Role-aware Multi-agent Reinforcement Learning for Coordinated Emergency Traffic Control
Authors: Ming Cheng
Hao Chen
Zhiqing Li
Jia Wang
Senzhang Wang
Venue: NeurIPS 2025 poster ()
Keywords: Reinforcement Learning
Primary area: reinforcement_learning

Abstract: Emergency traffic control presents an increasingly critical challenge, requiring seamless coordination among emergency vehicles, regular vehicles, and traffic lights to ensure efficient passage for all vehicles. Existing models primarily only focus on traffic light control, leaving emergency and regular vehicles prone to delay due to the lack of navigation strategies. To address this issue, we propose the ***R*ole-aware *M*ulti-agent *T*raffic *C*ontrol (RMTC)** framework, which dynamically assigns appropriate roles to traffic components for better cooperation by considering their relations with emergency vehicles and adaptively adjusting their policies. Specifically, RMTC introduces a *Heterogeneous Temporal Traffic Graph (HTTG)* to model the spatial and temporal relationships among all traffic components (traffic lights, regular and emergency vehicles) at each time step. Furthermore, we develop a *Dynamic Role Learning* model to infer the evolving roles of traffic lights and regular vehicles based on HTTG. Finally, we present a *Role-aware Multi-agent Reinforcement Learning* approach that learns traffic policies conditioned on the dynamically roles. Extensive experiments across four public traffic scenarios show that RMTC outperforms existing traffic light control methods by significantly reducing emergency vehicle travel time, while effectively preserving traffic efficiency for regular vehicles. The code is released at [https://github.com/mingchenghexi/RMTC](https://github.com/mingchenghexi/RMTC).

Role-aware Multi-agent Reinforcement Learning for Coordinated Emergency Traffic Control
Ming Cheng Central South University Changsha, China 1147010288csu.eda cn
Hao Chen
Zhiqing Li
City University of Macau
City University of Macau
Macao, China
Macao, China
sundaychenhao@gmal.cc com
zhiqinglizzy@@maill com
Jia Wang
Senzhang Wang" Central South University
Xi' an Jiaotong-Liverpool University Suzhou, China
Changsha, China szwangocsu. edu cn
jia. wang02xxjtlu edu. cn
Abstract
Emergency traffic control presents an increasingly critical challenge, requiring seamless coordination among emergency vehicles, regular vehicles, and traffic lights to ensure efficient passage for all vehicles. Existing models primarily only focus on traffic light control, leaving emergency and regular vehicles prone delay due to the lack of navigation strategies. To address this issue, we propose the Role-aware Multi-agent Traffic Control (RMTC) framework, which dy- namically assigns appropriate roles to traffic components for better cooperation by considering their relations with emergency vehicles and adaptively adjusting their policies. Specifically, RMTC introduces a Heterogeneous Temporal Traffic Graph (HTTG) model the spatial and temporal relationships among all traf- fic components (traffic lights, regular and emergency vehicles) at each time step. Furthermore, we develop a Dynamic Role Learning model to infer the evolving roles of traffic lights and regular vehicles based on HTTG. Finally, a Role-aware Multi-agens Reinforcement Learning approach that learns traffic policies conditioned on the dynamically roles. Extensive experiments across four public traffic scenarios show that RMTC outperforms existing traffic light control methods by significantly reducing emergency vehicle travel time, while effectively preserving traffic efficiency for regular vehicles. The code is released at https: / //gttttttb..... www.th....om.miaeoo....e...o..ee conmmiingchenghexi/ /RMTC.
present
Introduction
Quick arrival of Emergency Vehicles (EMVs), such fire trucks, ambulances, and police cars, is crucial in modern emergencies, including medical crises [1], fires [2], and accidents [3]. However, managing emergency traffic response is inherently complex, EMVs only impact the travel time of regular vehicles (REVs), but the congestion caused by REVs also affects the arrival time of EMVs. As illustrated Figure 1, when traffic congestion occurs, traditional traffic light control often routes emergency vehicles (EMVs) along the shortest path. In contrast, by integrating vehicle navigation, EMV's can be guided to bypass congested segments, while surrounding REVs are steered away from the paths that EMVs s will traverse. This coordination alleviates conflicts and improves overall traffic efficiency. In this context, the challenge lies how to navigate both emergency and regular vehicles
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(a) Congested (b]rraditioal Light Control
(meeggnncy Navigation
[d] Rogular Navigation
Figure 1: Visualization of (a) Road Network Congestion, (b) EMV Routes under Traditional light Control, (c) EMV Routing with Navigation, and (d) REV Routing with Navigation
while coordinating with traffic lights to achieve efficient traffic flow for both EMVs and REVs. This research can be integrated into navigation software, such as Google Maps and Amap, to help save property, lives, and other critical resources in real-world scenarios.
Existing models mainly focus on traflic light control and can be broadly classitied into two categories: rule-based methods and reinforcement learning (RL)-based methods. Rule-based methods usually give EMVs higher priority like more green lights to accelerate EMV movement. Greedy approaches [4, 5] switch the corresponding traffic signal to green while halting conflicting directions to facilitate EMV passage. RL-based models, on the other hand, use the arrival time of EMVs an optimization objective to adjust traffic light policies. For example, EMVLight [6] assigns distinct rewards to different traffic signals to encourage coordinated responses to EMVs, while RECAL [7] extends traffic control to scenarios involving multiple EMVs. However, these existing methods focus solely on controlling traffic lights and fail to coordinate the behavior of traffic lights and vehicles.
Though considering EMVs, REVs, and traffic lights is an attractive approach, but it introduces the following three key challenges when incorporating three types of traffic components: EMVs, REVs, and traffic lights. (1) Personalized behavior within the same type of components: Traffic lights and vehicles different positions should make distinct decisions. For instance, lights and vehicles near EMVs should provide assistance, while those farther away should operate normally. (2) Interdependencies between different types of components: Traffic lights directly influence the movement of vehicles, while EMVs and REVs mutually impact each other's 's navigation. (3) The seesaw problem between emergency and regular vehicles: Balancing the needs of EMVs and REVs is challenging, as facilitating EMVs often comes the expense of REVs' efficiency.
Motivated by these challenges, we propose the Role-aware Multi-agent Traffic Control (RMTC) framework, which dynamically considers the relationships among all traffic components, assigns appropriate roles to these components, and adjusts their policies accordingly. Since the relationships between traffic components evolve over time, RMTC introduces a Heterogeneous Temporal Traffic Graph (HTTG) model the spatial and temporal relationships among traffic components at each time step. Furthermore, we develop Dynamic Role Learning model to infer the evolving roles of traffic lights and REVs based on HTTG. Finally, we present Role-aware Multi-agent Reinforcement Learning approach to learn role-aware traffic policies conditioned on the dynamically roles. In summary, our contributions are as follows:
We explore the novel problem of multi-agent emergency traffic control by defining the optimization problem for traffic lights, EMVs, and REVs. This exploration can help improve real-world navigation software to save more property and lives.
We design a Heterogeneous Temporal Traffic Graph (HTTG) and corresponding Dynamic Role Learning model to infer the roles of individual traffic components.
We design tailored rewards and train Role-aware Multi-agent Reinforcement Learning model for the coordinated control of traffic lights, EMVs, and REVs.
Evaluations on four public traffic datasets demonstrate that RMTC consistently outperforms existing traffic light control methods in terms of both EMV travel time and overall traffic efficiency.

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2 Preliminaries
Notations We define a multi-agent traffic control consisting of three types of agents: EMVs agents Temv. REVs agents Zev and traffic light agents Zu Each agent functions within a dynamic traffic network where its actions influence and are influenced by other agents.
At each discrete time step I, an agent receives observation o that includes local traffic state. For a traffic light agent, the observation includes the current signal phase, the number of approaching vehicles, the queue length of stopped vehicles, lane occupancy, vehicle flow rate, the number of fully stopped vehicles, and traffic pressure. For vehicle agent, the observation includes the current intersection, current lane, destination intersection, and destination lane.
Based on its observation, each agent selects an action a1)) from its predefined action space. For traffic light agents, the action corresponds to selecting the next traffic phase from predefined set of non-conflicting options. When a phase activated, only vehicles on the associated lanes are allowed to move. For vehicle agents, the action space consists of routing decisions at intersections, including turning left, going straight, or turning right.
The environment subsequently evaluates these actions through extrinsic rewards. For each traffic light agent E Ig. the extrinsic reward provided by the environment based on key traffic metries collected from the lanes it controls, including queue length, average waiting time. traffic pressure, and vehicle delay. For each vehicle agent E Ize U Temv. the extrinsic reward is defined as the negative elapsed travel time since its last decision point, which encourages more efficient routing decisions [8]:
(8) Textr
-T(() clapsed
(1)
Problem Definition The coordinated control of traffic lights, EMVs navigation, and REVS naviga- tion aims to enable rapid passage of EMVs while maintaining overall traffic flow efficiency. This is formulated as learning a joint multi-agent policy (To, Jiez, parameterized by 6,, that maximizes the expected cumulative reward across all agents:
T ΣΣ
max (Te,)
ZE
where I = Temy UTe, U Uzi and y E (0, 1) is the discount factor, and T' denotes the episode length.
3 Role-aware Multi-agent Controlling
To jointly optimize vehicle navigation and traffic signal control in emergency scenarios, we propose RMTC, Role-aware Multi-agent Traffic Controlling framework. As illustrated in Figure 2, we construct heterogeneous temporal traffic graph based on the traffic environment and enable dynamic role learning through three constraints: EMV Position Role Impacting, EMV Trajectory Role Impacting, and Role Consistency Constraint. The learned dynamic role features are then used to compute each agent's 's state and reward. Through role-aware multi-agent reinforcement learning, agents are guided to make decisions that improve both EMV efficiency and overall traffic flow.
3.1 Heterogeneous Temporal Traffic Graph (HTTG)
The Heterogeneous Temporal Traffic Graph (HTTG), denoted as G(O) (I, E(1),, is used to represent the interactions among diverse traffic agents. The node set I comprises all agents, while the edge set E(1) evolves over time to reflect dynamic and heterogeneous relationships. Specifically, the HTTG is updated at each step f, allowing the graph structure to dynamically reflect real-time relationships among EMVs, REVs, and traffic light.
HTTG Edge Construction agents. Specifically, the edge set (xa) comprises four relation types, forming the relation set R used in role-aware message passing.
The HTTG is designed to capture the interaction relationships among
I) TI-REV edges: Each light node in connected to REVs iee currently on its incoming lanes, modeling the regulatory influence of lights.
3

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Heterogeneous Temporal Traffic Graph
Traffic Environment
Reinforcement Learning
State Resign
h
CONCAT
TL-REV mize TL-EMV
TL-TL edge EMV-REV elge
Reward Resign
Dynamic Role L earning
EMV Position Role
EMV Trajectory Role Impacting
Role Consistency Constraint
Impacting
The overview framework of RMTC. RMTC constructs heterogeneous temporal traffic graph based on the traffic environment, employs dynamic role learning to assign adaptive roles to agents, and leverages these dynamic roles to design states and rewards for reinforcement learning.
Figure 2:
2) TL-EMV edges: Each light node in connects to EMVs éemv within its control incoming lanes, enabling lights be aware of and responsive approaching EMVs.
3) TL-TL edges: Two lights nodes iu, and du. are connected if their intersections are adjacent in the road network, capturing coordination needs between neighboring intersections.
4) EMV-REV edges: REVs with motion patterns similar EMVs are more likely be influenced by their presence, such as adjusting their trajectories. Based on this intuition, EMVs are connected REVs located on the same inbound lanes controlled by traffic light, if their observed states are sufficiently similar. We use cosine similarity to measure this relationship, and add an edge when the similarity exceeds predefined threshold ò:
(femvs érev) cos(or (t)
1
(C) Sere
where o (*) and Oi denote the observed state of the EMVs and REVs, respectively. lan
Heterogeneous Role Convolution The heterogeneous graph- based role encoder facilitates struc- tural representation learning by capturing the interdependencies among agents and distinguishing their dynamic roles within the traffic cavironment. Specifically, it employs a message message-passing passing mechanism that iteratively updates each node's representation. This process aggregates and integrates information from neighhoring nodes, carefully considering the heterogeneous nature of node and edge types. The bassage-passing mechanism be expressed
h
Σ FER
Σ
h (1--1) 1) (())
(4)
(E) (6)
denotes the role feature of agent i at step and layer and is initialized h (1,0) o.00 Ng (i) denotes the set of neighbors of agent n connected by edge type r, where i {Temv, Ine. In}-
where h (t.f)
w(!) is the learaalee weight matrix for edge type layer I, ( is the bias term, and (()) is non-linear activation function. The layer index I denotes the propagation depth in the message- paassggg- process, where two-layer architecture is employed.
3.2 Dynamic Role Learning
Dynamic role learning captures role features of traffic agents through HTTG at each time These role features are influenced by the positions and trajectories of EMVs, leading to changes in the

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agents' behavior, such as yielding, accelerating, or changing lanes. At the same time, it is important that these behavioral patterns remain stable in the short term, avoiding rapid fluctuations. Based on these considerations, we design three learning objectives to incorporate the influence of EMVs into role representation learning while ensuring stability over time.
EMV Position Role Impacting The position of the EMV relative to other agents plays a crucial role shaping their behavior. Agents closer to the EMV are likely prioritize actions that allow the EMV to pass quickly, such as adjusting traffic signals or changing their movement. In contrast, agents farther away may exhibit different behaviors.
Therefore, we maximize a lower bound of the mutual information between each agent's role embed- ding ho and the EMV-related graph features h ensuring role embeddings reflect the EMV's positional impact effectiielyy. The lower bound of the mutual information can be expressed as (the proof is deferred to Appendix B):
N Σ
N Σ
( ))
7 (h
(+) Cmy
Th
cmIv
log
exp (Tu
(5)
N
where h/ and (1) denote the role embedding of the i-th agent and the EMVs features in the same graph, respectively. 2 denotes the number of agent at time I, I The function To (..)) is learnable critic parameterized by w.
EMV Trajectory Role Impacting The trajectory of the EMV not only influences its own future decisions but also affects the behavior of other agents in the current time step. For example, if the EMV is approaching light-controlled intersection, the light controllers at that intersection must anticipate the EMV's next decision (e.g., whether it will go straight or turn) and adjust the traffic light phases accordingly, even before the EMV arrives. Similarly, agents further along the EMV's path must adjust their actions based on the EMV's 's predicted trajectory to maintain smooth flow. The trajectory information of the EMV must be maximized within the role representations.
Specifically, we encode the EMV's observation history using an LSTM network and optimize the following mutual information lower bound:
Twr (h gemr)
1
exp
(h
Demv)
log
(6)
N
NNa exp (Tw (h (1)
(e) :
genv)
Jemv
GRU (eevv galn
(7)
where h denotes the role embedding of the i-th agent, and genv is the hidden state the GRU module at time step The function Twr (.,-) -) is learnable critic, parameterized by w'
Role Consistency Constraint Role consistency over short time intervals essential for stable decision-making. If role features rely only local observations, they may fluctuate significantly due to transient changes in the environment, leading to unstable or suboptimal behaviors.
Therefore, we promote temporal consistency in roles by smoothing their evolution over time. Specifi- cally, we apply contrastive objective that draws role embeddings from adjacent time steps closer while pushing them apart from those of unrelated agents or distant moments. This objective is formulated as:
exp (d(h)) h ((+1))/7))
log
(8)
cons
ΣLENçO exp (d(n()) h (ct1))/
where d(-) is distance metric incorporating behavior-relevant state changes, and is fixed temperature parameter. Therefore, the overall training loss can be formulated as:
Crote Σ (- (19) 122 (1h 9 + C(t) cons
(9)
1=1
5

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where the MI terms ensure V-ware adaptability, and the temporal consistency term (() main- tains smooth role transitions over consecutive time steps, together enabling the embeddings to be both responsive to dynamic changes and stable enough for effective decision-making
3.3 Role-aware Multi-Agent Reinforcement Learning
Based on the learned dynamic role, we develop a multi-agent reinforcement learning framework for traffic control, which consists of four components:
Policy Designing The state of agent is formed by concatenating its learned role feature embedding h()), which captures the agent's dynamic role, with environmental observations o(*), The concatenated state, integrating both environmental features and role information, is then input to the actor network, which maps the input to a distribution over actions:
Ta (a(a h (0) =fo (fconcat(o" (n)
(10)
where ol!) denotes the agent's observations at time (4) represents the learned role embedding, and fe is the policy network with parameters f mapping concatenated imputs actions.
Role-aware Intrinsic Reward Role embeddings capture behavior patterns of agents, but they re- quire feedback to guide role cole-consistent actions. We propose role-aware intrinsic reward mechanism that encourages agents to align with their roles under EMVs influence.
Specifically, we quantify the influence of EMVs on each agent by computing the cosine similarity between the agent's role embedding h and the EMVs feature vector h Using these similarity scores, we select the top-k agents whose role embeddings are most relevant to the current EMVs context. The intrinsic reward for each agent then computed as a weighted version of the EMVs' reward, where the weight is determined by the similarity score between the agent's 's role and the EMVs. Therefore, the intrinsic reward for each agent can be expressed as:
Nemu
re,intr
8 (t) (1) (
v i,ettr
Vn E (Trev Ju},
(11)
where, eemy denotes the extrinsic reward received by EMV i.
(t)
Finally, this role-aware intrinsic reward is combined with the agent's extrinsic reward (t) during training form the total reward:
(t) Tn.extr
A
(12)
where À is scaling factor adjusted according the magnitude of agent extrinsic reward.
Multi-Agent Optimization We adopt Proximal Policy Optimization (PPO), a widely used policy- based reinforcement learning algorithm that optimizes policy by maximizing expected cumulative rewards. In this multi-agent setting, each agent, including vehicles and traffic lights, learns its own policy based on local observations and role-specific features.
To improve scalability and coordination efficiency, a shared policy network adopted across agents with similar roles (e.g., vehicles traffic lights). This shared-par parameter strategy allows agents to learn from diverse experiences while significantly reducing the computational burden of maintaining separate policy for each agent. The training objective includes both policy and value updates, and the loss is defined as follows:
C(A, () =E min (0) clip ((()))), 1- c, (--c,+e)440 A() + (Vo(o(),,,0)) R(0) 2
(13)
T A
Σ ( (n.L)) (1 + + (((11))))))) Vo(o!0) /(0)) Vo(0(),,100),
(14)
1=0

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Table Comparison of EMV and REV Travel Times for RMTC and Baselines Across Four Datasets. indicates the statistical significance 0.01 compared to the best-performed baseline, indicates the statistical significance p 0.05 compared to the best-per rest-performed baseline. "Improve" denotes the percentage reduction in travel time relative to the best baseline.
Synthetic
Real-world
Datasets
Grid 4x4
Avenue 4x4
Cologne8
Fengl.in
Metrics
Temv
Teev
Tew
Tee
Temv
Tww
Temv
Twvv
FixedTime
158.00
209.12
237.00
666.90
48.00
114.96
98.00
262.74
MaxPressure
162.00
218.45
155.00
768.82
48.00
184.67
136.00
304.24
CoILight
170.00
239.93
262.00
520.71
52.00
98.53
55.00
307.12
IPPO
143.00
171.20
294.00
574.89
75.00
96.55
48.00
230.91
:MAPPO
118.00
171.44
250.00
519.92
53.00
95.38
35.00
398.76
X-Light
117.00
170.37
170.00
587.02
49.00
96.96
48.00
234.90
RECAL
129.00
296.39
162.00
757.83
47.00
159.93
38.00
276.91
EMVlight
117.00
176.03
254.00
625.85
47.00
148.9
34.00
293.79
RMTC
112,00**
163.62*
141.00**
491,49**
36.00**
87,90
26.00**
218.91**
Improve (%)
4.27%
3.96%
9.03%
5.46%
23.40%
7.84%
23.52%
5.19%
where (0) = Ta(a (a (00))
auau (a(9) loto (0) is the importance sampling ratio, Vo. is the value function ap- proximator with parameters Φ, and R2(() is the cumulative return time L. Additionally, A() the role-aware advantage function incorporating both extrinsic reward (t)) and intrinsic reward (0()) y the discount factor, ub the decay factor for multi-step advantage estimation.
'intr with
Experiments
4.1 Experimental Setup
Experiment Settings. We conduct our experiments using SUMO2, a widely adopted traffic simulator that provides control over vehicle movements and traffic signals through its TraCI API. Following common settings in prior work [9], we configure each intersection with eight signal phases to ensure stable signal transitions.
Datasets. Our datasets include two synthetic traffic scenarios, Grid4x4 and Avenue, well as two real-world scenarios, Cologne8 and FengLin [10, 11]. The synthetic scenarios are constructed with idcalized grid layouts containing uniformly distributed four-arm intersections, The real-world scenarios feature mix of two-arm, three-arm, and four-arm intersections.
In framework, each vehicle and each traffic signal is modeled number vehicles in each episode is determined by the vehicle generation rate and the duration of the traffic event, allowing us to simulate varying traffic densities. To simulate realistic emergency conditions, we schedule the EMVs to depart from the middle point of the traffic episode. Details about these datasets are shown in Appendix C.I.
individual agent. The total
Baselines. We compare our method with a range of widely used traffic signal control approaches, including both rule-based and reinforcement learning -RL)- methods. The rule-based baselines include Fixed- Time (FT) [12] and Max-Pressure (MP) [13], while the RL-based baselines include CoLight [14], IPPO [15], rMAPPO [16), X-Light [9], RECAL [7], and EMVlight [6].
To adapt these methods emergency scenarios, we adopt a standard signal preemption strategy that extends the green phase along the EMV's route, creating green wave prioritize its passage [17]. We apply this green wave mechanism to each baseline. Further implementation details are provided in Appendix C.2.
bttps://www.cdiise.or/aau

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w/o EMV Position Role impacting
w/o Role Consistency Constraint
RMTC
w/o EMV Trajectory Role impacting
w/o Role-aware Intrinsic Reward
Grid4x4 Temv
Grid4x4 Trev
Fenglin Temv
Fenglin Trev
200
300
120
300
180
260
270
90
160
220
240
60
140
180
210
30
120
140
180
100
100
0
150
Figure 3: Ablation study results between RMTC with its four variants on Grid 4x4.
Evaluation Metries Following prior studies [6, 9], we adopt the average travel time of both EMVs and REVs to assess overall traffic efficiency and emergency response performance. The average travel time of REVs is defined as:
Tre
(exii
eatry )
(15)
i=1
where denotes the total number of REVs, and e.tityy texit are the entry and exit times of vehicle i. The average travel time of EMVs Temv is computed in the same manner.
4.2
Main Results Analysis
The main comparison results for both EMVs and REVs are presented in Table From the results, can derive the following observations. RMTC achieves favorable balance ensuring the fast arrival of EMV' while maintaining overall traffic efficiency.
Overall Performance Analysis. Compared to the best-performing baseline methods significantly reduces EMV travel times across all datasets. Notably, it achieves over 15% reduction on three benchmarks: Cologne8 (23.40%) and FengLin (23.52%). Meanwhile, RMTC also improves REV travel times by 7. 7.84% and 5.1% on the same datasets, indicating that prioritizing EMV passage does not come at the cost of increased congestion for regular traffic.
Comparison with Rule-Based Methods. Rule-based methods with pre-emption strategies can improve EMV travel time but often disrupt regular traffic, resulting in increased delays. Their reliance on fixed strategies limits adaptability to dynamic traffic flow variations. Consequently, these methods tend to prolong green phases for EMV without accounting for downstream congestion, which exacerbates delays for regular vehicles. In contrast, RMTC dynamically coordinates signal control and vehicle decisions based on real-time traffic states, enabling responsive and balanced traffic management.
Comparison with RL-BBased Methods. Compared to existing RL-based approaches, RMTC achieves superior EMV response efficiency and lower average travel times for all vehicles across diverse traffic conditions. Traditional RL-based traffic signal control methods, such as CoLight and PressLight, focus primarily on ordinary traffic flows and do not explicitly account for emergency vehicles, limiting their responsiveness when EMV's appear and making it difficult to capture the dynamic traffic changes induced by EMV movements. RL-based methods tailored for emergency scenarios, including RECAL and Viigh,, effectively prioritize EMV clearance but offer limited benefits for ordinary vehicles, which can lead to suboptimal overall traffic performance. vehicle and signal policies, enabling adaptive coordination in dynamic traffic environments. This joint optimization ensures rapid EMV passage while substantially improving the efficiency of ordinary vehicles, resulting in balanced and globally enhanced traffic flow.
contrast, RMTC jointly learns both
8

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Temv
T
300
300
Average Travel Time
RMTC
RMIC
250
250
200
200
150
150
100
100
0.5
1.0
1.5
2.0
2.5
3.0
1.0
1.5
2.0
2.5
3.0
Vehicle Generation Rate (veh/s)
Vehicle Generation Rate (veh/s)
Figure 4: Performance comparison between FT and RMTC under varying congestion on Grid 4x4,
4.3 Ablation Study
To evaluate the contributions each component our model, we compare the full model with four ablated variants: (i) w/o EMV Position Role Impacting removes the constraint of EMV position; (ii) w/o EMV Trajectory Role Impacting removes the constraint of EMV action trajectory; (iii) w/o Role Consistency Constraint removes the temporal consistency constraint; (iv) w/o Role-aware Intrinsic Reward removes the awaee intrinsic reward, using only the environment reward. From the results shown in Figure 3, draw the following observations.
Effectiveness of components in the dynamic role loss. The performance degradation in w/o EMV Position Role Impacting and w/o EMV Trajectory Role Impacting demonstrates the effectiveness of enabling agents to learn roles by observing the position and behavioral tendencies of the EMVs. This alignment enables agents to learn role features that support coordination, allowing EMVs to pass through intersections more quickly. The inferior performance of w/o Role Consistency Constraint indicates that preventing abrupt shifts in role assignment across time steps is crucial. Without proper scheduling, the learned roles become volatile, which affects overall performance.
Effectiveness of components in the reward function.Tle poor performance of w/o Role-aware Intrinsic Reward indicates that, without a reward signal, agents lack proper incentives to prioritize actions that facilitate EMVs passage and minimize delays for other vehicles. Without a proper guidance, agents cannot coordinate effectively, failing to support the rapid movement of EMVs and maintain efficient overall traffic flow.
4.4 Performance Comparision under Different Traffic Flows
Figure 4 illustrates the performance of RMTC under different traffic generation rates, ranging from 1 vehicles per second, where the generation rate reflects the average number of vehicles entering the network per second and thus controls the overall traffic density. Across all rates, RMTC consistently achieves lower average travel times compared to the FT method, and its performance remains stable as traffic load increases, showing no significant degradation. These results confirm that the role-aware embeddings and reward redistribution mechanism enable RMTC to adaptively coordinate agents in real time, effectively prioritizing EMVs while preserving smooth traffic flow for ordinary vehicles even under dynamic and high-density conditions.
4.5 Impact of EMV Quantity on Traffic Efficiency
RMTC encodes the relationships between each agent and multiple EMVs using a heterogencous graph and learns role embeddings via mutual information maximization. This design allows agent roles to reflect varying importance to different EMV's based on their spatial positions. For example, an agent closer to EMV A than EMV B will have role embedding that more strongly captures EMV A's features, guiding its behavior accordingly. In addition, RMTC incorporates a cole-similarity-bbar reward redistribution mechanism, which enables soft prioritization when multiple EMVs coexist, ensuring coordinated decision makiin without severely disrupting regular traffic.
We compare RMTC with IPPO and rMAPPO on the Grid dataset under varying numbers of EMVs (from to 5), as summarized in Table 2. As the number of EMVs increases, the average travel time of REVs in IPPO and rMAPPO rises sharply due to growing route conflicts and the lack of explicit

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Table Comparison of travel times under varying numbers of EMVs on the Grid 4x4.
EMV Travel Time
Number of EMVs
2
3
5
IPPO
143.00
124.00
141.33
145.25
149.40
MAPPO
118.00
132.00
149.00
160.75
141.20
RMTC
112.00
108.50
125.00
133.50
136.60
REV Travel Time
Number of EMVs
IPPO
171.20
183.81
181.22
173.87
190.59
:MAPPO
171.44
185.26
173.08
172.37
179.04
RMTC
163.62
159.73
167.60
160.95
169.78
coordination among agents. In contrast, RMTC maintains relatively stable REV travel times even in dense EMV scenarios, benefiting from its ability to dynamically adapt agent behaviors through learned roles and coordinated signal-phase adjustments.
For EMVs, RMTC consistently outperforms the baselines across all settings, achieving significantly shorter travel times by leveraging structured role modeling, trajectory-awan coordination, and proac- tive traffic light control. This demonstrates that RMTC not only prioritizes emergency response but also preserves overall traffic efficiency. These results collectively highlight RMTC's robustness and scalability in handling complex multi-EMV environments while maintaining balanced performance for both emergency and regular vehicles.
Additional experimental results for RMTC are provided in the Appendix F.
Conclusion
Ensuring the rapid passage of EMVs in critical scenarios is essential for minimizing losses and saving lives. However, existing methods primarily focus on traffic light control while overlooking the collaborative between EMVs, REVs, and traffic signals. To address this issue, We propose the Role- aware Multi-agent Traffic Control (RMTC) framework, which jointly coordinates traffic lights and vehicle behaviors through dynamic role modeling. By constructing a Temporal Heterogeneous Traffic Graph and learning adaptive roles for each agent, RMTC enables fine-grad fine-grained policy adaptation based on evolving traffic contexts. Extensive experiments demonstrate that RMTC significantly accelerates EMVs arrivals while maintaining overall traffic efficiency.
Acknowledgement
This research was funded by the National Science Foundation of China (No. 62572489, No. 62502008), the Funding Scheme for Research and Innovation of FDCT (Grant No. 0021//025/TPPI), CIRP Open Fund of Radiation Protection Laboratories (CIRPFSHI20250033) Open Fund of Na- tional Key Laboratory of Information Systems Engincering (WDZC200552990007) and the High- Performance Computing Center of Central South University.
References
[1] Kunpeng Liu, Xiaolin Li, Cliff Zou, Haibo Huang, and Yanjic Fu. Ambulance dispatch via deep reinforcement learning. In Proceedings of the 28th International Conference on Advances in Geographic Information Systemx, SIGSPATIAL '20, page 123-126, New York. NY, USA, 2020. Association for Computing Machinery.
[2] Jianfeng Zhou, Genserik Reniers, and Valerio Cozzani. A petri-net approach for firefighting force allocation analysis of fire emergency response with backups. Reliability Engineering & System Safery, 229: 108847, 2023.
[3] Yu Lei, Guirong Zhang, Shan Lu, and Jiahuan Qian. Revealing the generation mechanism of cross-regional emergency cooperation during accidents and disasters rescue. Safety Science, 163: 106140, 2023.
10

---

[4] Eric J Nelson and Darcy Bullock. Impact of emergency vehicle preemption on signalized corridor operation: An evaluation. Transportation research record, 1727():1-11,,000 2000.
[5] Chakkaphong Suthaputchakun and Yue Cao. Ambulance-to-tricn traffie light controller communications for rescue mission enhancement: thailand use case. IEEE Communications Magazine, (7(11)911-97, 2019.
[6] Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, and Amit Chakraborty, Emvlight: A decentralized reinforcement learning framework for efficient passage emergency vehicles. Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 4593 4601, 2022.
[7] Miaomiao Cao, Victor O. K. Li, and Qiqi Shuai. A gain with no pain: Exploring intelligent traffic signal control for emergeney vehicles. IREE Transactions on Intelligent Transportation Systems, 23(10):17899 17909, 2022.
[8] Songsang Koh, Bo Zhou, Hui Fang, Po Yang, Zaili Yang, Qiang Yang, Lin Guan, and Zhigang Ji. Real-time deep reinforcement learning based vehicle navigation. Applied Soft Computing, 96: 16:106694, 2020.
[9] Haoyuan Jiang, Ziyue Li, Hua Wei, Xuantang Xiong, Jingqing Ruan, Jiaming Lu, Hangyu Mao, and Rui Zhao. X-light: cross-city traffic signal control using transformer on transformer as meta multi aulti-agent reinforcement learner. In Proceedings of the Thirty- Third International Joint Conference on Artificial Intelligence, pages 94-102, 2024.
[10] James Ault and Guni Sharon. Reinforcement learning benchmarks for traffic signal control. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021.
[11] Нaoyuan Jiang. Ziyue Li, Zhishuai Li, Lei Bai, Hangyu Mao, Wolfgang Ketter, and Rui Zhao. A general scenario-agnostic reinforcement learning for traffic signal control. IEEE Trunsactions on Intelligent Transportation Systems, 25(9):1 1330-11344, 2024.
[12] R.P. Roess, F.S. Prassas, and W.R. McShane, Traffic Engineering. Pearson, 2011.
[13] Pravin Varaiya. Max pressure control of a network of signalized intersections. IFAC-PupersOnnLine, 49(mov.):19-24,20 2013.
[14] Hua Wei. Nan Xu, Huichu Zhang, Guanjie Zheng. Xinshi Zang, Chacha Chen, Weinan Zhang, Yanmin Zhu, Kai Xu, and Zhenhui Li. Colight: Learning network- cooperation for traffic signal control. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19, page 1913-1922. New York, NY, USA, 2019. Association for Computing Machinery.
[15] James Ault and Guni Sharon. Reinforcement learning benchmarks for traffic signal control. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021.
[16] Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao, Yu Wang, Alexandre Bayen, and Yi Wu. The surpris- ing effectiveness of ppo in cooperative multi-agent games. Advances in neural information processing xystems, 155:24611-26644 2022.
[17] Laura Bieker- Walz and Michael Behrisch. Modelling green waves for emergency vehicles using connected traffic data. In Melanie Weber, Laura Bieker Walz, Robert Hilbrich, and Michael Behrisch, editors, SUMO User Conference 2019, volume 62 EPIC Series Computing, pages 10-20. EasyChair, 2019.
[18] Chakkaphong Suthaputchakun and Yue Cao. Anbulance-lo-traffic light controller communications for rescue mission enhancement: A thailand use case. IEEE Communications Magazine, 57(12)991-97, 2019.
[19] Seyed, Ali, Mohamad, Dehghan, Hamed, Homaei, S., R., and Hejazi. A new traffic light controller using fuzzy logic for a full singlejunction involving emergency vehicle preemption. Journal of uncertain systems, 9()):49-1,, 2015.
[20] Mladen Miletić, Borna Kapusta, and Edouard Ivanjko. Comparison of two approaches for preemptive traffic light control. In 2018 International Symposium ELMAR, pages 57-62, 2018
[21] Borna Kapusta, Mladen MiletiC, Edouard Ivanjko, and Miroslav Vujić. Preemptive traffic light control based on vehicle tracking and queue lengths. In 2017 International Symposium ELMAR, pages 11-16, 2017.
[22] Ranjita Chowdhury, Puneet Mishra, and Hitesh D. Mathur. Performance assessment of distribution system with electric vehicle charging station and forecasted loads. In Altaf Q H. Badar, M. Nabab Alam, and Asmae Berrada, editors, Smart Grid Security and Protection, pages Singapore, 2025. Springer Nature Singapore.

---

[23] Lijuan Wang, Guoshan Zhang, Qiaoli Yang, and Tianyang Han. An adaptive traffic signal control scheme with proximal policy optimization based deep reinforcement learning for single intersection. Engineering Applications of Artificial Intelligence, 149: 10440, 2025.
[24] Hua Wei, Chacha Chen, Guanjie Zheng. Kan Wu, Vikash Gayah, Kai Xu, and Zhenhui Li. Presslight: Learning max pressure control to coordinate traffic signals in arterial network. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, page 1290-1298, New York, NY, USA, 2019. Association for Computing Machinery.
[25] Hao Miao, Ziqiao Liu, Yan Zhao, Chenjuan Guo, Bin Yang. Kai Zheng, and Christian S Jensen, Less is more: Efficient time series dataset condensation via two-fold modal matching. PVLDB, 18(2):226-25) 2024.
[26] Chenxi Liu, Hao Miao, Qianxiong Xu, Shaowen Zhou, Cheng Long, Yan Zhao, Ziyue Li, and Rui Zhao. Efficient multivariate time series forccasting via calibrated language models with privileged knowledge distillation. In 2025 IEEE 41 International Conference on Data Engineering (ICDE), pages 3165 3178, 2025.
[27] Hao Miao, Ronghui Xu, Yan Zhao, Senzhang Wang, Jianxin Wang, Philip S Yu, and Christian S Jensen. A parameter-efficien federated framework for streaming time series anomaly detection via lightweight adaptation. TMC, (01):1-14, 2025.
[28] Renzhi Wang, Senzhang Wang, Hao Yan, and Xiang Wang. Wsip: Wave superposition inspired pooling for dynamic interactions-aware trajectory prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 4685-4692, 2023.
[29] Zichen Wang, Hao Miao, Senzhang Wang, Renzhi Wang, Jianxin Wang, and Jian Zhang. C2f-tp: A coarse-to-fine denoising framework for uncertainty-aware trajectory prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 12810-12817, 2025.
[30] Zhenyu Li, Tianyi Shang, and Pengjie Xu. Multi-modal attention perception for intelligent vehicle navigation using deep reinforcement learning, IEEE Transactions on Intelligent Transportation Systems, 2025.
[31] Qize Jiang, Jingze Li, Weiwei Sun, and Baihua Zheng. Dynamic lane traffic signal control with group attention and multi-timeseen reinforcement learning. IJCAL, 2021.
[32] Tonghan Wang, Heng Dong, Victor Lesser, and Chongjie Zhang. Roma: multi-agent reinforcement learning with emergent roles. In Proceedings of the 37th International Conference on Machine Learning. pages 9876-9886, 2020.
[33] Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng. Shimon Whiteson, and Chongjie Zhang, Rode: Learning roles to decompose multi-agent tasks. In International Conference on Learning Representations.
[34] Zican Hu, Zongzhang Zhang, Huaxiong Li, Chunlin Chen, Hongyu Ding, and Zhi Wang. Attention-guided contrastive role representations for multi-agent reinforcement learning. In The Twelfth International Conference on Learning Representations.
[35] Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. Mutual information neural estimation. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference Machine Learning, volume 80 of Proceedings of Machine Learning Research, pages 531 540. PMLR, 10-15 Jul 2018
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A Related Work
A.1 Traffic Signal Pre-emption for EMVs
To accelerate the elearance of EMVs, early studics [18. 19] adopt immediate green green-phase switching upon EMVs detection. This kind of greedy control ensures low delay for EMVs but may heavily disrupt regular traffic. To mitigate this, fuzzy logic-hased strategies have been explored. These approaches interpret traffic conditions (e.g., vehicle delays, queues, congestion levels) through fuzzy sets and apply rule-based logic grounded in expert knowledge [20, 21]. Comparative studies suggest fuzzy systems achieve better trade-off's than simpler methods. Optimization-base more precisely. For instance, elastic signal preemption (ESP) [22] reformulates the problem into a quadratic program triggered periodically as EMVs report their positions. Though potentially more effective, such methods are computationally demanding and may not scale well in practice. In contrast, learning-hasee haaeg methads provide practical alternative by using trained models to approximate optimal hehaviors without the need for frequent online computation, supporting hoth EMVs efficiency and overall traffic stability.
frameworks have also been proposed to handle signal scheduling
A.2 RI-base Traffic Signal Control
The core objective of adaptive traffic signal control is to dynamically adjust phase sequences to alleviate urban congestion. Early approaches, such as Fixed Time [12] and MaxPressure [13]. rely on handcrafted heuristics. Although these strategies are computationally lightweight and interpretable. their dependence striet assumptions limits their adaptability under complex or dynamic traffic conditions. address these limitations, recent studics have reformulated traffic signal control as a reinforeement learning (RL) problem, where cach intersection acts as an autonomous agent. PPO-TSC [23] designs a PPO-based adaptive controller for single intersections, CoLight [14] introduces graph attention facilitate inter-agent cooperation, and PressLight [24] embeds max-pressure into the reward function to enhance efficiency. Building this line of work, several methods further emphasize the importance of modeling temporal and spatiotemporal dependencies to improve coordination stability and responsiveness in dynamic enviromments [25, 26, 27]. In parallel, trajectory prediction techniques [28, 29] have been integrated into traffic control frameworks to provide fine- mne-grained forecasts of vehicle movements. By anticipating future trajectories. such approaches enable proactive adjustments signal timing and routing decisions, thereby improving long-term coordination and robustness. Moreover. RL approaches incorporating attention mechanisms [30, 31] and role representations [32, 33, 34] have shown strong potential for capturing inter-agent heterogeneity and learning adaptive communication patterns. These methods enhance cooperation in complex multi-agen environments but remain orthogonal to focus on the joint coordination of emergency vehicles (EMVs) and traffic lights. Unlike existing studies, approach explicitly balances the rapid passage of EMVs with the overall efficiency of regular traffic.
B Proof of Dynamic Role Learning
To prove that the mutual information objectives in Eq. 5 and Eq. 6 serve as valid training objectives for role representation leamning we follow a formulation inspired by the Mutual Information Neural Estimation (MINE) framework [35], which provides a tractable variational lower bound on mutual information.
Given two random variables h and he representing agent's role embedding and its associated EMV- related features, respectively, their mutual information is defined
(1 (0 (o...) = Dxi(p(hh (o |l p(h (P
(16)
Using the KL divergence, we obtain the following variational lower bound:
10 (1) h (4)
(h h ( log E
PCA (p (exp(IL(( (1 A ))]
(17)
Here, Te: is leamablee critic function parameterized by w. The first expectation taken over positive pairs sampled from the joint distribution, and the second over negative pairs sampled from the product of marginals.
In practice, we estimate the bound with empirical samples {(h))) ham 1==1 yielding:
NCC (To(h))) ( (alI)
(0)()) (4)
(E)
(h
(2) (() - log
1
Σ exp
(18)
This empirical form corresponds directly to Eg. 5 in the main text. A similar derivation applies to the trajectory objective in Eq. 6.
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Experiment Details
C.1
Dataset Details
Table presents the main attributes
datasets used in our experiments, including the amount intersections, and the amount of 2-arm 4-arm intersections. evaluate the adaptability our method across diverse road structures, we conduct experiments on the Cologne8 datasct, which contains both standard four-arm intersections and irregular junctions with asymmetric lane configurations. The results confirm the robustness and general applicability of method in real-world traffic networks.
Table 3: Statistics of the Datesets.
Datasets
Country
Type
#Total Int.
#2-arm
#3-arm
#4-arm
Grid 4x4
synthetic
region
16
0
Avenue 4x4
synthetic
region
16
0
0
Cologne8
Germany
region
8
1
3
Fenglin
China
corridor
7
0
2
5
C.2 Baseline Details
To benehmark the performance our approach, we compare it against variety methods, including traditional rulc-based strategics and modern multi-agent reinforcement learning (MARL) algorithms:
Traditional Control Strategies.
Fixed-Time (FT) [12]:
Change traffic signals phase a cyclical sequence with pre-set phase durations and randomly assigned offsets, independent of real-time traffic dynamics.
MaxPressure (MP) [13]: A classical queue-awar aware approach that greedily selects the phase with highest pressure, where pressure is defined as the difference between the sum of incoming and outgoing lane queues. It widely regarded as strong rule-based traffic control baseline.
Multi-Agent Reinforcement Learning Approaches.
Utilizes Graph Attention Networks (GAT) to capture spatial dependencies across neighboring intersections. Agents incorporate nearby traffic conditions into their decision maaiing to minimize local congestion and promote coordinated actions.
CoLight [14]:
IPPO [15]: Assigns an independent PPO agent to each intersection. Although agents share the same network architecture, they trained using local observations and rewards, allowing decentralized learning.
rMAPPO [16]: Enhances IPPO by integrating global traffic information during training, enabling agents to learn more coordinated strategies across the entire network via centralized value functions.
X-Light [9]: Applies hierarchical Transformer structure to capture both local intersection dynamics and glohal decision patterns, enabling effective policy generalization to unseen traffic networks.
RECAL [7]: Applies deep reinforcement learning to dynamically manage traffic signals for emergency vehicles, ensuring rapid passage while mitigating negative impacts on vehicles from conflicting directions.
Lighi [6]: Employs decentralized reinforcement learning framework jointly optimize emergency vehicle routing and traffic signal control, dynamically updating EMV routes real time while coordinating traffic signals to reduce both EMV travel time and average travel time for non-emergency vehicles.
D Model Details
Our complete training process is shown in Algorithm 1.
E
implementtation Details
We implement all baseline methods using their official codebases and adhere to the recommended settings outlined in their respective papers ensure fair comparisons. We employ Adam optimizer learning rate of 0.0005. The hidden layer dimension is set to 64. The PPO clip parameter is fixed at 0.2, and the Generalized Advantage Estimation (GAE) lambda parameter is set to 0.95. All experiments are conducted on single NVIDIA GeForce RTX 3090 GPU with 24GB of memory.
20

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Algorithm Pseudocode for RMTC
1: Input: mg Rgg> "9 "rme Policy networks for regular vehicles, lights and emergency vehicles
2:
Viev V/tl 1oo% Value networks for regular vehicles, lights and emergency vehicles
3: fg: Heterogeneous graph-base role encoder
4:
B:: Replay buffer
5:
Ta: Time interval for updating Heterogeneous graph convolutional network for each episode do
6:
7:
Reset environment
8:
Clear interaction records <- for each timestep
9:
to T do
10:
Construct heterogeneous graph G (I, E(!) Compute role embeddings: h t fo(o(!) for agent 1.2.....7 72 do
11:
12:
13:
Sample action a;
To(-10 ))
Execute a and observe extrinsic reward (t)
14:
Compute intrinsic reward (t)
15:
initt
Combine rewards n!!)) end for
16:
17:
18:
Store the trajectory to in B end for
19:
20:
if episode mod Tu = 0 then
21:
Compute dynamic role loss foll Update role encoder fy using Crol
22:
end if
24:
Update To, V
end for
Additional Experiments
F1
Hyperparameter Analysis
We conducted hyperparameter analysis of the balance coefficient X, which controls the weighting hetween the intrinsic reward and the environment reward in our framework. As the reward magnitudes for EMVs and regular vehicles comparable, and traffic signal agents differs significantly. Therefore, we analyze the performance impact of varying for signal agents in the range of (0.001, 0.005, 0.01, 0.05, 0.1).
fix À for regular vehicles. However, the reward scale between EMVs
As shown in Figure 5, the overall performance first improves with increasing X. reaches peak at À = 0.01, and then begins to decline. This indicates that a moderate emphasis on the intrinsic objective is beneficial, while weightigg it may lead performance degradation. Based on this analysis, we set À 0.01 for traffic signal agents experiments.
Grid4x4 Temv
Grid4x4 Trev
200
200
0.001
160
160
0.005
120
120
0.01
80
80
0.05
40
40
0.1
0
Figure 5: Parameter sensitivity analysis of the balance coefficient À for signal agents on EMVs travel time and REVs travel time.
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Table Ablation study on RMTC showing episode time, inference time, and reward for different
signal settings.
Signal
Method
Episode Time (s)
Inference Time (s)
Reward
RMTC
122.12
0.0275
-0.561
signal-8
w/o GNN
101.25
0,0269
-0.780
w/o MI
106.09
0.0235
-0.665
RMTC
160.90
0,0424
-0.144
signal-16
w/o GNN
130.97
0.0396
-0.157
w/o MI
142.02
0.0422
-0.275
RMTC
239.92
0.0507
-0.487
signal-25
w/o GNN
222.61
0.0421
-0.511
w/o MI
236.00
0.0469
-1.229
F.2
Case Study
To further evaluate the role mechanism and its influence on agent behavior, we conduct case study using the Cologne8 dataset. Specifically, we visualize the trajectories of an EMV from its entry into the road network its arrival at the destination at two time steps. As shown in Figure 6, the EMV's trajectory is highlighted in Vehicles highlighted in dark blue represent those influenced by EMV, while light blue denotes all other vehicles.
The visualized trajectories reveal that vehicles close proximity to the EMV in space or along path tend to steer clear of the EMV's trajectory to prevent potential conflicts, In contrast, vehicles farther away do not exhibit yiclding behavior, indicating obvious response the EMV. This distinction illustrates that the learned role embeddings enable local agents perceive and respond EMV presence, while preserving normal behavior in unaffected areas.
Figure 6: Visualization of EMV and REV Trajectories from EMV Entry to Destination.
F.3 Efficiency Analysis
We report episode training time and inference time averaged over 100 episodes for traffic networks with 8, 16. traffic signals. shown the table below, episode time RMTC increases from 122.12: (8 signals) to 160.90s (16 signals) and 239,92 (25 signals). The inference time similarly grows from 0.0275s to 0.0424s and 0.0507s. Despite the increasing network size, the growth trend remains close to linear, demonstrating that RMTC scale efficiently with larger traffic scenarios. Removing the GNN module reduces training time by 35.7%
22

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(16-signal case), and the MI component increases training time by 13%. with minimal impact on inference. Despite the added cost, full model achieves the highest final reward among all variants, demonstrating better learning performance without hindering practical deployment.
G
Limitation
One practical limitation of approach is that it relies the presence of emergency events. The proposed coordination strategy is specifically designed respond EMVs; is only when such events occur that role-aware coordination mechanism is activated and contributes to improved traffic flow. Another limitation is the assumption that all vehicles strictly follow their assigned navigation routes. In practice, drivers may deviate from recommended paths due to personal preferences external constraints, potentially reducing the effectiveness of coordinated control strategy.
23


Reviews:
[{"review_id": "e7ReVGR5RC", "reviewer": "Reviewer_Q23t", "summary": "The paper proposes RMTC, a role-aware multi-agent RL framework that jointly controls traffic-lights, regular vehicles, and emergency vehicles (EMVs). It builds a heterogeneous temporal traffic graph, learns dynamic role embeddings driven by EMV position/trajectory, and adds a role-aware intrinsic reward. Experiments on two synthetic and two real SUMO scenarios show 4 – 26 % EMV travel-time reduction while keeping regular traffic efficient.", "questions": "- Shouldn’t there be a rule-based baseline that prioritizes EMV completely to get an idea about the shortest time for EMV?\n- How does RMTC scale up to a large-city map? How to apply this method to real-world scenarios?\n- How are the baselines compared? What factors are controlled, and how were hyperparameters tuned separately for each baseline? Please clarify your controlled experiments.\n- How to handle contentions among many simultaneous emergencies?", "limitations": "- Framework only activates when EMV is detected; benefits vanish in normal conditions.\n- Relies on idealized communication, accurate, low-latency global state; noisy detection or non-cooperative drivers may break assumptions.\n- It is hard to gauge its contribution to the RL or role-based MARL without experiments in other domains, given that this paper is evaluated in the RL track.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n- Addresses a practically important but under-studied three-party coordination problem.\n- Solid technical integration: graph message passing + mutual-information role learning + intrinsic reward.\n- Thorough and towards real-world experiments setups: four benchmarks, ablations, congestion, and multi-EMV stress tests, code released.\n- Empirical gains over strong RL baselines (rMAPPO, X-Light) on both EMV and regular travel time.\n\n**Weaknesses**\n\n- Novelty incremental: many elements (GNN, MI role learning, PPO) exist in prior MARL/traffic-RL work like [1] and its prior works; contribution is mainly a combination.\n- Unclear evaluation: \n   - It is unclear how the dataset is split into train & test to report the evaluation metrics.\n   - It is unclear how the method performs under normal scenarios\n- Scalability unclear beyond ~16 intersections; compute overhead of graph & MI terms not reported.\n- Assumes perfect sensing, V2X comms, and strict driver compliance, which may limit its real-world application.\n- Related Work:\n  - Related work lacks recent attention-based or graph-meta RL traffic control methods and possibly role-based MARL\n- Poor writing quality: \n  - several typos (“Tough”, “implementtation”)\n  - Equation (14), it is unclear whether V is value function of extrinsic reward only or extr+intr\n  - missing reference to PPO by shulman et al.,\n  - A.2 what is Rl-base → RL-based?\n\n[1] Goel, H., Omama, M., Chalaki, B., Tadiparthi, V., Pari, E. M., & Chinchali, S. (2025). R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning. arXiv preprint arXiv:2505.24265.", "was_revised": false, "final_justification": "", "rebuttal": "First and foremost, we sincerely thank you for pointing out the issues. Your suggestions are invaluable in enhancing the quality of this paper. Below is our answer to your questions.\n\n> W1. Incremental contribution and Novelty.\n\nThank you for your comment. We will clarify our key contributions in the revised version.\n\n- **New Problem Setting**: We formulate emergency traffic coordination as a multi-agent problem involving EMVs, REVs, and traffic lights—going beyond prior TSC works that only control signals.\n- **Framework Design**: Instead of combining existing techniques, we customize MI-based role learning, heterogeneous temporal graphs, and role-conditioned RL to address behavior differentiation and cross-agent interaction in emergency scenarios.\n- **EMV-aware Role Learning**: We further design three losses based on EMV states and trajectories to enable dynamic role representation, and introduce a role-aware intrinsic reward to balance global traffic efficiency with EMV priority.\n\n> W2. Unclear evaluation:\n>\n> Dataset Splitting for Training and Testing\n>\n> Method Applicability in Normal Traffic Scenarios\n\nThank you for your comment.\n\n- **Evaluation Protocol**: Our evaluation is conducted in the SUMO simulation environment. Unlike supervised learning, reinforcement learning does not rely on static training/testing splits. We evaluates performance by varying initial environment conditions. This approach is widely adopted in traffic control tasks[1,2].\n- **Degraded Setting**: In scenarios without emergency vehicles, our method naturally reduces to a basic actor-critic model without role embeddings or intrinsic rewards.\n\n> W3 & Q2. Scalability, computational overhead, and applicability to large-scale real-world scenarios\n\nThank you for your comment. We address this from the following four perspectives:\n\n- **New experiment on Larger traffic Network Size.** Following prior works[1,2,3], we conduct our main experiments on commonly used traffic networks. To further assess scalability with respect to the number of intersections, we introduce a larger 5×5 grid network comprising 25 intersections. The results show that our method consistently maintains effective performance and coordination as the network size increases.\n- **Computational Overhead**. We report episode training time and inference time averaged over 100 episodes for traffic networks with 8, 16, and 25 traffic signals. As shown in the table below, the episode time of RMTC increases from 122.12s (8 signals) to 160.90s (16 signals) and 239.92s (25 signals). The inference time similarly grows from 0.0275s to 0.0424s and 0.0507s. Despite the increasing network size, the growth trend remains close to linear, demonstrating that RMTC can scale efficiently with larger traffic scenarios.\n- **Impact of GNN and MI on Computation:** Removing the GNN module reduces training time by 35.7% (16-signal case), and the MI component increases training time by 13%, with minimal impact on inference. Despite the added cost, our full model achieves the highest final reward among all variants, demonstrating better learning performance without hindering practical deployment.\n- **Scalability to Large Real-World Scenarios**. In practice, EMV dispatch is organized by region, as emergency services such as fire stations and hospitals are distributed across districts. Our method supports region-wise training and deployment, making it well-suited for scalable application in large cities, rather than requiring training over the entire city.\n\n**Comparison on Grid 5×5**\n\n| Method | Grid 5×5 $T_{emv}$ | Grid 5×5 $T_{ve}$ |\n| ------ | ------------------ | ----------------- |\n| FT     | 551                | 628.49            |\n| mappo  | 223                | 427.24            |\n| RMTC   | 199                | 410.91            |\n\n**Efficiency experiment**\n\n|           |         | **Episode Time** (s) | **Inference Time**(s) | Reward |\n| --------- | ------- | -------------------- | ---------------------- | ------ |\n| signal-8  | RMTC    | 122.12               | 0.0275                 | -0.561 |\n|           | w/o GNN | 101.25               | 0.0269                 | -0.780 |\n|           | w/o MI  | 106.09               | 0.0235                 | -0.665 |\n| signal-16 | RMTC    | 160.90               | 0.0424                 | -0.144 |\n|           | w/o GNN | 130.97               | 0.0396                 | -0.157 |\n|           | w/o MI  | 142.02               | 0.0422                 | -0.275 |\n| signal-25 | RMTC    | 239.92               | 0.0507                 | -0.487 |\n|           | w/o GNN | 222.61               | 0.0421                 | -0.511 |\n|           | w/o MI  | 236.00               | 0.0469                 | -1.229 |\n\n\n> W4. Assumes perfect sensing, V2X comms, and strict driver compliance, which may limit its real-world application.\n\nWe appreciate the reviewer’s concern. Our framework assumes V2X-based communication, where vehicles can exchange information with traffic lights and nearby vehicles. This assumption is widely adopted in EMV coordination research. Additionally, we consider each vehicle’s destination, which allows integration with real-world navigation systems to assist both emergency and regular drivers in cooperative routing during emergencies.\n\n> W5. Related Work: Related work lacks recent attention-based or graph-meta RL traffic control methods and possibly role-based MARL\n\nThank you for your comment. We will add a discussion of recent works on attention-based or graph-meta reinforcement learning for traffic control [4,5] and role-based multi-agent reinforcement learning methods [6,7,8] to the related work section in the revised version. These methods differ from ours in both problem formulation and data modalities, and thus were not directly compared in our experiments.\n\n> Q1 & Q3. EMV Priority Baseline Design and Comparison Protocol\n\nThank you for your valuable comment.  We provide detailed clarifications as follows:\n\n- **Baseline Settings**: Our baselines consist of existing traffic signal control methods, each implemented with the same settings and hyperparameters as reported in their original works to ensure fair comparison.\n- **EMV Priority in Baselines**: All baselines incorporate a rule-based EMV priority(switching lights when EMV is detected under safe conditions), reflecting common practical strategies for emergency vehicle prioritization.\n- **Greedy EMV Baseline**: We follow the suggestion to add a rule-based baseline that switches the signal to green as soon as an EMV is detected and keeps it green until the EMV leaves. While this design minimizes EMV travel time, it significantly increases delay for regular vehicles, showing poor overall efficiency balance.\n\n| Method              | grid $T_{emv}$ | grid $T_{ve}$ | avenue $T_{emv}$ | avenue $T_{ve}$ | cologne $T_{emv}$ | cologne $T_{ve}$ | Fenglin $T_{emv}$ | Fenglin $T_{ve}$ |\n| ------------------- | -------------- | ------------- | ---------------- | --------------- | ----------------- | ---------------- | ----------------- | ---------------- |\n| Greedy EMV Baseline | 104            | 207.14        | 117              | 664.86          | 45                | 117.06           | 24                | 261.93           |\n\n> Q4. How to handle contentions among many simultaneous emergencies?\n\nThank you for the insightful question.\n\n- Our method encodes the relation between each agent and multiple EMVs using a heterogeneous graph, and learns role embeddings via mutual information maximization. This ensures that agent roles reflect varying importance to different EMVs based on their spatial positions. For example, if an agent is spatially closer to EMV A than EMV B, its role embedding will more strongly reflect EMV A’s features, guiding behavior accordingly. We further design a role-similarity-based reward redistribution mechanism, enabling soft prioritization when multiple EMVs coexist.\n\n- We tested our model in scenarios with multiple concurrent EMVs, as reported in the main paper (Table 2). The results demonstrate that our method maintains high EMV efficiency while preventing excessive delay for REVs.\n\n[1]Wei, Hua, et al. \"Colight: Learning network-level cooperation for traffic signal control.\" *Proceedings of the 28th ACM international conference on information and knowledge management*. 2019.\n\n[2]Wei, Hua, et al. \"Presslight: Learning max pressure control to coordinate traffic signals in arterial network.\" *Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining*. 2019.\n\n[3]Jiang, Haoyuan, et al. \"X-Light: cross-city traffic signal control using transformer on transformer as meta multi-agent reinforcement learner.\" *Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*. 2024.\n\n[4]Li, Zhenyu, Tianyi Shang, and Pengjie Xu. \"Multi-Modal Attention Perception for Intelligent Vehicle Navigation Using Deep Reinforcement Learning.\" *IEEE Transactions on Intelligent Transportation Systems* (2025).\n\n[5]Jiang, Qize, et al. \"Dynamic lane traffic signal control with group attention and multi-timescale reinforcement learning.\" IJCAI, 2021.\n\n[6]Wang, Tonghan, et al. \"ROMA: multi-agent reinforcement learning with emergent roles.\" *Proceedings of the 37th International Conference on Machine Learning*. 2020.\n\n[7]Wang, Tonghan, et al. \"RODE: Learning Roles to Decompose Multi-Agent Tasks.\" *International Conference on Learning Representations*.\n\n[8]Hu, Zican, et al. \"Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning.\" *The Twelfth International Conference on Learning Representations*."}, {"review_id": "eXKS2LPRUS", "reviewer": "Reviewer_HR9y", "summary": "This paper proposes a novel framework called RMTC (Role-aware Multi-agent Traffic Control) that coordinates emergency vehicles (EMVs), regular vehicles (REVs), and traffic lights for efficient traffic control during emergencies. It introduces a Heterogeneous Temporal Traffic Graph (HTTG) to represent spatial-temporal relations among traffic agents and employs a dynamic role-learning mechanism to adaptively assign behaviors. A role-aware reinforcement learning model is trained using both extrinsic and intrinsic rewards derived from EMV influence. Experiments on four datasets (synthetic and real-world) show improved EMV travel time and overall traffic efficiency compared to baselines.", "questions": "1) While the proposed role-learning loss appears intuitively plausible, it remains largely heuristic and lacks theoretical justification. In particular, the paper does not explain why maximizing mutual information between role embeddings and EMV state features would necessarily lead to better coordination or policy learning.  Can you showcase how the dynamic role influences the reinforcement learning process? 2) The paper introduces a role embedding mechanism, but lacks evidence that these embeddings are meaningful or useful. Could you please elaborate further on this point? 3)  The interaction and potential conflicts between the different loss components (e.g., role consistency vs. responsiveness to EMVs) are not analyzed or theoretically grounded. 4) While the proposed framework involves reinforcement learning as a core optimization engine, the paper lacks critical evidence of actual policy learning. There are no training curves, no sample efficiency comparisons, and no analyses of training stability, convergence, or the effects of reward shaping.", "limitations": "Yes, the authors discussed limitations in the appendix. However, it would be better to discuss some limitations in the main body.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The studied problem is interesting and important. The strengths: 1) Problem Setup: Formulates a novel multi-agent emergency traffic control problem that jointly coordinates EMVs, REVs, and traffic lights, beyond conventional traffic light-only control; 2) HTTG + Role Learning: Proposes the Heterogeneous Temporal Traffic Graph to model time-varying interactions and designs a dynamic role learning module based on mutual information maximization and role consistency; 3) Role-aware Reinforcement Learning: Combines role-conditioned observations with intrinsic and extrinsic rewards to guide traffic control policies: 4) Empirical Validation: Demonstrates clear improvements on EMV and REV travel time across multiple datasets, including ablation and stress testing under traffic load variations. Weaknesses: 1) The related work section is not comprehensive and overlooks many recent studies. 2) It’s not clear either in Figure 2 or the main content how the dynamic role influences the reinforcement learning process; 3) There is no latest traffic signal control baseline methods or methods specifically designed for traffic signal control with emergency vehicles, such as EMVLight or RECAL; 4) The paper does not report variance or error bars across different random seeds, limiting confidence in the reliability of performance gains; 5) The number of agents used in each task is not specified. If each vehicle requires an independent policy, the computational burden could be substantial. This important detail should be clearly addressed in the paper.", "was_revised": true, "final_justification": "I appreciate the authors’ efforts in preparing the thoughtful rebuttal. I have carefully reviewed the authors' rebuttal and the comments of other reviewers. The responses have addressed some of the questions, but do not fully address my concerns: 1) the effectiveness and efficiency of the RL engine, 2) the impacts of the learned embedding. These unsolved concerns are central to the effectiveness and practical significance of this work. These unresolved concerns weigh heavily in my overall assessment.  Thus, my overall recommendation for this work remains unchanged.", "rebuttal": "First and foremost, we sincerely thank you for pointing out the issues. Your suggestions are invaluable in enhancing the quality of this paper. Below is our answer to your questions.\n\n> W1. The related work section is not comprehensive and overlooks many recent studies. \n\nThank you for your helpful comment. In the revised version, we will expand the related work section to include recent advances in traffic signal control and studies specifically addressing emergency vehicle scenarios[1,2,3], which are closely related to our problem setting.\n\n> W2 & Q2 & Q1. How are dynamic role embeddings learned and how do they influence the reinforcement learning process? \n\nThank you for your comments. We address them from three perspectives:\n\n- **Role Embedding Learning:** Our approach learns compact role embeddings that focus on essential EMV-related information by maximizing the mutual information (MI) between the embeddings and the local EMV state. This ensures the roles capture task-critical semantic cues, serving as informative context variables that help the policy network concentrate on critical features. Prior works such as ROMA [4], RODE [5], and ACORM[6] demonstrate that maximizing mutual information between role embeddings and key state features improves policy learning and efficiency. This supports our use of EMV-centered mutual information to enhance role semantics and policy performance.\n\n- **Role Influence on Policy:** Dynamic roles affect RL behavior in two ways: role embeddings are combined with local states as inputs to the policy network, enabling EMV-aware decisions. Additionally, a role-conditioned intrinsic reward encourages agents to take EMV-friendly actions. Together, these mechanisms guide both behavior and coordination, improving training efficiency.\n\n- **Case Study:** Following the reviewer’s comment, we showcase how the dynamic role influences the reinforcement learning process with the following case. TL 1, one hop ahead of the EMV, has a high cosine similarity (0.49) between its role embedding and the EMV feature, which reflects the level of EMV-awareness and contextual relevance. TL 2, with no nearby EMV, shows low similarity (0.17) and changes its signal independently. This shows role embeddings capture EMV proximity and influence signal behavior accordingly.\n\n  | Agent | Relative to EMV | Cosine Sim (Role vs EMV feat) | Action                                 |\n  | ----- | --------------- | ----------------------------- | -------------------------------------- |\n  | TL 1  | EMV 1 hop ahead | 0.49                          | gGGGgrrrrgGGGgrrrr->gGGGgrrrrgGGGgrrrr |\n  | TL 2  | No EMV nearby   | 0.17                          | gGGGgrrrrgGGGgrrrr->grrrgGGGGgrrrgrrrr |\n\n*Action indicates the signal phase before and after the decision. Each character corresponds to a lane direction: 'g' denotes green and 'r' denotes red. For TL 1, the EMV enters when the signal is green, and the phase remains unchanged to allow passage.*\n\n> W3&W4. Baselines and performance stability\n\nThanks for your suggestion. To address recent concerns, we include EMV-specific baselines, and report mean ± standard deviation over 3 random seeds for our method.\n\n\n| Method   | grid4*4 $T_{emv}$ | grid4*4 $T_{ve}$ | avenue $T_{emv}$ | avenue $T_{ve}$ | cologne $T_{emv}$ | cologne $T_{ve}$ | Fenglin $T_{emv}$ | Fenglin $T_{ve}$ |\n| -------- | -------------- | ------------- | ---------------- | --------------- | ----------------- | ---------------- | ----------------- | ---------------- |\n| RECAL    | 129            | 296.39        | 162              | 757.83          | 47                | 159.93           | 38                | 276.91           |\n| EMVlight | 117            | 176.03        | 254              | 625.85          | 47                | 148.9            | 34                | 293.79           |\n| RMTC     | 108±9.64       | 162.71±0.87   | 151.33±11.67     | 508.47±18.87    | 46±8.08           | 89.02±4.84       | 30.66±3.29        | 225.61±5.72      |\n\n> W5. The number of agents used in each task is not specified. If each vehicle requires an independent policy, the computational burden could be substantial. This important detail should be clearly addressed in the paper\n\nThank you for pointing this out. \n\n- **Vehicle agent number setting.** In our framework,  each vehicle and traffic light is modeled as an agent. The number of vehicles is determined by the vehicle generation rate and episode duration. Specifically, the Grid and Cologne datasets contain approximately 600 vehicles, while the Arterial and Fenglin datasets include around 1000 vehicles per episode.\n\n- **Shared policy.** All agents share a centralized policy network (parameter sharing). This design enables agent-specific behavior while avoiding the computational burden of maintaining independent policies for each agent, ensuring scalability and learning efficiency in large-scale environments. We will include a detailed explanation of this design choice in the final version\n\n> Q3. The interaction and potential conflicts between the different loss components (e.g., role consistency vs. responsiveness to EMVs) are not analyzed or theoretically grounded\n\nThank you for the suggestion. We address the concern from two aspects:\n\n- **Temporal stability**: The temporal consistency loss is designed to ensure smooth role transitions across adjacent time steps. It does not constrain adaptation to major changes. When an EMV changes its road segment, the mutual information loss actively guides the role embedding to adapt, ensuring responsiveness. Thus, the two objectives are functionally complementary rather than conflicting.\n\n- **Experiment**: We experimented with different values of the weight $\\alpha$ in the role loss function, which is defined as follows. \n\n  $\\mathcal{L}\\_{\\text{role}} = \\sum\\_{t=1}^{T}\\left( - \\alpha \\left( I^{(t)}(h^{(t)};h^{(t)}\\_{\\text{emv}}) + I(h^{(t)}; g\\_{\\text{emv}}^{(t)}) \\right) + \\mathcal{L}\\_{\\text{cons}}^{(t)}\\right)$​\n\n  The results show that setting \\($\\alpha$ = 1\\) yields the best balance, achieving the lowest EMV travel time and vehicle travel time. This indicates a stable and effective interaction between the mutual information terms and the consistency loss.\n\n| $\\alpha $ |grid4*4 $T_{emv}$ |grid4*4 $T_{ve}$ |\n| --------- | --------- | -------- |\n| 0.5       | 139       | 194.38   |\n| 2         | 133       | 196.15   |\n| 1         | 112       | 163.62   |\n\n> Q4. While the proposed framework involves reinforcement learning as a core optimization engine, the paper lacks critical evidence of actual policy learning. There are no training curves, no sample efficiency comparisons, and no analyses of training stability, convergence, or the effects of reward shaping.\n\nThank you for the valuable comment. Below, we provide a detailed analysis of RMTC’s training stability, convergence, and sample efficiency:\n\n- The first table shows the episodic rewards of RMTC and its ablations across different training stages. RMTC consistently achieves higher rewards and faster convergence. Stability is measured by the standard deviation of evaluation rewards, where RMTC exhibits the lowest standard deviation (1.480), indicating superior stability.\n- The second table compares RMTC and MAPPO across different timesteps. RMTC achieves better performance at each stage, especially in early training (e.g., at 5k and 10k timesteps), demonstrating stronger sample efficiency and faster learning.\n\n**Reward Ablation Study on RMTC Components Across Episodes**\n\n|                                   | Episode10 | Episode20 | Episode50 | Episode100 | Stability |\n| --------------------------------- | --------- | --------- | --------- | ---------- | --------- |\n| RMTC                              | -0.509    | -0.222    | -0.139    | -0.119     | 1.480     |\n| w/o EMV Position Role Impacting   | -1.393    | -0.770    | -0.301    | -0.289     | 2.387     |\n| w/o Role Consistency Constraint   | -1.237    | -0.644    | -0.511    | -0.203     | 3.205     |\n| w/o EMV Trajectory Role Impacting | -1.152    | -0.293    | -0.185    | -0.187     | 3.147     |\n| w/o Role-aware Intrinsic Reward   | -1.198    | -0.407    | -0.232    | -0.278     | 3.099     |\n\n**Sample efficiency Comparison Between RMTC and MAPPO**  \n\n|       | Environment steps 5k | Environment steps  10k | Environment steps  25k | Environment steps  50k |\n| ----- | -------------------- | ---------------------- | ---------------------- | ---------------------- |\n| mappo | -1.739               | -0.880                 | -0.1557                | -0.149                 |\n| RMTC  | -0.891               | -0.600                 | -0.1445                | -0.127                 |\n\n[1]Yao, Jiarong, et al. \"Incorporating vision-based artificial intelligence and large language model for smart traffic light control.\" *Applied Soft Computing* (2025): 113333.\n\n[2]Wang, Lijuan, et al. \"An adaptive traffic signal control scheme with Proximal Policy Optimization based on deep reinforcement learning for a single intersection.\" *Engineering Applications of Artificial Intelligence* 149 (2025): 110440.\n\n[3]Alruwaili, Madallah, et al. \"LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations.\" *Scientific Reports* 15.1 (2025): 6011.\n\n[4]Wang, Tonghan, et al. \"ROMA: multi-agent reinforcement learning with emergent roles.\" *Proceedings of the 37th International Conference on Machine Learning*. 2020.\n\n[5]Wang, Tonghan, et al. \"RODE: Learning Roles to Decompose Multi-Agent Tasks.\" *International Conference on Learning Representations*.\n\n[6]Hu, Zican, et al. \"Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning.\" *The Twelfth International Conference on Learning Representations*."}, {"review_id": "K0OeUw3TGn", "reviewer": "Reviewer_5jVo", "summary": "The paper introduces RMTC, a role-aware multi-agent reinforcement learning framework for emergency traffic control. It dynamically assigns roles to traffic lights and vehicles using a Temporal Traffic Graph, enabling coordinated behavior based on EMV position and trajectory. The method is tested against several benchmarks demonstrating an improvement in the EMV travel time without hurting the overall traffic efficiency.", "questions": "1. Mainly about the sensitivity and scalability of the method, I am not sure how well it would scale to a real-life problem and if not what are the limitations.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Interesting framework, unifying both signal control and navigation.\n2. Results support the claim that this approach can benefit EMVs and should be explored further.\n\nWeaknesses:\n1. Limited modeling as EMVs are part of the system and treated as such, instead of allowing for individual decisions.\n2. Scalability of the method is unclear. two aspects are relevant, how the method scales with the number of intersections (that seems to scale at least to small-medium networks), and how it scales with growing number of EMVs.", "was_revised": true, "final_justification": "This is a usufull paper. I retain my score after rebuttal.", "rebuttal": "First and foremost, we sincerely thank you for pointing out the issues. Your suggestions are invaluable in enhancing the quality of this paper. Below is our answer to your questions.\n\n> W1. Limited modeling as EMVs are part of the system and treated as such, instead of allowing for individual decisions.\n\nThank you for the comment. In our framework, EMVs are modeled as independent learning agents with their own observations, policies, and rewards. This allows each EMV to make individual decisions rather than being passively controlled by the environment or rule-based logic. \n\n> W2 & Q1. Scalability and Real-World Applicability\n\nThank you for your comment. We address this from the following four perspectives:\n\n- **New experiment on Larger traffic Network Size.** Following prior works[1,2,3], we conduct our main experiments on commonly used traffic networks. To further assess scalability with respect to the number of intersections, we introduce a larger 5×5 grid network comprising 25 intersections. The results show that our method consistently maintains effective performance and coordination as the network size increases.\n- **Computational Overhead**. We report episode training time and inference time averaged over 100 episodes for traffic networks with 8, 16, and 25 traffic signals. As shown in the table below, the episode time of RMTC increases from 122.12s (8 signals) to 160.90s (16 signals) and 239.92s (25 signals). The inference time similarly grows from 0.0275s to 0.0424s and 0.0507s. Despite the increasing network size, the growth trend remains close to linear, demonstrating that RMTC can scale efficiently with larger traffic scenarios.\n- **Impact of GNN and MI on Computation:** Removing the GNN module reduces training time by 35.7% (16-signal case), and the MI component increases training time by 13%, with minimal impact on inference. Despite the added cost, our full model achieves the highest final reward among all variants, demonstrating better learning performance without hindering practical deployment.\n- **Scalability to Large Real-World Scenarios**. In practice, EMV dispatch is organized by region, as emergency services such as fire stations and hospitals are distributed across districts. Our method supports region-wise training and deployment, making it well-suited for scalable application in large cities, rather than requiring training over the entire city.\n\n**Comparison on Grid 5×5**\n\n| Method | Grid 5×5 $T_{emv}$ | Grid 5×5 $T_{ve}$ |\n| ------ | ------------------ | ----------------- |\n| FT     | 551                | 628.49            |\n| mappo  | 223                | 427.24            |\n| RMTC   | 199                | 410.91            |\n\n**Efficiency experiment**\n\n|           |         | **Episode Time** (s) | **Inference Time**(s) | Reward |\n| --------- | ------- | -------------------- | ---------------------- | ------ |\n| signal-8  | RMTC    | 122.12               | 0.0275                 | -0.561 |\n|           | w/o GNN | 101.25               | 0.0269                 | -0.780 |\n|           | w/o MI  | 106.09               | 0.0235                 | -0.665 |\n| signal-16 | RMTC    | 160.90               | 0.0424                 | -0.144 |\n|           | w/o GNN | 130.97               | 0.0396                 | -0.157 |\n|           | w/o MI  | 142.02               | 0.0422                 | -0.275 |\n| signal-25 | RMTC    | 239.92               | 0.0507                 | -0.487 |\n|           | w/o GNN | 222.61               | 0.0421                 | -0.511 |\n|           | w/o MI  | 236.00               | 0.0469                 | -1.229 |\n\n[1]Jiang, Haoyuan, et al. \"X-Light: cross-city traffic signal control using transformer on transformer as meta multi-agent reinforcement learner.\" *Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*. 2024.\n\n[2]Wei, Hua, et al. \"Colight: Learning network-level cooperation for traffic signal control.\" *Proceedings of the 28th ACM international conference on information and knowledge management*. 2019.\n\n[3]Wei, Hua, et al. \"Presslight: Learning max pressure control to coordinate traffic signals in arterial network.\" *Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining*. 2019."}, {"review_id": "Xh191Ty53q", "reviewer": "Reviewer_2Wmh", "summary": "This paper proposes Role-aware Multi-agent Traffic Control (RMTC), a novel framework designed to enhance traffic coordination in scenarios involving emergency vehicles. RMTC introduces a Heterogeneous Temporal Traffic Graph (HTTG) to comprehensively capture the spatial-temporal interactions among traffic lights, regular vehicles, and emergency vehicles. Building on this representation, the framework incorporates a Dynamic Role Learning module that adaptively infers the roles of different traffic agents based on evolving traffic conditions. These roles are then leveraged in a Role-aware Multi-agent Reinforcement Learning algorithm to guide decision-making and coordination. Through extensive evaluations on four benchmark scenarios, RMTC demonstrates superior performance in reducing emergency vehicle delays while maintaining overall traffic efficiency, highlighting the benefits of incorporating dynamic roles into multi-agent traffic control.", "questions": "Q1. Could the authors explain how role features are used to produce distinct behaviors across roles, and how this is reflected in the policy learning process?\n\nQ2. Please provide more detailed descriptions for the training of the Dynamic Role Learning module. This would help readers better understand and reproduce the approach.\n\nQ3. How does the framework handle scenarios with multiple emergency vehicles? Specifically, how is the intrinsic reward in Eq.11 extended or adapted to such cases? Clarification on this point would strengthen the technical rigor of the paper.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths: \nS1. The paper is generally well-organized, with a logical flow of methodology. The equations and definitions are formal and clear.\n\nS2. The authors explicitly formulate the coordination of emergency vehicles, regular vehicles, and traffic lights as a joint multi-agent decision-making problem, which is a relatively new perspective in traffic control and provides new insights for related research.\n\nS3. The proposed integration of a heterogeneous temporal traffic graph and role-aware reinforcement learning is reasonable and effective. The experiments are sufficient, the settings and data preparation are well-illustrated, and the results are well-discussed.\n\nWeaknesses: \nW1. The paper introduces role-aware reinforcement learning, but it is unclear how different roles actually lead to different behavior patterns in agents.\n\nW2. The multi-agent modeling does not provide enough details, especially about how the Dynamic Role Learning module is trained, making it hard to fully understand or reproduce.\n\nW3. Some formulas in the manuscript are incomplete or simplified. For example, Eq.11 assumes only one EMV in the scenario, but the paper does not clarify how the intrinsic reward would be computed when multiple EMVs are present.", "was_revised": false, "final_justification": "", "rebuttal": "First and foremost, we sincerely thank you for pointing out the issues. Your suggestions are invaluable in enhancing the quality of this paper. Below is our answer to your questions.\n\n> W1&Q1.  The paper introduces role-aware reinforcement learning, but it is unclear how different roles actually lead to different behavior patterns in agents.\n\nThank you for your comments. We address them from two perspectives:\n\n- **Role Influence on Policy:** Dynamic roles affect RL behavior in two ways: role embeddings are combined with local states as inputs to the policy network, enabling EMV-aware decisions. Additionally, a role-conditioned intrinsic reward encourages agents to take EMV-friendly actions. Together, these mechanisms guide both behavior and coordination, improving training efficiency.\n- **Case Study:** Following the reviewer’s comment, we showcase how the dynamic role influences the reinforcement learning process with the following case. TL 1, one hop ahead of the EMV, has a high cosine similarity (0.49) to the EMV feature and maintains its green phase to facilitate passage. TL 2, with no nearby EMV, shows low similarity (0.17) and changes its signal independently. This shows role embeddings capture EMV proximity and influence signal behavior accordingly.\n\n| Agent | Relative to EMV | Cosine Sim (Role vs EMV feat) | Action                                 |\n| ----- | --------------- | ----------------------------- | -------------------------------------- |\n| TL 1  | EMV 1 hop ahead | 0.49                          | gGGGgrrrrgGGGgrrrr->gGGGgrrrrgGGGgrrrr |\n| TL 2  | No EMV nearby   | 0.17                          | gGGGgrrrrgGGGgrrrr->grrrgGGGGgrrrgrrrr |\n\n*Action indicates the signal phase before and after the decision. Each character corresponds to a lane direction: 'g' denotes green and 'r' denotes red. For TL 1, the EMV enters when the signal is green, and the phase remains unchanged to allow passage.*\n\n> W2&Q2. The multi-agent modeling does not provide enough details, especially about how the Dynamic Role Learning module is trained, making it hard to fully understand or reproduce.\n\nThank you for your comments.\n\n- **Role Embedding Learning:** We first train the Heterogeneous Role Convolution module using the role learning objective in Eq.9. This module captures spatial-temporal interactions in the heterogeneous traffic graph and outputs dynamic role embeddings for each agent. These embeddings are learned to reflect agent role, especially in relation to nearby EMVs.\n\n- **Role-aware Policy Learning:** The learned role embeddings are then concatenated with the agent’s state to form role-aware observations. These enhanced inputs are fed into the policy networks, allowing each agent to condition its decisions on both local traffic state and semantic role information. Role learning and policy optimization proceed in an alternating training loop to ensure consistency and coordination.\n\n> W3&Q3. Some formulas in the manuscript are incomplete or simplified. For example, Eq.11 assumes only one EMV in the scenario, but the paper does not clarify how the intrinsic reward would be computed when multiple EMVs are present.\n\nThank you for your valuable suggestion. Our method encodes the relations between each agent and multiple EMVs through a heterogeneous graph, learning role embeddings via mutual information maximization. This design ensures that agent roles dynamically reflect their varying relevance to different EMVs based on spatial positions. Additionally, we propose a role-similarity-based reward redistribution mechanism to enable soft prioritization when multiple EMVs coexist. We have revised Eq.11 to the following form to support multi-EMV scenarios:\n\n$r\\_{n,\\text{intr}}^{(t)} = \\sum\\_{i=1}^{N_{emv}} s\\left(h\\_n^{(t)}, h\\_{\\text{emv},m}^{(t)}\\right) \\cdot r\\_{\\text{emv},i,\\text{extr}}^{(t)}, \\quad \\forall n \\in \\{ I_{\\text{rev}}, I_{\\text{tl}} \\}$\n\nWe will update Eq.11 and the corresponding explanations in the manuscript to clearly present this multi-EMV reward computation."}]

Author rebuttal:

Meta-review: This paper studies the challenge of emergency traffic control and the limitations of existing models. The authors propose the RMTC framework, which uses HTTG, dynamic role learning, and role-aware multi-agent RL to coordinate traffic components. The authors and reviewers had thorough rebuttal discussions, and finally, the reviewers had not converged to accept or reject this paper. The score is right on the borderline. After reading the paper & discussions, I personally think this paper is acceptable for publication in NeurIPS this year.
Decision: Accept (poster)