The Impacts of Congestion on Commercial Vehicle Tours

Transcription

1 Figliozzi 1 The Impacts of Congestion on Commecial Vehicle Tous Miguel Andes Figliozzi Potland State Univesity Maseeh College of Engineeing and Compute Science figliozzi@pdx.edu 5124 wods + 7 Tables + 2 Figues = 7374

2 Figliozzi 2 THE IMPACTS OF CONGESTION ON COMMERCIAL VEHICLE TOURS Miguel Andes Figliozzi ABSTRACT Congestion is a common phenomenon in all majo cities of the wold. Inceased tavel time and uncetainty bought about by congestion impacts the efficiency of logistics opeations. Recent studies indicate that a significant popotion of commecial vehicle kilometes taveled (VKT) and vehicle hous taveled (VHT) ae geneated by tip-chains o multi-stop tous. This pape pesents eseach demonstating the impact of congestion on multi-stop tous in uban aeas. An analytical model, numeical expeiments, and eal-wold tou data ae used to undestand the impact of congestion on tou chaacteistics, caies costs, VKT, and VHT. This eseach shows that tavel time/distance between custome and depot is a cucial facto that exacebates the negative impacts of congestion. Tavel time vaiability is not as significant when the tavel time between the depot and the customes is small in elation to the maximum tou duation and when the outes ae not highly constained. As congestion inceases, the numbe of vehicles needed to complete the tou also inceases. This is accompanied by an incease in the pecentage of total diving time and the aveage distance tavelled pe custome. Congestion impacts on caies costs ae also consideable since congestion not only inceases caies opeating costs but also affects caies cost stuctue. As congestion wosens the elative weight of labo costs wages and ovetime escalates. This pape categoizes tous into thee classes based on tou efficiency and the elative weight of time and distance elated costs. These ae intuitive and valuable tools to monito congestion, epesent eal-wold tou data, and classify tous in egads to thei sensitivity to congestion. KEYWORDS: Congestion Modeling, Caie Costs, Uban Feight, City Logistics

3 Figliozzi 3 1. Intoduction Inceased tavel times and the uncetainty bought about by congestion impacts the efficiency of logistics opeations. Diect and indiect costs associated with congestion have been widely studied and epoted. Most of the studies have focused on passenges value of tavel time, shippes value of time and maket access costs, poduction costs, and labo poductivity costs (Weisbod et al., 2001). Howeve, the modeling and study of the specific impacts of uban congestion on commecial vehicle tous have eceived scant attention. The lack of studies is lagely explained by lack of disaggegated and compehensive commecial vehicle data, which due to pivacy o competitive easons, is expensive to collect o unattainable at the desied level of detail. This eseach studies the impact of congestion on commecial vehicle tous in uban aeas. Recent studies indicate that a high popotion of uban commecial vehicle kilometes taveled (VKT) oiginate at distibution centes (DC), waehouses, o depots (Cambidge Systematics, 2003, Outwate et al., 2005) and constitute tip-chains o multi-stop tous. Commecial vehicle tous ae ubiquitous in uban aeas; fo example, in Denve appoximately 50% of single and combination tuck tous include 5 to 23 stops pe tou (Holguin-Veas and Patil, 2005). The tems oute, tou, and tip-chain ae used intechangeably in this eseach to designate the activity of a commecial vehicle that stats at a depot, visits one o moe customes, and then etuns to the depot; fo the sake of bevity the wod tou is used heein. This eseach contibutes to the undestanding of the impacts of congestion on commecial vehicle tous. The specific contibutions of this eseach ae theefold: a) it pesents an analytical model to undestand the impact of congestion on tou chaacteistics and costs, b) it discusses congestion costs fom a caie s pespective, c) it uses a novel and intuitive classification of uban distibution tous accoding to thei efficiency and susceptibility to congestion. Real-life disaggegated tou data is also analyzed to validate analytical insights of the model. The eseach is oganized as follows: section two povides a liteatue eview of congestion impacts on caie opeations and costs. Section thee intoduces a model to analyze the impacts of congestion on commecial vehicle tous. Section fou pesents analytical insights fo time constained tous. Section five contains a numeical analysis and discusses tou efficiency. Section six discusses the impact of

4 Figliozzi 4 congestion on caies costs. Section seven examines eal-life tou data; it also poposes a classification of tous based on thei efficiency and vulneability to congestion. Section eight ends with conclusions. 2. Liteatue Review It is widely ecognized that congestion seiously affects logistics opeations. McKinnon (1999) pesents the esults of in-depth inteviews with DC manages and discusses the negative effects of congestion on logistics opeations. Diect and indiect costs of congestion on passenges tavel time, shippes tavel time and maket access, poduction, and labo poductivity have been widely studied and epoted. Fo a systematic eview of this congestion liteatue the eade is efeed to the wok of Weisbod et al. (2001). Sizeable pogess has been made in the development of econometic techniques to study the joint behavio of caies and shippes in egads to congestion (Henshe and Puckett, 2004, Henshe and Puckett, 2005). Suvey esults suggest that the type of feight opeation has a significant influence on how congestion affects caies opeations and costs. Data fom a Califonia suvey indicate that congestion is peceived as a seious poblem fo companies specializing in LTL (less-than-tuckload), efigeated, and intemodal cago (Golob and Regan, 2001). In addition, Golob and Regan (2003) found a positive elationship between the level of local congestion and the puchase of outing softwae. Caies that do not follow egula outes, e.g. fo-hie caies, tend to place a highe value on the usage of eal-time infomation to mitigate the effects of congestion (Golob and Regan, 2005). Anothe banch of the liteatue models the elationship between industial/commecial pocuement and congestion. The negative impact of just-in-time (JIT) poduction on vehicle tip geneation has been analyzed and modeled (Rao et al., 1991, Moinzadeh et al., 1997). Using data fom Auckland, New Zealand, Sankaan et al (2005) study the impact of congestion and eplenishment ode sizes on caies with time-definite deliveies. Sankaan and Wood (2007) continues this wok and pesents a model using Daganzo s (1984, 1991) appoximations to outing poblems. Fo poblems whee outes have 10 o moe deliveies Sankaan and Wood (2007) indicate that congestion costs incease with the aveage numbe of ounds pe day and decease with the wokday length, the squae oot of the numbe of deliveies, and that congestion is invaiant with fixed o vaiable stop times. Routing constaints limit the numbe and chaacteistics of the feasible set of tous that caies can use to meet custome demand. Figliozzi (2006) models tou constaints using Daganzo s (1991) appoximations

5 Figliozzi 5 to outing poblems and analyzes how constaints and custome sevice time affect tip geneation using a tou classification based on supply chain chaacteistics and oute constaints. Building on this wok, Figliozzi (2007) poposes an analytical famewok to study the impact of policy o netwok changes on uban feight VKT; the model shows that a decease in tavel speed seveely affects tous with time window constaints while capacity constained tous ae less affected. Figliozzi (Figliozzi, 2007) also indicates that changes in both VKT and vehicle hous taveled (VHT) diffe by type of tou and outing constaint. Confidentiality issues ae usually an insumountable baie that pecludes the collection of detailed and complete feight data. To the best of the autho s knowledge, all feight congestion studies pesent aggegated data with the exception of Figliozzi et al. (2007) whee seveal months of detailed tuck activity have been analyzed And the disaggegated tou data was used to eveal tou chaacteistics, speed vaiability, and data collection challenges. Tanspotation agencies ae inceasingly using tavel time eliability as a congestion measue (Chen et al., 2003); eliability is often associated to a buffe time index (Lomax et al., 2003, Bemme et al., 2004). The buffe time index epesents the exta time a tavele needs to allow in ode to aive on time 95 pecent of the time. Caies also use buffes to mitigate the effects of tavel time o demand vaiability. Reliability as a constaint o objective has also been incopoated in opeations eseach models whee outes and sevice aeas ae designed using continuous appoximations (Eea, 2000, Novaes et al., 2000) o on stochastic pogamming (Lapote and Louveaux, 1993, Kenyon and Moton, 2003). In paticula, Eea (2000) poposes continuous appoximations to estimate expected detou and distances in stochastic vesion of the capacitated vehicle outing poblem. To the best of the autho s knowledge, no model has been developed to study the impacts of congestion on tou chaacteistics, costs, and VKT/VHT. 3. The Tou Model This eseach consides a system with one DC o depot and n customes. A tou is defined as the closed path that a tuck follows fom its depot to visit one o moe customes in a sequence befoe etuning to its depot duing a single dive shift. A tou is compised of seveal tips; a tip is defined by its oigin and destination and chaacteized by its distance and tavel time attibutes. The expession used to appoximate the length of a set of tous stating and ending at the depot is: n m ln ( ) = 2m+ kl an (1) n

6 Figliozzi 6 whee: ln: ( ) the total length of the tous needed to seve n : the numbe of stops o customes m : the numbe of outes n customes k l and : paametes to be estimated by linea egession k m a : the extent of the aea of sevice compised by the n customes : the aveage distance fom the depot to the n customes Expession (1) is a obust appoximation to pedict the aveage length of tous in a divese set of andomly geneated instances and eal-life uban netwoks. This expession has been tested with diffeent pattens of custome spatial distibution, time windows, custome demands, and depot locations. The fit of expession (1) to simulated and eal-life data is high with a -squae of 0.96 to 0.99 and a mean absolute pecentage eo (MAPE) of less than 5% (Figliozzi, 2008). The fist component of expession (1) epesents the connecting distance to the delivey aea (including the etun tip) and the second component is the distance connecting customes o stops. The value of the k l paamete is detemined by linea egession and captues the influence of factos such as spatial custome distibution, depot location, and time windows. Expession one oute seves all customes, and maximized when m (1) is minimized when m = 1, i.e. = n, i.e. one oute pe custome. Let custome ii, I= {1,..., n} have a location x i and a distance i to the depot; d epesents the distance between customes i, j I. The set of tous to seve all customes in I is denoted R = {1,..., m}, whee m indicates the numbe of outes o sevice egions and is the numbe of n ij customes in oute L = n + 1. R. Any given oute R is fomed by a set of n + 1 links denoted L, whee Let: t u = Time to load/unload a unit of poduct t o = Fixed time when stopping at a custome t l = Aveage tavel time in link l

7 Figliozzi 7 f t l = Fee-flow tavel time in link l s = d / t = Aveage tavel speed in link l l l l s = Aveage tavel speed in any given oute σ l = Standad deviation of the tavel time in link l υ = σ / t = Coefficient of vaiation in link l l l l ρ kl = Coelation between the distibutions of tavel time in links k and l w = Tou duation constaint b = Vehicle capacity q = Amount deliveed at custome ii, I= {1,..., n} i q = Aveage amount deliveed pe custome in oute, such that nq b Thee ae seveal constaints associated with the opeation of an uban commecial vehicle fleet: the type and capacity of the vehicles, dives woking hous o maximum tou lengths, sevice time, and the design of balanced tous (Bodin et al., 2003). Fo a given set of custome equests, the fleet opeato delineates tous that satisfy these equests and constaints. In uban tous, commonly binding constaints fo sevice, package delivey, and LTL 1 tous ae sevice time (moning/aftenoon custome visits) and tou duation. A common assumption when continuous appoximations ae utilized is that outes ae balanced, i.e. outes have a simila numbe of customes (Daganzo, 1984, Daganzo, 1991). Assuming balanced outes, the binding constaint fo each oute with an aveage of aveage speed s can be expessed as: n = n/ m 1 n m n n (2 + kl an ) + to + tu q w (2) s nm m m customes and The tou duation can be limited by one o seveal constaints such as: a) sevice consideations, e.g. tou duations of less than eight hous to ensue deliveies duing nomal business hous, b) dive woking hous, e.g. by county o state law the numbe of consecutive hous that a tuck dive can dive is esticted, usually the theshold is between 10 and 12 consecutive hous, and c) cost consideations, e.g. afte a cetain numbe of hous the caie must pay ovetime, which can be a significant cost in congested aeas as discussed in Section 6. 1 LTL stands fo less-than-tuckload

8 Figliozzi 8 4. Analysis of the Impact of Congestion on Duation Constained Tous The impact of congestion on distibution tous can be boken down into thee cases: (a) the incease in aveage tavel time, (b) the incease in tavel time vaiability, and (c) the inteaction effect between a simultaneous incease in aveage tavel time and vaiability. A constaint coefficient is defined to contol fo the elative impact of congestion on outing constaints fo each custome. (a) Incease in aveage tavel time no uncetainty An incease in aveage tavel time can be expessed by a coefficient αl 1 that eflects the tavel time incease with espect to the fee-flow tavel time: tl = α and f t l sl 1 f = s l (3) α By using this coefficient, expession (2) can be estated as: α n m n n (2 + kl an) + to + tu q w (4) s nm m m f If tavel time inceases and the tou duation constaint (4) is violated, the numbe of outes, m, inceases to estoe feasibility. An incease in aveage tavel time inceases not only diving time but may also distance the numbe of tous and the total distance taveled. Theefoe, the diect impact on VHT alone is insufficient to descibe the effects of congestion; the impact on VKT must also be consideed. Howeve, fo any given α, the pecentage-wise incease in VHT is always lage than the pecentage-wise incease in VKT. If pecentage time diving is calculated as the atio between time diving and tou duation, then an incease in aveage tavel time inceases the pecentage time diving wheeas the custome time is unaffected. (b) Incease in tavel time vaiability If the tavel times ae not constant, the buffe σ z must be added to (2) in ode to guaantee a custome sevice level: 1 n m n n (2 + kl an / m ) + to + tu q w σ z (5) s n m m Assuming nomally distibuted tavel times, the coefficient z is elated to the pobability of completing the tou within the allowed tou duation. The oute tavel time standad deviation can be expessed as:

9 Figliozzi 9 n 2 k kl k l k L k= 1 σ = σ + ρ σ σ l = k+ 1 k, l L (6) Unlike pevious studies and modeling appoaches, the coelation between tavel times is included because empiical data suggests that a positive coelation may not be negligible (Figliozzi et al., 2007). To minimize costs, a caie will educe the numbe of vehicles needed as well as the total oute lengthduation without violating custome sevice constaints. Fo a given set of customes and depots, this is equivalent to minimizing the numbe of outes subject to constaint (5). The oute tavel time vaiance, expession (6), gows with the numbe of customes pe oute o with an incease in vaiability (vaiances ae non-negative numbes). An incease in tavel time vaiability affects the ight-hand tem of expession (5) which is deceased by the tem the constaint and theefoe m σ z. This may lead to a violation of must incease in ode to estoe feasibility. Aveage oute duations decease when the buffe incease makes expession (5) binding and m inceases. Hence, on aveage, oute duations shoten but the sum of the oute duation plus the buffe tends to emain constant. Despite this, oute duation vaiability inceases. Unlike case (a), only if m inceases, distance taveled, time diving, and pecentage time diving ae inceased wheeas aveage diving speed does not change but effective speed may incease. (c) Inteaction effect between a simultaneous incease in tavel time and vaiability If thee is an incease in congestion and aveage tavel time inceases while the coefficient of vaiation emains constant the impact of congestion is amplified. A widespead appoach to indicate the degee of uncetainty in the distibution of a andom vaiable is to calculate the coefficient of vaiation. Assuming nomal distibutions and using coefficients of vaiations υ, expession (6) can be estated as: n 2 = ( k tk) + kl ktk ltl l = k+ 1 k, k L k= 1 l L σ υ ρ υ υ Assuming a constant coefficient of vaiations, υ = υk = υl kl, L, then: n 2 k kl k l k L k= 1 σ = υ t + ρ t t l = k+ 1 k, l L (7)

10 Figliozzi 10 Expessing the tavel time using the fee-flow tavel times, t l = α t f l n f 2 f f = ( k ) + kl k l = + 1, k L k= 1 σ υα t ρ t t l k k l L (8) f Denoting σ as the fee-flow standad vaiation of the oute tavel time in oute σ n f f 2 f f = ( k ) + ρkl k l = + 1, k L k= 1 t t t l k k l L (9) σ = υα σ (10) f α n m n n f (2 + kl an) + to + tu q w υασ z (11) s nm m m f Thee is a multiplicative inteaction between υ and α which affects the ight-hand tem of expession (11). Since υ and α ae non-negative a simultaneous incease in tavel time and vaiability can have a lage impact on the buffe size. A decease in tavel speed inceases the aveage time to complete the oute, as seen in the left-hand tem of (11). A decease also inceases the equied buffe, as seen in the ight-hand tem of (11). Links with long tavel times have a significant contibution on the final value of f σ as shown in (9). In tous with a long tavel time fom a DC to a sevice aea that is followed by shot inte-custome tips, the value of the standad vaiation is detemined by the ound tip fom/to the depot. (d) Constaint Coefficient Fo each custome it is possible to define a coefficient φ that can povide an indication the elative impact of congestion on outing constaints fo each custome. α 2 / sf + to + tuqi φi ( α, υ) = f w υασ z (12) i Whee the time standad deviation fo the etun tip to custome i is defined as: σ f i = + f 2 ( ti ) (2 ρ) Congestion has a distinct impact on each custome due to its location and sevice chaacteistics. When eaches the value of one, it is infeasible to seve custome i with the desied level of sevice z. φ i

11 Figliozzi Sensitivity Analysis To illustate how changes in tavel time affect tou chaacteistics as well as VHT/VKT, a sensitivity analysis based on a eal-wold situation 2 and tou data epoted in the liteatue is pefomed. Tou data fom diffeent cities indicate that the aveage numbe of stops pe tou in uban aeas is equal o geate than 5 stops pe tou: appoximately 6 in Calgay (Hunt and Stefan, 2005), 5.6 in Denve (Holguin-Veas and Patil, 2005), and 6.2 in Amstedam (Vleugel and Janic, 2004). Custome sevice is highly elated to the numbe of opeations to be pefomed and the numbe of pallets/packages to be loaded/unloaded; sevice time can be as shot as a few minutes (package delivey). In Amstedam, unloading/loading time pe stop is 21 minutes on aveage; LTL data fom Sydney indicate a median stop time 30 minutes and an aveage time of appoximately 40 minutes (Figliozzi et al., 2007). Routing scenaios wee constucted assuming a sevice aea of 39.5 squae kilometes containing 30 customes, a maximum tou duation of eight hous ( w = 8), thee diffeent ound tip distances to the depot ( 2 = 25, 50, and 75 kilometes), fou diffeent coefficient of vaiations (υ = 0.0, 0.2, 0.4, and 0.6), and fou custome sevice times anging fom 15 to 60 minutes ( t o + t u q = 15, 30, and 45 minutes). Tous wee designed using a constuction and impovement outing heuistics fo thee aveage speeds: 50, 25 and 12.5 km/h. Aveage Speed Sevice Time ( t c ) 15 min 30 min 45 min Tous Diving Time 50 km/h Distance Tous Diving Time 25 km/h Distance Tous Diving Time 12.5 km/h Distance Table 1 Aveage Incease Factos fo diffeent custome sevice times ( t c ) The sensitivity to congestion as a function of custome sevice time is pesented in Table 1. Each of the sections contains the aveage 3 incease factos with espect to fee-flow speed and assuming 2 = 25 km, 2 Tou chaacteistics and scenaios ae based on data pesented in Figliozzi et al. (2007). The sevice aea epesents the industial distict of Bankstown in the city of Sydney, Austalia. 3 The aveage among the thee vaiability factos: 0.2, 0.4, and 0.6

12 Figliozzi 12 s = 50, and a constant sevice time pe custome. Fo example, keeping the sevice time constant, = 45 min, but educing the speed by half ( s = 25km/h), the numbe of tous needed is 1.25 times lage than the numbe of tous needed when s = 50 km/h. A eduction of tavel speed has a significant effect on VKT and VHT. t c The impact of congestion on VKT and VHT is highe when customes have longe sevice times. The coesponding constaint coefficients follow a simila tend as shown in Table 2. Aveage Speed Sevice Time ( t c ) 15 min 30 min 45 min 50 km/h km/h km/h Table 2 Constaint coefficients φ assuming υ = 0.0 i The sensitivity to congestion as a function of distance to the depot is pesented in Table 3; this table is oganized by the thee etun distances (25, 50, 75 km) to depot 2. Each of the sections contains the aveage 4 incease factos with espect to fee-flow speed and no vaiability, i.e. s = 50, υ = 0.0, and fo a given etun distance to depot 2. Fo example, by setting 2 = 50km, the numbe of tous needed when speed is halved ( s = 25km/h) and vaiability inceases fom υ = 0.0 to υ = 0.6 is 1.60 times lage than the numbe of tous needed when s = 50 km/h, and υ = 0.0. Seveal obsevations can be made fom the analysis of Table 3: (a) ound tip distance to the depot is a cucial facto. The impact of congestion is amplified as the depot moves futhe away fom its customes; (b) vaiability is an impotant facto fo tavel times that ae elatively long with espect to the tou duation. Fo shot aveage distances to/fom the depot and high speeds the buffe is too small with espect to w and the impact of vaiability is negligible; (c) when congestion is high, i.e. low speeds and high vaiability, it becomes infeasible to seve moe distant customes with a high sevice level; (d) in all cases, the incease in the numbe of tous is geate than the incease in total distance taveled. Hence, the distance pe tou deceases as congestion inceases, i.e. tous become shote on aveage; and (e) as congestion inceases, the numbe of tous needed inceases and with it the pecentage of time diving and 4 The aveage among the fou custome times: 15, 30, and 45 minutes

13 Figliozzi 13 the aveage distance pe custome. The coesponding constaint coefficients follow a simila tend as shown in Table 4. Distance to Depot ( 2 ) Coefficient of Vaiation Aveage Tou Speed 50 km/h 25 km/h 12.5 km/h Tous Diving Time Distance km Tous Diving Time Distance Tous Diving Time Distance km Tous Diving Time Distance Tous Diving Time Distance km Tous Infeasible Diving Time fo z=1.64 Distance Table 3 Aveage Incease Factos fo Diffeent Custome-depot distances ( 2 ) Distance to Depot Aveage Speed 50 km/h 25 km/h 12.5 km/h 25 km km km Table 4 Constaint coefficients assuming υ = 0.0 As shown in Table 2 and Table 4, the analysis of the constaint coefficients φ i is a fast and effective poxy to study the sensitivity of tous to changes in congestion levels. Conceptually, the impact of congestion on VKT/VHT can be analyzed as a function of the custome distance to the depot and the level of the constaint coefficient φ i, see Table 5. When congestion is such that the constaint coefficients ae close o above one and custome sevice level has to be maintained, new depots ae equied o the

14 Figliozzi 14 sevice of customes located fa away fom the depot must be tansfeed to anothe tanspot povide, fo example a thid paty logistics company (3PL). Distance to Depot Shot Medium Long Vey High Constaint Coefficient Low High Low Medium High Vey High Appoaching Infeasibility Appoaching Infeasibility Use 3PL o New Depot Table 5 Conceptual Impact of Congestion on VKT/VHT 6. Impact on Costs Congestion not only inceases caies costs but also changes the elative weight of daily opeational costs such as fuel and labo needed pe custome. Table 6 pesents congestion elated fuel and wage inceases as a function of tavel speed and its coefficient of vaiation. The column named total incease facto shows the total cost incease using as a base scenaio a tavel time of 50 km/h and no tavel speed vaiation. The columns named sevice time, diving time, and fuel espectively indicate thei shae as a pecentage of the total fuel plus labo costs 5. Aveage Tou Speed Coefficient of Vaiation Total incease Facto Sevice Time Diving Time Fuel 50 km/h 25 km/h 12.5 km/h % 13% 27% % 14% 28% % 24% 32% % 27% 36% % 40% 40% % 47% 45% Table 6 Impact of Congestion on Tou Costs 5 The fuel and labo costs wee calculated using fuel consumption of a medium-size delivey tuck of appoximately thee kilometes pe lite of diesel at a cost of $1.25 pe lite of diesel. Fuel consumption was inceased to account fo lowe fuel efficiency at low speeds and congested diving conditions. Labo cost was assumed as $20 pe dive hou. Retun distance to distance was assumed to be 50 kilometes and the sevice time pe custome 30 minutes.

15 Figliozzi 15 Fo a given numbe of customes seved, total custome sevice time is not affected by congestion i.e. time spent at the customes locations does not change wheeas fuel and wages ae diectly affected by the amount of VHT and VKT. As congestion inceases, labo and fuel costs elated to time and distance diven become dominant. Fom a caie s pespective, the ultimate monetay impact of congestion depends on how much a caie can chage customes o pass on congestion costs along the supply chain. If diect distance between distibution cente and custome location is the main basis to pice tanspot sevices, caies cannot eadily and tanspaently tansfe the costs bought about by congestion. Table 6 does not include costs associated with the inceases in fleet size needed fo the incease in the numbe of tous. In addition, fo a caie opeating in an uban aea, the costs of congestion may be compounded by: (a) custome sevice employee time to handle custome complaints and escheduling issues; (b) stiff penalties due to JIT (just-in-time) late deliveies o the cost of lage time-buffes; (c) capital and opeational costs of eal time infomation systems, sophisticated vehicle outing and tacking softwae needed to mitigate the impact of congestion (Regan and Golob, 1999); (d) tolls oad usage to avoid highly congested steets tucks that come on/off main tolled highways seveal times a day to access diffeent delivey aeas can accue a significant toll cost (Figliozzi et al., 2007); and (e) paking fees and/o the payment of taffic/paking fines in dense uban aeas lacking loading zones (Mois et al., 1998). In tanspot o highway planning studies, commute/passenge congestion costs ae taditionally estimated as the sum of thee diffeent components: 1) the poduct of the tavel time delays and the value of time pe vehicle-dive, 2) a cost due to tavel time uneliability, and 3) highe opeating and envionmental costs. Caies costs ae hade to quantify; the impact of congestion is heavily dependent on netwok (e.g. depot location), oute type (e.g. numbe of stops and its density) and custome sevice chaacteistics (e.g. time windows) that may geatly vay among caies. 7. Real-wold Tou Data Classification and Repesentation This section elates the insights of pevious sections to empiical tou data obtained fom a company based in Sydney, Austalia. The company s customes ae located in seveal industial sububs of Sydney and ound tips between the depot and these industial sububs ange between 14 to 40 kilometes. Fo this paticula distibution opeation time windows ae not an oveiding concen; howeve, deliveies must take place within the pomised day. In addition, deliveies have to be caied out within nomal business hous (most customes pefe moning deliveies); theefoe, the stating time of the tou and its

16 Figliozzi 16 length ae constained to meet this condition. This is clealy evealed in the tou data: 13% of the deliveies took place befoe 8 am, 45% of the deliveies took place befoe 11 am, 76% of the deliveies took place befoe 2 pm, and 99% of the deliveies took place befoe 5 pm. A detailed desciption and analysis of the tou data is pesented in Figliozzi et al. (2007). 160 Time Diven pe Stop (min) y = x R² = Distance Tavelled pe Stop (km) 100 Figue 1 Time and Distance pe Custome Seved In many eal-wold distibution outes, to educe distibution costs customes ae seved and clusteed accoding to thei equiements and geogaphical location. At the disaggegate tou level, time diven and distance diven pe custome (o tou) ae highly coelated as shown in Figue 1. Moe insights egading tou chaacteistics and classifications can be obtained plotting pecentage of time diving (in a given tou) and distance taveled pe custome (in a given tou); see Figue 2. The pecentage of time diving can be expessed as a function of aveage distance taveled pe stop ( d ), aveage tavel speed ( s ), and sevice time pe custome ( t c ): d d + t s c Hence, pecentage of time diving is not diectly elated to tou duation o total distance tavelled and bette descibes the efficiency of the tou. Accoding to thei location in Figue 2 tous can be divided into thee distinct classes; a summay of these tou chaacteistics is shown in Table 7.

17 Figliozzi 17 90% 80% 70% II. FEWER STOPS LARGER DELIVERY AREA III. FEWEST STOPS FARTHER AWAY FROM DEPOT % Tou Time Diving 60% 50% 40% 30% 20% 10% 0% I. MANY STOPS IV. INFEASIBLE REGION Avg Distance Pe Stop (Km) Figue 2 - Tou Classification by Pecentage Time Diving and Distance Taveled pe Stop Class I tous, Figue 2 lowe left, have many stops and a low pecentage of the tou duation is spent diving. The aveage diving speed is low because the pecentage of local and access oads/steets used inceases with the numbe of customes visited. Despite the low aveage tavel speed, tous ae highly efficient fom the distibuto pespective because many customes ae seved diving a shot distance and a low pecentage of time is spent diving. On the uppe ight section of the gaph (Class III), tous have few stops and a high pecentage of the tou duation is spent diving. The tou distance is long because customes ae located futhe away fom each othe and/o the depot is fa fom the distibution aea. Tou duation is high and vey few customes can be seved. The aveage diving speed is high because the pecentage of local and access oads/steets used is small and main highways ae used to connect the depot with the distibution aea. Compaing delivey costs, class III tous have a delivey cost pe custome that is appoximately 3 times highe than class I tous. In addition, class III tous ae moe constained; the aveage constaint coefficient fo class I customes is 0.20 wheeas fo class III customes is Class II tous ae not as efficient as class I tous as the aveage distance pe stop is significantly highe because the density of stops is lowe than in class I tous. Given that the daily design of tous is based on what feight is available on a paticula day, caies cannot always utilize outes that ae tight both in tems of custome locations and total tou duation. In class II tous, moe customes could have been added to the tou if moe demand had mateialized. Finally, on the lowe ight section of the gaph (Class

18 Figliozzi 18 IV o infeasible tous), below the feasibility bounday, thee is an aea whee feasible tous cannot be found at pactical tavel speeds. Tou Class % Time Diving Dist. pe stop (km) Stops pe Tou Tou Duation (h) Tou Distance (km) Tou Speed (km/h) Effective Tou Speed (km/h) Class I 43% Class II 58% Class III 65% Table 7 Summay of Tou Chaacteistics by Class (Aveages) Fo a given custome time, tou efficiency and the elative weight of time and distance elated costs can be classified based on pecentage of time diving and the aveage distance pe custome. As congestion wosens the elative weight of labo costs wages and ovetime escalates and the popotion of class II and III tous will gows since educed tavel time and lage buffes peclude the design of tight o efficient outes. As indicated in sections 4 and 5, the impact of congestion ae moe sevee in tous that have a elatively long distance between depot and customes and thee is a significant eduction in tavel speed. Given the tou classification pesented in this section, class III tous ae moe exposed to negative congestion impacts: (a) congestion on feeways dastically educes fee-flow tavel speed; (b) the longe distance between depot and customes exacebates the incease in diving tavel time; and (c) using local steets may not be feasible due to thei lowe tavel speed. Despite the simplifying assumptions made in the analytical modeling of congestion impacts to ensue analytical tactability, the intuition and insights obtained can be applied to the chaacteization and analysis of eal-wold tou data and netwoks.

19 Figliozzi Conclusions This eseach analyzes the impact of congestion on commecial vehicle tous. An analytical model, numeical expeiments, and eal-life tou data ae used to undestand changes in tou chaacteistics, caies costs, and VKT-VHT. An incease in aveage tavel time inceases not only diving time but also distance taveled. Theefoe, the diect impact on VHT alone is insufficient to descibe the effects of congestion; the impact on VKT must also be consideed. This eseach shows that long tavel time/distance between custome and depot is a cucial facto that exacebates the negative impacts of congestion. Tavel time vaiability is not as significant when the tavel time between the depot and customes is small in elation to the maximum tou duation and when the outes ae not highly constained. Congestion impacts on caies costs ae also consideable since congestion not only inceases caies opeating costs but also affects caies cost stuctue. It is shown that as congestion wosens labo costs, wages and ovetime, may outweigh othe opeating costs. The poductivity of the caie can be measued in tems of tou time and distance equied to seve a custome. Pecentage of time diving and the aveage distance taveled pe custome ae tou chaacteistics suitable to indicate the efficiency of an individual tou because they ae diectly elated to diving time and invesely elated to custome time. This pape categoizes tous into thee classes based on tou efficiency and the elative weight of time and distance elated costs. The poposed classification is based on pecentage of time diving and the aveage distance pe custome. In addition fo a given custome time, a feasibility bounday that is a function of pecentage of time diving and aveage distance pe stop can be established. The tou classification and feasibility boundaies ae valuable and intuitive paametes epesent eal-wold tou data and classify tous in egads to thei sensitivity to congestion.

20 Figliozzi 20 Refeences BODIN, L., MANIEZZO, V. & MINGOZZI, A. (2003) Steet Routing and Scheduling Poblems. IN HALL, R. W. (Ed.) Handbook of Tanspotation Science, 2nd Edition. Nowell, Ma., Kluwe Academic Publishes. BREMMER, D., COTTON, K. C., COTEY, D., PRESTRUD, C. E. & WESTBY, G. (2004) Measuing congestion - Leaning fom opeational data. TRANSPORTATION RESEARCH RECORD (1895), CAMBRIDGE SYSTEMATICS (2003) Accounting fo Commecial Vehicles in Uban Tanspotation Models - Task 3 - Magnitude and Distibution. pepaed fo Fedeal Highway Administation- pepaed by Cambidge Systematics, Inc.Cambidge, MA. CHEN, C., SKABARDONIS, A. & VARAIYA, P. (2003) Tavel-time eliability as a measue of sevice. Tanspotation Reseach Recod 1855, DAGANZO, C. F. (1984) The Distance Taveled To Visit N-Points With A Maximum Of C-Stops Pe Vehicle - An Analytic Model And An Application. Tanspotation Science, 18, DAGANZO, C. F. (1991) Logistics Systems-Analysis. Lectue Notes In Economics And Mathematical Systems, 361, ERERA, A. (2000) Design of Lage-Scale Logistics Systems fo Uncetain Envionments. Ph D dissetation, Univesity of Califonia-Bekeley. FIGLIOZZI, M. A. (2006) Modeling the Impact of Technological Changes on Uban Commecial Tips by Commecial Activity Routing Type. Tanspotation Reseach Recod 1964, FIGLIOZZI, M. A. (2007) Analysis of the efficiency of uban commecial vehicle tous: Data collection, methodology, and policy implications. Tanspotation Reseach Pat B, 41, FIGLIOZZI, M. A. (2008) Planning Appoximations to the Aveage Length of Vehicle Routing Poblems with Vaying Custome Demands and Routing Constaints. Poceeding of the 87th Tanspotation Reseach Boad Annual Meeting CD om - Washington DC. USA. FIGLIOZZI, M. A., KINGDON, L. & WILKITZKI, A. (2007) Analysis of Feight Tous in a Congested Uban Aea Using Disaggegated Data: Chaacteistics and Data Collection Challenges. Poceedings 2nd Annual National Uban Feight Confeence, Long Beach, CA. Decembe. GOLOB, T. F. & REGAN, A. C. (2001) Impacts of highway congestion on feight opeations: peceptions of tucking industy manages. Tanspotation Reseach Pat A-Policy And Pactice, 35, GOLOB, T. F. & REGAN, A. C. (2003) Taffic congestion and tucking manages' use of automated outing and scheduling. Tanspotation Reseach Pat E-Logistics And Tanspotation Review, 39, GOLOB, T. F. & REGAN, A. C. (2005) Tucking industy pefeences fo tavele infomation fo dives using wieless Intenet-enabled devices. Tanspotation Reseach Pat C-Emeging Technologies, 13, HENSHER, D. & PUCKETT, S. (2004) Feight Distibution in Uban Aeas: The ole of supply chain alliances in addessing the challenge of taffic congestion fo city logistics. Woking Pape ITS-WP HENSHER, D. A. & PUCKETT, S. M. (2005) Refocusing the modelling of feight distibution: Development of an economic-based famewok to evaluate supply chain behaviou in esponse to congestion chaging. Tanspotation, 32, HOLGUIN-VERAS, J. & PATIL, G. (2005) Obseved Tip Chain Behavio of Commecial Vehicles. Tanspotation Reseach Recod 1906, HUNT, J. & STEFAN, K. (2005) Tou-based micosimulation of uban commecial movements. pesented at the 16th Intenational Symposium on Tanspotation and Taffic Theoy (ISTTT16), Mayland, July KENYON, A. S. & MORTON, D. P. (2003) Stochastic vehicle outing with andom tavel times. Tanspotation Science, 37, LAPORTE, G. & LOUVEAUX, F. V. (1993) The Intege L-Shaped Method Fo Stochastic Intege Pogams With Complete Recouse. Opeations Reseach Lettes, 13, LOMAX, T., SCHRANK, D., TURNER, S. & MARGIOTTA, R. (2003) Selecting Tavel Reliability Measues. Texas Tanspotation Institute monogaph (May 2003). MCKINNON, A. (1999) The Effect of Taffic Congestion on the Efficiency of Logistical Opeations. Intenational Jounal of Logistics: Reseach & Applications, 2, MOINZADEH, K., KLASTORIN, T. & BERK, E. (1997) The impact of small lot odeing on taffic congestion in a physical distibution system. Iie Tansactions, 29, MORRIS, A. G., KORNHAUSER, A. L. & KAY, M. J. (1998) Uban feight mobility - Collection of data on time, costs, and baies elated to moving poduct into the cental business distict. Feight Tanspotation. Washington, Natl Acad Sci.

21 Figliozzi 21 NOVAES, A. G. N., DE CURSI, J. E. S. & GRACIOLLI, O. D. (2000) A continuous appoach to the design of physical distibution systems. Computes & Opeations Reseach, 27, OUTWATER, M., ISLAM, N. & SPEAR, B. (2005) The Magnitude and Distibution of Commecial Vehicles in Uban Tanspotation. 84th Tanspotation Reseach Boad Annual Meeting - Compendium of Papes CD- ROM. RAO, K., GRENOBLE, W. & YOUNG, R. (1991) Taffic Congestion and JIT. Jounal of Business Logistics, 12, REGAN, A. C. & GOLOB, T. F. (1999) Feight opeatos' peceptions of congestion poblems and the application of advanced technologies: Results fom a 1998 suvey of 1200 companies opeating in Califonia. Tanspotation Jounal, 38, SANKARAN, J., GORE, K. & COLDWELL, B. (2005) The impact of oad taffic congestion on supply chains: insights fom Auckland, New Zealand. Intenational Jounal of Logistics: Reseach & Applications, 8, SANKARAN, J. & WOOD, L. (2007) The Relative Impact of Consignee Behaviou and Road Taffic Congestion on Distibution Costs. Tanspotation Reseach pat B, 41, VLEUGEL, J. & JANIC, M. (2004) Route Choice and the Impact of 'Logistic Routes'. IN TANIGUCHI, E. & THOMPSON, R. (Eds.) LOGISTICS SYSTEMS FOR SUSTAINABLE CITIES. Elsevie. WEISBROD, G., DONALD, V. & TREYZ, G. (2001) Economic Implications of Congestion. NCHRP Repot #463. Washington, DC, National Coopeative Highway Reseach Pogam, Tanspotation Reseach Boad.

22 Figliozzi 22 Table 1 Aveage Incease Factos fo diffeent custome sevice times ( t c ) Table 2 Constaint coefficients φ assuming υ = i Table 3 Aveage Incease Factos fo Diffeent Custome-depot distances ( 2 ) Table 4 Constaint coefficients assuming υ = Table 5 Conceptual Impact of Congestion on VKT/VHT Table 6 Impact of Congestion on Tou Costs Table 7 Summay of Tou Chaacteistics by Class (Aveages)... 18