Freight Demand Modeling: State of the Art and Practice

Transcription

1 1 Freight Demand Modeling: State of the Art and Practice José Holgín-Veras (shamelessly stealing the credit and great work of E. Thorson, Q. Wang, N. X, C. González-Calderón, I. Sánchez-Díaz, and J. Mitchell) Center for Infrastrctre, Transportation, and the Environment (CITE) VREF s Center of Excellence on Sstainable Urban Freight Systems

2 Empirical Evidence on Urban Freight Tors 2

3 Average nmber of stops/tor Characterization of Urban Freight Tors (UFT) 3 Nmber of stops per tor depends on: Contry, city, type of trck, the nmber of trip chains, type of carrier, service time, and commodity transported Alphen Schiedam Amsterdam Apeldoorn 10, ,000 1,000,000 10,000,000 Poplation Denver New York City

4 Characterization of Urban Freight Tors (UFT) 4 Denver, Colorado (Holgín-Veras and Patil,2005): Stops/Tor Single Trck Combination Trck Average tor/day tors/day tors/day Port Athority of NY and NJ (HV et al., 2006): By type of company: Common carriers: 15.7 stops/tor Private carriers: 7.1 stops/tor By origin of tor: New Jersey: 13.7 stops/tor New York: 6.0 stops/tor

5 Characterization of Urban Freight Tors (UFT) 5 NYC and NJ (Holgin-Veras et al. 2012): Average: 8.0 stops/tor 12.6%: 1 stop/tor 54.9%: < 6 stops/tor 8.7% do > 20 stops Parcel deliveries: stops/tor

6 Urban Freight Tor Models 6

7 Urban Freight Tor Models 7 The UFT models cold be sbdivided into: Simlation models Hybrid models Analytical models

8 Simlation Models 8

9 Simlation Models 9 Simlation models attempt to create the needed isomorphic relation between model and reality by imitating observed behaviors in a compter program Examples inclde: Tavasszy et al. (1998) (SMILE) Boerkamps and van Binsbergen (1999) (GoodTrip) Ambrosini et al., (2004) (FRETURB) Liedtke (2006) and Liedtke (2009) (INTERLOG) Stefan et al. (2005) and Hnt and Stefan (2007) describe the Calgary model

10 Hybrid Models 10

11 Hybrid Models 11 Hybrid models incorporate featres of both simlation and analytical models (e.g., sing a gravity model to estimate commodity flows, and a simlation model to estimate the UFTs and logistical patterns) Examples inclde: van Din et al. (2007) Wisetjindawat et al. (2007) Donnelly (2007) describes the Oregon model

12 Analytical Models 12

13 Analytical Models 13 Analytical models attend to achieve isomorphism sing formal mathematic representations based on behavioral, economic, or statistical axioms Two main branches: Spatial Price eqilibrim models (disaggregate) Entropy Maximization models (aggregate) Examples inclde: Holgín-Veras (2000), Thorson (2005) X (2008), X and Holgín-Veras (2008) Holgín-Veras et al. (2012) Wang and Holgín-Veras (2009), Sanchez and Holgín-Veras (2012)

14 Entropy Maximization Tor Flow Model 14

15 Entropy Maximization Tor Flow Models 15 Based on entropy maximization theory (Wilson, 1969; Wilson, 1970; Wilson, 1970) Comptes most likely soltion given constraints Key concepts: Tor seqence: An ordered listing of nodes visited Tor flow: The flow of vehicle-trips that follow a seqence The problem is decomposed in two processes: A tor choice generation process A tor flow model

16 Entropy Maximization Tor Flow Model 16 Tor choice: To estimate sensible node seqences Tor flow: To estimate the nmber of trips traveling along a particlar node seqence Tor choice generation Tor flows 2013/5/6

17 Static version of EM Tor Flow Model 17

18 The eqivalent model of formlation 2: Entropy Maximization Tor Flow Model 18 MIN Z M m 1 ( t m ln t m t m ) Sbject to: M aimtm m 1 M ct t m m m 1 M ch t m m m 1 t m 0 O i C C T H ( i ) ( 1 ) ( 2 ) Trip prodction constraints Total travel time constraint Total handling time constraint

19 Entropy Maximization Tor Flow Model 19 First-order conditions (tor distribtion models) Formlation 1: t * m N * * * exp( i aim cm) exp( i a i 1 N i 1 im * )exp( c m ) Formlation 2: t * m N i 1 * 1 * 2 * exp( a )exp( c c i im Tm Hm ) Traditional gravity trip distribtion model t * ij AO B i i j D j * exp( c ij ) Formlation 1 and the traditional GM model have exactly the same nmber of parameters

20 Entropy Maximization Tor Flow Model 20 The optimal tor flows are fond nder the objective of maximizing the entropy for the system The tor flows are a fnction of tor impedance and Lagrange mltipliers associated with the trip prodctions and attractions along that tor Sccessflly tested with Denver, Colorado, data: The MAPE of the estimated tor flows is less than 6.7% given the observed tors are sed Mch better than the traditional GM

21 Case Stdy: Denver Metropolitan Area 21 Test network 919 TAZs among which 182 TAZs contain home bases of commercial vehicles 613 tors, representing a total of 65,385 tor flows / day Calibration done with 17,000 tors (from heristics) Estimation procedre Sorting inpt data: aggregate the observed tor flows to obtain trip prodctions and total impedance Estimation: estimate the tor flows distribted on these tors sing the entropy maximization formlations Assessing performance: compare the estimated tor flows with the observed tor flows

22 Estimated vs. observed tor flows Performance of the Models

23 Spatial Price Eqilibrim Tor Models 23

24 General principles 24 The models estimate commodity flows and vehicle trips that arise nder competitive market eqilibrim Conceptal advantages: Accont for tors Provide a coherent framework to jointly model the joint formation of commodity flows and vehicle trips Based on the seminal work of Samelson (1952), as it seeks to maximize the economic welfare associated with the consmption and transportation of the cargo, taking into accont the formation of UFTs

25 Integrated Shipper-Carrier Operations 25 Five sppliers deploy tors from their bases (rhomboids) to distribte the cargo they prodce to varios consmer (demand) nodes (circles) Legend: Spplier Receiver Empty trips Loaded trips made by sppliers (Contested nodes are shown as shaded circles)

26 A Spatial Price Eqilibrim UFT Model (P2) 26 MAX Sbject to: SN NSP S ( E ) C i 1 S i i ( E i ) i j 0 E i E i e ij we l V s( x) dx Q 0, p j ij i ( t t t t ) 0 L 0i 1 L i, p i 1 j i 1 1 we p L 1 l 1 i, p j p ij 1 j, j ij ( 0,1) i, j, e 0, c, c 0 ij D T H l, p Q l 1 c d i t 0, j, i, j L (Net Social Payoff) (Area nder excess spply fnction) (Excess spply) (Linking flows to vehicle-trips) T (Capacity constraint) (Tor length constraint) (Conservation of flow) (Integrality) (Non-negativity)

27 However 27 P2 is a nasty combinatorial and non-linear problem that is notoriosly difficlt to solve To solve it, frame it as: A dispersed SPE problem A problem of profit maximization sbject to competition (which is eqivalent to the NSP formlation prodced by Samelson) A dynamic problem in which competitors adjst decisions based on the market competition reslts Use heristics

28 Conclding Remarks 28

29 Knowledge, Models, and Data 29 Data do not necessarily lead to Knowledge, D K Models cannot be developed withot Knowledge Knowledge/Model inform Data collection: K+M D We need integrative developments: K+M+D Knowledge Models Data

30 The allre (and trap) of the low hanging frit 30 We love the low hanging frit thogh we may not realize that we are benefiting from the trees planted by others If that s all we plan to do we are in troble we need to plant trees Gerard van Honthorst, circa 1632

31 What shold we do? 31 We need to plant trees of different varieties to see which ones provide the best frit this is basic research We need to evalate the trees to see which ones are better this is applied research Then, we need to take the best trees and their frit to the market this is development

32 Thanks! Qestions? 32

33 References 33

34 References 34 Ambrosini, C., J. L. Rothier and F. Toilier (2004). How do rban policies work on the rban goods transport flows? WCTR, Istanbl. Boerkamps, J. and A. van Binsbergen (1999). Goodtrip - A New Approach for Modelling and Evalation of Urban Goods Distribtion. City Logistics I, Cairns, Astralia. Donnelly, R. (2007). A hybrid microsimlation model of freight flows. Proceedings of the 4th International Conference on City Logistics, Jly 11-13, E. Tanigchi and R. G. Thompson. Crete, Greece, Institte for City Logistics: Figliozzi, M. A. (2007). "Analysis of the efficiency of rban commercial vehicle tors: Data collection, methodology, and policy implications." Transportation Research Part B: Methodological 41(9): Figliozzi, M. A. (2010). "The impacts of congestion on commercial vehicle tor characteristics and costs." Transportation Research Part E: Logistics and Transportation Review 46(4): Holgín-Veras, J. (2000). A Framework for an Integrative Freight Market Simlation. IEEE 3rd Annal Intelligent Transportation Systems Conference ITSC- 2000, Dearborn Michigan, IEEE Holgín-Veras, J. (2002). "Revealed Preference Analysis of Commercial Vehicle Choice Process." Jornal of Transportation Engineering 128(4): 336. Holgín-Veras, J. (2008). "Necessary Conditions for Off-Hor Deliveries and the Effectiveness of Urban Freight Road Pricing and Alternative Financial Policies." Transportation Research Part A: Policy and Practice 42A(2): Holgín-Veras, J. (2011). "Urban Delivery Indstry Response to Cordon Pricing, Time-Distance Pricing, and Carrier-Receiver Policies " Transportation Research Part A 45: Holgín-Veras, J. and G. Patil (2005). "Observed Trip Chain Behavior of Commercial Vehicles." Transportation Research Record 1906: Holgín-Veras, J. and G. Patil (2007). "Integrated Origin-Destination Synthesis Model for Freight with Commodity-Based and Empty Trip Models." Transportation Research Record 2008: Holgín-Veras, J. and G. Patil (2008). "A Mlticommodity Integrated Freight Origin destination Synthesis Model." Networks and Spatial Economics 8(2): Holgín-Veras, J., M. Silas and J. Polimeni (2008). An Investigation on the Attitdinal Factors Determining Participation in Cooperative Mlti-Carrier Delivery Systems. Innovations in City Logistics IV. E. Tanigchi and R. Thomson, Nova Science Pblishers: Holgín-Veras, J., M. A. Silas, J. Polimeni and B. Crz (2007). "An Investigation on the Effectiveness of Joint Receiver-Carrier Policies to Increase Trck Traffic in the Off-Peak Hors: Part I: The Behaviors of Receivers." Networks and Spatial Economics 7(3): Holgín-Veras, J., M. A. Silas, J. Polimeni and B. Crz (2008). "An Investigation on the Effectiveness of Joint Receiver-Carrier Policies to Increase Trck Traffic in the Off-Peak Hors: Part II: The Behaviors of Carriers." Networks and Spatial Economics 8(4): Holgín-Veras, J., E. Thorson and J. Mitchell (2012). "Spatial Price Eqilibrim Model of Independent Shipper-Carrier Operations with Explicit Consideration of Trip Chains." Transportation Research Part B (in review). Holgín-Veras, J., E. Thorson and K. Ozbay (2004). "Preliminary Reslts of Experimental Economics Application to Urban Goods Modeling Research." Transportation Research Record: Jornal of the Transportation Research Board 1873(-1): Holgín-Veras, J. and Q. Wang (2011). "Behavioral Investigation on the Factors that Determine Adoption of an Electronic Toll Collection System: Freight Carriers." Transportation Research Part C 19(4): Holgín-Veras, J., Q. Wang, N. X, K. Ozbay, M. Cetin and J. Polimeni (2006). "Impacts of Time of Day Pricing on the Behavior of Freight Carriers in a Congested Urban Area: Implications to Road Pricing." Transportation Research Part A: Policy and Practice 40 (9):

35 References 35 Holgín-Veras, J., N. X and J. Mitchell (2012). "A Dynamic Spatial Price Eqilibrim Model of Integrated Prodction-Transportation Operations Considering Freight Tors." (in review). Hnt, J. D. and K. J. Stefan (2007). "Tor-based microsimlation of rban commercial movements." Transportation Research Part B: Methodological 41(9): Liedtke, G. (2006). An actor-based approach to commodity transport modelling. Ph.D., Karlsrhe Universita t. Liedtke, G. (2009). "Principles of micro-behavior commodity transport modeling." Transportation Research Part E: Logistics and Transportation Review 45(5): McFadden, D., C. Winston and A. Boersch-Span (1986). Joint Estimation of Freight Transportation Decisions Under Non-Random Sampling. Discssion Paper, Harvard University. Ran, M., J. Lin and K. Kawamra (2012). "Modeling rban commercial vehicle daily tor chaining." Transportation Research Part E: Logistics and Transportation Review 48(6): Samelson, P. A. (1952). "Spatial Price Eqilibrim and Linear Programming." American Economic Review 42(3): Samelson, R. D. (1977). Modeling the Freight Rate Strctre. Cambridge MA, Center for Transportation Stdies, Massachsetts Institte of Technology. Stefan, K., J. McMillan and J. Hnt (2005). "Urban Commercial Vehicle Movement Model for Calgary, Alberta, Canada." Transportation Research Record: Jornal of the Transportation Research Board 1921: Tavasszy, L. A., B. Smeenk and C. J. Rijgrok (1998). "A DSS for Modelling Logistics Chains in Freight Transport Systems Analysis." International Transactions in Operational Research 5(6): Thorson, E. (2005). The Integrative Freight Market Simlation: An Application of Experimental Economics and Algorithmic Soltions. Ph.D., Rensselaer Polytechnic Institte. van Din, J. H. R., L. A. Tavasszy and E. Tanigchi (2007). "Real time simlation of actioning and re-schedling processes in hybrid freight markets." Transportation Research Part B: Methodological 41(9): Vlegel, J. and M. Janic (2004). Rote choice and the impact of logistic rotes. Logistics Systems for Sstainable Cities. E. Tanigchi and R. Thompson, Elsevier. Wang, Q. (2008). Tor Based Urban Freight Travel Demand Models. PhD, Rensselaer Polytechnic Institte. Wang, Q. and J. Holgín-Veras (2008). "Investigation of Attribtes Determining Trip Chaining Behavior in Hybrid Microsimlation Urban Freight Models." Transportation Research Record: Jornal of the Transportation Research Board 2066: 1-8. Wang, Q. and J. Holgín-Veras (2009). Tor-based Entropy Maximization Formlations of Urban Commercial Vehicle Movements Annal Meeting of the Transportation Research Board. CDROM. Wilson, A. G. (1969). "The se of entropy maximising models in the theory of trip distribtion, mode split " Jornal of Transport Economics and Policy Wilson, A. G. (1970). Entropy in Urban and Regional Modelling. London, Pion. Wilson, A. G. (1970). "The se of the concept of entropy in system modelling." Operational Research Qarterly 21(2): Wisetjindawat, W., K. Sano, S. Matsmoto and P. Raothanachonkn (2007). Micro-Simlation Model for Modeling Freight Agents Interactions in Urban Freight Movement. 86th Annal Meeting of the Transportation Research Board