Freight Demand Modeling and Logistics Planning for Assessment of Freight Systems Environmental Impacts

Transcription

1 EPA Freght Demand Modelng and Logstcs Plannng for Assessment of Freght Systems Envronmental Impacts PI/Co-PIs: Tam C. Bond, Yanfeng Ouyang, Chrstopher P. L. Barkan, Bumsoo Lee Students: Taesung Hwang, Lang Lu, Sungwon Lee March 5, 2014

2 Outlne 1. Background 2. Inter-regonal Freght Demand Modelng Forecastng freght demand consderng economc growth factors Freght transportaton mode choce and ts envronmental mpacts Freght shpment demand network assgnment under congeston Integrated decson-support software 3. Intra-regonal Freght Demand Modelng Logstcs systems plannng for regonal freght delvery Urban freght truck routng under stochastc congeston and emsson consderatons 4. Conclusons and Future Research Plan 2

3 Background Rapd globalzaton and ever-ncreasng demand for freght movements Emsson problems from freght transportaton Most freght transportaton modes are powered by desel engnes Sgnfcant sources of natonal ar pollutants (e.g., NO X, PM) and greenhouse gases (e.g., CO 2 ) (ICF Consultng, 2005) Emssons from freght transportaton actvtes Clmate change (on global scale) Ar qualty and human health (n regonal and urban areas) Freght delvery systems need to be thoroughly nvestgated to understand ther mpacts on envronment 3

4 Emsson projectons today Input-output model Technology & nfrastructure model and tomorrow Hybrd model Economy-wde model Separate economc sectors Apply emsson coeffcent to actvty n each sector (+): response to economc envronment, e.g. fuel swtchng (-): Lttle How-to engneerng component Stuaton-specfc Data ntensve, requrng fleet composton, traffc lnks, etc. Emssons from specfc condtons & vehcle types (+): Realstc emssons that can be connected to polcy decsons (-): Dffcult to extrapolate 4 to other stuatons Actvty and growth drven by nput-output model Lnked to technology choce usng general theoretcal prncples Models (e.g. emsson rates) constraned by observatons whenever possble

5 Inter-regonal freght For heavy-duty trucks and ral S. Smth, PNNL B. Lee, S. Lee, Urban Plannng Y. Ouyang, T. Hwang, Transportaton (CEE) T. Bond, L. Lu, F. Yan, Ar Qualty (CEE) 5

6 Intra-regonal freght For medum duty trucks; not presented here Urban development scenaros (compact/polycentrc/bau) Spatal autoregresson model S. Smth, PNNL B. Lee, S. Lee, Urban Plannng Delvery actvty Rng-sweep algorthm Y. Ouyang, T. Hwang, Transportaton (CEE) T. Bond, L. Lu, F. Yan, Ar Qualty (CEE) 6

7 Framework Global Economc Forecasts Models Urban Spatal Structure and Input-Output Models Freght Transportaton Systems Models Varous Economc Factors Projectons Input-Output Commodty Value Forecasts Inter-regonal Freght Flow Trp Generaton Trp Dstrbuton Mode Splt Traffc Assgnment Employment Dstrbuton wthn Domestc Regons Regonal Level Truck Freght Delvery Intra-regonal Freght Flow Pont-to- Pont Truck Routng n Urban Freght Delvery Freght demand and logstcs modelng: Develop and ntegrate a set of U.S. freght transportaton system models to capture nterdependences on future economc growth and urban spatal structure changes Scope () Inter-regonal freght flow; e.g., from Los Angeles to Chcago () Intra-regonal freght flow; e.g., wthn Chcago metropoltan area () Pont-to-pont delvery routng Ar Qualty and Clmate Impacts Models Long-haul Truck and Ral Emsson Short-haul Truck Emsson Ar Qualty and Clmate Impacts n the U.S. Regons Global Ar Qualty and Clmate Impacts 7

8 Inter-regonal Freght Demand Four-step freght commodty transportaton demand forecastng model (NCHRP Report 606, 2008) Economc growth factor forecast for each geographcal freght analyss regon zone (FAZ) Trp Generaton Enterng and extng freght demand (attractons and productons) by zone Truck Ral Producton Trp Dstrbuton Zonal O/D freght demand Mode Splt Zonal O/D freght demand by shppng mode Traffc Assgnment Traffc flow, average speed on each lnk Shp Attracton Whch modes wll be How What Where much routes wll the demand wll be used? freght wll used? go? be made? 123 domestc Freght Analyss Zone (FAZ) 8

9 Introducton Trp Generaton Trp Dstrbuton Mode Splt Traffc Assgnment Objectve Forecast future freght demand that begns and ends n each FAZ, and dstrbute them on all O/D pars Methodology: RAS algorthm (Stone, 1961; Stone and Brown, 1962) Basc Ideas Forecast of economc growth factors are gven for all FAZs Current FAZ structure does not change (.e., nether new zone wll appear nor currently exstng zone wll dsappear) Dstrbuton of future freght demand s proportonal to that of base-year demand 9

10 Freght Demand Generaton/Dstrbuton Structure of base-year freght demand dstrbuton data Orgn zone o O y, o P o Destnaton zone d D D od A d y, d For commodty type ሼ1,2,, ሽ, O D P o A d y, o y, d D od = orgn zone set, {1, 2,, Z} = destnaton zone set, {1, 2,, Z} = base-year total producton of commodty n an orgn zone o = base-year total attracton of commodty n a destnaton zone d = growth rate of commodty producton n an orgn zone o for future year y = growth rate of commodty attracton n a destnaton zone d for future year y = freght volume of commodty movng from orgn zone o to destnaton zone d 10

11 Freght Demand Generaton/Dstrbuton Step 0. Generate base-year freght demand O/D matrx for commodty : D od Let be base-year commodty freght movement from orgn o to destnaton d Step 1. Estmate future producton and future attracton for all FAZs: y, Multply each P o o by Defne by, y, y V P, W, and each A, o O, d D. o o o d d d A d y, d O D d.. Z Gven P roducton Future P roducton O D d.. Z Gven P roducton Future P roducton : : : o D od P o o D od P o V o : : : Z. Z Gven Attracton Future Attracton A d Gven Attracton Future Attracton A d W d 11

12 Step 2. Snce future nput and output commodty growth are modeled separately, Total future producton summed across all orgn zones Freght Demand Generaton/Dstrbuton o O V o Total future attracton summed across all destnaton zones d D W d Assume freght commodty productons are derved by attractons Multply future productons of all orgn zones by the same factor: W d d D Update V o V o, o O. V o o O Then, o O V o = d D W d O 1 2 : o : D Z Gven Attracton Future Attracton d.. Z D od A d W d Gven P roducton P o Future P roducton V o 12

13 Step 3. Apply RAS algorthm: O Modfy each entry D od teratvely to match wth the future producton n each row and the future attracton n each column 1 2 : o : D Z Gven Attracton Future Attracton d.. Z D od A d W d Gven Future P roducton Producton P o Freght Demand Generaton/Dstrbuton V o Defne tolerance 1, and let L large postve nteger and n 1. V o Wd Defne R o, o O, and C d, d D. D D d D od Whle {( n L ) and ( R 1 for some o O or C 1 for some d D )} { Set D od R o D od, o O, d D, Wd U pdate C d, d D, D o O Set D od C d D od, o O, d D, Vo Update R o, o O, D d D Update n n 1, } o od od o O od d 13

14 Freght Transportaton Mode Choce Trp Generaton Trp Dstrbuton Mode Splt Traffc Assgnment Goal Draw connectons among varous economc and engneerng factors, freght transportaton modal choce, and subsequently freght transportaton emssons Sgnfcant dfference n emssons across modes CO 2 Emsson Factor (kgco 2 /ton-mle) CH 4 Emsson Factor (gch 4 /ton-mle) N 2 O Emsson Factor (gn 2 O/ton-mle) On-Road Truck Ral Waterborne Craft Arcraft Source: EPA (2008) Ref: Hwang, T.S. and Ouyang, Y. (2014) Freght shpment modal splt and ts envronmental mpacts: An exploratory study. Journal of the Ar & Waste Management Assocaton, 64(1):

15 Freght Transportaton Mode Choce Focus on two domnatng freght modes: Truck and Ral Macroscopc bnomal logt market share model for mode choce o Dependent varable: Annual market % share of shpments between modes (between 0 and 1) o Explanatory varables for each commodty type: Commodty value per ton ($/ton): VALUE Avg. shpment dstance for truck (mle): DIST T Avg. shpment dstance for ral (mle): DIST R Crude ol prce ($/barrel): OILPRC o Data: Observed modal splt for each O/D par 15

16 Mode Choce: Bnomal Logt Market Share Model Utlty of truck for commdty n : U n a b VALUE c DIST d OILPRC, T 1n 1n 1n T 1n Utlty of ral for commdty n : U n a b VALUE c DIST d OILPRC, R 2n 2n 2n R 2n n n n T U T U R U n e e Market share of truck for commdty n: P T n n n n, UT U R U T U R e e e 1 n e 1 Market share of ral for commdty n: PR n n n n, UT U R U T U R e e e 1 n P T n n ln U U n T R 1 PT (a a ) (b b ) VALUE (c ) DIST ( c ) DIST (d d ) OILP RC. 1n 2n 1n 2n 1n T 2n R 1n 2n Generalzed lnear form wth four explanatory varables Intercept and coeffcents estmated va lnear regresson n U R 16

17 Data Sources and Processng Freght Transportaton Data Freght Analyss Framework (FAF) database from the U.S. DOT Datasets Verson 2 (FAF 2 ) for year 2002 and verson 3 (FAF 3 ) for year 2007 Commodty Flow Survey (CFS) data from the U.S. Census Bureau Freght transportaton actvtes n years 1993 and 1997 Average shpment dstances of truck and ral West Texas Intermedate (WTI) crude ol prce from Economagc.com o Merged nto one useable database (69,477 observatons) Dvde the database nto two sets for each commodty type. Tranng set for estmaton: 2/3 of the total observatons. Test set for valdaton: 1/3 of the total observatons Statstcal software package, R (verson ) 17

18 Estmaton Results and Goodness of Ft (a) Estmaton results Type 1 Type 2 Type 3 Type 4 Type 5 Type 6 Type 7 Type 8 Type 9 Type 10 Estmate 1.989E E E E E E E E E E-01 Intercept z -statstc P r(> z ) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Estmate 2.428E E E E E E E E E E-03 Value z -statstc per ton P r(> z ) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Avg. truck dstance Avg. ral dstance WTI c rude ol prce Estmate z -statstc P r(> z ) Estmate z -statstc P r(> z ) Estmate z -statstc P r(> z ) E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E < E <0.001 (b) Number of data used 3,802 5,468 3,753 3,105 5,883 6,068 6,035 5,100 5,041 2,062 (c) Pseudo McFa dden R-squared Nagelkerke

19 Estmaton Results and Goodness of Ft All estmates are statstcally sgnfcant (all p-values 0.001) Interpretatons and nsghts Postve Intercept: Everythng else beng equal, truck s more lkely to be chosen Postve Value per ton : Truck tends to shp hgher value goods than ral Negatve Avg. truck dstance : As shppng dstance ncreases, utlty of truck decreases Negatve Avg. ral dstance : As shppng dstance ncreases, ral s preferred Negatve WTI crude ol prce : As ol prce ncreases, ral s preferred 19

20 Traffc Assgnment Trp Generaton Trp Dstrbuton Mode Splt Traffc Assgnment Goal: Assgn freght traffc onto modal networks for all shpment O/D pars Route choce rule: User equlbrum (Wardrope, 1959; Sheff, 1985) Each motorst selects the shortest travel tme route between O/D All used routes connectng each O/D par have the same cost/travel tme whch s less than or equal to the costs of unused routes Algorthm 1. Convex combnatons algorthm (Frank and Wolfe, 1956, coded n VC++) 2. Input: graph representaton of modal networks, demand for all O/D pars 3. Output fle: assgned traffc flow, average speed on each lnk, lnk cost, etc. 20

21 Truck Traffc Assgnment Model development Standard network assgnment problem under user equlbrum prncple (Sheff, 1985) Bureau of Publc Roads (BPR) lnk cost functon (Bureau of Publc Roads, 1970) modfed to nclude background traffc volume Data for graph representaton of freght truck network 1) O/D nodes: 120 centrods of FAF 3 regons boundary Exclude Hawa (2 zones) and Alaska (1 zone) 2) U.S. road network: FAF 3 network Consder only major nterstate hghways Background traffc (AADT) and lnk capacty n Year 2007 Data for truck freght demand FAF 3 truck shpment database (FHWA U.S. DOT, 2011) Real truck freght demand data (n tonnage) n Year

22 Truck Traffc Assgnment: Data (2) Smplfed U.S. major hghway freght truck road network FAF 3 zones Major Interstate Hghway 1) 178 nodes Centrods of domestc FAZs (120 nodes) Major junctons n the nterstate hghway network (58 nodes) 2) 14,400 O/D pars Each centrod of 120 FAF 3 zones s both orgn and destnaton of freght demand 3) 588 lnks Mostly major nterstate hghways Some local roads: for FAF 3 centrods located far from the major nterstate hghway network 22

23 Truck Traffc Assgnment: Data (3) Parameters - Average truckload (tons per truck) = 16 (FHWA U.S. DOT, 2007; EPA and NHTSA, 2011) - Passenger-car equvalents (assumng rollng terran) = 2.5 (HCM, 2000) - Hours of operaton of the freght truck delvery system = Truck free flow speed (mph) = 65 (Ba et al., 2011) - Background traffc = AADT/(2 24) - BPR lnk cost functon modfed to nclude background traffc volume b t( ) t f 1 C where t f lnk free flow travel tme (hr), assgned traffc volume # of veh/hr, b background traffc volume # of veh/hr, C lnk capacty # of veh/hr, 0.15, and 4 23

24 Truck Traffc Assgnment Results FAF3 zones 0 Assgned flow < Assgned flow 1,200 1,000 1,000 1,2 < Assgned flow 1,800 1,500 1,500 1,8 < Assgned flow 2,400 2,000 2,000 2,400 < Assgned flow 3,000 2,500 2,500 3,000 < Assgned flow 3,600 3,000 3,000 3,600 < Assgned flow * Unt of assgned flow: # of vehcles (passenger cars) per hour t Total Cost = (Lnk Travel Tme Assgned Lnk Flow) = a x a x a = 699, (veh-hr/hour) a A Convergence s reached wthn a tolerance of % after 12 teratons (0.640 sec CPU tme) Output: lnk and node number, lnk dstance, total and assgned traffc volume, lnk cost (lnk travel tme), average lnk speed at equlbrum 24

25 Model Valdaton Freght traffc dstrbuton (annual tonnage) on the U.S. hghway (red), ral (brown), and nland waterways (blue) networks n Year 2007 (FHWA U.S. DOT, 2011) 25

26 Model Valdaton Truck traffc dstrbuton on the U.S. hghway network Trend consstent n a hgh level: Washngton, Oregon, Calforna, Florda, the Mdwest states near Chcago, and northeastern regons Less emphaszed n our result: Some man hghway lnks that connect Southern Calforna, Arzona, and Oklahoma 26

27 Ral Traffc Assgnment Ral network operates very dfferently from hghway network - Lnk traffc flow n opposte drectons shares the same track nfrastructure - Assgn b-drectonal traffc flow on one shared undrected lnk (.e., undrected graph) Ralroad-specfc lnk cost functon (Krueger 1999; La and Barkan, 2009) For undrected ralroad lnk e E d t e e T e e 100 e e e e, e E, where, e T e d e e, e = lnk free flow travel tme (hour) = lnk length (mle) = the total ral lnk flow (# of trans/day) = parameters unquely determned by ral operatng condtons Ref: Hwang, T.S. and Ouyang, Y. Assgnment of freght shpment demand n congested ral networks. Transportaton Research Record. In press. 27

28 Ral Traffc Assgnment: Methodology Equvalent drected graph representaton of the undrected ral network Each undrected lnk s replaced by two separate drected lnks n opposte drectons x j Ralroad lnk cost functon for the drected graph j d j 100 j x j x x T e j j j j j t x x j j, (, j ) A x j (+ x j ) x j (+ x j ) Lnk travel tmes on both drected lnks (from node to j and from node j to ) are dentcal Modfy conventonal convex combnatons algorthm - Consder traffc volume n both drectons whenever lnk cost s updated j 28

29 Ral Traffc Assgnment: Data (1) Data for graph representaton of ral network 1) O/D nodes: 120 centrods of FAF 3 regons boundary Exclude Hawa (2 zones) and Alaska (1 zone) 2) U.S. ral network: Ral network GIS data (ATLAS, 2011) Select ral network man lnes on whch Class І ralroads (AMTK, BNSF, CSXT, KCS, NS, UP, CN, CP n the database) operate Incorporated double track nformaton obtaned from Rchards and Cobb (2010) Data for ral freght demand FAF 3 ral shpment database (FHWA U.S. DOT, 2011): freght demand n 2007 Converted the freght shpment demand n tonnage nto equvalent numbers of tranloads based on the types of commodtes (AAR, 2007; Cambrdge Systematcs, Inc., 2007) Parameters: operaton days per year = 365; free flow speed (mph) = 60 (Krueger, 1999) 29

30 Ralroad Traffc Assgnment: Data (2) Smplfed U.S. ral network FAF 3 zones Selected Ralroad Track 1) 183 nodes Centrods of domestc FAZs (120 nodes) Major ntersectons n the selected ral network (63 nodes) 2) 40,909 O/D pars Consder both shpment O/D pars and commodty types 3) 566 lnks Mostly major ralroad tracks on whch Class І ralroads operate Some tracks on whch other mnor ralroads operate: for FAF 3 centrods located far from the major ral network 30

31 Ralroad Traffc Assgnment: User Equlbrum Results FAF3 zones 0 Assgned flow < Assgned flow < Assgned flow < Assgned flow < Assgned flow < Assgned flow < Assgned flow * Unt of assgned flow: # of trans per day tj x j x j xj Total Cost = (Lnk Travel Tme Assgned Lnk Flow) = (, j ) A = 75,426 (tran-hr/day) Convergence s reached wthn a tolerance of 0.001% after 2,569 teratons ( sec CPU tme) Output: lnk number, lnk orgn and destnaton node, lnk dstance, freght shpment volume (for each commodty type), lnk cost (lnk travel tme), average lnk speed 31

32 Model Valdaton Ral traffc dstrbuton on the U.S. ral network Trend consstent at a hgh level: Washngton, Calforna, Wyomng, Montana, the Mdwest states near Chcago, northeastern regons, and some man lnks that connect Southern Calforna, Texas, and Kansas More emphaszed n our result: Idaho, Oregon, and southeastern regons 32

33 Software Development Integrated decson-support software for four-step nter-regonal freght demand forecastng Vsual Basc Applcatons (VBA) n Mcrosoft Excel platform Overvew of the software Input Man Program Output Included n one Excel fle 33

34 Software Development Procedure of the program Input Man Program Output 34

35 Software Development Procedure of the program Input Man Program Output Input worksheets - Each step n the four-step analyss requres dfferent nput worksheets to conduct the analyss - Total eghteen dfferent nput worksheets Trp generaton and Trp dstrbuton Attracton_S1, Attracton_S2, Attracton_S3, Attracton_S4, Producton_S1, Producton_S2, Producton_S3, Producton_S4, and 2007Demand Modal splt TruckDst, RalDst, and ModalSplt Network assgnment TruckDemand, RalDemand, TruckNetwork, RalNetwork, TruckNode, and RalNode 35

36 Software Development Procedure of the program Input Man Program Output Output worksheets Results from dfferent steps wll be recorded n seven dfferent output worksheets - Trp generaton Trp_Generaton - Trp dstrbuton Trp_Dstrbuton - Modal splt Modal_Splt - Truck freght demand network assgnment TruckResult and TruckMap - Ral freght demand network assgnment RalResult and RalMap 36

37 Software Development Vsualzaton of the fnal results TruckMap worksheet RalMap worksheet Help decson-makers explore atmospherc mpacts of future freght shpment actvtes n varous economc scenaros 37

38 Illustratve Examples of Model Applcaton Sample Questons: How would economc growth affect nter-regonal freght transportaton? How would fuel prce affect freght modal choce? How could congeston n current transportaton nfrastructure restrct freght movements, and what are the mpacts of capacty nvestments? 38

39 Future Freght Demand Forecast Forecast future freght demand dstrbuton wthn the U.S. from 2010 to 2050 n fve-year ncrements Four scenaros Data Scenaro 1 (S1): Hgh GDP growth & Busness as usual Scenaro 2 (S2): Hgh GDP growth & Clmate polcy Scenaro 3 (S3): Low GDP growth & Busness as usual Scenaro 4 (S4): Low GDP growth & Clmate polcy 1. Base-year freght demand dstrbuton matrx: Freght Analyss Framework data verson 3 (FAF 3 ) for Year 2007 Orgn, Destnaton, Commodty type, Freght demand (n tonnage) 2. Future I/O commodty value growth estmates for all scenaros: Exogenously gven from the nput-out model ( n fve-year ncrements) 39

40 Future Freght Demand Forecast Freght demand forecastng results (a) Scenaro (b) Year Algorthm converged n a short tme Future freght demand s generated (360, 120-by-120 matrces) Scenaro 1: Scenaro 2: Scenaro 3: Scenaro 4: Hgh GDP growth wth busness as usual Hgh GDP growth wth clmate polcy Low GDP growth wth busness as usual Low GDP growth wth clmate polcy (c) Total freght demand forecasted (thousand ton) (g) % change (d) Total freght demand forecasted (thousand ton) (h) % change (e) Total freght demand forecasted (thousand ton) () % change (f) Total freght demand forecasted (thousand ton) (j) % change ,059, ,059, ,059, ,059, ,703, ,648, ,528, ,494, ,501, ,438, ,929, ,890, ,431, ,780, ,355, ,742, ,438, ,650, ,755, ,023, ,693, ,780, ,271, ,435, ,034, ,945, ,725, ,747, ,697, ,356, ,523, ,339, ,574, ,893, ,377, ,903, ,673, ,621, ,351, ,573, Sutable for long-term economc forecasts Global economc forecasts models: hard to capture unexpected short-term economc fluctuatons (e.g., recesson n ) 40

41 Model Applcaton - Emsson Estmaton Modal splt and the followng emsson estmatons for a range of WTI crude ol prce Select one arbtrary data record: Commodty type 5 (basc chemcals, chemcal and pharmaceutcal products) from Texas to Colorado Freght value per unt weght Avg. truck dstance Avg. ral dstance $1,240.85/ton 1,005 mles 1,332 mles Total annual freght shpment demand n data = 328,000 ton Forecast annual freght shpment splt for dfferent ol prce range Estmate total emsson and greenhouse gas nventory Emsson factors adopted from EPA (2008) and NRDC (2012) CO 2 emsson factor (kgco 2 /ton-mle) CH 4 emsson factor (gch 4 /ton-mle) N 2 O emsson factor (gn 2 O/ton-mle) PM 10 emsson factor (gpm 10 /ton-mle) Truck Ral

42 Model Applcaton - Emsson Estmaton (a) (b) (c) (d) (e) (f) (g) (h) () (j) (k) (l) (m) (n) (o) WTI crude Truck share Ral share Truck CO 2 Ral CO 2 Total CO 2 Truck CH 4 Ral CH 4 Total CH 4 Truck N 2O Ral N 2O Total N 2O Truck PM 10 Ral PM 10 Total PM 10 ol prce predcton predcton emsson emsson emsson emsson emsson emsson emsson emsson emsson emsson emsson emsson ($/barrel) (%) (%) (ton) (ton) (ton) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) (kg) % 33.2% 65,412 3,654 69, , ,262 1,885 22, % 36.5% 62,163 4,019 66, , ,256 2,073 21, % 40.0% 58,784 4,399 63, , ,209 2,269 20, % 43.5% 55,304 4,791 60, , ,131 2,471 19, % 47.1% 51,758 5,189 56, , ,033 2,677 18, % 50.8% 48,181 5,591 53, , ,925 2,885 17, % 54.4% 44,613 5,993 50, , ,820 3,091 16, % 58.0% 41,091 6,389 47, ,729 3,296 16, % 61.5% 37,651 6,776 44, ,663 3,495 15, % 64.9% 34,324 7,150 41, ,632 3,688 14, % 68.2% 31,140 7,508 38, ,646 3,873 13, % 71.3% 28,120 7,848 35, ,711 4,048 12, % 74.2% 25,282 8,167 33, ,832 4,213 12, % 76.9% 22,638 8,464 31, ,012 4,366 11,379 Natonal emsson estmaton Aggregate emsson calculatons across all shpment O/D pars and all commodty types 42

43 Ral Network Capacty Expanson and Its Effect on Network Assgnment Ral freght demand: projected to ncrease 88% by Year Sever congeston s expected (Cambrdge Systematcs, Inc., 2007) - Infrastructure nvestment may be needed near potental chokeponts Wll affect future ral freght demand assgnment patterns Before and After comparson for Year Acton: on the most congested ralroad lnks n 2035 Average lnk speed 10 mph - Sngle tracks wll be expanded to full double tracks 43

44 Ral Network Capacty Expanson and Its Effect on Network Assgnment Congeston predcton n Year 2035 wthout nfrastructure nvestment Our model Cambrdge Systematcs, Inc. (2007) FAF3 zones 50 < Avg. speed 40 < Avg. speed < Avg. speed < Avg. speed < Avg. speed 20 0 < Avg. speed 10 * Unt of Average speed: mph 44

45 Ral Network Capacty Expanson and Its Effect on Network Assgnment Congeston predcton from our model n 2035 after capacty expanson FAF3 zones 50 < Avg. speed 40 < Avg. speed < Avg. speed < Avg. speed < Avg. speed 20 0 < Avg. speed 10 * Unt of Average speed: mph (a) Capacty expanson Before After % reducton (b) Total cost (10 3 tran-hr/day) 2,025 1, (c) Total ton-mle (10 3 ton-mle/day) 10,496,597 10,411, Decrease n total ton-mles - Less detour toward shpment destnatons - Improvements n ral freght delvery effcency 45

46 Framework Global Economc Forecasts Models Urban Spatal Structure and Input-Output Models Freght Transportaton Systems Models Varous Economc Factors Projectons Input-Output Commodty Value Forecasts Inter-regonal Freght Flow Trp Generaton Trp Dstrbuton Mode Splt Traffc Assgnment Employment Dstrbuton wthn Domestc Regons Regonal Level Truck Freght Delvery Intra-regonal Freght Flow Pont-to- Pont Truck Routng n Urban Freght Delvery Freght demand and logstcs modelng: Develop and ntegrate a set of U.S. freght transportaton system models to capture nterdependences on future economc growth and urban spatal structure changes Scope () Inter-regonal freght flow; e.g., from Los Angeles to Chcago () Intra-regonal freght flow; e.g., wthn Chcago metropoltan area () Pont-to-pont delvery routng Ar Qualty and Clmate Impacts Models Long-haul Truck and Ral Emsson Short-haul Truck Emsson Ar Qualty and Clmate Impacts n the U.S. Regons Global Ar Qualty and Clmate Impacts 46

47 Introducton Bulk of freght arrvng at the destnatons (.e., termnals) n each FAZ - Broken for delvery to dstrbuted ndvdual customers - Also, freght needs to be collected from a large number of supply ponts to the set of orgns (.e., termnals) n each FAZ Freght delvery actvtes wthn large urban areas are crtcal ssues - Emssons from freght shpments comprse a large share of toxc ar pollutants n most metropoltan areas worldwde (OECD, 2003) - Resdents n metropoltan areas are more lkely to be affected by the ar polluton problems than those n rural areas Need to nvestgate freght shpment modelng and logstcs plannng at the ntra-regonal level 47

48 Introducton Logstcs systems model for freght dstrbuton wthn an FAZ Vehcles need to serve spatally dstrbuted customer demand whch mght be large scale (Large-scale Vehcle Routng Problem) Estmate network delvery effcency Methodology: Contnuum Approxmaton (Newell and Daganzo, 1986a) () Assume contnuous customer demand densty that may vary slowly over space () Sutable for large-scale estmaton (asymptotc approxmaton) Objectve: Estmate near-optmum total delvery dstance Total travel dstance wthn a delvery regon = Total lne-haul dstance + Total local travel dstance Possble zonng and delvery plan example (Ouyang, 2007) 48

49 Wthn FAZ Delvery Procedure Applcaton of the rng-sweep algorthm to estmate regonal freght delvery Each FAZ s composed of a set of mutually dsjonted census tracts Freght delvery regon (.e., FAZ) Assumptons Freght demand n each census tract s concentrated at the centrod of the census tract Freght demand wll be assgned to the nearest termnal (f multple termnals) Freght s delvered by dentcal short-haul trucks wth constant low speed (e.g. 30 mph) Eucldean metrc roadway network Objectve: Estmate the total transportaton cost (.e., total travel dstance) 49

50 Wthn FAZ Delvery Total delvery cost to serve freght demand wthn an FAZ = Total lne-haul dstance (L 1 ) + Total local travel dstance (L 2 ) dstrbuton and collecton (1) Total lne-haul dstance d = dstance from the termnal to the centrod of the census tract E j = number of employees n an ndustry type j n the census tract I = total number of census tracts J = total number of ndustry types consdered C = truck capacty (n tonnage) D = total freght demand n a gven FAZ (tons per day) Total lne-haul dstance ( L ) = 1 I J 1 j 1 I J 2 D E C 1 j 1 E j j d 50

51 Wthn FAZ Delvery (2) Total local travel dstance N = total number of demand ponts n a gven FAZ I N 1 where N = total number of demand ponts n each census tract J j1 a j a j = average number of employees per frm n an ndustry type j - represents how many employees are served on average by one truck vst - may vary across ndustres N δ = unformly dstrbuted demand pont densty n a gven FAZ A where A = area of an FAZ E j Total local travel dstance ( L 2 ) = 0.57N 51

52 Applcaton Estmate regonal freght delvery cost and the related emssons (CO 2, NO X, PM, and VOC) n 36 FAZs that cover 27 major Metropoltan Statstcal Areas (MSAs) from 2010 to 2050 Data () Forecast of employment dstrbutons (from urban spatal structure model): wholesale trade, retal trade, and manufacturng ndustres () Future truck and ral freght demand for each FAZ (from four-step nter-regonal freght demand model) Three urban development scenaros () Scenaro 1 Busness as usual : current urban sprawl contnues n the U.S. () Scenaro 2 Polycentrc development : CBD (current trend), sub-centers (hgh-growth) ()Scenaro 3 Compact development : both CBD and sub-centers (hgh-growth) Inter-regonal freght demand scenaro: hgh GDP growth under busness as usual Freght collecton and dstrbuton delveres from truck and ralroad termnals are modeled separately Commodtes are delvered separately consderng dfferent ndustry types Lght and medum trucks: capacty = 4 tons (FHWA U.S. DOT, 2007; Davs et al., 2012), avg. speed = 30 mph 52

53 Applcaton Regonal freght delvery from truck termnals A number of truck termnals are located near the junctons of major hghways 1. Commodtes related to the wholesale and retal trade ndustres for termnal k I k 2 k k k k f k 1 k k f where N f a E j and k 1 j1 2 D D E d 0.57N I 2 N 1 k W R 1 j1 j L L =, f f 1 =, f 2 I 2 A C E j f 1 k 1 j1 2. Commodtes related to the manufacturng ndustry for termnal k I k k k 2 D E d 0.57N k p k 2 I k k N 2 M 3 k p L k 1 =, L p2 =, where N p E 3 and p p1 I k 1 a A C E 3 1 p 2 k 3. Total freght delvery cost n the FAZ summed across all truck termnals k K k k k k G K T L f 1 L f 2 L p1 L p2 k1 53

54 Applcaton Regonal freght delvery from ralroad termnals Several ralroad termnals are located near the ntersectons of major ralroad lnks 1. Commodtes for drect shpments from ralroad termnals - Trucks are not nvolved n freght delvery 2. Commodtes for short-haul truck delvery from ralroad termnals (1) Commodtes related to the wholesale and retal trade ndustres for termnal q 2 D D I 2 q E d q q q 0.57N I 2 N and s q 1 j1 A q 1 W R 1 j1 j q s q 1 q q s L s1 =, L =, where N s a E I 2 s2 j C E j 1 q 1 j1 s (2) Commodtes related to the manufacturng ndustry for termnal q I q q q 2 D E d q 0.57N m q 2 I q q N m L q = 2 M 1 3 q m1, L m2 =, where N m E 3 and m. q 1 a A C I E m h 3. Total freght delvery cost n the FAZ summed across all ralroad termnals qq Q q q h h G R q1 L s1 L s2 L m1 L m2 54

55 Case Study Scenaros 1: Busness as usual 2: Polycentrc development 3: Compact development MSA Los Angeles San Francsco Chcago New York MSA # of FAZ Scenaro Freght shpment (10 3 ton-mle) 2010 % 2020 % 2030 % 2040 % 2050 % 1 3, , , , , Los Angeles 1 2 3, , , , , , , , , , , , , , , San Francsco 1 2 1, , , , , , , , , , , , , , , Chcago 2 2 5, , , , , , , , , , , , , , , New York 3 2 5, , , , , , , , , , Scenaro CO 2 (10 3 kg per day) NO X (kg pe r da y) P M (kg per day) VOC (kg per day) , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , *Emsson factors (TRL, 1999) 55

56 Framework Global Economc Forecasts Models Urban Spatal Structure and Input-Output Models Freght Transportaton Systems Models Varous Economc Factors Projectons Input-Output Commodty Value Forecasts Inter-regonal Freght Flow Trp Generaton Trp Dstrbuton Mode Splt Traffc Assgnment Employment Dstrbuton wthn Domestc Regons Regonal Level Truck Freght Delvery Intra-regonal Freght Flow Pont-to- Pont Truck Routng n Urban Freght Delvery Freght demand and logstcs modelng: Develop and ntegrate a set of U.S. freght transportaton system models to capture nterdependences on future economc growth and urban spatal structure changes Scope () Inter-regonal freght flow; e.g., from Los Angeles to Chcago () Intra-regonal freght flow; e.g., wthn Chcago metropoltan area () Pont-to-pont delvery routng Ar Qualty and Clmate Impacts Models Long-haul Truck and Ral Emsson Short-haul Truck Emsson Ar Qualty and Clmate Impacts n the U.S. Regons Global Ar Qualty and Clmate Impacts 56

57 Introducton Improvements n fleet operatons from truckng servce sector Reducton n vehcle emssons Huge benefts (urban ar qualty, human exposure) Roadway congeston n large urban areas s stochastc Real tme nformaton technology Avod heavy congeston by dynamcally choosng the mnmum expected cost path Shortest path problem n a stochastc network settng (Mller-Hooks and Mahmassan, 2000; Waller and Zlaskopoulos, 2002) Cost component: travel delay (focus on mnmzng the expected total travel tme) 57

58 Introducton Traffc congeston n large urban areas Responsble for ar polluton and related human health problems (Copeland, 2011) Truckng freght delvery contrbute to the largest share of ar pollutants n metropoltan areas (ICF Consultng, 2005) Envronmental cost caused by truck actvtes (CO 2, VOC, NO X, and PM) Penaltes for late or early truck arrval at destnaton (ensure delvery punctualty) Total cost = [ Total delvery tme + Emssons + Penalty ] Mnmum expected travel tme soluton (classcal shortest path approach) Does not necessarly guarantee the mnmum expected total cost soluton 58

59 Model Formulaton Consder urban roadway networks Represented by a graph D(V, A) where V = node set and A = drected lnk set From orgn to destnaton, truck drver needs to decde the next lnk whenever he/she arrves at each node to mnmze the expected total cost Assumptons () Truck speed on each lnk s stochastc (unquely determned by stochastc congeston state on the lnk) () Truck speed on each lnk follows a certan probablty dstrbuton - Fxed throughout the perod of routng study (e.g., mornng rush hour) - Not necessarly dentcal across the lnks () Consder only major arteral roads or freeways to represent urban network lnks - Queue formed on a lnk does not spll over nto mmedate downstream lnks - Congeston states are ndependent across the lnks 59

60 Model Formulaton orgn g, destnaton s d j = length of lnk ሺ, ሻ ܣ (mle) U = stochastc truck speed (mph) on lnk ሺ, ሻ ܣ j W( ) = emsson rate (g/veh-mle), functon of the truck speed U j = assgnment of vehcle on lnk ሺ, ሻ ܣ x j Total travel tme Emssons Penalty Mnmze d j d j xj dj xj W U j P x j, j j (, ) A U j (, )A (, j ) A U j 1, f g, subject to x j x j 1, f s, { j (, j ) A } { j ( j, ) A } x j (, 0,1, j) A. 0, otherwse, Mnmzes expected total travel cost Flow conservatons at all network nodes Bnary decson varables ($/hr), ($/gram) parameters to convert unts P( ) penalty ($) for late or early arrval, functon of the total travel tme ( T ) E scheduled travel tme (hr), shppers preference on the total delvery tme (gven) If T E, no penalty; otherwse, assgn penalt y 60

61 Soluton Approaches: (1) Dynamc Programmng Stage: each node V n a gven network State: truck arrval tme m [0, ) at each stage V Decson: choce from a fnte set of decsons on the next lnk to move onto {(, j ) (, j) A} Truck speed: postve, contnuous random varable whch follows a certan probablty densty functon Algorthm can be wrtten nto a recursve Bellman equaton wth backward nducton Optmal soluton Mnmum expected total cost of the freght truck from ts orgn 61

62 Soluton Approaches: (2) Determnstc Shortest Path Heurstc In many real roadway networks, truck drvers need to select the next travel lnk n real tme (.e., wthn several seconds) Heurstc to fnd - Feasble soluton n a very short computaton tme even for very large networks - Upper bound to the optmum soluton Shortest path from orgn to destnaton s obtaned usng the expected lnk cost consderng only lnk travel tme and the related emssons Once truck reaches the destnaton, penalty cost s added 62

63 Numercal Examples Tested on four examples: small networks and large-scale urban transportaton networks Orgn Destnaton Orgn Destnaton 5-node and 13-lnk network (Powell, 2011) Orgn node and 25-lnk network (Papadmtrou and Stegltz, 1998) Destnaton Orgn Destnaton 24-node and 76-lnk Soux Falls network (Bar-Gera, 2009) 416-node and 914-lnk Anahem network (Bar-Gera, 2009) 63

64 Numercal Examples Assgn a hgh penalty for late but a low penalty for early arrval 100( T E ), f T E, PT ( ) where, T = total travel tme (hr), 10(T E), otherwse. E = scheduled travel tme (hr) Truck emsson rate functons (g/veh-mle) for CO 2, VOC, NO X, PM (TRL, 1999) Parameters that convert weght of emssons and tme nto monetary values 280 ($/tonco 2 ), 200 ($/tonvoc), 200 ($/tonno X ), 300 ($/tonpm 10 ) (Muller and Mendelsohn, 2007; Wnebrake et al., 2008), 20 ($/hr) (Ba et al., 2011) Truck speed on each lnk follows a randomly generated log-normal dstrbuton mean = unform [20, 60] (mph), s.d. = unform [10, 15] (mph) 64

65 Numercal Examples: Computatonal Results (a) Network 5-node and 7-lnk network 15-node and 25-lnk network 24-node and 76-lnk Soux Falls network 416-node and 914-lnk Anahem network (b) Algorthm Shortest path heurstc Dynamc programmng (D = 0.025) Shortest path heurstc Dynamc programmng (D = 0.030) Shortest path heurstc Dynamc programmng (D = 0.050) Shortest path heurstc Dynamc programmng (D = 0.040) (c) Mn. expected (d) Gap (e) Soluton tme total cost ($) (%) (sec) ,

66 Numercal Examples: Computatonal Results Benchmark routng = Ignorng emsson cost n selectng the route Proposed routng = Consderng emsson cost n selectng the route Cost dfference = Cost from the benchmark routng - Cost from the proposed routng (a) Network 5-node and 7-lnk network 15-node and 25-lnk network 24-node and 76-lnk Soux Falls network 416-node and 914-lnk Anahem network (b) Scenaro (c) Mn. expected total cost ($) (d) Travel tme ($) (e) Emssons ($) (f) Penalty ($) Benchmark desgn P roposed approach Cost dfference % -3.67% 21.42% 6.41% Benchmark desgn Proposed approach Cost dfference % -5.97% 24.38% 2.08% Benchmark desgn P roposed approach Cost dfference % 40.90% 40.82% % Benchmark desgn P roposed approach Cost dfference % -0.44% 11.04% % 66

67 SPEW-Trend fleet model Represent how emssons are affected by technology change and modal choce 67

68 CO 2 emsson projecton Clmate polcy (carbon tax) causes modal shft to ralway BUT not enough (Commodty-lmted) 68

69 Fuel use projecton Heavy-duty vehcle fuel use No congeston (by tonne-km only) Wth projected eff. mprovement* Wth congeston GCAM (nput-output) (* regresson!) 69

70 Emsson projecton ar pollutants (congeston case) 70

71 Conclusons Envronmental problems from freght shpment actvtes Clmate change (on global scale) Ar qualty and human health (n regonal and urban areas) Choce of freght mode and routng them between/wthn geographcal regons sgnfcantly affect regonal and urban ar qualty Freght demand models are developed to reflect dependences on future economc growth and urban spatal changes Scope of the freght transportaton Inter-regonal freght flow: Four-step freght demand forecastng model Intra-regonal freght flow: Varous network optmzaton models and soluton approaches 71

72 Contrbutons In ths nterdscplnary project, we Develop a comprehensve freght shpment modelng framework rangng from ntal collectng systems, to freght movements and routng at the natonal scale, and then to fnal dstrbutng systems Provde deeper understandng of the nterdependences and connectons among multple tradtonally separated research felds Ad decson-makers n evaluatng freght handlng decsons that contrbute to reducng adverse mpacts on ar qualty and clmate change Facltate decson-makng processes n the freght ndustres or the government agences by provdng an ntegrated decson-support software Extend and apply to other studes such as transportaton network capacty expanson and mantenance as well as traffc safety predcton Enhance human health and socal welfare 72

73 Future Research (1) Trp generaton and trp dstrbuton Dstrbuton of future freght demand s proportonal to that of the base-year freght demand can be relaxed Gravty model for freght demand dstrbuton Once newer verson of FAF database becomes avalable More recent base-year to mprove forecast accuracy (2) Modal splt Update the models usng addtonal/newer verson freght demand data Estmaton of precse envronmental mpacts of freght transportaton systems 73

74 Future Research (3) Network assgnment Impacts of nfrastructure nvestment n the ral network on modal splt Enhanced level of servce and ts effect on future ral freght demand (.e., aganst other modes n a compettve freght shpment market) (4) Stochastc urban freght truck routng problem Apply tme-dependent stochastc congeston state on each lnk Lnk travel tme and followng emssons wll be affected by stochastc truck speed as well as truck arrval tme at the lnk orgn node Include local and collector roads n the urban transportaton networks Truck speed on downstream and upstream lnks may be correlated Apply envronmental mpacts from transportaton actvtes to other stochastc network optmzaton problems 74

75 Thank you! Any questons? 75

76 Freght Analyss Zone (FAZ) Background Defned n Freght Analyss Framework to represent the U.S. geographcal regons wth regard to freght actvtes Composed of 123 domestc regons n total 74 metropoltan areas 33 regons representng the remanng parts of the states that these 74 metropoltan areas belong to 16 remanng regons, each of whch represents an entre state Map of domestc FAZs n Freght Analyss Framework verson 3 76

77 10 Commodty Types Commodty type Commodty descrpton Agrculture products and fsh Gran, alcohol, and tobacco products Stones, nonmetallc mnerals, and metallc ores Coal and petroleum products Basc chemcals, chemcal and pharmaceutcal products Logs, wood products, and textle and leather Base metal and machnery Electronc, motorzed vehcles, and precson nstruments Furnture, mxed freght, and mscellaneous manufactured products Commodty unknown 77