteristics affect public acceptance and trust towards shared autonomous vehicles?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    19. Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    20. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3822-:d:247908. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.