Demand for freight transport results from the spatial separation of shippers and recipients. Only after these two parties agreed to a relationship are all other logistics and transport decisions made. Thus, the relationships between shippers and recipients are of great importance. For this reason, the allocation of shippers and recipients, also is worth addressing from a modeling perspective. To arrive at a synthetic population of shippers and recipients, a heuristic optimization method that combines priority labeling and a simulated annealing algorithm was developed. The model was applied to a real-life case study of last-mile deliveries in food retail in Berlin, which was based on existing nationwide survey data and other open-source data.
Ben-AkivaM.de JongG.The Aggregate-Disaggregate-Aggregate (ADA) Freight Model System. In Freight Transport Modelling (Ben-AkivaM.MeersmanH.Van de VoordeE., eds.), Emerald Group Publishing Limited, Bingley, 2013, pp. 69–90.
2.
AnderstigC.BerglundM.EdwardsH.SundbergM.PWC Matrices: New Method and Updated Base Matrices. WSP, Stockholm, 2015.
3.
HoviI.GrønlandS.MadslienA.Development of an Improved Decision Support Tool for Freight Transport Planning in Norway. Paper Presented at European Transport Conference, Frankfurt, Germany, 2013.
4.
De JongG.BaakJ.RuijsK.PietersM.BellemansT.JanssensD.WetsG.Passenger and Freight Transport in Flanders 2010-2040 Under Three Scenario’s. Paper Presented at European Transport Conference, Glasgow, 2010.
5.
Holguin-VerasJ.JallerM.Comprehensive Freight Demand Data Collection Framework for Large Urban Areas. In Sustainable Urban Logistics: Concepts, Methods and Information System (Gonzalez-FeliuJ.SemetF.RouthierJ. L., eds.), Springer, Berlin, Heidelberg, 2014, pp. 91–112.
6.
IvanovaO.Modelling Inter-Regional Freight Demand with Input-Output, Gravity and SCGE Methodologies. In Modelling Freight Transport (TavasszyL.de JongG., eds.), Elsevier, London, 2014, pp. 13–42.
7.
PaniA.SahuP. K.Planning, Designing and Conducting Establishment-Based Freight Surveys: A Synthesis of the Literature, Case-Study Examples and Recommendations for Best Practices in Future Surveys. Transport Policy, Vol. 78, 2019, pp. 58–75.
8.
VierthI.LindgrenS.LobigA.MatteisT.LiedtkeG.BurgschweigerS.NiératP.FALCON Handbook Understanding What Influences Modal Choice. CEDR Contractor Report 2017–07. Conference of European Directors of Roads (CEDR), Brussels, 2017.
9.
WisetjindawatW.SanoK.MatsumotoS.Micro-Simulation Model for Modeling Freight Agents Interactions in Urban Freight Movement. Paper No. 07-2224. Proc., 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.
10.
SakaiT.BhavathrathanB. K.AlhoA.HyodoT.Ben-AkivaM.Commodity Flow Estimation for a Metropolitan Scale Freight Modeling System: Supplier selection Considering Distribution Channel Using an Error Component Logit Mixture Model. Transportation, Vol. 47, 2020, pp. 997–1025.
11.
GentileG.VigoD.Movement Generation and Trip Distribution for Freight Demand Modelling Applied to City Logistics. European Transport\Trasporti Europei, 54, Paper No 6, 2013.
12.
De JongG.Ben-AkivaM.A Micro-Simulation Model of Shipment Size and Transport Chain Choice. Transportation Research Part B: Methodological, Vol. 41, No. 9, 2007, pp. 950–965.
13.
de BokM.TavasszyL.KourouniotiI.ThoenS.EggersL.NielsenV. M.StrengJ.Simulation of the Impacts of a Zero-Emission Zone on Freight Delivery Patterns in Rotterdam. Transportation Research Record: Journal of the Transportation Research Board, 2021. 2675: 776–785.
14.
KawamuraK.MohammadianA.SamimiA.PourabdollahiZ.KlekotkaM.Development of Freight Policy Analysis Tool for Northeastern Illinois and the United States. Report No.CFIRE 02–29. National Center for Freight & Infrastructure Research & Education, Department of Civil and Environmental Engineering, College of Engineering, University of Wisconsin–Madison, 2010.
15.
MahmoudifardS. M.ShabanpourR.GolshaniN.MohammadianK.MohammadianA. K.Supplier Evaluation Problem, a Decision Tree and an Ordered Probit Analysis on a Freight Establishment Survey. No. 17-01210. Paper No. 17-01210, Proceedings, 96th Annual Meeting of the Transportation Research Board, Washington, D.C., 2017.
16.
KunnumkalS.TopalogluH. (2011). Transportation Resource Management. In Wiley Encyclopedia of Operations Research and Management Science (eds CochranJ.J.CoxL.A.KeskinocakP.KharoufehJ.P.SmithJ.C.).
17.
DanielisR.RotarisL.MarcucciE.Urban Freight Policies and Distribution Channels: A Discussion Based on Evidence From Italian Cities, European Transport, Vol. 46, 2010, pp. 114–146.
18.
WermuthM.NeefC.WirthR.Kraftfahrzeugverkehr in Deutschland 2010 (KiD2010). Projekt Nr. 70.0829/2008, Schlussbericht, 2012.
19.
KirkpatrickS.GelattC. D.VecchiM. P.Optimization by Simulated Annealing, Science, Vol. 220, 1983, pp. 671–680.
20.
SchröderS.LiedtkeG.Modelling and Analyzing the Effects of Differentiated Urban Freight Measures – A Case Study of the Food Retailing Industry. Paper No. 14-5015, Proceedings, 93trd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
21.
SchrimpfG.SchneiderJ.Stamm-WilbrandtH.DueckG.Record Breaking Optimization Results Using the Ruin and Recreate Principle. Journal of Computational Physics, Vol. 159, No. 2, 2000, pp. 139–171.
22.
HansenW. G.How Accessibility Shapes Land Use. Journal of the American Institute of Planners, Vol. 25, No. 2, 1959, pp. 73–76.
HuntJ. D.StefanK. J.BrownleeA. T.McMillanJ. D. P.FarhanA.TsangK.AtkinsD.IshaniM.A Commercial Movement Modelling Strategy for Alberta’s Major Cities. Proc., 2004 Annual Conference of the Transportation Association of Canada, Québec City, Canada, 2004.
25.
GliebeJ.CohenO.HuntJ. D.Dynamic Choice Model of Urban Commercial Activity Patterns of Vehicles and People. Transportation Research Record: Journal of the Transportation Research Board, 2003. 1: 17–26.
