Directory UMM :Data Elmu:jurnal:T:Transportation Research Part B Methodological:Vol34.Issue2.Feb2000:

Continuum modeling of multiclass trac ¯ow

Serge P. Hoogendoorn

*

_{, Piet H.L. Bovy}

Faculty of Civil Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands

Received 7 August 1998; received in revised form 31 March 1999; accepted 8 April 1999

Abstract

In contemporary macroscopic trac ¯ow modeling, a distinction between user-classes is rarely made. However, it is envisaged that both the accuracy and the explanatory ability of macroscopic trac ¯ow models can be improved signi®cantly by distinguishing classes and their speci®c driving characteristics. In this article, we derive such a multiple user-class trac ¯ow model. Starting point for the derivation of the macroscopic ¯ow model is the user-class speci®c phase±space density, which can be considered as a gen-eralization of the traditional density.

The gas-kinetic equations describing the dynamics of the multiclass Phase±Space Density (MUC-PSD) are governed by various, interacting processes, such as acceleration towards a class-speci®c desired velocity, deceleration caused by vehicle interactions and the in¯uence of lane changing. The gas-kinetic equations serve as the foundation of the proposed macroscopic trac ¯ow models, describing the dynamics of the class-dependent spatial density, velocity and velocity variance. The modeling approach yields explicit re-lations for both the velocity and the velocity variance. These equilibrium rere-lations show competing pro-cesses: on the one hand, drivers accelerate towards their class-dependent desired velocity, while on the other hand, they need to decelerate due to interactions with vehicles from their own class and asymmetric in-teractions with vehicles from other classes. Using the operationalized model, macroscopic simulations provide insight into the model behavior for di€erent scenarios. Ó _{2000 Published by Elsevier Science Ltd.}

All rights reserved.

1. Introduction

Contemporary trac control measures aim to pursue a more ecient use of existing infra-structure by recognizing the importance of distinguishing user-classes, such as trucks, buses, passenger-cars, and high-occupancy vehicles, and managing these classes di€erently. Examples of

*

Corresponding author. Tel.: +33-15-278-5475; fax: +33-15-278-3179; e-mail: s.hoogendoorn@ct.tudelft.nl

0191-2615/00/$ - see front matterÓ_{2000 Published by Elsevier Science Ltd. All rights reserved.}

such control instruments are uninterrupted passage for buses at ramp-metered on-ramps, dynamic truck overtaking prohibitions, and dynamic lane allocation control.

Due to the complexity of controlling heterogeneous trac ¯ow operations, caused by among others the interaction between user-classes and the interplay between the available trac, a model-based approach is needed. That is, future research should focus on theory development regarding generic trac ¯ow theory for the synthesis and analysis of heterogeneous trac ¯ow. Also, emphasis should be on analyzing the problem of optimizing trac control of heterogeneous ¯ows. To this end, macroscopic multiple user-class trac ¯ow models are invaluable. This article presents a macroscopic trac ¯ow model that distinguishes user-classes having distinct trac characteristics.

The macroscopic model described here is derived from mesoscopic principles. Mesoscopic models describe the behavior of small groups of vehicles of a speci®c user-class, classi®ed by their position, velocity and desired velocity at an instant in time. These groups are aggregated with respect to the velocity and the desired velocity of the vehicles of a user-class:

Z

velocity

Z

desired velocity

mesoscopic!macroscopic …1†

resulting a macroscopic model. The proposed model generalizes the models proposed by Helbing (1996) by macroscopically describing trac ¯ow operations for multiple user-classes.

The article is organized as follows. We ®rst present a very short overview of the state-of-the-art in multiclass continuum trac ¯ow modeling. Subsequently, we derived the multiclass gas-kinetic ¯ow equations that are the mesoscopic foundation of the macroscopic model derived in the en-suing section. In this section we also discuss the expressions for the equilibrium velocity and velocity variance following from the modeling approach. We conclude the article with results from macroscopic simulation to show the plausibility of the multiclass model.

2. Trac ¯ow modeling approaches

In this article, we aim to develop a macroscopic trac ¯ow model that enables the synthesis and analysis of multiclass trac ¯ow. Examples of these user-classes are person-cars, recreational trac, vehicles at various levels of driver-support, and articulate and non-articulate trucks. Such a model is necessary to improve modeling the e€ects of (class-speci®c) trac control on the het-erogeneous trac stream.

mesoscopic or macroscopic, respectively describing groups of vehicles or the collective vehicular stream. Furthermore, we only consider deterministic analytical models.

Macroscopic modeling approaches and modeling issues: Several macroscopic continuum models have been proposed in the literature. Basically, each of these can be generalized towards a mul-ticlass trac ¯ow model. A well-known model is the so-called kinematic model or simple con-tinuum model®rst presented by Lighthill and Whitham (1955). This model consists of the so-called conservation-of-vehicle equation, describing the dynamics of the densityrin relation to the ¯owq:

o_r o_t‡

o_q

o_xˆ0: …2†

The model is completed by a speed±densityrelation V ˆVe…r†, and the relationqˆrV.

This simple model has been successfully applied and captures the basic properties of the ve-hicular ¯ow. However, several problems with this oversimpli®ed description of trac have been reported (cf. Lyrintzis et al., 1994; Kerner et al., 1996):

1. The kinematic model does not admit deviations from the speed±densityV…x;t† ˆVe…r…x;t†† re-lation. In other words, driversreact instantaneously to changing trac conditions.

2. Discontinuous solutions result irrespective of the smoothness of the initial solution. This con-tradicts the observed real-life ¯ow behavior.

3. The LWR-model is unable to capture the formation of localized structures, phantom-jams, hysteresis, and stop±start waves under speci®c conditions. This is unfortunate, since realistic modeling these phenomena is imperative for describingreal-life trac ¯ow.

ThePayne-type models1remedy these ¯aws by adding to the conservation-of-vehicles equation a dynamic equation describing the dynamics of the velocityV. In its most general form, this velocity equation equals:

where P is the so-called trac pressure and g the trac viscosity coecient. In Lyrintzis et al. (1994) and Kerner et al. (1996) di€erent model speci®cations (i.e. choices forP, g, andVe_{r†) are}

successfully used to simulate trac ¯ow operations. Indeed, introducing a dynamic velocity equation remedies the issues (1)±(3). However, in Daganzo (1995). the following points of interest not considered by Payne-type models are stated:

1. Vehicles areanisotropicparticles that mainly react to downstream trac conditions.

2. The interaction between the vehicle in the ¯ow is asymmetric in that theslow vehiclesremain virtually una€ectedby faster vehicles.

3. Unlike particles in a ¯uid, vehicles havepersonalitythat remains una€ected by prevailing trac conditions.

Finally, Helbing (1996) proposes a third dynamic equationfor the velocity varianceH:

o_H

whereP ˆrH, He…r† the equilibrium velocity variance, andj denotes the kinematic coecients. In addition to resolving the aforementioned issues, the model of Helbing (1996) also considers: 1. Finite spacing requirements of the vehicles, important due to the relative scale of processes of

interest.

2. The quantitative e€ects of ®nite reaction and braking times.

The model presented in the article can be considered as a multiclass generalization of the model of Helbing (1996). Consequently, it inherits the desirable properties of the latter model, which will be illustrated in the sequel of this article.

Multiclass approaches to mesoscopic and macroscopic ¯ow modeling: The number of me-soscopic or macroscopic models developed is small. In Daganzo (1994b) a generalized theory to model freeway trac in the presence of two vehicle types and a set of lanes reserved for one of the vehicle classes is presented. It refers to the case of a long homogeneous freeway, described with the Cell-Transmission model (cf. Daganzo, 1994a). However, all vehicles have identical driving characteristics (acceleration times, free-¯ow velocities, etc.) and are only di€erent in their lane-use behavior. In other words, the di€erences in among other acceleration performance and behavior, maneuverability, and maximum velocities are not described. The model can be con-sidered as a generalization of the original LWR-model, thereby inheriting the shortcomings of the latter.

In Hoogendoorn and Bovy (1996) asemi-discrete (that is, continuous in time, and discrete in space) multiclass generalization of the Payne model (cf. Payne, 1971) is proposed. This model describes the asymmetric interaction between vehicles of the respective user-classes heuristically, and lacks theoretical foundation. Finally, let us note that Helbing (1997) presents a tentative multiclass model.

Outline of the derivation of macroscopic MUC model: In this article we present a macroscopic multiclass trac ¯ow model based on gas-kinetic principles. The derivation approach can be considered as a multiclass generalization of the approach followed in, among others, Prigogine and Herman (1971) and Helbing (1996). Let us brie¯y outline the approach used to establish the macroscopic multiple user-class trac ¯ow model.

First, we derive the multiclass gas-kinetic equations describing the dynamics of the multiclass Phase±Space Density. These equations enable modeling the process of drivers accelerating to-wards the class speci®c desired velocity and the process of drivers decelerating due to impeding vehicles of the same or other classes. Subsequently, themethod of momentsis applied to the MUC gas-kinetic equations, in order to establish partial di€erential equations describing the dynamics of the class speci®c density, velocity and velocity variance. From this modeling approach, ex-pressions for the equilibrium velocityand equilibrium velocity variance result. These convey both

acceleration processestowards the class dependent desired velocity as well asdeceleration processes

due the within and between user-class interactions.

3. Gas-kinetic modeling of multiclass trac

presented in Paveri-Fontana (1975). We will show that these multiclass gas-kinetic equations are given by the following set of partial di€erential equations:

…5†

for allu2U, whereUdenotes the set of user-classesu. Eq. (5) expresses the dynamic changes in the so-calledmulticlass Phase±Space Density(MUC-PSD)q_u due to:

1. Convection because of the in¯ow and out¯ow of vehicles (term C of (5)).

2. Acceleration towards theacceleration velocityxu…v;v0†(termAof (5)). The acceleration velocity

re¯ects theexpected velocityto which a vehicle of classuwith velocityvand desired velocityv0 accelerates.

3. Interactions without immediate overtaking opportunity, expressed by the multiclass collision equations(term I of (5)).

In the remainder of this section we will elaborate upon the MUC-PSD, and present the derivation approach of the gas-kinetic equations (5).

Multiclass Phase±Space Density: The gas-kinetic equations derived in this section describe dynamic changes in the MUC-PSD. This notion is de®ned as follows. The joint probability density function /_u…v;v0jx;t† describes the joint probabilities of the velocity V_u and desired ve-locityV0_u. Using /_u;we de®ne the MUC-PSDq_u…x;v;v0_{;t† by the expected number of vehicleson}

…x;t†having a velocityv and a desired velocityv0 per unit roadway length:

q_u…x;v;v0;t†,/_u…v;v0jx;t†ru…x;t†; …6†

whereru…x;t†equals the expected number of vehicles of classuper unit roadway length. Note that

for thedensityr…x;t†, being the expected number of vehicles per unit roadway length, it holds that r…x;t† ˆP

uru…x;t†.

Derivation of the multiclass gas-kinetic equations: Several very complex and interacting pro-cesses cause the dynamic behavior of the trac stream: drivers accelerate towards their desired velocity, vehicles interact, followed by either an immediate lane change or by a deceleration, or drivers adapt their desired velocity to prevailing road, ambient or weather conditions. In this section, we present dynamic equations describing temporal changes in the trac conditions caused by these processes, resulting in the multiclass gas-kinetic equations.

These mesoscopic trac dynamics are governed by what will be referred to as continuum

and non-continuum processes. Continuum processes yieldsmooth changes in the MUC-PSD due to in¯ow and out¯ow in the phase±space Y consisting of vectors yˆ …x;v;v0_†.

Non-con-tinuum processes re¯ect non-smooth changes in the MUC-PSD, caused by among others vehic-ular interactions followed by either an immediate lane change or deceleration of the impeded vehicle.

‰xÿdx=2;x‡dx=2†. In the multiclass gas-kinetic derivation, we will distinguish convection, ac-celeration, and deceleration processes. Let us elaborate upon these processes.

1. Convection: The MUC-PSDq_u…x;v;v0_{;t†changes due to the in¯ow of vehicles from cellxÿdx,}

and the out¯ow of vehicles to cellx‡dx(A and B).

2. Acceleration: Letxu…x;v;v0;t†denote the acceleration velocity of vehiclesqu…x;v;v

0_{;t†. Vehicles}

from classuin cellxdriving at velocityvaccelerate in order to attain their acceleration velocity xu resulting in an increasequ…x;w;v0;t† at the expense ofqu…x;v;v0;t† (see (C)). Vehicles

accel-erating fromw<v tovcause q_u…x;v;v0_{;t† toincrease (D).}

3. Interaction: When a vehicle driving with velocity v catches up with a slower vehicle, it either needs to reduce its velocity, or perform an immediate lane change. In the ®rst case, the number of vehicles inq_u…x;v;v0;t† decreases (E);q_u…x;v;v0;t†increases when fast vehicles are impeded by vehicles driving with velocityv (F).

Derivation of the gas-kinetic equations: Before presenting the multiclass gas-kinetic equations, let us brie¯y discuss the assumptions underlying the gas-kinetic ¯ow equations:

1. The desired velocity is an observable concept that is meaningful to drivers. 2. The lengths of the vehicles are neglected. 2

3. No user-class transitions are allowed.

4. Free-¯owing vehicles accelerate towards their desired velocity; constrained vehicles accelerate to the expected velocity of the platoon-leaders.

5. Impeded vehicles not able to immediately overtakeinstantaneouslyadapt to the velocity of the impeding vehicle; immediately overtaking vehicles remain at the same velocity.

6. Impeding vehicles are una€ected by the impeded vehicle.

2

The in®nitesimal vehicles assumption is remedied in the macroscopic MUC model.

Continuum processes(Convection,acceleration and desired velocity adaptation): By balancing the ¯ows through the boundaries of a di€erential volume …dx;dv;dv0;dt†, we can establish the fol-lowing multiclass generalization of gas-kinetic model of Paveri-Fontana (1975) (for details, we refer to Hoogendoorn and Bovy, 1998a).

o_q

… †_NC re¯ects the changes caused by non-convective processes. Let us ®rst specify the respective terms in…dy=dt†:

Clearly, the total derivative dx=dtequals the velocity v:

dx

dtˆv: …9†

The total derivative dv=dt re¯ects the acceleration process. Let us assume that the acceleration processes can be described realistically by an exponential acceleration law:

dv

the acceleration time. In contrast to the original mesoscopic single-class trac ¯ow model pro-posed by Prigogine and Herman (1971) this term describes individual relaxation towards the desired velocity, instead of the collective relaxation towards a trac composition dependent equilibrium velocity. This is a very important model feature. We argue that it is more realistic to assume that drivers aim to traverse the roadway at their individual desired velocity and are possibly restricted as a result of interactions with slower vehicles, than to assume that drivers relax towards a velocity which they chose based on the prevailing average trac conditions.

Di€erent speci®cations of xu and su have been proposed to model the acceleration process in

the single-class case. These describe either of the two extreme cases:

1. All vehicles in the trac stream are able to accelerate towards their individual desired velocity v0_{, given their class-speci®c acceleration times}0

u(equivalent to single-class model of

Paveri-Fon-tana, 1975).

2. Only free-¯owing vehicles accelerate towards their desired velocityv0 given their acceleration time s0

u. Constrained (platooning) vehicles do not accelerate at all (see Helbing, 1997).

Case 1 yields the following speci®cation of the acceleration law:

xu ˆv0 and suˆs0u )

assume that free-¯owing drivers accelerate towards their desired velocity v0, given their acceler-ation times0

u, while platooning drivers accelerate towards the average acceleration velocity of the

platoon-leaders, given the acceleration times of the latter vehicles. For free-¯owing vehicles (in-dicated by the index f) we then have:

xf_uˆv0 and sf_u ˆs0_u: …13†

Hoogendoorn (1998) shows that the aggregate-class expected acceleration time sf_{v† and the}

ag-gregate-class desired velocity xf_{v† offree-¯owingvehicles driving with velocity vsatis®es:}

1=sf…v† ˆ

respectively, where the free-¯owing MUC-PSDqf

u,…1ÿhu†qu. We assume that all vehicles in a

platoon have the same velocity v, and the acceleration velocity xc _{and time s}c _{of vehicles in a}

platoon is equal to the acceleration velocity and time of the free-¯owing platoon-leader. Fur-thermore, we assume that the leader of a platooning vehicle driving with velocity v is drawn uniformly from the population of free-¯owing vehicles driving with velocityv. Then, usingxf_and

sf _{we ®nd for the constrained vehiclesc of classu:}

xc_uˆxf and sc_u ˆsf: …16†

Finally, we assume that drivers do not change their desired velocity during their trip. That is, the derivative of desired velocity with respect to time satis®es:

dv0

dt ˆ0: …18†

By substitution of (9), (17), and (18) into (7), we ®nd:

o_q

Non-continuum processes(vehicular interaction): Let us now consider the right-hand side of (19). We can distinguish two types of non-continuum processes, namely adaptation of the desired ve-locity distribution to a reasonable desired velocity distribution, and the deceleration caused by vehicle interactions. With respect to the former, the assumption that the desired velocity distri-bution is adapted quickly to the reasonable desired speed justi®es neglecting the adaptation process. Let us thus elaborate only upon the deceleration process.

vehicle is referred to as thepassive party. The used terminology stems from the concepts of active and passive overtaking (cf. Leutzbach, 1988).

Let us now consider interacting vehicles ofq_u…x;v;v0_{;t†. Interactions can either yield an increase}

or a decrease inq_u…x;v;v0;t†, respectively denoted by…o_q

wˆ …w;w0_{† respectively denoting the velocity and desired velocity of vehicles 1 and 2. In other}

words,q_u;sdenotes the expected number of vehicle pairs of a vehicle of classuhaving a velocityv and a desired velocityv0 _{and a vehicle of class shaving a velocitywand a desired velocityw}0 _per

unit roadway length. Let puˆpu…x;v;w;t† denote the probability that a vehicle of class u can

immediately overtake the slower vehicle. Then, using the results established in Leutzbach (1988), we can show that the decrease-rate …o_q

u=ot†

where we have assumed that the slower vehicles are una€ected by the interaction.

The class±speci®c immediate overtaking probabilitypu ˆpu…x;v;w;t† depends on a number of

factors, such as the trac conditions on the destination lanes, the maneuverability and length of the vehicle, the di€erence between the current velocityvand the velocitywof the impeding vehicle, the composition of trac (e.g. percentage of heavy-vehicles), etc.

The number of interactions between vehicles having speedvandwrespectively are equal to the `interaction rate' equal to the relative speedwÿv and the mutual phase density. Paveri-Fontana (1975) established that assuming vehicular chaos justi®ed the factorization approximation q_u;sˆq_uq_s, re¯ecting probabilistic independence of the vehicles. Expressions (21) and (22) con-stitute a multiclass generalization of the so-called collision equations (see Leutzbach, 1988).

Hoogendoorn (1998) shows that the assumption of vehicular chaos is ¯awed, except for free-¯ow conditions. That is, for increasingly dense trac, the assertion that velocities are independent is not realistic. To remedy this, the author proposes a trac ¯ow theory based on description of the trac stream as a collection ofvehicle platoonsrepresented by their platoon leaders (the free-¯owingvehicles) to describe the increase in velocity-correlation for increasingly dense trac (see Hoogendoorn, 1999). Assuming that only the unconstrained platoon leaders (rather than all

3

vehicles) move independently infers substituting qf

s for qs into expressions (21) and (22) (see

Hoogendoorn, 1998), whereqf

s equals the free-¯owing vehicles inqs. Substitution of the resulting

equations (21) and (22) into (19) establishes the multiclass gas-kinetic equations. Finally, let us remark that in the remainder of this article, we assume that the resulting expressions for (21) and (22) can be approximated by:

s;and wherepu;sdenotes the…v;v0†-independent expected immediate

overtaking probability of vehicles of class uovertaking a vehicle of class s:

4. Macroscopic multiclass trac ¯ow model

We establish in this section the macroscopic multiclass trac ¯ow equations on the basis of the gas-kinetic equations presented in the Section 3. The derivation approach is similar to the ap-proaches followed by among others, Prigogine and Herman (1971), Leutzbach (1988) and Helbing (1996). To establish the dynamics of the class-speci®c density, velocity and velocity variance the method of moments is applied, resulting in a non-closed set of equations of the moments of the velocity distribution. Subsequently, additional modi®cations to the model are proposed resulting in the following closed-form approximate system of partial di€erential equations, describing the dynamics of the class-speci®c density ru, expected velocity Vu and velocity variance Hu:

o_ru

where the equilibrium velocity Ve

u and the equilibrium variance H e

u;s re¯ects the in¯uence of interacting vehicles of classes uand s which follows from its

In the remainder, we will derive these partial di€erential equations. However, we will ®rst derive the main dependent variables (e.g. densityru;velocityVu, velocity varianceHu, desired velocityVu0,

and velocity covarianceCu) from the MUC-PSD.

Velocity moments: In the following the method of moments is applied to density functions describing the distribution of the velocities, with the aim to establish equations describing the dynamics of the velocity moments. We show that the class-speci®c densities, velocities and velocity variances are completely determined by these velocity moments. Consequently, establishing the dynamics of the velocity moments also reveals the dynamics of the densities, velocities and ve-locity variances.

where/_u denotes the joint probability density function of the velocity and the desired velocity. Then, the jointkth velocity andl-th desired velocity momentM_u…k;l† is de®ned by:

M_u…k;l†,Dvk …v0_†lE

That is, the velocity variance HuˆHu…x;t† of user-class u can be determined from the second

velocity moment and the mean velocity of user-classu. Finally, the third velocity moment equals:

M_u…3† ˆV_u3‡3VuHu‡Cu; …33†

whereCuˆCu(x,t) denotes the skewness of the velocity distribution.

Let us now consider some speci®c moments forlˆ1. Consideringkˆ1, the zeroth-covariance moment equals the expected desired velocityV0

u of class u:

M_u…0;1†,v0_uˆV_u0: …34† The covarianceCu ˆCu…x;t† between the actual and the desired velocity is determined from the

®rst covariance momentkˆlˆ1, the expected actual velocity and the expected desired velocity. That is:

M_u…1;1†,vv0_uˆV_u0Vu‡Cu: …35†

Finally, let us de®ne the interaction momentsI…k†

where the reduced MUC-PSD_~q_sis de®ned by_~q_s…x;v;t†,R

The multiclass velocity moments dynamics: In applying the method of moments, the special MUC continuity equations are multiplied withvk_{, and subsequently integrated with respect tov}

and v0_{. By doing so, the following equations describing the dynamics of the velocity moments}

M_u…k†ˆM_u…k†…x;t†are established (for details, we refer to Hoogendoorn and Bovy, 1998a):

wherepu is the expected immediate overtaking probability, andsu ˆsu…x;t†denotes the expected

acceleration time. The dynamic equation shows how thekth velocity momentM_u…k†changes due to convection (term C), acceleration (term A), and deceleration (term D). The in¯uence of deceler-ation is the result of a termP

s2UR …k†

u;s re¯ecting changes over time inM …k†

u due to vehicles of classu

interacting with vehicles of classson the one hand, and the probability on immediate overtaking …1ÿpu† of vehicles of classu on the other hand.

Derivation of the multiclass macroscopic equations: In this section we establish the macroscopic MUC equations for the density, the velocity and the velocity variance. To this end, Eq. (39) is assessed fork ˆ0;1;and 2 respectively, yielding the conservation of vehicle equation, the velocity dynamics and the velocity variance dynamics.

The conservation of vehicles equation: Fork ˆ0; and usingI…0†

u;s‡X …0†

u;s ˆ0 we ®nd Eq. (25) as

the expected result. This expression can be considered as the multiclass generalization of the traditional conservation-of-vehicle equation. It states that the density changes according to the balance between the in¯ow and out¯ow of vehicles of user-classu.

The velocity dynamics equation: Next, Eq. (39) is assessed for k ˆ1. By using of the con-servation-of-vehicle equation (25) in the expansion of the left-hand side of (39) for k ˆ1 we ®nd:

where the so-called trac pressurePu for classuis de®ned by Pu ˆruHu. The following dynamic

equation thus holds for the velocityVu:

where the equilibrium velocity V_ueis de®ned by:

V_ue,V_u0ÿ …1ÿpu†su

Eq. (41) shows the changes in the trac conditions experienced by an observer moving along with the vehicles of classu. These changes are caused by a term considered as ananticipationterm4

Aon the one hand, and a relaxation termRon the other hand. TermAshows how the total time derivative of the velocityVu changes due to spatial changes in the trac pressureP.

Eq. (42) speci®es the equilibrium velocity in accordance with the gas-kinetic multiclass ¯ow equations (5). It can be interpreted as a theoretical result concerning the dependence of the equilibrium velocity on the microscopic processes of trac ¯ow. According to (42), the equilib-rium velocity is given by the expected class-dependent desired velocity V0

u reduced by the term

arising from forced instantaneous deceleration due to within-class and between-class interactions.

The velocity variance equation: Derivation of the equations describing the dynamics of the velocity varianceHu involves the evaluation of Eq. (39) forkˆ2. Similar to the dynamics of the

class-speci®c expected velocityVu we can determine the following dynamic equations:

dHu

i_u, and the equilibrium velocity variance is de®ned by:

Expression (44) re¯ects the velocity variance due to relaxation of the velocity variance towards the covarianceCuˆhvv0iuÿV

0

uVu of classuon the one hand, and the changes in the variance due to

interactions on the other hand.

Drivers anticipation and trac viscosity: In ¯uidic or gas-¯ows, viscosity expresses the in¯uence of friction between the particles in the ¯uid or gas. The e€ect of the internal friction is expressed by a second order term with respect to the velocity present in the momentum dynamics. However, several authors (e.g. Payne, 1971, 1979; Kerner and Konhauser, 1995; Kerner et al., 1996; Helbing, 1996) have argued that trac viscosity cannot be plausibly interpreted from gas-kinetic theory, since due to the spatial one-dimensionality of the trac ¯ow equations, the viscosity term cannot be shear viscosity (originating from friction between e.g. the boundaries of the ¯ow-re-gion).

However, viscosity can be interpreted from the viewpoint of transitions in the driver's attitude towards prevailing trac conditions: it describes the higher-order anticipation-behavior of driv-ers. In other words, if drivers travel in a region of spatial acceleration (o2_V=ox2_{>0) they will}

anticipate, and accelerate (i.e. dV_u=dt>0) (cf. Kerner et al., 1996). In, among others, Payne (1971) and Kerner et al. (1996) the spatial derivative of the trac pressure (in the multiclass caseo_Pu=o_x†

in Eq. (41) has been related to driver's anticipation. The authors argue that when the pressure increases spatially, driver's anticipate on these worsening downstream conditions and decelerate

(for the multiclass case, i.e. dVu=dt<0). This justi®es modeling this anticipation-behavior by

adding a di€usive term to the trac pressure, implyingPu :ˆPuÿguoV=ox, yielding Eq. (26). In

4

this expression, V ˆP

s2UrsVs=

P

s2Urs is the expected aggregate-class velocity, where gu is the

bulk-viscosity coecient.

Critique on the low-order driver's anticipation: We can easily show that the interpretation of term Ain (41) for the perspective of driver's anticipation (see among others Payne, 1971; Payne, 1979; Kerner and Konhauser, 1995; Kerner et al., 1996; Helbing, 1996) is not justi®ed, since term Ashows that drivers only anticipate on changes in the pressure of their own classu. For instance, if the pressure Pu of class u decreases spatially (oPu=ox<0), only drivers of this class u will

ac-celerate, even if the aggregate-class pressure P ˆP

sPs does not (oP=oxˆ0).

For a macroscopic multiclass (multilane) trac ¯ow this implies that the interpretation of the term o_Pu=o_{x from the perspective of drivers' anticipations is incorrect. Rather, the term stems}

from the convection of groups of vehicles with di€erent variations in their velocities (see Hoo-gendoorn, 1999). However, if we extend this conception, the classical interpretation of theo_Pu=o_x

is equally incorrect for traditional Payne-type macroscopic ¯ow models.

Thus, describing the trac process as a mixed stream of di€erent user-classes rather than considering each class separately leads to the misinterpretation of the pressure term. In terms of gaining correct insights into the mechanisms of trac ¯ow operations, a multiclass distinction is hence invaluable.

Closure of the model equations: The system of partial di€erential equations (25), (41) and (43) is

not closed, in the sense that the dynamics of the velocity variance contains three unspeci®ed moments, namely the expected desired velocity V_u0, the covariance Cu, and the ¯ux of velocity

variance Ju. For the single-class case, Helbing (1997) proposes dynamic equations for the

ag-gregate-class velocity±covariance equations. However, for the multiclass case, we assume that the dynamic variations in the expected desired velocityV0

u and the covarianceCucan be neglected, and

that both can be described by functions of the densities rof the respective classes rˆfrug:

V_u0ˆV_u0…r† and CuˆCu…r†: …45†

Due to the increasing fraction of platooning vehicles, these functions are monotonically de-creasing with respect to the class-speci®c densities (e.g.o_V0

u=ors60). In Hoogendoorn (1999) the

speci®cation of both terms is discussed in more detail.

If we assume that even in dynamic situations the velocity distributions are Gaussian distributed random variates with mean Vu…x;t† and variance Hu…x;t†, then by de®nition, the ¯ux of velocity

variance equalsJu…x;t† ˆruh…vÿVu† 3

i_u ˆ0. In Helbing (1996) it is shown that since drivers need a ®nite amount of time to adapt their velocities caused by their ®nite reaction and braking times

(re¯ected by the adaptation of the velocity distributionto thelocal equilibrium distribution). This causes a non-zero skewness of the velocity distribution. As a consequence, additional transport terms result, that can be expressed by the term:

Ju ˆ ÿju

o_Hu

o_x : …46†

Using this expression, the velocity variance dynamics equation (43) yields Eq. (27). See Helbing (1996) for details.

pla-tooning vehicles and their free-¯owing platoon-leaders of the multiclass model presented in the paper.

Finite space requirements: The analogy between a ¯uidic ¯ow and a vehicular ¯ow is based on the presumption that vehicles can be adequately modeled as in®nitesimal particles. However, this assumption is not realistic and causes both theoretical as well as practical problems. To resolve such problems, we propose incorporation of the space needed by each of the vehicle due to both its user class dependent physical length and the additional velocity dependent safety margin. To this end, let lu…v† be the average roadway space needed by a vehicle of user-class u to safely

traverse the roadway at speedv. This amount of roadway space can be determined by considering the car-following behavior of drivers. For recent empirical ®ndings on car-following behavior, we refer to Dijker et al. (1998). We will use the model of Jepsen (1998) to describe the space re-quirementslu…v†of a vehicle of class u driving with velocityv:

lu…v† ˆLu‡du‡vTu‡v2Fu; …47†

whereLu denotes the length of the vehicle, du the minimal gap between two vehicles, Tu the

re-action time of drivers, and ®nally Fu the speed-risk factor following from drivers aiming to

minimize the potential damage or injuries. If we assume that these parameters are equal for all vehicles in a class, then we can de®ne the dimensionless space-requirement correction factor dˆ d…x;t† 2 ‰1;1†by (see Hoogendoorn, 1999):

d, 1

1ÿP

s2Urs…Ls‡ds‡VsTs‡ …Vs2‡Hs†Fs†

: …48†

Using this correction factor, we introduce themodi®ed trac ¯ow variables. For instance, we can de®ned the modi®ed density^ru of class u by:

^

ru,dru: …49†

The modi®ed density equals the expected number of vehicles of class u per unit non-occupied roadway space. If we assume that the changes in the trac conditions experienced by an observer moving along with vehicles of class uchange very slowly (the so-called adiabatic elimination; cf. Haken, 1983), we ®nd themodi®ed multiclass conservation-of-vehicle equation:

o_^ru o_t ‡

o_^ruVu†

o_x ˆ0: …50†

Similarly, we can include the vehicle spacing requirements in the dynamics of the velocity and the velocity variance. Let the `hat' indicate multiplication of the respective variable or parameter with the correction factor d…x;t†. We need not include the resulting model equations here, since the structure of the equations does not change due to incorporation of the ®nite-space requirements. Rather, the modi®ed density_^ru; pressureP^u, viscosity coecient^gu, kinematic coecientj^u, and

®nally the interaction term R^…k†

u;s replace their regular counterparts in Eqs. (26) and (27),

respec-tively. We can consequently show that when the densities ru increase, the available space

de-creases, and the correction factor d increases accordingly. As a consequence, the in¯uence of vehicle-interaction expressed by the modi®ed interaction moments R^…k†

u;s, dR …k†

introduction of the space-requirements yields an increase of the interaction rates). This increase results in a larger decrease in the modi®ed equilibrium velocity. 5

This concludes the derivation of the multiclass trac ¯ow model. That is, we have now shown how the macroscopic multiclass equations can be determined from the mesoscopic gas-kinetic equations using the method of moments. Moreover, we have brie¯y discussed the closure of the model, the introduction of viscosity, and the ®nite-space-requirements. For details, see Hoo-gendoorn (1999), HooHoo-gendoorn and Bovy (1998b) and Helbing (1996).

The equilibrium relations and vehicular interactions: Now that we have established the macro-scopic multiclass ¯ow model, let us discuss some of the model properties. That is, in this section we will discuss the interactions of vehicles in the same user-class, and the asymmetric interactions among vehicles from di€erent user-classes.

Within-class interactions (Interactions): Let us illustrate the mechanisms in the macroscopic model that re¯ect the in¯uence of acceleration and deceleration due to vehicle interactions. Let us ®rst consider the case where only one user-class u is present. In this case, we can show that:

R…1†

u;u , …1ÿhu† vk

Z …v

ÿw†~q_u…w†dw

u

ˆ …1ÿhu†Pu …51†

yielding:

V_ueˆV_u0ÿ …1ÿpu†…1ÿhu†suPu: …52†

In other words, the equilibrium velocity Ve

u is determined by the mean desired velocity V 0 u of the

user-class, decreased by the mean rate at which vehicles of the same user-class actively interact with each other …1ÿhu†Pu, without being able to overtake these vehicles (with probability

…1ÿpu†), multiplied by the acceleration timesu. This mean interaction rate is relative to the trac

pressure. From (52) we observe that if the acceleration timesu is small, the in¯uence of

interac-tions is reduced due to vehicles being able to increase their velocity rapidly after interacting. Let us ®nally remark that expression (52) is similar to the expression describing the equilibrium velocity derived by Helbing (1996), albeit the latter does not consider the e€ect of relaxing the vehicular chaos assumption.

If we consider the equilibrium velocity variance in the single-class case, then Hoogendoorn and Bovy (1998a) show that He_u equals:

He_u ˆC_ueÿ1

2…1ÿpu†…1ÿhu†suJu; …53†

which is again similar to the single-class equation describing the equilibrium variance derived by Helbing (1996).

Between-class interactions: Next, let us consider the in¯uence of interactions between vehicles of di€erent classes. In contrast to the velocity dynamics derived in Helbing (1996), interaction be-tween user-classes is present in the equations, both directly in the equilibrium velocity, and in-directly in the trac pressure. This novelty of our macroscopic MUC trac ¯ow model is re¯ected in the way in which the distinguished classes interact. That is, the model does not only

5

consider in¯uences on the velocity and on the velocity variance caused by within-class interac-tions; also between-class interactions are considered. These in¯uences are directly re¯ected in the equilibrium velocity and the equilibrium velocity variance.

Changes in the equilibrium velocity (28) of classuthat are caused by interactions with vehicles from class s are re¯ected by the term …1ÿpu†suR…1†u;s for u6ˆs. Let us now illustrate class

inter-action by assuming that the supports of classes u and s are disjoint. Let us ®rst assume that Vu <Vs. In other words, each vehicle of classuis drivingslowerthan any vehicle of classs. In this

case we have (see Hoogendoorn and Bovy, 1998a):

R…1†

u;s ˆ0 …54†

and thus

V_ueˆV_u0ÿ …1ÿpu†…1ÿhu†suPu: …55†

We can observe that the equilibrium velocity (28) of vehicles from classudoes not change due to interaction with vehicles in user-classs. That is, since each vehicle in user-classs is driving faster than any vehicle in user-classu, no interaction exists. This holds equally for the equilibrium ve-locity variance of user-class u.

However, from the viewpoint of the other classs, we can show: R…1†

s;u ˆ …1ÿhu†ru…Hu‡Hs‡ …VuÿVs† 2

† …56†

that is

V_seˆV_s0ÿ ÿ …1ÿps†ss…1ÿhu†ru…Hu‡Hs‡ …VuÿVs† 2

†: …57†

In this case, interaction between vehicles is present due to velocity variances of both classes, and the squared di€erence in mean speed between the user-classes, causing a reduction in the equi-librium velocity of user-classs. This holds equally for the equilibrium velocity variance of user-class s.

Competing processes: In Helbing (1996) and Kerner et al. (1996) competing processes in single user-class trac ¯ow operations are observed and interpreted. The equilibrium velocityV_ueand the equilibrium variance He_u re¯ect these processes. That is, they describe how drivers accelerate to traverse along the roadway at their desired velocity on the one hand, while they may need to decelerate due to impeding, slower vehicles on the other hand.

Kerner et al. (1996) approximate the varianceH by a constantH0 and assume that the

equi-librium velocityVe_{r†is an explicit function of the trac density r:}

VeˆV0ÿs…1ÿp†rH0; …58†

wherepˆp…r† is the immediate overtaking probability. When the equilibrium velocity is deter-mined explicitly, the probability of immediate overtaking can be deterdeter-mined fromVe…r†:

p…r† ˆ1ÿV

0_ÿVe_r†

rsH0 : …59†

Considering these competing processes for single user-class trac ¯ow, the relaxation term:

VeÿV s ˆ

V0ÿV

s ‡ …1ÿp†H

describes the dynamic changes in the velocityV due to relaxation towards the equilibrium velocity Ve_{. Here, the term…V}0_{ÿV†=sre¯ects the acceleration oractive process caused by drivers aiming}

to traverse the roadway at their desired velocity. Conversely, the term …1ÿp†H0 _{re¯ects a}

de-celeration or damping due to vehicles interacting.

In Kerner and Konhauser (1995) a stability analysis is performed by considering spatial per-turbations in the mean density. It is shown that, if the amplitude of the ¯uctuation in the density exceeds a certain threshold value, the density perturbation causes a change in the mean velocity which itself leads to an increase in the trac density. This `avalanche' occurs when the spatial ¯uctuations are large enough to overcome the in¯uence of trac viscosity and relaxation.

We can also identify these competing processes for the multiclass case. Consider Eq. (26) de-scribing dynamic changes in the velocity. In this equation, the term:

represents the relaxation process towardsVe

u. Also for the MUC case, the processes comparable to

the single user-class case can be identi®ed. On the one hand, drivers aim to traverse along the roadway at the class-speci®c desired velocityV0

u. These acceleration processes are re¯ected by the

term A. On the other hand, the term Dresults from both within and between-class interactions. This term re¯ects a deceleration process due to vehicles interacting. As shown in the previous sections, main di€erence between the single-class case and the multiclass case is impact of vehicle interactionbetweenthe classes. We remark that in the velocity variance equation (27), comparable processes can be identi®ed.

Equivalence with other macroscopic models: In Hoogendoorn and Bovy (1998a) it is shown that our generalized multiclass model collapses to well-known macroscopic models in case of a single user-class. For instance, if we consider only one user-class u, and choose hˆ0; the system of equations (25), (26), and (27) yield Helbing's improved single user-class macroscopic trac ¯ow model presented in Helbing (1996). In the Payne-model (cf. Payne, 1971), the equilibrium velocity VeˆVe…r† is speci®ed a priori. Assuming a constant velocity variance Hˆc2₀, assuming jˆ0;gˆ0, and ®nally choosing p…r†:

p…r† ˆc

2

0srÿ …V

0_ÿVe_r††

c2 0sr

…62†

reduces our multiclass model reduces to the Payne model.

5. Fundamental issues and macroscopic simulation

It is the purpose of this section to investigate whether the issues raised by among others, Daganzo (1995) and Helbing (1996), are remedied by the developed MUC model. Some of these issues can be checked straightaway by considering the model equations. Others, such as the anisotropy and the una€ected slow vehicles, are investigated using macroscopic simulations.

system of partial di€erential equations describing the dynamics of the user-class speci®c density, velocity and velocity variance. To this end, several numerical approaches have been developed. These are based on recasting the model equations using appropriate variables (the so-called

conservative variables). The following results have been computed by applyingRoe's Approximate Riemann solver to the multiclass ¯ow equations. This scheme is based on exact solutions of the inviscid multiclass model (e.g. g_u ˆju ˆ0), operating under equilibrium conditions.

Approxi-mations at timestep tk are represented as piecewise constant functions in x. Locally, piecewise

constant initial conditions yield the so-calledRiemann-problem, which can be solved analytically for the inviscid multiclass equations (assuming equilibrium conditions). These solutions are then used to approximate the (partial) solution at the next timestep: The in¯uence of relaxation and deceleration are subsequently determined. Finally the in¯uence of the higherorder terms are ef-fectuated using central ®nite-di€erences. These results are combined to determine the approxi-mation at the next timesteptk‡1. For details, we refer to Hoogendoorn and Bovy (1998a).

Description of the test-cases(Speci®cation of the relations and parameters): In this section we will illustrate macroscopic multiclass simulation using a hypothetical test case (Fig. 2). The case de-scribes a two-lane ringroad of 30 km length. We identify two classes, namely person-cars and trucks. These di€er with respect to their mean lengths (4 and 11 m, respectively), their (constant) desired velocities (31 and 23 m/s), and relations for the covarianceCu…r†.

The overtaking probabilities are speci®ed by considering the gap-distribution (see Hoogen-doorn, 1999): having assumed that these gap-distributions are a function of the total density, the probabilitypu that a vehicle is able to immediately change lanes equals the probability that a gap

is available that is larger than the gap needed by the interacting vehicle. The gap that is needed equals 2.5 times the length of the vehicle increased by a safety margin, which is dependent on the

reaction time of the drivers and their velocity (cf. Jepsen, 1998). Since the overtaking probability distribution function is a decreasing function of the gap needed, the overtaking probability of a truck is in general smaller than the overtaking probability of person cars.

The reaction timeTuequals 1.6 and 2.6 s for person-cars and trucks, respectively. For the

speed-risk factor, and the minimal distance headway, we respectively chooseFu ˆ0:022 m2/s2 anddu ˆ1

m (see Jepsen, 1998). The acceleration timesuis assumed constant and equals 10 s for both classes.

For the kinematic viscosity coecient and the kinematic coecient we have usedg_u ˆ150ruand

ju ˆ150ru.

Case I(Multiclass trac operations in homogeneous regions): The ®rst case is characterized by the following initial conditions: at time tˆ0, the trac conditions are characterized by three regions of di€erent person-car densities, respectively equal to 10, 20 and 50 veh/km. In the regions, the truck densities are equal, namely 4 veh/km. The regions are respectively located atX1ˆ[3 km,

6 km), X2ˆ[9 km, 12 km) and X3ˆ[15 km, 18 km). We assume that initially, the trac is in

equilibrium (in Hoogendoorn, 1999 the author presents an algorithm to determine the initial equilibrium conditions). As a result, in each region the velocity of the vehicles is di€erent (inX3the

vehicles are nearly stationary).

Fig. 3 depicts the density contour plots and the trajectories of person-cars and trucks on the ringroad during a 15-minute period. In the two moderate density regionsX1 andX2 the velocities

of the persons-cars and the trucks are high, while in the high density region X3 the velocities are

nearly equal to zero (congested trac conditions). Clearly, in neither the free-¯ow, nor the congested regions, vehicles¯ow back into the empty upstream regions. This strengthens our belief that thediscrete MUC-model satis®es the anisotropy condition, stating that drivers mainly react to stimuli in front of them. We can also see how the individual drivers are a€ected by the trac conditions if we consider the vehicle trajectories. Note that the mean velocity of theperson-carsis reduced signi®cantly in each density region, while this reduction is more profound in the higher-density regions. Only in the high-higher-density regionX3 the velocities of the trucks are reduced as well.

That is, from the trajectories we can see that the trucks-velocities are virtually una€ected by the person-cars present in the regionsX1andX2. In the high-density regionX3however, the number of

interactions with slow person-cars is such that the truck-velocity is a€ected signi®cantly. More-over, in this region the velocity of person-cars and trucks in nearly identical. Concluding, similar to trac operations in real-life trac, the slow users (trucks) remain virtually una€ected by fast users (person-cars), at least in non-congested trac conditions. This implies that the una€ected slow-users critique of Daganzo (1995) in remedied in our multiclass model. In Hoogendoorn (1999) it is discussed how the multiclass modelling of trac loosens theinvariant personalityissue of Daganzo (1995).

this region are low, passenger-car/truck interactions also increase substantially. Consequently, eventually the truck velocities are a€ected by the person±car disturbances (tˆ6 min at xˆ5±7 km). This example shows that our model is able to describe the seemingly spontaneous formation of trac jams.

Macroscopic modeling issues: The test-cases illustrate the models aptness with respect to the issues raised by Daganzo (1995) (anisotropy, una€ected slow vehicles, and driver's personality). Furthermore, the multiclass model also considers the issues of Lyrintzis et al. (1994) and Kerner et al. (1996) (deviation from the speed±density relation, trac hysteresis, formation of localized structures; see Hoogendoorn, 1999 for details).

Finally, let us note that the remarks of Helbing (1996) have explicitly been taken into account: the velocity variance is a model variable, the ®nite space requirements have been explicitly troducing using the modi®ed density, while the ®nite braking and reaction times have been in-troduced by the inclusion of the ¯ux of velocity variance.

6. Conclusions

In this article, we have presented a multiple user-class generalization of a macroscopic single-class trac ¯ow model. The model, being derived from mesoscopic foundations, features single- dependent competing processes: on the one hand, drivers aim to traverse the roadway at a class-dependent desired velocity, while on the other hand, drivers are slowed down by slower vehicles from their own and other classes. Depending on the prevailing trac conditions, these competing processes may result in the formation of localized structures.

Other model features are: incorporation of dynamic equations describing class-speci®c velocity variance, vehicle interactions within and between the distinguished classes, and incorporation of class-speci®c parameters, such as acceleration time, reaction time, desired velocity, and vehicle lengths. Additionally, critical issues raised by among others, Daganzo (1995), Lyrintzis et al. (1994) and Helbing (1996), do not hold for our model.

From a model-application perspective, the usefulness of the macroscopic model stems among other from multiclass trac control. For one, the developed model may provide insight into the multiclass response±behavior of heterogenous trac. Moreover, it can be applied in among others model-based trac control approaches of multiclass control options, automated multiclass inci-dent-detection, data-checking and data-completion. Finally, a by-product of the model are (equilibrium) multiclass travel-time functions, that can be used in multiclass dynamic assignment. The paper has presented a new multiclass model formulated in so-called primitive variables, namely the density, the velocity, and the velocity variance. It appears that the model advanta-geously can be reformulated in the so-called conservatives (see Hoogendoorn, 1999). The ad-vantages of this approach are among others the simpli®ed and elegant model derivation, and the availability of improved numerical solution approaches. Its main disadvantage is the lack of in-terpretation of the resulting model equations from the driver's perspective.

Acknowledgements

We would like to express our gratitude to the anonymous referees whose comments have contributed to improve both the clarity and contents of this paper.

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