IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v112y2023ics0966692323001837.html
   My bibliography  Save this article

Integrating link count data for enhanced estimation of deterrence functions: A case study of short-term bicycle network interventions

Author

Listed:
  • Melo, Lucas Eduardo Araújo de
  • Isler, Cassiano Augusto

Abstract

Owing to the lack of demand-related data from interim timespans between consecutive household surveys, it is difficult to assess short-term interventions in the bicycling network and their impact on potential accessibility. In this study, we propose a novel method that incorporates easier and cheaper demand-related data for estimating the parameters of deterrence functions of gravity-based distribution models. In particular, an origin-destination (OD) matrix-updating procedure that uses bicycle count data was incorporated into a two-step line-search algorithm for estimating a two-parameter deterrence function. The method was applied to a case study in the city of São Paulo, where potential accessibility was calculated during the years 2014–2017, which was a period interim to the two most recent household surveys. Three different models were used to estimate the generalized costs of the network links. This method proved useful for capturing accessibility variations over short timespans. Results show how demand is affected by the bicycling network and provide insights into how short-term assessments can benefit decision-making such as providing well-connected and continuous bicycling infrastructure positively influences accessibility. However, they fail as a unique solution apart from land-use policies. Finally, our proposed method can become a valuable tool for decision-making from an accessibility perspective in the short term.

Suggested Citation

  • Melo, Lucas Eduardo Araújo de & Isler, Cassiano Augusto, 2023. "Integrating link count data for enhanced estimation of deterrence functions: A case study of short-term bicycle network interventions," Journal of Transport Geography, Elsevier, vol. 112(C).
    • Handle: RePEc:eee:jotrge:v:112:y:2023:i:c:s0966692323001837
    DOI: 10.1016/j.jtrangeo.2023.103711
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692323001837
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2023.103711?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Menghini, G. & Carrasco, N. & Schüssler, N. & Axhausen, K.W., 2010. "Route choice of cyclists in Zurich," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(9), pages 754-765, November.
    2. Ehrgott, Matthias & Wang, Judith Y.T. & Raith, Andrea & van Houtte, Chris, 2012. "A bi-objective cyclist route choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(4), pages 652-663.
    3. Faghih Imani, Ahmadreza & Miller, Eric J. & Saxe, Shoshanna, 2019. "Cycle accessibility and level of traffic stress: A case study of Toronto," Journal of Transport Geography, Elsevier, vol. 80(C).
    4. Broach, Joseph & Dill, Jennifer & Gliebe, John, 2012. "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1730-1740.
    5. Liu, Chengxi & Tapani, Andreas & Kristoffersson, Ida & Rydergren, Clas & Jonsson, Daniel, 2020. "Development of a large-scale transport model with focus on cycling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 164-183.
    6. Arellana, Julián & Saltarín, María & Larrañaga, Ana Margarita & González, Virginia I. & Henao, César Augusto, 2020. "Developing an urban bikeability index for different types of cyclists as a tool to prioritise bicycle infrastructure investments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 310-334.
    7. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    8. Bera, Sharminda & Rao, K. V. Krishna, 2011. "Estimation of origin-destination matrix from traffic counts: the state of the art," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 49, pages 2-23.
    9. Seungkyu Ryu, 2020. "A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    10. Bocarejo S., Juan Pablo & Oviedo H., Daniel Ricardo, 2012. "Transport accessibility and social inequities: a tool for identification of mobility needs and evaluation of transport investments," Journal of Transport Geography, Elsevier, vol. 24(C), pages 142-154.
    11. Owens, Susan, 1995. "From 'predict and provide' to 'predict and prevent'?: Pricing and planning in transport policy," Transport Policy, Elsevier, vol. 2(1), pages 43-49, January.
    12. Zuo, Ting & Wei, Heng, 2019. "Bikeway prioritization to increase bicycle network connectivity and bicycle-transit connection: A multi-criteria decision analysis approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 52-71.
    13. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    14. Cascetta, Ennio, 1984. "Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 289-299.
    15. Bar-Gera, Hillel & Boyce, David, 2006. "Solving a non-convex combined travel forecasting model by the method of successive averages with constant step sizes," Transportation Research Part B: Methodological, Elsevier, vol. 40(5), pages 351-367, June.
    16. Pritchard, John P. & Tomasiello, Diego Bogado & Giannotti, Mariana & Geurs, Karst, 2019. "Potential impacts of bike-and-ride on job accessibility and spatial equity in São Paulo, Brazil," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 386-400.
    17. Hesham Rakha & Mazen Arafeh, 2010. "Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data," Transportation Science, INFORMS, vol. 44(2), pages 151-168, May.
    18. Hallgrimsdottir, Berglind & Wennberg, Hanna & Svensson, Helena & Ståhl, Agneta, 2016. "Implementation of accessibility policy in municipal transport planning – Progression and regression in Sweden between 2004 and 2014," Transport Policy, Elsevier, vol. 49(C), pages 196-205.
    19. Banister, David, 2008. "The sustainable mobility paradigm," Transport Policy, Elsevier, vol. 15(2), pages 73-80, March.
    Full references (including those not matched with items on IDEAS)

    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. Seungkyu Ryu, 2020. "A Bicycle Origin–Destination Matrix Estimation Based on a Two-Stage Procedure," Sustainability, MDPI, vol. 12(7), pages 1-14, April.
    2. Ospina, Juan P. & Duque, Juan C. & Botero-Fernández, Verónica & Montoya, Alejandro, 2022. "The maximal covering bicycle network design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 222-236.
    3. S. Travis Waller & Sai Chand & Aleksa Zlojutro & Divya Nair & Chence Niu & Jason Wang & Xiang Zhang & Vinayak V. Dixit, 2021. "Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    4. Louis Grange & Felipe González & Shlomo Bekhor, 2017. "Path Flow and Trip Matrix Estimation Using Link Flow Density," Networks and Spatial Economics, Springer, vol. 17(1), pages 173-195, March.
    5. Cunha, Isabel & Silva, Cecília, 2023. "Assessing the equity impact of cycling infrastructure allocation: Implications for planning practice," Transport Policy, Elsevier, vol. 133(C), pages 15-26.
    6. Menon, Aditya Krishna & Cai, Chen & Wang, Weihong & Wen, Tao & Chen, Fang, 2015. "Fine-grained OD estimation with automated zoning and sparsity regularisation," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 150-172.
    7. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
    8. Bielli, Maurizio & Reverberi, Pierfrancesco, 1996. "New operations research and artificial intelligence approaches to traffic engineering problems," European Journal of Operational Research, Elsevier, vol. 92(3), pages 550-572, August.
    9. Shao, Hu & Lam, William H.K. & Sumalee, Agachai & Chen, Anthony & Hazelton, Martin L., 2014. "Estimation of mean and covariance of peak hour origin–destination demands from day-to-day traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 52-75.
    10. Bierlaire, M. & Toint, Ph. L., 1995. "Meuse: An origin-destination matrix estimator that exploits structure," Transportation Research Part B: Methodological, Elsevier, vol. 29(1), pages 47-60, February.
    11. Hai Yang & Qiang Meng & Michael G. H. Bell, 2001. "Simultaneous Estimation of the Origin-Destination Matrices and Travel-Cost Coefficient for Congested Networks in a Stochastic User Equilibrium," Transportation Science, INFORMS, vol. 35(2), pages 107-123, May.
    12. Giannotti, Mariana & Tomasiello, Diego B. & Bittencourt, Taina A., 2022. "The bias in estimating accessibility inequalities using gravity-based metrics," Journal of Transport Geography, Elsevier, vol. 101(C).
    13. Sherali, Hanif D. & Narayanan, Arvind & Sivanandan, R., 2003. "Estimation of origin-destination trip-tables based on a partial set of traffic link volumes," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 815-836, November.
    14. McArthur, David Philip & Hong, Jinhyun, 2019. "Visualising where commuting cyclists travel using crowdsourced data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 233-241.
    15. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    16. Lo, H. P. & Zhang, N. & Lam, W. H. K., 1999. "Decomposition algorithm for statistical estimation of OD matrix with random link choice proportions from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 33(5), pages 369-385, June.
    17. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    18. Xuesong Zhou & George F. List, 2010. "An Information-Theoretic Sensor Location Model for Traffic Origin-Destination Demand Estimation Applications," Transportation Science, INFORMS, vol. 44(2), pages 254-273, May.
    19. Juha-Matti Kuusinen & Janne Sorsa & Marja-Liisa Siikonen, 2015. "The Elevator Trip Origin-Destination Matrix Estimation Problem," Transportation Science, INFORMS, vol. 49(3), pages 559-576, August.
    20. Łukawska, Mirosława & Paulsen, Mads & Rasmussen, Thomas Kjær & Jensen, Anders Fjendbo & Nielsen, Otto Anker, 2023. "A joint bicycle route choice model for various cycling frequencies and trip distances based on a large crowdsourced GPS dataset," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).

    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:eee:jotrge:v:112:y:2023:i:c:s0966692323001837. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    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.