Skip to main content
SpringerLink
Log in

The Value of Freight Accessibility: a Spatial Analysis in the Tampa Bay Region

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

With the recent growth in markets in the U.S., freight transportation systems have become more intricate, and are affected by the variety of static and dynamic elements associated with roadways. It is imperative to find effective ways to manage freight systems with smart transportation solutions so that governments can facilitate policy decisions to use their resources more efficiently. To make such concrete policy decisions for a specific study region, freight network, supply chain, and freight transportation trends should be studied carefully. As such, this study evaluates the accessibility of freight warehouses to intermodal freight facilities using Geographical Information Systems (GIS)-based tools with a focus on Value of Time (VOT), spatial distributions of warehouses and intermodal freight facilities, and traffic characteristics. A Value of Freight Accessibility (VoFA) metric is developed for traffic analysis zones (TAZs) in the Tampa Bay region as a function of accessibility to warehouses, number of trucks, and VOT. Results indicate that there is a need for smart transport solutions such as dedicated truck-only lanes, and Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) in order to help decrease the travel time needed for freight transportation and increase the freight accessibility. Findings are critical in terms of informing Florida transportation agencies to pinpoint freight bottleneck areas and enhance their freight transportation plans and policies to alleviate these bottlenecks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Ayar, B., & Yaman, H. (2012). An intermodal multicommodity routing problem with scheduled services. Computational Optimization and Applications, 53(1), 131–153.

    Google Scholar 

  • Becker, G. S. (1965). A theory of the allocation of time. Economic Journal, 75(299), 493.

    Google Scholar 

  • Ben-Akiva, M., Bierlaire, M., Burton, D., Koutsopoulos, H. N., & Mishalani, R. (2001). Network state estimation and prediction for real-time traffic management. Networks and Spatial Economics, 1(3), 293–318.

    Google Scholar 

  • Bergkvist, E. (2001a). Freight Transportation: Valuation of Time and Forecasting of Flows. Umea_Economic Studies 549. Umea_University, Sweden.

  • Bergkvist, E. (2001b). The value of time and forecasting of flows in freight transportation. Paper presented at the 41st European Regional Science Association Congress. Zagreb, Croatia. Available at: http://www-sre.wuwien.ac.at/ersa/ersaconfs/ersa01/papers/full/271.pdf. Accessed April 2018.

  • Beuthe, M., & Bouffioux, C. (2008). Analysing qualitative attributes of freight transport from stated orders of preference experiment. Journal of Transport Economics and Policy, 42(1), 105–128.

    Google Scholar 

  • Bock, S. (2010). Real-time control of freight forwarder transportation networks by integrating multimodal transport chains. European Journal of Operational Research, 200(3), 733–746.

    Google Scholar 

  • Bureau of Labor Statistics (BoLS). (2017). CPI Inflation Calculator. https://www.bls.gov/data/inflation_calculator.htm , Accessed 7/27/2017.

  • Cartenì, A. (2014). Accessibility indicators for freight transport terminals. Arabian Journal for Science and Engineering, 39(11), 7647–7660.

    Google Scholar 

  • Center for Transportation Analysis (CUTA) (2016). Freight Analysis Framework, http://faf.ornl.gov/fafweb/, Accessed 7/25/2016.

  • Chen, X., Liu, Q., & Du, G. (2011). Estimation of Travel Time Values for Urban Public Transport Passengers Based on SP Survey. Journal of Transportation Systems Engineering and Information Technology, 11(4), 77–84.

    Google Scholar 

  • Cho, J. H., Kim, H. S., & Choi, H. R. (2012). An intermodal transport network planning algorithm using dynamic programming-a case study: From Busan to Rotterdam in intermodal freight routing. Applied Intelligence, 36(3), 529–541.

    Google Scholar 

  • Citilabs (2017). http://www.citilabs.com/, Accessed 7/25/2017.

  • De Jong, G., Gommers, M., and Klooster, J. (1992). Time Valuation in Freight Transport: Method and Results, Proceedings of the PTRC European Transport Seminar, September 14–18, Volume P357, pp. 195–206.

  • De Jong, G., Europe, R., Leeds, I., Bakker, S., Pieters, M., & Donselaar, P. W.-V. (2014). New SP-values of Time and Reliability for Freight Transport in the Netherlands. Transportation Research Part E, 64, 71–87.

    Google Scholar 

  • DeSerpa, A. C., & De Serpa, A. C. A. (1971). Theory of the Economics of Time. Economic Journal, 81(324), 828–846.

    Google Scholar 

  • District 7 Office. (2015) Tampa Bay Regional Planning Model. TBRPM v8.0 User’s Procedural Guide, Technical Report 2.

  • Feo-Valero, M., García-Menéndez, L., & Garrido-Hidalgo, R. (2011). Valuing freight transport time using transport demand modelling: A bibliographical review. Transport Reviews, 31(5), 625–651.

    Google Scholar 

  • Florian, M., Mahut, M., & Tremblay, N. (2008). Application of a simulation-based dynamic traffic assignment model. European Journal of Operational Research, 189(3), 1381–1392.

    Google Scholar 

  • Florida Department of Transportation. (2017) District 7 Office. http://www.fdot.gov/info/moreDOT/districts/dist7.shtm., accessed 7/27/2017.

  • Florida Department of Transportation District 7. (2015). Tampa Bay Regional Planning Model.

  • Florida Department of Transportation, District 7 Office (2016). Tampa Bay Strategic Freight Plan, Accessed 7/25/2016.

  • Fowkes, A. S., Firmin, P. E., Tweddle, G., & Whiteing, A. E. (2004). How highly does the freight transport industry value journey time reliability—And for what reasons? International Journal of Logistics Research and Application, 7(1), 33–43.

    Google Scholar 

  • Fowkes, A., Firmin, P., Whiteing, A., Tweddle, G. (2001). Freight road user valuations of three different aspects of delay, Proc. AET Eur. Transp. Conf. HELD 10-12 Sept. 2001, Homert. Coll. CAMBRIDGE, UK. https://trid.trb.org/view.aspx?id=728922. Accessed 13 March 2018.

  • Fridstrom, L., Madslien, A. (1995). A stated preference analysis of wholesalers’ freight choice. Institute of Transport Economics, Oslo.

  • Goel, A. (2010). The value of in-transit visibility for supply chains with multiple modes of transport. International Journal of Logistics Research and Applications, 13(6), 475–492.

    Google Scholar 

  • Gonzalez-Feliy, J., and Mercier, A.A. (2013). Combined People-freight Accessibility Approach for Urban Retaling and Leisure Planning at Strategic Level, 5th International Conference on Urban Freight Transport, Long Beach, CA.

  • Griffin, J. (2017) Tampa Bay Times, Have a Look Inside the 1-Million-Square-Foot Amazon Fulfillment center in Ruskin, Accessed 7/27/2017.

  • Islam, M. S., & Aktar, S. (2011). Measuring physical accessibility to health facilities--a case study on Khulna City. World Health & Population, 12(3), 33–41.

    Google Scholar 

  • Jiang, M., & Morikawa, T. (2004). Theoretical analysis on the variation of value of travel time savings. Transportation Research Part A, 38(8), 551–571.

    Google Scholar 

  • Jovicic, G. (1998). Application of models based on stated and revealed preference data for forecasting Danish international freight transport. Aalborg: Tetraplan AS.

    Google Scholar 

  • Kamga, C., & Yazici, M. A. (2014). Temporal and weather related variation patterns of urban travel time: Considerations and caveats for value of travel time, value of variability, and mode choice studies. Transportation Research Part C, 45, 4–16.

    Google Scholar 

  • Kawamura, K. (2000). Perceived value of time for truck operators. Transportation Research Record, No., 1725, 31–36.

    Google Scholar 

  • Kocatepe, A., E.E. Ozguven, H. Ozel, M.W. Horner, and R. Moses. (2016). Transportation Accessibility Assessment of Critical Emergency Facilities: Aging population-focused case studies case Studies in Florida, Human Aspects of IT for Aged Population: Design for Aging, Part 1, pp. 407.

  • Lei, F., Wang, Y., Lu, G., & Sun, J. (2014). A Travel Time Reliability Model of Urban Expressways with Varying Levels of Service. Transportation Research Part C, 48, 453–467.

    Google Scholar 

  • Lin, P. S., Ozkul, S., Guo, R., & Chen, C. (2018). Assessment of countermeasure effectiveness and informativeness in mitigating wrong-way entries onto limited-access facilities. Accident Analysis & Prevention, 116, 79–93.

    Google Scholar 

  • Mahmassani, H. S. (2001). Dynamic network traffic assignment and simulation methodology for advanced system management applications. Networks and Spatial Economics, 1(3), 267–292.

    Google Scholar 

  • Miller-Hooks, E., Zhang, X., & Faturechi, R. (2012). Measuring and maximizing resilience of freight transportation networks. Computers and Operations Research, 39(7), 1633–1643.

    Google Scholar 

  • O’Sullivan, D., Morrison, A., & Shearer, J. (2000). Using desktop GIS for the investigation of accessibility by public transport: An Isochrone approach. International Journal of Geographical Information Science, 14(1), 85–104.

    Google Scholar 

  • Ozel, H., Ozguven, E. E., Kocatepe, A., & Horner, M. W. (2016). Aging Population–Focused Accessibility Assessment of Multimodal Facilities in Florida. Transportation Research Record, 2584, 45–61.

    Google Scholar 

  • Patterson, Z., Ewing, G., and Haider, M. (2007). Mode and carrier choice in the Quebec City-Windsor corridor: A random parameters approach TRISTAN-VI.

  • Peeta, S., & Ziliaskopoulos, A. K. (2001). Foundations of dynamic traffic assignment: The past, the present and the future. Networks and Spatial Economics, 1(3/4), 233–265.

    Google Scholar 

  • Saliba, D., Buchanan, J., & Kington, R. S. (2004). Function and response of nursing facilities during community disaster. American Journal of Public Health, 94, 1436–1441.

    Google Scholar 

  • Shams, K., Asgari, H., & Jin, X. (2017). Valuation of travel time reliability in freight transportation: A review and meta-analysis of stated preference studies. Transportation Research Part A, 102, 228–243.

    Google Scholar 

  • Small, K.A., Noland, R., Chu, X., and Lewis, D. (1999). Valuation of travel-time savings and predictability in congested conditions for highway user-cost estimation, NCHRP Report, Vol., 431, Transportation Research Board, National Academy Press.

  • Taylor, N. B. (2003). The CONTRAM dynamic traffic assignment model. Networks and Spatial Economics, 3(3), 297–322.

    Google Scholar 

  • Thomas, I., Hermia, J.-P., Vanelslander, T., & Verhetsel, A. (2003). Accessibility to freight transport networks in Belgium: A geographical approach. Journal of Economic and Social Geography, 94(4), 424–438.

    Google Scholar 

  • US Department of Transportation (2015). The Value of Travel Time Savings: Departmental Guidance for Conducting Economic Evaluations Revision, Accessed 7/25/2017.

  • Van den Heuvel, F. P., Rivera, L., van Donselaar, K. H., de Jong, A., Sheffi, Y., de Langen, P. W., & Fransoo, J. C. (2014). Relationship between freight accessibility and logistics employment in U.S.counties. Transportation Research Part A, 59, 91–105.

    Google Scholar 

  • Woudsma, C., and J.F. Jensen (2013) Accessibility and Freight: Transportation and Land Use—Exploring Spatial-Temporal Dimensions. Access to Destinations, Emerald Group Publishing Limited, pp. 267–295.

  • Zamparini, L., and Reggiani, A. (2016). The Value of Travel Time in Passenger and Freight Transport: An Overview, Chapter 8, Policy Analysis of Transport Networks, by Van Geenhuizen, M., and Rietveld, P., London, Routledge.

  • Zamparini, L., Layaa, J., & Dullaert, W. (2011). Monetary values of freight transport quality attributes: A sample of Tanzanian firms. Journal of Transport Geography, 19(6), 1222–1234.

    Google Scholar 

  • Ziliaskopoulos, A. K. (2000). An internet-based geographic information system that integrates data, Models and Users for Transportation Applications. Transportation Research Part C, 8(1–6), 427–444.

    Google Scholar 

Download references

Acknowledgements

We thank FDOT for providing roadway data. The opinions, results, and findings expressed in this manuscript are those of the authors and do not necessarily represent the views of the US Department of Transportation, FDOT, the Center for Accessibility and Safety for an Aging Population, Florida State University, Florida A&M University, or the University of North Florida.

Funding

This project was supported by US Department of Transportation grant DTRT13-G-UTC42 and administered by the Center for Accessibility and Safety for an Aging Population (ASAP) at Florida State University (FSU), Florida A&M University (FAMU), and the University of North Florida (UNF).

Author information

Authors and Affiliations

  1. Travel Modeling and Analytics, Connetics Transportation Group, 3111 N University Drive, Suite 725, Coral Springs, FL, 33065, USA

    Ayberk Kocatepe

  2. Operations and Supply Chain Faculty, Marketing Department – Muma College of Business, University of South Florida, 4202 E. Fowler Ave., BSN 3403, Tampa, FL, 33620, USA

    Seckin Ozkul

  3. Department of Civil and Environmental Engineering, FAMU–FSU College of Engineering, 2525 Pottsdamer Street, Tallahassee, FL, 32310, USA

    Eren Erman Ozguven, John Olusegun Sobanjo & Ren Moses

Authors
  1. Ayberk Kocatepe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seckin Ozkul
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eren Erman Ozguven
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John Olusegun Sobanjo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ren Moses
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eren Erman Ozguven.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kocatepe, A., Ozkul, S., Ozguven, E.E. et al. The Value of Freight Accessibility: a Spatial Analysis in the Tampa Bay Region. Appl. Spatial Analysis 13, 527–546 (2020). https://doi.org/10.1007/s12061-019-09314-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-019-09314-6

Keywords

Search

Navigation