What we need to know about the germ-free animal models

1.   Introduction.

Modeling and analysing the dynamics of network flows is an important problem that has applications in many different areas such as transportation planning [7,25,42], air traffic control [32,48], communication networks [1,6,12,22], processor scheduling [49] and supply chain optimization [34,17]. Flow models are crucial for understanding the response of networked systems under different boundary conditions, estimating the state of the system, measuring system performance under different tunable parameters and devising the appropriate control strategies for efficient operation of the system. For example, in transportation networks, flow models are used for traffic estimation [53], dynamic traffic assignment or demand response assessment [30], traffic signal control [28], ramp-metering control [43] and rerouting [47]. This article focuses on modeling heterogeneous (multi-path) traffic flow through a network with a single source and multiple sinks with the objective of analytically expressing the delays at each node of the network as a function of the boundary flows at the source. This is a critical requirement when solving certain control and optimization problems over the network, where flow entering the network is one of the direct or indirect control parameters [46]. We present this model in the context of a transportation network modeling, but the results can be applied to any single origin network with no merge junctions that satisfies the following properties; 1) link flows are capacity restricted, 2) the flow through each junction satisfies the first-in-first-out (FIFO) condition, and 3) there is no holding of flow, i.e. the flow through a junction is maximized subject to the FIFO condition.

There are many types of traffic dynamics models, including microsimulation [18], cellular automaton [33], first-order or higher-order macroscopic models [19,26,44,51] and point queue models [52], listed roughly in decreasing model complexity. Microsimulation models simulate the movement of individual vehicles by applying car following models and lane changing models. This approach enables the modeling of very specific and detailed characteristics of driver behavior and networks dynamics [18] such as route choice modeling and the implications of the precise network geometry (e.g. road curvature). Therefore, microscopic models are very popular in transportation planning at an urban scale, since planners have the ability to observe the behavior on the entire network due to modifications such as road closures and changes in traffic signal plans. However, these models can not be expressed in closed form, are very difficult to calibrate [20] and have an extremely high computational overhead associated with them.

Another related approach is to use cellular automaton models to simulate traffic [33]. Typically, cellular automaton models divide the roadway into cells, each of which can be empty or occupied by only one vehicle. This approach as also been extended to model heterogeneous flow [21,23], where the behavior around freeway off-ramps are considered. Cellular automation models are similar to microscopic models in the sense that they both scale very poorly with the problem size and are generally computationally intractable since they track individual vehicles.

The third and perhaps most common approach is the use of macroscopic traffic flow models. Among these models, the most well known is the continuum or fluid-dynamic model based on the Lighthill-Williams-Richards (LWR) partial differential equation [26,44,27]. This model uses first-order partial differentiation equations to describe the relationship between aggregate traffic flow variables (flux and density), under the principle of mass conservation. Due to its simplicity, the LWR model does not capture individual driver behavior and cannot be used to model heterogeneous flows where different groups of drivers exhibit different behaviors. However, there have been extensions to the LWR model that can account for such heterogeneous flow [8,9].

The most popular solution method to the LWR PDE is the Godunov scheme, also known as the cell transmission model (CTM) [7,25]. The CTM discretizes space into cells and keeps track of the vehicle density in each cell instead of individual vehicles. Another approach for solving the LWR PDE is using the Moskowitz function. The Moskowitz function numbers vehicles as they pass a reference location and it describes the evolution of this number over time and space. The Moskowitz function can be interpreted as the cumulative vehicle count at a reference location, also known as cumulative curves, which is similar to our approach. Cumulative curves have been proposed together with triangular shaped fundamental diagram (relationship between flow and density) to simplify the traffic analysis based on the LWR model [35,36,37], because the adoption of cumulative curves automatically guarantees conservation. However, while cumulative curves do provide a nice graphical interpretation of the traffic flow, they do not present the ability to describe the network behavior as a function of variable boundary flows.

The final approach, which is also the approach used in this work, is to model the traffic via a network of point queues that are typically represented using ordinary differential equations (ODE's). One of the first point queue models was presented in the seminal work of Vickrey [52] to study a roadway with a single bottleneck. This approach has since been extended to handle networks with multiple commodities [11]. One of the main limitations of this ODE-based approach is that it does not capture the spatial propagation of traffic along a roadway (unlike the PDE-based macroscopic models) and assumes that all delayed vehicles wait at the point of the bottleneck, as if vehicles were packets in a data network. As a result, point queue models are generally unable to capture queue spillback. However, some recent work [41,40] shows how point queue models can be adapted to capture queue spillback using two queues for each bottleneck.

There is a vast literature in the transportation community on network flow models based on both ordinary differential equation (ODE) based point queues and the LWR PDE based spatio-temporal propagation. These models are also commonly referred to as network loading models [2] in dynamic traffic assignment (DTA) [31]. While the existing literature provides a rich portfolio of modeling techniques, of which some recent works we summarize below, this work presents a new technique that adds to the existing literature by enabling the analytical expression of delays within the network as a function of the inflows at the origin and the bottlenecks that are active. This however comes at the cost of some restrictions in terms of the types of networks that can be analyzed, as we will explain in detail below.

There have been a number of recent works on extending point queue models for network loading in DTA. In [3,4], the authors take a similar approach to ours in terms of extending the Vickrey point queue model (with no spillback). A time discretized iterative numerical scheme is provided to obtain a dynamic user equilibrium solution and the existence of the solution is proven under the assumptions of the model. The convergence rate of the iterative scheme is not explicitly addressed. Similarly, [11] considers a point-queue model with no spillback, but is able to model congestion delay during the free-flow phase of the fundamental diagram for hyperbolic fundamental diagrams. In contrast, [29] uses a point queue network with multiple queues to capture spillback, based on the double queue technique in [41]. The existence and uniqueness of the solution to the network loading problem is not addressed, but an existence result for the dynamic system optimal solution is provided. While the work in [11] allows for merge junctions and the model in [29] captures some queue spillback, none of the works above can analytically express the delays within the network as a function of the input flows¹.

¹We wish to incorporate the approach in [41] for capturing queue spillback into the proposed model as future work.

A series of articles by Han et al. [14,15,16] reformulates the Vickrey model using a PDE-based approach and extends the results to general networks. This reformulation enables more rigorous mathematical analysis of the network loading model, better numerical methods for solving the network loading problem and a proof of existence for a route and departure time user equilibrium. In a follow up work, [5] shows the existence and well-posedness of this PDE-based queuing model. Furthermore, [13] presents a very important contribution to the literature by providing a continuous time network loading model with both existence and well-posedness guarantees. This model can be considered a continuous time counterpart of the Link Transmission Model [39,54]. The results on existence and well-posedness are however limited to certain junction models. In particular, the results require fixed turning ratios at junctions, which does hold in dynamic traffic assignment.

In addition, there is a rich literature on ODE-based network flow propagation for various packet networks [1,6,12,22], but a large majority of these dynamics models violate the FIFO and no holding requirements that are essential in phyisical flow networks. The models proposed for transportation network flows, as described above, can in fact satisfy these physical requirements, but cannot analytically prescribe the internal delays of the network as a function of the boundary flows. Therefore, a new framework is required for the problem that we consider.

Our approach can be summarized as follows. We assume that the traffic flow is differentiated by the destination of the flow (i.e. Lagrangian flow) and that the different flow groups satisfy the FIFO condition at each junction. The network is limited to a contain a single origin and no merge junctions. The input flows are assumed to be piecewise continuous in time and the link capacities are assumed to be piecewise constant in time. The queuing in the network is assumed to be contained at each junction node and spill-back to the previous junction is ignored². We show that this model leads to a well-posed ordinary differential equation for computing the dynamics of the network as a function of the boundary flows and prove that the solution is unique through a rigorous mathematical derivation of the model properties. The main benefit of this framework is the ability to analytically prescribe the delays at any junction in the network and across any sub-path as a function of the the boundary flows, which can be an important requirement when solving certain control and optimization problems, such as demand allocation problems, where the flow entering the network is one of the direct or indirect control parameters. This is achieved via the creation of a time mapping operator that maps the traffic flow at a given node at a given time to the corresponding flow at the origin of the network when that flow entered the network. We also show that this model can be solved numerically using a simple and efficient forward simulation approach. Finally, we demonstrate the application of the model by applying it to two example networks, a single path of multiple bottlenecks and a diverge junction with complex junction dynamics including time varying capacities.

²This is a restrictive assumption in the case where the queue length at a given node exceeds the length of the incoming link because the queue would have interrupted the flow at the previous junction. The model allows for these cases of queue spill-back to be identified by comparing the queue length to the queue capacity, and for control actions to be taken to mitigate the spill-back. However, in general, it may not be possible to prevent spill-back under certain input flow profiles.

The article is organized as follows. Section 2 introduces the network properties and junction dynamics. Then section 3 formalizes the time mapping operator, shows the well-posedness of the problem and proves the uniqueness of the solution to this model. Section 4 demonstrates the practical application of the mathematical framework by showing that the off-ramp model posed by Newell [38] can be modeled using this framework. Section 5 concludes the article.

2.   A point queue model for network flow.

The traffic network with a single source is modeled as an arborescence³. The congestion at each bottleneck is modeled as a vertical queue that is located at the start of the bottleneck. Thus, the physical propagation of the queue forming at the bottleneck is not modeled. This modeling choice is only restrictive when the queue propagates upstream to the preceding junction, as the change in dynamics at the junction due to the queue is not taken into account, but the model is equivalent to a horizontal queuing model otherwise.

³An arborescence is a directed rooted tree where all edges point away from the root.

2.1.   Network definitions.

A node $v$ denotes a junction in the network and $V$ is the set of all nodes. A link $l=(v^{\text{in}}_l,v^{\text{out}}_l)$ is a couple consisting of an origin node $v^{\text{in}}_l$ and a destination node $v^{\text{out}}_l$, and $L$ is the set of all links.

The congestion-free travel time on link $l$ is denoted by $T_{l}$, an agent that enters link $l$ at time $t$ will exit link $l$ at time $t+T_{l}$. The congestion-free travel time between nodes $v_1$ and $v_2$ is denoted by $T_{(v_1,v_2)}$, an agent that enters node $v_1$ at time $t$ will reach node $v_2$ at time $t+T_{(v_1,v_2)}$

The set of incoming links to node $v$ is denoted by $L^{\text{in}}_{v}$, the set of outgoing links from node $v$ is denoted by $L^{\text{out}}_{v}$ and the set of all links $l$ connected to node $v$ is denoted by $L_{v}$.

A node $v$ is a source if it admits no incoming link $(L^{\text{in}}_{v} = \emptyset )$. A node $v$ is a sink if it admits no exiting link $(L^{\text{out}}_{v} = \emptyset )$. The set of sinks is denoted by $S$.

The set of nodes $V$ and the set of links $L$ compose a network. Due to the network being an arborescence, it contains a unique source indexed by $v_0$. For all nodes $v \in V \backslash \{v_0\}$, $L^{\text{in}}_{v}$ is a singleton. The element of this singleton is called the parent node and is denoted by $\pi_v$: $L^{\text{in}}_{v}=\{(\pi_{v},v)\}$.

We define a path $p_{(v_{\text{orig}},v_{\text{dest}})}$ as a finite sequence of distinct nodes from an origin node $v_{orig}$ to a destination node $v_{dest}$ such that there is a link connecting each pair of subsequent nodes.

There is a unique path from any source to any destination since the network is tree structured. For each sink $s$, let $p_{s}$ be the path starting at the origin $v_{orig}$ and ending at node $v_s = s$, and $V_{p_s}$ be the sequence of nodes on path $p_s$. The set of paths $P_v$ is the set of all paths $p$ for which $v \in p$. The set of paths $P_l$ is the set of all paths $p$ for which $l \in p$.

Remark 1. The path sets $P_l$ where $l$ is a link in $L_v^{out}$ form a partition of $P_v$

2.2.   Modeling the flow of agents.

The traffic flow at a node is measured by counting the number of agents that pass through the node between an arbitrary initial time $t_{\text{initial}}$ and any given time $t$.

For a node $v \in V \backslash v_0$ (that is not the source) and path $p \in P_v$, the arrival curve $A^{p}_{v}(t)$ gives the total number of agents on path $p$ that arrive at node $v$ during the time interval $(t_{\text{initial}},t]$. Similarly, for a node $v \in V \backslash S$ (that is not a sink) and $p \in P_v$, the departure curve $D^{p}_{v}(t)$ gives the total number of agents on path $p$ that leave node $v$ during the time interval $(t_{\text{initial}},t]$.

Remark 2. The arrival curve $A^{p}_{v}(t)$ (resp. departure curve $D^{p}_{v}(t)$) also gives the agent number of the last agent on path $p$ to arrive at (resp. leave) node $v$ by $t$. Arrival and departure curves are non-decreasing: if $t_1 < t_2$, $A(t_2)-A(t_1)$ (resp. $D_p(t_2)-D_p(t_1)$) is the total number of agents who arrive at (resp. pass) node $v$ in the interval $(t_1, t_2$], and is therefore non-negative.

Definition 2.1. Acceptable cumulative arrival and departure curves $\mathbf{A}(t_{\text{initial}},t_{\text{final}}]$, $\mathbf{D}(t_{\text{initial}},t_{\text{final}}]$

Given times $t_{\text{initial}}$ and $t_{\text{final}}$, a function on $(t_{\text{initial}},t_{\text{final}}]$ is an acceptable cumulative curve on $(t_{\text{initial}},t_{\text{final}}]$ if it is continuous, piecewise $C^1$, and strictly increasing functions on $(t_{\text{initial}},t_{\text{final}}]$.

The assumption that the cumulative curves are strictly increasing is made for mathematical convenience, but can be relaxed⁴. Cumulative curves are required to be piecewise $C^{1}$ in order to be able to define flows.

⁴We could relax the assumption that the cumulative curves are strictly increasing and allow for non-decreasing curves. However, this results in the time mapping function $T^{,\pi_{v}}(v)$ introduced in section 3.2 being a correspondence instead of a function and makes the analysis significantly more complicated. Therefore, for mathematical convenience, we make the assumption that the cumulative curves are strictly increasing.

The outgoing flow $\lambda^{v}_{p}$ at a node $v$ is the piecewise continuous derivative of the departure curve $D^{v}_{p}$

Remark 3. Zero congestion-free travel time

Let $\pi_{v},v$ be two consecutive nodes on path $p$. Agents on path $p$ leaving node $v$ at time $t$ arrive at node $v$ at $t+T_{(\pi_{v},v)}$. For all links $(\pi_{v},v)$ and paths $p \in P_v$, without loss of generality we set the congestion-free travel time $T_{(\pi_{v},v)}$ to be zero: $T_{(\pi_{v},v)}=0$. This implies that:

This modeling choice is made purely for mathematical convenience, since the goal of this framework is to analyze delays in the network. The total travel time for each agent can be easily reconstructed a posteriori by adding the actual congestion-free travel time for each link of the path traveled by the agent.

Thus, for all links $(\pi_{v},v) \in L$ and paths $p \in P$ we have:

2.3.   Queuing and diverge model.

This section defines the model dynamics for queuing and the flow propagation through a junction, which will then lead to a definition of the feasible departure curves that the model admits.

The capacity $\mu_{l}(t)$ of a link $l$ is the maximum flow that can enter the link from its input node $v^{\text{in}}_l$ at time $t$. Road capacity may vary with time due to weather conditions, accidents, or other factors. Thus, capacity is a time varying quantity.

Requirement 1. Capacity constrained flows

The inflow entering a link is always no greater than the links capacity.

If the flows arriving at a node $v$ are larger than available outflow capacity, a queue will form at node $v$.

Definition 2.2.Queue length $n_{v,p}(t)$

We define the path queue length $n_{v,p}(t)$ at node $v$ as the number of agents on path $p$ that arrive at node $v$ by time $t$ and are yet to depart node $v$

The total queue length $n_{v}(t)$ at node $v$ is the sum of the path queue lengths.

Remark 4. Let $[D^{v}]^{-1}$ be the inverse of the departure curve $D^{v}$. Since $D^{v}$ is strictly increasing, $t_k = [D^{v}]^{-1}(k)$ gives the time at which agent number $k$ leaves node $v$.

Definition 2.3. Delay in queue $v$

We define $\delta _{v,p}(t)$ as the delay encountered in queue $v$ by the agent that entered the queue at time $t$.

As $D^{v}_{p}$ is continuous, piecewise $C^1$, and strictly increasing, its inverse is continuous, piecewise $C^1$ and strictly increasing. Since the composition of functions preserves continuity (see [45] for the $C^0$ case and [10,24] for the $C^k$ case), the function $[D^{v}_{p}]^{-1}\circ D^{\pi_{v}}_{p}$ is continuous and piecewise $C^1$, and therefore, the delay $\delta_{v,g}$ is also continuous and piecewise $C^1$.

Remark 5. If $n_{v,p}(t)=0 \forall p \in P_v$, then $D^{v}_{p}(t)=A^{v}_{p}(t) \forall p \in P_v \implies \delta_{v,p}(t)=0 \forall p \in P_v$. If $\exists p' \in P_v : n^{v}_{p'}(t)>0$, then $D^{v}_{p'}(t)<A^{v}_{p'}(t) \implies [D^{v}_{p}]^{-1}\left({A^{v}_{p}(t)}\right) > t$ and $\delta_{v,p}(t)>0 \forall p \in P_v$.

Therefore,

We now describe the diverge model with a graphical illstration of a one to two diverge junction given in Figure 1.

Requirement 2. First-in-first-out (FIFO) property

The model satisfies the FIFO property. The delay encountered in queue $v$ at time $t$ is identical for all paths $p$ in $P_v$.

FIFO property implies that agents exit the queue in the same order that they enter the queue regardless of which path they belong to.

Interpreting $A^{v}_{p}$ (resp $D^{v}_{p}$) as the identifier of the agent which arrives in (resp. leaves) queue $v$ at time $t$, we can see that the queues respect the FIFO rule for each path $p$. Let $x_1$ and $x_2$ be two agents: agent $x_1$ enters queue $v$ at time $t_1^{in}$ such that $A^{v}_{p}(t_1^{in}) = x_1$ and leaves queue $v$ at time $t_1^{out}$ such that $D^{v}_{p}(t_1^{out}) = x_1$, agent $x_2$ entered in queue $v$ at time $t_2^{in}$ such that $A^{v}_{p}(t_2^{in}) = x_2$ and leaves queue $v$ at time $t_2^{out}$ such that $D^{v}_{p}(t_2^{out}) = x_2$. As $A^{v}_{p}$ and $D^{v}_{p}$ are both strictly increasing functions, $t_1^{in} \leq t_2^{in} \Rightarrow x_1 \leq x_2 \Rightarrow t_1^{out} \leq t_2^{out}$, which means that if $x_1$ is enters queue $v$ before $x_2$, it will leave $v$ before $x_2$.

Proposition 1. FIFO implies conservation of the ratio of flows

If $p_1$ and $p_2$ are two paths in $P_v$ such that $\lambda^{\pi_{v}}_{p_1}, \lambda^{\pi_{v}}_{p_2}>0$, then the ratio of their flows is conserved when exiting node $v$

Proof. Let $t$ be an arbitrary time. The FIFO assumption gives $\delta_{v,p}(t)=\delta_{v}(t)$. By definition of delay $\delta_{v,p}(t)$,

Taking the derivative with respect to $t$ and using $\delta_{v,p}(t)=\delta_{v}(t)$,

Using equation (8) we obtain,

Therefore, it follows that

Definition 2.4. Queue state $\eta{v}$ -state transitions

We define queue state as the boolean valued function $\eta_{v}(t)$:

If $\eta_{v}=1$, queue $v$ is said to be active, or in active state

If $\eta_{v}=0$, queue $v$ is said to be inactive, or in inactive state

A queue state transition happens at time $t$ if

When queue $v$ is inactive, $D^v = D^{\pi_{v}}$.

Definition 2.5. Link constraint $c_{l,v}(t)$

Let $v \in V \backslash \{v_0 \cup S\}$ be a node which is not a source or a sink. For all links $l \in L^{\text{out}}_{v}$, we define the link constraint $c_{l,v}(t)$ as the ratio of arriving flows at time $t$ on capacity at queue $v$ when this flow leaves queue $v$⁵.

⁵The dissipation rate of the point queue at the node is only governed by the capacities of the outgoing links. This model can be extended to also impose a discharge rate constraint based on the capacity of the incoming link, but increases the complexity of the notation and the proofs.

⁶When there is a tie, one of them is chosen arbitrarily.

Definition 2.6. Active link $\gamma{v}{t}$ and set of active paths $\Gamma{v}{t}$ of a node

We define the active link $\gamma_{v}(t)$ of a node $v$ at time $t$ as the most constrained link⁶ in $L^{\text{out}}_{v}$:

⁶When there is a tie, one of them is chosen arbitrarily.

We define the set of active paths $\Gamma_{v}(t)$ in queue $v$ as the set of paths in the most constrained link $\gamma_{v}(t)$

Remark 1 gives $\Gamma_{v} \subset P_{v}$.

Requirement 3. Full capacity discharge property

The model satisfies the full capacity discharge property. For each node $v$ and time $t$, if queue $v$ is active at $t$, then the active link $\gamma_{v}(t)$ discharges at full capacity.

With this last property, we complete the definition of the dynamics model.

Definition 2.7. Feasible flows

A feasible flow $\lambda^{v}_{p}$ at a node $v$ is a flow that satisfies the FIFO, capacity constraint and full capacity discharge properties from requirements 1, 2 and 3.

The definition of the initial conditions on the network completes the definition of the model.

Definition 2.8. Initial times for each non-source node

Given a set of initial delays at each node $\delta^{v}(t_{\text{initial}}) \geq 0, \forall v \in V\backslash(S\cup \{v_0\})$ and an initial time $t_{\text{initial}}$, we define the set of initial times over which the departure curves are defined for each non-source node recursively as follows:

2.4.   Existence and uniqueness of the solution to the model

Now that we have fully defined the model dynamics, we consider the well-posedness of the model. In other words, given a network, link capacities and the departure functions at the source, we want to know whether the dynamics of the model admits a unique solution.

Probleme 1: General network problem

Input. An arborescence $(V,L)$ with source $v_0$ and sink set $S$, capacities $\mu_{l}(t), \forall l \in L, t \in [t_{\text{initial}}, t_{\text{final}}]$, acceptable departure functions from the source $D^{v_0}_{p} \in$ $\mathbf{D}(t_{\text{initial}},t_{\text{final}}) \forall {p\in P_{v_0}}$ and initial delays $\delta_{v}(t_{\text{initial}}) \geq 0, \forall v \in V\backslash(S \cup \{v_0\})$}

Question. Does a corresponding set of feasible flows exist for all internal nodes $v\in V\backslash v_0$ and are they unique?

Theorem 1 states that the solution to problem 1 both exists and that the solution is unique, under certain conditions on the departure curves at the origin and the link capacities of the network.

Theorem 1. Existence and uniqueness of the solution to problem 1

Problem 1 admits a unique solution under the following conditions.

1) the path flows at the origin $\lambda^{0}_{p}(t)$ are piecewise polynomial,

2) link capacities $\mu_{l}$ are piecewise constant over time. Note that neither of the assumptions of the theorem are restrictive in a practical sense.

⁷Neither of these assumptions are restrictive in a practical sense, because any piecewise continuous function on a closed interval can be approximated to an arbitrary accuracy by a polynomial of appropriate degree (Stone-Weierstrass theorem [50]) and link capacities do not evolve in a continuous manner. LiIf the flows arriving at a nodenk capacities are typically subject to discrete changes due to incidents such as accidents and changes in weather.

The next section is devoted to a constructive proof of Theorem 1. The general flow of the proof is as follows. Sections 3.1-3.3 first develop a set of differential equations for delays in the network. In section 3.4, we then prove that a unique solution to differential equation on delays also implies a unique solution to problem 1. Section 3.5 proves that the differential equations on the delay at each node always admit an unique solution, which finally leads to the proof of Theorem 1.

3.   A solution based on time mapping.

This section builds a constructive proof of Theorem 1. Throughout sections3.1-3.3, we require that the flows at the origin are acceptable departure curves as defined in definition 2.1 and that the outflows at each node satisfy the model requirements (i.e. result in feasible flows as defined in definition 2.7).

3.1.   Local study of point queues.

We begin by proving Proposition 2, which gives an analytical expression for the derivative of the delay at node as a function of its downstream capacities and outgoing flow at its parent nodes.

Proposition 2. Evolution law of a single queue

If queue $v$ is active at time $t$,

Proof. From Equation (17),

Using the full capacity discharge property from Requirement 3,

3.2.   Time mapping.

The evolution law stated above foWe now introduce the following time mapping function r any given node $v$ depends on the outgoing flows $\lambda^{\pi_{v}}_{p}$ at the parent node. However, this it not an input of Problem 1. In this section, we introduce the notion of time mapping to obtain a modified law for the delay evolution that replaces the outgoing flows at the parent node with the outgoing flow at the origin.

3.2.1.   Definition of time mapping functions.

The evolution law from Proposition 2 gives a non-linear ordinary differential equation (ODE) that governs the evolution of $\delta_{v}(t)$. The evolution of delay encountered by an agent $x$ entering queue $v$ at time $t$ depends on the flows entering the queue at $t$ and the capacity of the active link(s) $\gamma_{v}$ at time $t+\delta_{v}(t)$ when agent $x$ leaves the queue. The non-linearity of the ODE makes directly computing the dynamics along a path algebraically complex. Therefore, we introduce a time mapping function.

Let $v$ be an internal node of the network and its parent node be $\pi_{v}$. An agent leaving node $\pi_{v}$ at time $t$ will leave node $v$ at time $t+\delta_{v}(t)$. We now introduce the following time mapping function:

Definition 3.1. Node time mapping function $T^{{v,\pi_{v}}}$

We define the time mapping function $T^{{v,\pi_{v}}}$ by

an agent leaving node $\pi_{v}$ at time $t$ will leave node $v$ at time $T^{{v,\pi_{v}}}(t)$

The notation $T^{{v,\pi_{v}}}$ (variable ordering) is chosen for mathematical convenience with respect to the derivatives of the function, as will be apparent in the rest of the discussion. In equation (26), $T^{{v,\pi_{v}}}$ takes a time with a physical meaning at the exit of node $\pi_{v}$ on its right hand side, and gives back a time with a physical meaning at the exit of node $v$ on its left hand side.

Proposition 3. $T^{{v,\pi_{v}}}$ is strictly increasing and bijective

The function $T^{{v,\pi_{v}}}$T is strictly increasing and thus bijective from its domain to its image. Its derivative is

Physically, this means that the FIFO assumption is respected: i.e. an agent $x_2$ entering queue $v$ after another agent $x_1$ will also leave the queue after $x_1$

Proof. Taking the derivative of equation (26) and applying equation (25) in Proposition 2 gives,

The departure curves at the origin are strictly increasing since they must be acceptable departure curves. The full capacity discharge property from requirement 3 requires that one outgoing link at each node discharges at full capacity. Finally, these properties combined with Proposition 1, which states that the outflows at a node are proportional to the inflows, give us the result that $\left.\dfrac{dT^{{v,\pi_{v}}}}{dt}\right|_t > 0$.

Thus $T^{{v,\pi_{v}}}$ is invertible and its inverse is an increasing function.⁸

⁸If the acceptable set of departure curves $\mathbf{D}$ is relaxed to allow non-decreasing instead of strictly increasing functions, $T^{v,\pi_{v}}$ becomes a correspondence, and the mathematical treatment would be more involved.

Definition 3.2. Node time mapping function $T^{{\pi_{v}}{v}}$

Given an internal node $v$, we define the function $T{\pi_{v}}{v}$ as the inverse of $T^{{v,\pi_{v}}}$

We now consider the unique path $(v_0,v_1, \cdots,v_{n-1},v_n)$ which leads from the source $v_0$ to some node $v_n$. As each node has a unique parent, we can recursively trace the path from node $v$ back to the source node $v_0$. Let $t^{v_n}$ be a fixed time. If an agent $x$ leaves node $v_n$ at the time $t^{v_n}$, we can recursively define the following:

1) $t^{v_{n-1}} = T^{{v_{n-1},v_n}}(t^{v_n})$ is the time that agent $x$ left $v_{n-1}$, $t^{v_n} = t^{v_{n-1}} + \delta_v(t^{v_{n-1}})$

2) $t^{v_{n-2}} = T^{{v_{n-2},v_{n-1}}}(t^{v_{n-1}})$ is the time that agent $x$ left $v_{n-2}$, $t^{v_n} = t^{v_{n-2}}+ \delta_{v_{n-1}}(t^{v_{n-2}}) + \delta_v(t^{v_{n-2}} + \delta_{v_{n-1}}(t^{v_{n-2}}))$

3) $t^{v_{n-3}} = T^{{v_{n-3},v_{n-2}}}(t^{v_{n-2}})$ is the time that agent $x$ left $v_{n-3}$, $\cdots$

As $T^{{v,\pi_{v}}}$ and $T^{{\pi_{v},v}}$ are bijective for all internal nodes $v$, we can give the following definition

Definition 3.3. Time mapping function from and to the origin $T^{{v}{v_0}}$ and $T^{{v_0}{v}}$

Let $v_n$ be a node, and $(v_0,v_1,v_2,\cdots,v_{\pi_{n}},v_n)$ be a path from the origin $v_0$ to node $v$. We define the time mapping function to the origin as the composition of the node time mapping function on the path between the source and $v_n$

an agent that leaves node $v_n$ at time $t$ left the origin $v_0$ at time $T^{{v_0,v_n}}(t)$.

an agent that leaves the origin at time $t$ will leave node $v_n$ at time $T^{{v_n,v_0}}(t)$

A sample path from the origin $v_0$ to a node $v_n$ is illustrated in figure 2. We can now define the time mapping function between any arbitrary pair of nodes.

Definition 3.4. Time mapping function between two arbitrary nodes

We define the time mapping function $T^{i,j}$ between node $i$ and node $j$ as follows.

1) There exists a path between nodes $i$ and $j$ (for example nodes $v_2$ and $v_n$ in figure 2),

Let $x$ be an agent that leaves node $j$ at time $t$. $T^{i,j}(t)$ is the time that agent $x$ leaves node $j$.

2) There does not exist a path between nodes $i$ and $j$ (for example nodes $v_2$ and $v_x$ in figure 2),

Let $x_j$ be an agent that leaves node $j$ at $t$. From definition 3.3 we know that $x_j$ leaves the origin at time $T^{0,j}(t)$. Let $x_i$ be an agent that also leaves the origin at time $T^{0,j}(t)$. Then $T^{i,j}(t)$ is the time that agent $x_i$ leaves node $i$.

Definition 3.5. Time mapping operator $\mathbf{T}^{i,j}$

We define the time mapping operator $\mathbf{T}^{i,j}$ on the set $F$ of time dependent functions as follows:

We now consider the physical interpretation of $T^{i,j}$.

3.2.2.   Time mapping of model quantities

This section first studies the relationship between departure curves at different nodes and the time mapping function. We then define the time mapped versions of the other quantities in the model. The time mapping operators allow for mapping any quantity from one node to the other. This definition of a time mapped quantities thus allows any quantity to be defined with respect to the source node of the network.

Proposition 4. Physical interpretation of the time mapping function

Let $p$ be a path, and $(v_{0},v_{1},v_{2},\cdots,v_{n})$ be a sequence of consecutive nodes on the path.

Let $x = D^{v_0}_{p}(t^{v_0})$ be an agent on path $p$ that leaves the origin at time $t^{v_0}$ and $t^{v_i}=T^{v_i,0}(t^{v_0}) \forall v_i \in p$.

Proof. Proof by induction on the length of the sequence $k$. If $k=0$, the result is trivial. Let $k\in [1,i]$ be an integer. By the induction hypothesis, we assume that the result is true for to $k=i-1$, i.e. $D^{v_{i-1}}_{p} = D^{v_0}_{p} \circ T^{0,v_{i-1}}$. By the definition of path delay $\delta_{v,p}, D^{v_i}_{p}{t+\delta_{v_i,p}(t)}=D^{v_{i-1}}_{p}(t), \forall t$, which means $D^{v_{i-1}}_{p}= D^{v_{i}}_{p}\circ T^{v_{i},v_{i-1}}$. Composing both sides of the equality with $T^{v_{i-1},v_i}$ we get $D^{v_{i}}_{p}= D^{v_{i-1}}_{p}\circ T^{v_{i-1},v_{i}}$. Substituting the induction hypothesis and simplifying the results completes the proof.

Equation (36) follows directly from equation (35)

Remark 6. As function $T^{i,j}$ is the inverse of $T^{j,i}$, the operator $\mathbf T^{j,i}$ is the inverse of $\mathbf T^{i,j}$.

We can now reformulate the first equation of Proposition 4 as follows:

Proposition 5. Time mapping of departure curve $D^{v}_{p}$

Let $i$ and $j$ be two nodes on path $p$.

Proof. Using definition 3.5 we have,

Proposition 6. Time mapping and flows

Let $v$ be a node on path $p$.

Proof. From the definition of flow, $\lambda^{v}_{p} = \dfrac{dD^{v}_{p}}{dt}$. The result is obtained by simply taking the derivative of the equation $D^{i}_{p}=D^{j}_{p} \circ T^{j,i}$ (from Proposition 5) with respect to time.

Remark 7. The time mapping and derivative operators do not commute.

Definition 3.6. Time mapping of delay $\delta^{i}_{j}$

Let $v$ be an internal node⁹. We define the time mapped delay in queue $v$ at node $\pi_{v}$, $\delta^{\pi_{v}}_{v}$ as the delay encountered in queue $v$ by an agent leaving node $\pi_{v}$:

⁹An internal node is a node $v$ which is neither a sink nor the source $k \in K \backslash (\{0\} \cup S)$.

Let $i$ be an arbitrary node and $j$ be an internal node. We define the time mapped delay in queue $j$ at node $i$, $\delta^{i}_{j}$ as

Physically, if nodes $i$ and $j$ are on the same branch with $i \prec j$ (resp. $i \succ j$), then $\delta^{i}_{j}(t)$ is the time that an agent which leaves queue $i$ at time $t$ will be (resp. has been) delayed at in queue $j$.

Definition 3.7. Time mapping for capacity

We define the time mapped capacity of a link $l$, $\mu^{v^\text{in}_{l}}_{l}$ as the capacity encountered by an agent at queue $v^\text{in}_{l}$ in link $l$

Let $l$ be an arbitrary link and $v$ an internal node. We define the time mapped capacity of link $l$ at node $v$ as

Physically, if link $l$ and node $v$ are on the same branch with $v^\text{in}_{l} \prec v$ (resp. $v^\text{in}_{l} \succ v$), then $\mu^{l}_{v}(t)$ is the capacity an agent that leaves queue $v$ at time $t$ encountered (resp. encounters) at link $l$.

Proposition 7. Physical interpretation of mapped delay and mapped capacity

Let $v_j$ be an arbitrary node, $p$ be a path, and $(v_{0},v_{1},v_{2},\cdots,v_{n})$ be a sequence of consecutive nodes on the path $p$. Also, let $t^{v_i}=T^{v_i,0}(t^{v_0}), \forall v_i \in p$.

Let $l$ be an arbitrary link.

Proof. Let $i$ be an arbitrary node and $j$ be an internal node. From definition (3.6) for time mapped delay we have.

Therefore, $\delta^{v_i}_{v_j}(t^{v_i})=\delta^{v_{j-1}}_{v_j}(t^{v_{j-1}}),\forall v_i \in p$, which proves equation (43). The proof for equation (44) is identical.

Definition 3.8. Time mapping of active link and active paths

Let $v$ be an internal node. We define mapped active link $\gamma^{\pi_{v}}_{v}$ as the active link for flow exiting node $\pi_{v}$ at queue $v$, and mapped active paths $\Gamma^{\pi_{v}}_{v}$ as the active paths for flow exiting node $\pi_{v}$ at queue $v$.

Let $j$ be an arbitrary node, we define the mapped active link and mapped paths for flow exiting queue $v$ at node $j$ as

Physically, if node $j$ and node $v$ are on the same branch with $j \prec v$ (resp. $j \succ v$), then $\gamma^{j}_{v}(t)$ is the active link that an agent leaving node $j$ at time $t$ will encounter (resp. encountered) at queue $v$, and $\Gamma^{v}_{j}(t)$ are the corresponding active paths.

Definition 3.9. Time mapped link constraint

Let $v$ be a internal node and $l \in L^{\text{out}}_{v}$. We define the mapped link constraint $c^{\pi_{v}}_{v,l}$ as the link constraint at link $l$ for an agent leaving node $\pi_{v}$.

Let $j$ be an arbitrary node, we define the mapped link constraint for link $l$ at node $j$ as

Physically, if node $j$ and node $v$ are on the same branch with $j \prec v$ (resp. $j \succ v$), then $c^{j}{v,l}(t)$ is the link constraint that an agent leaving node $j$ at time $t$ will encounter (resp. encountered) at link $l$.

Remark 8. The notation of the link constraint can be simplified for convenience as follows when time mapped.

We use the simplified notation in the rest of the discussion.

Proposition 8. The mapping of link constraints and active links is coherent

For all non-sink nodes $j \in V\backslash S$, internal nodes $v\in V\backslash (S \cup \{0\}$ and time $t \in (t_{\text{initial}}, t_{\text{final}}]$, we have

Proof. Let $v$ be an internal node and let $t^{j}$ be a time. Let $t^{\pi_{v}}=T^{\pi_{v}}_{j}(t^{j})$. Proving the proposition is equivalent to proving the following set equality

From the definition of the link constraint in equation (20) we have

By definition of $\mu^{v}_{l}$ in equation (41), we have $\mu_{l}(t^{\pi_{v}}+\delta{v}(t^{\pi_{v}}))$ = $\mu^{v}_{l}(t^{\pi_{v}}+\delta_{v}(t^{\pi_{v}}))$ and defining $t^{v}$ $\doteq$ $T^{v,\pi_{v}}(t^{\pi_{v}})$ = $t^{\pi_{v}}+\delta_{v}(t^{\pi_{v}})$, we obtain

$\mu_{l}(t^{\pi_{v}}+\delta_{v}(t^{\pi_{v}}))$ = $\mu^{v}_{l}(T^{v,\pi_{v}}(t^{\pi_{v}}))=\mu^{v}_{l}(t^{v})$. Equation (44) finally gives

Moreover, using equation (38) gives $\lambda^{\pi_{v}}_{p}(t^{\pi_{v}})\cdot \left.\dfrac{dT^{\pi_{v},j}}{dt}\right|_{t^{j}}=\lambda^{j}_{p}(t^{j})$. Summing on all paths $p$ in $P_l$, we obtain

Substituting equations (55) and (56) in the right hand side of equation (54) and using the time mapped link constraint from equation (50), we obtain

For all $l\in L^{\text{out}}_{v}$, $c_{v,l}(t^{\pi_{v}})$ and $c^{j}_{l}(t^{j})$ are proportional (and the proportionality ratio is independent from $l$). Therefore, the ${\arg \max }$ in equation (53) are the same. which concludes the proof.

Definition 3.10. Capacity of the active link

For notational simplicity we denote the capacity of the active link of an agent that enters queue $v$ at time $t$ as follows:

Definition 3.11. Time mapped capacity of the active link

Let $v$ be a internal node. We define the time mapped active link capacity $Q^{\pi_{v}}_{v}$ as the capacity of link $\gamma_{v}$ as seen by an agent at node $\pi_{v}$.

Let $j$ be an arbitrary node, we define the mapped active link capacity for link $\gamma_{v}(t)$ as seen by an agent at node $j$ as

Physically, if node $j$ and node $v$ are on the same branch with $j \prec v$ (resp. $j \succ v$), then $Q^{j}_{v}(t)$ is the active link capacity that an agent leaving node $j$ at time $t$ will encounter (resp. encountered) at link $\gamma^{j}_{v}(t)$.

Definition 3.12 Time mapping of queue state

Let $i$ be an arbitrary node and $j$ be an internal node. We define the time mapped queue state of queue $j$ at node $i$, $\eta^{i}_{j}$ as the queue state at queue $j$ as seen by an agent at queue $i$

Physically, if queue $i$ and node $j$ are on the same branch with $i \prec j$ (resp. $i \succ j$), then $\eta^{i}_{j}(t)$ is the queue state an agent that leaves node $i$ at time $t$ encounters (resp. encountered) at queue $j$.

3.3.   Global evolution of delay.

We now have the necessary tools to define the evolution of delays at any node of the network with respect to the flows at any upstream node in the network.

Definition 3.13. First active upstream node

Let $v$ be an internal node. We define the first active upstream node of $v$ as

For notational convenience we also define the following:

Theorem 2. Evolution law for delay at an arbitrary internal node $v$ mapped to any node $j$

Given an arbitrary internal node $v \in V \backslash (S \cup \{0\})$ such that queue $v$ is active, if the flows at the origin are acceptable departure curves and the model requirements are satisfied, the evolution law for delay mapped to any upstream node $j \in V \backslash S$ is

Proof. Let $t$ be a time and $v$ be a node. Evolution law (25) in Proposition 2 gives

By the definition of the time mapping functions we have, $\delta^{\pi_{v}}_{v}(t) \doteq \delta_{v}(t), Q^{\pi_{v}}_{v}(t) \doteq Q_{v}(t), \Gamma^{\pi_{v}}_{v}(t) \doteq \Gamma_{v}(t)$. Thus, equation (70) becomes:

Case 1: If node $v$ is not the first active node of path $p$ and $\Upsilon_{v}(t)$ exists.

Let for an arbitrary node $j$, $t^{j}=T^{j,\pi_{v}}(t)$. Since all the nodes between $\Upsilon_{v}(t)$ and $\pi_{v}$ are inactive by the definition of $\Upsilon_{v}(t)$ (note that $\Upsilon_{v}(t)$ may be equal to $\pi_v$), we have

Furthermore, since $\hat{\eta}^{\pi_{v}}_{v}(t) = 1$, and the full capacity discharge of active links (assumption 3), we have

Thus:

By replacing the constant $1$ in equation (71) with the above result we get,

This gives us the result for $j = \pi_{v}$. We will now map this result to any node $j \in V \backslash S$. By definition of time mapping, we have

Taking its derivative with respect to time, we obtain

Equation (38) on flow mapping gives

Substituting this result and the simple time mapping transformations of $\lambda$ and $Q$ into equation (79) gives the final result

Case 2: If node $v$ is the first active node of path $p$, we leave the constant 1 in equation (71) and follow the same remaining steps as in case 1 to obtain the result.

Applying Theorem 2 with $j=0$, we see that the delays with respect to the flows at the origin $\delta^{0}_{v}$ are solutions to the ordinary differential equations in definition 3.14.

Definition 3.14. Time mapped delay evolution differential equation

• If $v$ is not an active node and the flow on its active link $\gamma^{v}_{0}$ is within capacity, then $\dfrac{d\delta^{0}_{v}}{dt} = 0$.

• If $v$ is an active node or its active link $\gamma^{v}_{0}$ is over capacity, then

where the time mapping functions are redefined from delays as follows:

Proposition 9. Delay evolution does not depend on departure curves

All the time mapped quantities in equations (84) can be computed using only the initial delays, departure curve at the origin and the link capacities. It does not require the departure curves for any internal nodes $v \in V\backslash v_0$.

Proof. The time mapping function only depends on the delay functions from definition 3.1. The time mapped flows can be obtained using the time mapping function using Proposition 6. The other time mapped quantities are by definition constructed using the time mapping function as given in section 3.2.2.

3.4.   Equivalence of departure curves and delays.

We prove Theorem 1 on the existence and uniqueness of Problem 2 by first showing the equivalence between Problem 2 and Problem 2 (defined below), and then proving the existence and uniqueness of Problem 2 in the next section.

Problem 2: General delay problem

Input. An arborescence$(V,L)$ with source $v_0$ and sink set $S$, capacities $\mu_{l}(t), \forall l \in L, t \in [t_{\text{initial}}, t_{\text{final}}]$, departure functions from the source $D^{v_0}_{p} \in \mathbf{D}(t_{\text{initial}},t_{\text{final}})$ $\forall {p\in P_{v_0}}$ and initial delays $\delta_{v}(t_{\text{initial}}) \geq 0, \forall v \in V\backslash(S \cup \{v_0\})$}

Question. Does a solution to the time mapped delay function from definition 3.14 for each node $v \in V\backslash v_0$ exist and is it unique?

Theorem 3. Problem (1) and problem (2) are equivalent

Proof. The inputs to both problems are identical. Therefore, we only need to prove that the existence of a solution to one problem implies a unique and feasible corresponding solution to the other problem.

$(\Rightarrow)$ Suppose first that Problem 1 admits a solution.

By the definition of delay,

By the definition of time mapped delay we obtain,

which can be made a function of only $D^{v}_{p}$ by equation (86).

Theorem 2 then ensures that the delay functions thus defined satisfy the time mapped delay evolution from definition 3.14, i.e. a feasible solution to problem 2. Furthermore, the solution is unique from equation (89), since $D^{v}_{p}$ is a strictly increasing function.

$(\Leftarrow)$ Suppose now that Problem 2 admits a solution $\delta^{0}_{v}(t)$. We can build the corresponding departure curves $D^{v}_{p}(t)$ as follows.

The inverse departure curve $[D^{0}_{p}]^{-1}(x)$ can be constructed from $D^{0}_{p}(t)$, since the departure curve is strictly increasing.

The departure curve $D^{v}_{p}(t)$ can also be constructed from $[D^{v}_{p}]^{-1}(x)$ due to the strictly increasing nature of the functions.

We now show that the departure curves thus defined are feasible departure curves, i.e. a feasible solution to problem 1.

1. $D^{0}_{p}$ is continuous and piecewise $C^1$ because $\lambda^{0}_{p}$ is piecewise continuous. Furthermore, since $T^{j,0}$ is strictly increasing for all nodes $j$, $D^{v}_{p}$ is continuous and piecewise $C^1$.

2. The capacity constraint on links is imposed by equation (84) due to Proposition 8.

3.The FIFO condition is satisfied by construction since the delay $\delta^{0}_{v}$ is not a function of the path $p$.

4.The full capacity discharge of the active queues is also imposed by by equation (84) due to Proposition 8.

3.5.   Existence and uniqueness of the time mapped delay evolution.

This section proves Theorem 4 on the existence and uniqueness of the solution to Problem 2.

Theorem 4.Existence and uniqueness of the solution to problem (2)

The solution to problem (2) exists and is unique on the time interval of the problem $[t_{\text{initial}},t_{\text{final}}]$, if the following conditions are satisfied.

1. the path flows at the origin $\lambda^{0}_{p}(t)$ are piecewise polynomial,

2. link capacities $\mu_{l}$ are piecewise constant over time.

The proof of this theorem is fairly technical and requires several definitions and lemmas. Theorem 1 is a direct corollary of this result due to to Theorem 3 on the equivalence of the two problems.

The main goal of the proof of Theorem 4 is to show that there are a finite number of possible transitions, and to integrate equation (84) across the transitions. The next definitions and lemmas enables to establish these properties.

Definition 3.15 Depth of a node $d(v)$

We define the depth $d(v)$ of a node $v$ as the number of links on the unique path from the origin $v_0$ to node $v$

Definition 3.16 Link constraint comparators $B_{c_{l_1},c_{l_2}}(t)$ and $B_{c_{l}}(t)$

Given a node $v$ and two distinct links $(l_1,l_2)$, we define the boolean comparator $B_{c_{l_1},c_{l_2}}(t)$ as follows:

Given a node $v$ and link $l \in L^{\text{out}}_{v}$, we define the boolean comparator $B_{c_{l}}(t)$ as follows:

Definition 3.17. Time segment of constant link constraint $J$

A time segment $J$ is a segment of constant link constraint if and only if

1. for each $l \in L$, the boolean $B_{c_{l}}(t)$ is constant on $J$,

2. for each pair of nodes $\left(l_1, l_2\right) \in L$, the boolean $B_{c_{l_1},c_{l_2}}(t)$ is constant on $J$.

3. for each $l \in L$, the time mapped link capacity $\mu^{0}_{l}(t)$ is constant on $J$.

Lemma 10. Under the assumptions on flows and capacities, there are a finite number of segments of constant link constraint

Proof.  Consider a pair of links $\left(l_1, l_2\right)$. Since capacities are piecewise constant and flows are piecewise polynomial, there are a finite number of segments on which the capacities are constant and flows are polynomial. On any such a segment, $\frac{\sum_{p \in P_{l_1}}\lambda^{0}_{p}(t)}{\mu^{0}_{l_1}(t)}$ and $\frac{\sum_{p \in P_{l_1}}\lambda^{0}_{p}(t)}{\mu^{0}_{l_1}(t)} - \frac{\sum_{p \in P_{l_2}}\lambda^{0}_{p}(t)}{\mu^{0}_{l_2}(t)}$ are polynomials. Therefore, the number of times each expression crosses zero is bounded by the degree of the polynomial, which implies that there are a finite number of segments of constant link constraint.

Lemma 11. Constant active link

If $J$ is a segment of constant link constraint, the active link $\gamma_{v}^0$ of any node $v$ is constant on $J$.

Proof. The result comes directly from the definition of a segment of constant constraint.

Definition 3.18. Solution of depth $n$

A solution of problem (2) for depth $n$ is a set of solutions $\delta_v$ for all nodes $v$ such that $d(v) < n$ . It can be rigorously defined because the equations for $\delta_v$ only depend on variables associated with nodes of depth less than $n$ .

Definition 3.19. Elementary time segment $T^e(v)$

Given a node $v$ and a solution of depth $d(v)-1$ (if $v$ is not the origin), an elementary segment for node $v$ is a time segment $T^e(v)$ such that

• $T^e(v)$ is a segment of constant constraint,

• If $v$ is not the origin, for each node $j \in V$ such that $d (j) < d (v)$ , the node state $\mu_j (t)$ is constant on $T^e(v)$ .

Lemma 12. Single transition of node state on an elementary segment

If there exists a solution to problem (2) up to depth $d{(v-1)}$, and if $T(v)=[t_0,t_f]$ is an elementary segment for node $v$, then there is a solution $\delta_{v}^0$ of the problem and node $v$ admits at most one transition in $T(v)$.

Proof. As for each node $j\in V$ such that $d(j)<d(v)$, the node state $\eta_{j}(t)$ is constant on $T^e(v)$, the first active upstream node $\Upsilon_{v}$ is constant over time. Moreover, as $T^e(v)$ is a segment of constant constraint, Lemma 11 gives that active link $\gamma_{v}$ and first active upstream link $\hat{\gamma}_{v}$ are constant on $T^e(v)$, and the sign of $\dfrac{\sum_{p \in \Gamma^{0}_{v}(t)}\lambda^{0}_{p}(t)}{Q^{0}_{v}(t)} - \dfrac{\sum_{p \in \hat{\Gamma}^{0}_{v}(t)}\lambda^{0}_{p}(t)}{\hat{Q}^{0}_{v}(t)}$ is constant on $T^e(v)$.

Let us now consider the following four cases:

1. $B_{(c_{\hat{\gamma}{v},\gamma{v}})}(t_0) = 1, \eta^{0}_{v}(t) = 1$ $\implies \left.\dfrac{d\delta^{0}_{v}}{dt}\right|_t > 0$ and since the queue state is already active no transition will occur.

2. $B_{(c_{\hat{\gamma}{v},\gamma{v}})}(t_0) = 1, \eta^{0}_{v}(t) = 0$ $\implies \left.\dfrac{d\delta^{0}_{v}}{dt}\right|_t > 0$ and the queue state will immediately transition to being active $\eta^{0}_{v}(t) = 1$. No further transitions will occur as shown above.

3. $B_{(c_{\hat{\gamma}{v},\gamma{v}})}(t_0) = 0, \eta^{0}_{v}(t) = 1$ $\implies \left.\dfrac{d\delta^{0}_{v}}{dt}\right|_t \leq 0$ and the queue at node $v$ starts dissipating. There will be a transition in the queue state to inactive $\eta^{0}_{v}(t) = 0$ if the queue dissipates by time $t_f$ and the queue state will remain active otherwise.

4. $B_{(c_{\hat{\gamma}{v},\gamma{v}})}(t_0) = 0, \eta^{0}_{v}(t) = 0$ $\implies \left.\dfrac{d\delta^{0}_{v}}{dt}\right|_t \leq 0$ and the only possibility is the strict equality case and the queue state remains inactive.

Lemma 13. Unique solution on an elementary segment

Let $T^e(v)$ be an elementary segment for node $v$. Assuming a solution of depth $d(v)-1$ (if $v$ is not the origin), then solution of equation $(84)$ for node $v$ exists is unique on $T^e(v)$.

Proof. By Lemma 12, there can be at most one state transition of node $v$ in $T^e(v)$. This splits $T^e(v)$ into at most two sub-segments where $\eta_{v} = 0 \text{ or }\eta_{v} = 1$. From Lemma 11 we have that active link $\gamma_{v}$ and the first active upstream link $\Upsilon_{v}$ are constant on $T^e(v)$. Therefore, the quantities $\Gamma_{v}, \hat{\gamma}_{v}, Q_{v} \text{ and } \hat{Q}_{v}$ are constant on $T^e(v)$. Equation (84) states that

• If $v$ is not an active node $(\eta_{v} = 0)$ and the flow on its active link $\gamma_{v}$ is within capacity, then $\dfrac{d\delta^{0}_{v}}{dt} = 0$.

• If $v$ is an active node $(\eta_{v} = 1)$ or it's active link $\gamma_{v}$ is over capacity,

As all the variables in equation (94) other than the flow $\lambda^{0}_{p}(t)$ are constant during an elementary segment $T^e(v)$ and the flow $\lambda^{0}_{p}(t)$ is continuous in $t$ for all $t\in T^e(v)$, we can show that equation (94) admits a unique solution on the interval $T^e(v)$ by the Picard-Lindelöf theorem.

We have now all the ingredients to prove Theorem 4.

Proof of Theorem 4 We will prove the following proposition: The time interval of interest $(t_{\text{initial}}, t_{\text{final}}]$ can be partitioned into a finite set of elementary segments, and the solution to problem (2) exists and is unique

The proof is done inductively over the depth of the network. If the network contains a single node $v_0$, $[t_{\text{initial}},t_{\text{final}}]$ is an elementary segment for $v_0$, $(t_{\text{initial}},t_{\text{final}}] \in T^e(v_0)$ and there is a unique solution by Lemma 13. By the induction hypothesis, let us now assume that $(t_{\text{initial}}, t_{\text{final}}]$ can be partitioned into a finite number of elementary segments with respect to all nodes of depth $n$ and that the solution exists and is unique. Let $t_0,t_1,\cdots,t_{m}$ be times such that $E_n = \{(t_i,t_{i+1}], \forall i\in[0,m-1]\}$ is the set of elementary segments for nodes of depth $n$, and let $\delta_{v}$ for all $v \in \{V \vert d(v) \leq n\}$ be the unique solution of depth $n$.

Let $K_n$ be the non-empty set of nodes of depth $n$, and let $v\in K_n$ be a node in this set. Lemma 12 gives that for each $v \in K_n$, there is at most one state transition on $(t_i,t_{i+1}]$. Let $F_n(v)$ be the set of times at which these transitions occur for node $v$. Since there are $m$ elementary segments, there can at most be $|F_n\left(v\right)|\leq m$ transitions. If $F_n$ is the set of times at which the transitions for all nodes of depth $n$ happen, $|F_n|\leq m\cdot K_n$.

Let $\{t'_0,t'_1,\cdots,t'_{m'}\} = \{t_0,t_1,\cdots,t_m\} \cup F_n$ be the $m'$ segments created by splitting $E_n$ at each of the state transitions for nodes of depth $n$. The total number of segments $m'$ satisfies $m' \leq{m\cdot (K_n + 1)}$, since $|F_n|\leq m\cdot K_n$. By the definition of the $t'_i$, for each $i \in [0,m']$ we have

• for all $v\in K_n$, $\eta {v}$ is constant on $(t'_i, t'_{i+1}]$,

• $(t'_i, t'_{i+1}]$ is a segment of constant constraint $J$, since it is subset of an elementary segment, which is already by definition a segment of constant constraint.

Thus, $[t'_i, t'_{i+1}]$ is an elementary segment for all nodes of depth $(n+1)$. Furthermore, by Lemma 13, this implies that there is an unique solution to all nodes of depth $n+1$, which concludes the proof.

This also completes the proof of Theorem 1.

Proof of Theorem 1 Problem 1. is equivalent to Problem 2 by Theorem 3 and Problem 2 admits a unique solution by Theorem 4.

In some applications, it is also important to be able to computing the total delay experienced by an agents that takes a particular path. A provides analytical expressions for the total delay along a path.

4.   Applications

The solution to problem (1) models the flows in the network given the departure time functions at the origin and the initial delays by providing the departure time functions for all the other nodes in the network. The solution can be obtained by first solving problem (2), which provides the agent delay function at each node. Practically, problem (2) easier to directly solve than problem (1), because it corresponds to an explicit automaton that is easy to implement for numerical simulations.

Given a discretization time step $\Delta t$ and the initial conditions $\delta_{v}(0)$, algorithm 1 gives a numerical solution to the discretized problem (2). The algorithm numerically integrates the ordinary differential equation (ODE) given in equation (84) over time to obtain the solution. The algorithm relies on the fact that each discretized time step is an elementary segment, because the path flows and capacities are assumed to be constant (discrete approximation) during each time step.

4.1.   Single route with multiple bottlenecks.

The first case we will study is that of a simple single path network with multiple queues due to several capacity bottlenecks, as illustrated in figure 3. This network can be modeled as a tree with a single sink, i.e. a single path. Thus, we will remove the path index from the notation in this section. Each internal node $v$ has a unique child, thus the internal nodes can be indexed by the integers $v_0,\cdots,v_n$ and the unique path of the tree is $[v_0,v_1,\cdots, v_n,v_s]$. Moreover, as they model a succession of queues on the same road, we can assume that the capacity of each link $(v_i,v_{i+1})$ is constant and equal to capacity of the corresponding road segment $\forall v, \mu_{v_i,v_{i+1}} = \mu$. From Theorem 5, we know that the evolution of delay is given by

Since, the link with the smallest capacity will always be the last active link $\tilde{\mu} = \min(\mu_{v_i,v_{i+1}})$:

Thus, the evolution of total delay is equivalent to the evolution law for one queue of capacity $\tilde{\mu}$, and the network can be simplified to a unique internal node $v$ followed by a link of capacity $\tilde{\mu}$.

If the capacity of the links is time varying and $\bar{\mu}(t)$ is the capacity of the most constrained link that the agent entering the network at time $t$ is subjected to,

4.2.   Off-Ramp bottleneck

The next application is to compute the the dynamics of a congested freeway off-ramp, using the off-ramp model presented by Newell [38]. This example shows the versatility of the proposed framework, since Newell's the model includes non-FIFO dynamics at the off-ramp. This is accommodated by introducing an additional node and state dependent capacities on two links. The description of the model is as follows. As seen in figure 4(a), there are two flows $\lambda_{h}$ and $\lambda_{e}$ that enter the network, which has a capacity of $\mu_{h}$. Therefore, $\lambda_{h}(t) + \lambda_{e}(t) \leq \mu_{h}$. The exiting flow $\lambda_{e}$ is restricted by a capacity constraint of $\mu_{e}$ at the exit. There are four possible states of queuing dynamics that can occur based on the flow values. Figure 5 illustrates the transitions between the states.

Case 1: $\lambda_{e} \leq \mu_{e}$. If $\lambda_{e}(t) \leq \mu_{e}$, no queues will form in the network and there will be no delay.

Case 2: $\lambda_{e} > \mu_{e}$ and $\lambda_h \leq \mu_{r}$. If $\lambda_{e}(t) > \mu_{e}$, an exit queue will start forming at the entrance to the exit as seen in figure 4(b), which will then restrict the capacity of the freeway from $\mu_{h}$ to $\mu_{r}$.

Case 3: $\lambda_{e} > \mu_{e}$, $\lambda_h > \mu_{r}$ and $\frac{\mu_{r}}{\lambda_h}\cdot\lambda_e \geq \mu_{e}$. If the freeway flow $\lambda_h > \mu_{r}$, then a second freeway queue will start forming behind the exiting agent queue, as seen in figure 4(c), since the freeway demand is greater than the new reduced freeway capacity $\mu_{r}$. This second freeway queue will contain both freeway and exiting agents and therefore the flow exiting the queue will be subject to the first-in-first-out (FIFO) condition. As a result, since the freeway flow $\lambda_h$ is restricted to a rate of $\mu{r}$, the exiting agent flow at the freeway queue will be restricted to $\lambda_e^{\prime} = \frac{\mu_{r}}{\lambda_h}\cdot\lambda_e$.

Case 4: $\lambda_{e} > \mu_{e}$, $\lambda_h > \mu_{r}$ and $\frac{\mu_{r}}{\lambda_h}\cdot\lambda_e < \mu_{e}$. Now, if $\lambda_e^{\prime} < \mu_{e}$, then the off-ramp queue will start decreasing since the flow is less than the capacity and the queue will disappear. Thus, in this case, an off-ramp bottleneck created a second bottleneck that in turn removed the off-ramp bottleneck, which is an unstable equilibrium. Therefore, as explained in [38], there will be a single queue of both freeway and exiting agents that occurs at the off-ramp, as seen in figure 4(d), and the freeway flow through the bottleneck will be $\lambda_h^{out} = \frac{\mu_{e}}{\lambda_e}\cdot\lambda_h$ according to the FIFO condition.

The uniqueness and existing properties hold even with the state dependent capacities, since the flows are assumed to be piecewise polynomial and therefore lead to a finite number of state transitions. This implies that the link capacities are piecewise constant. Therefore, we can solve for the delays in this network using algorithm 1. Furthermore, this subnetwork can be part of a larger network over which we wish to compute the system delays.

Figure 6 shows the flow and delay profiles for a numerical example of the off ramp network with the following link capacities: $\mu_E=5,\mu_H=30$ and $\mu=45$. We can observe the following state transitions during the simulated time window.

• At $\verb=t==\verb=92=$ Appearance of exiting agent queue.

• At $\verb=t==\verb=121=$ Appearance of freeway queue.

• At $\verb=t==\verb=222=$ Disappearance of exiting agent queue.

•At $\verb=t==\verb=372=$ Disappearance of freeway queue.

One interesting observation is that freeway congestion caused by the exiting agent bottleneck persists well beyond the time at which the exiting agent queue disappears.

5.   Conclusion

This article presented a mathematical framework for modeling traffic flow through a network with a single source and multiple sinks. The model satisfies the standard laws of flow dynamics and is shown to lead to a well-posed dynamics problem with an unique solution. The main benefit of this framework is the ability to analytically prescribe the delays at each junction as a function of the boundary flows at any other upstream junction and the delay over any sub-path with respect to the boundary flow at the source node of the sub-path. This is a critical requirement when solving control and optimization problems over the network, since solving an optimization problem over simulation models is generally intractable in terms of computational complexity. The versatility of computing the delays as a function of the inflow at any point in the network is achieved though a mathematical framework for time mapping the delays. An algorithm for computing a discrete approximation of the system numerically is also provided. The application of the framework is illustrated using two examples, a single path consisting of multiple bottlenecks and a diverge junction with complex junction dynamics. The main limitation of this framework is the requirement that the network only have a single source. The time mapping framework presented can however be generalized to any non-cyclic (tree) network. Thus, the next step would be to introduce merging dynamics into the model to obtain a more general network model.

A.   Total path delay.

In some applications, it is also important to be able to computing the total delay experienced by an agents that takes a particular path. In this section, we provide analytical expressions for the total delay along a path.

Definition A.1. Total delay of a path $p$

We define the total delay $\Delta^{0}_{p}$ encountered on a path $p$ at time $t$ as the total delay encountered by agent on path $p$ that enters on the network at $t$ throughout its entire path to the sink node.

where $v_{p_n}$ is the last non-sink node on path $p$ . We define the time mapped total delay $\Delta^{j}_{p}$ as the total delay in path $p$ as seen by an agent that is at node $j$ at time $t$ .

Proposition 14. Total delay $\Delta^{j}_{p}$ as a function of queue delay $\delta$

The time mapped total delay $\Delta^{j}_{p}$ encountered on a path is equal to the sum of delay encountered by the agent on its path.

where $t^{j}$ is the time that the agent is at node $j$ .

Proof. Let $t^{i} = T^{i,j}(t^{j})$ . We obtain the result as follows using the definition of delay and a series of time mappings.

Definition A.2. Active link of the last active queue of a path $p$ at time $t$ $\left(a_{p}(t)\right)$

Let $p$ be a path and $t$ be the time that an agent departs node $j$ . We define the last active queue of the path $p$ time mapped to passing node $j$ at time $t$ as

For notational convenience we also define the following:

Theorem 5. Evolution law for total delay $\Delta^{0}_{p}$

Let $p$ be a path, $t$ be a time. The evolution law for total delay at time $t$ is

Proof. Taking the dfrac of equation (100) for $j=0$, we obtain

Note that $\gamma^{0}_{v}(t) = 1$ implies node $v$ is active when the source flow at time $t$ reaches node $v$ . From Theorem 2 with $j=0$ we have,

Plugging this into equation (107) gives a telescopic series, since it only considers the active nodes of the path and $\tilde{Q}^{0}_{p}(t)$ gives the capacity of the last active link of path $p$ . Thus, we obtain

If $p$ does not contain an active queue there is no queuing in the path, which means there is no change in the queue length and therefore no change in the delay.

Remark 9. Note that this theorem can be extended to any subpath ${p}_{ij} \in p$ such that

Received June 2016; revised December 2016.