Assessing the impact of urban off-hour delivery program using city scale simulation models

  • Satish V. UkkusuriEmail author
  • Kaan Ozbay
  • Wilfredo F. Yushimito
  • Shri Iyer
  • Ender F. Morgul
  • José Holguín-Veras
Research Paper


This paper describes two different types of models to assess the traffic impacts of an off-hour delivery program for the New York City (NYC) borough of Manhattan. Traffic impacts are measured in New York City metropolitan region using both a regional travel demand model and a mesoscopic simulation model. Analysis is conducted to determine the effectiveness and impacts of the scenarios modeled; focusing on the changes predicted by the traffic models. The results from both models are compared and analyzed, and a discussion on the usage of these models is presented. While macroscopic models can be used to measure traffic effects in a large urban region, mesoscopic models similar to the one used in this paper have their advantages in terms of better quantifying traffic impacts of system-wide benefits. However, simulation time makes it impractical to use mesoscopic simulation for large urban regions. In this work, both the macroscopic regional travel demand model and a mesoscopic sub-simulation network show a measurable impact to congestion and network conditions. However, even when the results show an increasing benefit in terms of travel time savings and increasing speeds, cost–benefit analysis show that when compared with the costs (in this case implementation costs by providing incentives), only small receiver participation justifies the costs of the off-hour deliveries (OHD) program. As incentive amounts increase, receiver participation increases greatly, though the monetized traffic benefits do not necessarily increase at the same rate. Additional analysis was also performed with a targeted program where large traffic generators and large businesses were the recipients of the incentive. The benefits of the targeted program are estimated to be roughly equivalent to the cheapest scenario run for the broad-based program ($5,000 tax incentive assumption) at a fraction of the cost.


Freight modeling Off-hour deliveries Planning model Traffic simulation Urban freight 



The authors would like to acknowledge USDOT for funding this innovative research study as a part of the Integrative Freight Demand Management in the New York City Metropolitan Area Project Phase I between 2007 and 2010, as well as New York/New Jersey-area transportation agencies such as NYMTC, NYSDOT, NYCDOT, NJDOT, NJTA and PANYNJ for providing data and models used in this study. Their support is appreciated. This work was completed while the first and third authors were at Rensselaer Polytechnic Institute, and the second, fourth and fifth authors were at Rutgers University.


  1. Alexiadis V, Jeannotte K, Chandra A (2004) Traffic analysis toolbox volume I: traffic analysis tools primer. Federal Highway Administration, Publication No. FHW A-HRT-04-038, Washington DCGoogle Scholar
  2. Alexiadis V (2008) Integrated corridor management analysis, modeling, and simulation experimental plan for the test corridor. US Department of Transportation, Publication No. FHWA-JPO-08-035 EDL 14415, Washington DCGoogle Scholar
  3. Balakrishna R, Antoniou C, Koutsopoulos HN, Wen Y, Ben-Akiva M (2011) Calibrating Speed-Density Functions for Mesoscopic Traffic Simulation. 75 Years of the Fundamental Diagram for Traffic Flow Theory (Transportation Research Circular No. E-C149)Google Scholar
  4. Burghout W, Koutsopoulus H, Andréasson I (2005) Hybrid mesoscopic-microscopic traffic simulation. Transp Res Rec 1934:218–255CrossRefGoogle Scholar
  5. Caliper Corporation (2008) TransCAD 5.0: transportation planning software user’s guideGoogle Scholar
  6. Caliper Corporation (2009) TransModeler user’s guideGoogle Scholar
  7. Caliper Corporation (2010) TransModeler traffic simulation software, version 2 built 930Google Scholar
  8. Crainic TG, Ricciardi N, Storchi G (2004) Advanced freight transportation systems for congested urban areas. Transp Res Part C 12:119–137CrossRefGoogle Scholar
  9. Federeal Highway Administration (FHWA) (2002) Freight analysis framework overview. Publication No. FHWA-OP-03-2006Google Scholar
  10. Holguín-Veras J, Brom M (2008) Trucking costs: comparison between econometric estimation and cost accounting. In: 87th Annual meeting of the Transportation Research Board, Washington DCGoogle Scholar
  11. Holguín-Veras J, List GF, Meyburg AH, Ozbay K, Paaswell RE, Teng H et al (2001) An assessment of methodological alternatives for a regional freight model in the NYMTC region. Region II University Transportation Research Center, New YorkGoogle Scholar
  12. Holguín-Veras J, Ozbay K, Cerreño A (2005) Evaluation Study of the Port Authority of New York and New Jersey’s Time of Day Pricing Initiative. Federal Highway Administration/US Department of Transportation, Publication No. FHWA/NJ-2005-005, Washington DCGoogle Scholar
  13. Holguín-Veras J, Perez N, Cruz B, Polimeni J (2006a) Effectiveness of financial incentives for off-peak deliveries to restaurants in Manhattan, New York. Transp Res Rec 1966:51–59CrossRefGoogle Scholar
  14. Holguín-Veras J, Wang Q, Xu N, Ozbay K, Cetin M, Polimeni J (2006b) The impacts of time of day pricing on the behavior of freight carriers in a congested urban area: Implications to road pricing. Transp Res Part A: Policy Pract 40(9):744–766Google Scholar
  15. Holguín-Veras J, Silas MA, Polimeni J, Cruz B (2007) An Investigation on the effectiveness of joint receiver-carrier policies to increase truck traffic in the off-peak hours: Part I: the behavior of receivers. Netw Spat Econ 7(3):277–295CrossRefGoogle Scholar
  16. Holguín-Veras J, Silas MA, Polimeni J, Cruz B (2008) An investigation on the effectiveness of joint receiver-carrier policies to increase truck traffci in the off-peak hours: Part II: the behavior of carriers. Netw Spat Econ 8(4):327–354CrossRefGoogle Scholar
  17. Holguín-Veras J, Ozbay K, Kornhauser A, Shorris A, Ukkusuri S (2010) Integrative freight demand management in the New York City metropolitan area. United States Department of Transportation Cooperative Agreement #DTOS59-07-H-0002, Final ReportGoogle Scholar
  18. Holguín-Veras J, Jaller M, Destro L, Ban X, Lawson C, Levinson H (2011a) Freight generation, freight trip generation, and the perils of using constant trip rates. Transp Res Rec 2224:68–81CrossRefGoogle Scholar
  19. Holguín-Veras J, Ozbay K, Kornhauser A, Brom MA, Iyer S, Yushimito WF, Ukkusuri S, Allen B, Silas MA (2011b) Overall impacts of off-hour delivery programs in New York City metropolitan area. Transp Res Rec 2238:68–76CrossRefGoogle Scholar
  20. Holguín-Veras, I. Sánchez-Díaz. C. Lawson, M. Jaller, S. Campbell, H. Levinson (2013) Transferability of freight trip generation models. Transp Res Rec 2379:1–8CrossRefGoogle Scholar
  21. Ioannou P, Anastasios C, Bose A, Jula H, Kanaris A, Pourhammadi H et al. (2001) Modeling and route guidance of trucks in metropolitan areas. Metrans Transportation Center, Center for Advanced Transportation Technologies, Universty of Southern CaliforniaGoogle Scholar
  22. Jaller M, Wang X, Holguín-Veras J (2012) Large traffic generators: opportunities for city logistics initiatives. J Transp Land Use (JTLU) (accepted)Google Scholar
  23. Lawson C, Holguín-Veras J, Sanchez I, Jaller M, Campbell S, Powers E (2012) Estimation of freight trip generation based on land use. Transp Res Rec 2269:65–72CrossRefGoogle Scholar
  24. Montero L, Codina E, Barcelo J, Barcelo P (1998) Combining macroscopic and microscopic approaches for transportation planning and design of road networks. In: Proceedings of the 19 th ARRB transport research conference, Sydney, pp 93–108Google Scholar
  25. New York City Department of Transportation (NYCDOT). (2007) New York City Bridge and Tunnel Counts 2007 ReportGoogle Scholar
  26. New York Metropolitan Transportation Council (2005a) 2005–2030 Regional Transportation PlanGoogle Scholar
  27. New York Metropolitan Transportation Council (2005b) New York Best Practice Model Final ReportGoogle Scholar
  28. Ozbay K, Yanmaz-Tuzel O, Yazici MA, Ozbay K, Holguín-Veras J (2006) The impacts of time-of-day pricing initiative at NY/NJ port authority facilities car and truck movements. In: 85th Annual meeting of the Transportation Research Board, Washington DCGoogle Scholar
  29. Ozbay K, Yanmaz-Tuzel O, Bartin B, Mudigonda S, Berechman J (2007) Cost of transporting people in New Jersey—Phase 2. Federal Highway Administration, Publication No. FHWA/NJ-2007-003, Washington DCGoogle Scholar
  30. Parsons Brinckerhoff Quade & Douglas, Inc. (2005) Transportation models and data initiative: New York best practice model (NYBPM). New York Metropolitan CouncilGoogle Scholar
  31. Rousseau G, Scherr W, Yuan F, Xiong C (2009) An implementation framework for integrating regional planning model with microscopic traffic simulation. In: Liu R, Zhang J, Guan C (eds) Logistics Chapter 559, pp 3816–3825Google Scholar
  32. Siegel J, Coeymans JE (2005) An Integrated Framework for Traffic Analysis Combining Macroscopic and Microscopic Models. Transp Plan Technol 28(5):135–148CrossRefGoogle Scholar
  33. Silas MA, Holguin-Veras J (2009) A behavioral micro-simulation for the analysis of off-hour delivery policies. Transp Res Rec 2097:43–50CrossRefGoogle Scholar
  34. Singh V, Huey SB, Lethco T, Dunn P, Sanagavarapu S (2009) Microsimulation model design in lower Manhattan: a street management approach. In: 88th Annual meeting of the Transportation Research Board, Washington, DCGoogle Scholar
  35. Xie C, Kockelman K, Waller ST (2010) Maximum entropy method for subnetwork origin-destination trip matrix estimation. Transp Res Rec 2196:111–119CrossRefGoogle Scholar
  36. Yannis G, Golias J, Antoniou C (2006) Effects of urban delivery restrictions on traffic movements. Transp Plan Technol 29(4):295–311CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and EURO - The Association of European Operational Research Societies 2015

Authors and Affiliations

  • Satish V. Ukkusuri
    • 1
    Email author
  • Kaan Ozbay
    • 2
  • Wilfredo F. Yushimito
    • 3
  • Shri Iyer
    • 4
  • Ender F. Morgul
    • 2
  • José Holguín-Veras
    • 5
  1. 1.Lyles School of Civil EngineeringPurdue UniversityWest LafayetteUSA
  2. 2.Department of Civil and Urban Engineering, Polytechnic School of Engineering, Center for Urban Science + Progress (CUSP)New York University (NYU)BrooklynUSA
  3. 3.Department of Engineering and SciencesUniversidad Adolfo IbáñezViña Del MarChile
  4. 4.MTA New York City Transit, Operations Planning-System Data and ResearchNew YorkUSA
  5. 5.Department of Civil and Environmental EngineeringJohnsson Engineering CenterTroyUSA

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