DynLight: Realize dynamic phase duration with multi-level traffic signal control



Introduction
============

Signalized intersections are one of the commonest types in urban environments, and traffic signal control (TSC) plays an important role in urban traffic management. Nowadays, using reinforcement learning (RL) @rl for traffic signal control is increasingly popular. RL-based methods can learn directly through trail-and-errors without the strict assumptions in traditional methods. MPLight @mplight and CoLight @colight have demonstrated superior performance and capacity for large-scale TSC. Efficient-XLight @efficient and QL-XLight @ql adopt more effective state representations to further improve the control performance. AttendLight @attend develops a universal model to handle different topologies of intersections. RL-based methods have become a promising solution for adapting traffic signal control.

Considering the control logic, most RL-based TSC methods (such as MPLight @mplight, CoLight @colight, Efficient-XLight @efficient, and AttendLight @attend) use fixed action duration and select the green phase for the next state. Such a control logic is the same as in most games (such as Atari games @atari), which are designed for people to play with. Under this control logic, the duration of each phase cannot adapt to dynamic traffic conditions, and the capability of RL methods is strongly limited. Moreover, Efficient-XLight @efficient and QL-Light @ql have demonstrated that the action duration can significantly influence the model performance.

In several studies, the RL agent can change the duration of green lights to adapt to the changing traffic flow @liang [@prglight; @ipdalight], but they are not flexible enough as expected. In @liang, the duration of one and only one phase in the next cycle is the current duration added or subtracted by 5 seconds. In the multi-intersection scenario, PRGLight @prglight and IPDALight @ipdalight change the phase duration for all the intersections rather than set different phase duration for each intersection. Realizing dynamic phase duration according to real-time traffic for different intersections is needed.

Although RL-based TSC methods develop rapidly, most have limited capacity for deployment in the real world. A major obstacle to implementation in practice is the non-cyclical phase actuation, which can actuate any phase in any sequence. Specifically, drivers prefer a cyclical phase structure for traffic signals. This arbitrary phase selection can be confusing for travelers expecting regular signal patterns and is therefore unacceptable for some city traffic engineers. Although a non-cyclical phase selection may improve throughput, its limitations include potentially unbounded waiting times and the appearance of phases being "skipped" for waiting drivers. Also, some intersections require additional conditional phase orders such as go-straight must follows turn left with the existence of a waiting area @leftturn2. Few RL methods support cyclical phase structure.

Considering the evaluation metric, average travel time and throughput are the most used to evaluate TSC performance @colight [@survey]. However, average travel time and throughput are not fair. The throughput is also influenced by the vehicle travel pattern except for the control methods. For example, if few vehicles enter the traffic network in the final period and cannot pass through, then the throughput may all be the same under different methods. The average travel time is influenced by the throughput because it calculates the average travel time of throughput. However, the throughput is different under different control methods, and the calculation of average travel time is not fair. In this way, average travel time and throughput can evaluate which method is better, but cannot explain one method "how" better than another.

In summary, there are still some challenges for TSC:

-   Most RL methods use fixed action duration and the phase duration cannot adapt to changing traffic flow, which is not dynamic and flexible.

-   Most RL methods have limited capacity to be deployed in the real world when the phase order is determined in a cyclical order. In addition, most RL also cannot be modified for a cyclical phase structure.

-   Average travel time and throughput are not fair metrics to evaluate model performance, a true fair metric is needed.

To address these challenges, we develop a multi-level traffic signal control framework DynLight that determines the phase in the first stage and determine the corresponding phase duration in the last stage. With DynLight well trained, the phase selection policy can be replaced to realize cyclical phase actuation, and further be capable of real-world deployment. Finally, we rethink average travel time and throughput and propose adjusted average travel time that extends the testing time to ensure all the vehicles can pass through. All the methods in this article use adjusted average travel time for comparison.

Related work
============

Traditional methods for TSC
---------------------------

Traditional methods heavily rely on hand-crafted traffic signal plans or rules. FixedTime @fixedtime control requires pre-timed cycle length, fixed phase sequence, and phase split. Actuated control @sotl2013 triggers traffic signals according to pre-defined rules and real-time traffic data. Adaptive control @scats2 [@scoot2] decides a set of traffic plans and chooses one that is optimal for the current traffic situation based on traffic volume from loop sensors.

Optimization based control @mp2013 formulate traffic signal control as an optimization problem under a particular traffic model and determines the traffic signal according to observed traffic data. Recently, some optimization based methods such as MaxPressure @mp2013, Efficient-MaxPressure @efficient, and Max-QueueLength @ql support multi-intersection TSC and have demonstrated superior performance than RL methods (such as MPLight @mplight and FRAP @frap).

RL based methods
----------------

RL methods improve the TSC performance mainly in three ways: (1) design an effective state and reward; (2) develop a new network structure; (3) introduce advanced RL techniques. LIT @LIT introduces a very simple state and reward design and gets significant performance improvement from IntelliLight @intellilight. PressLight @presslight introduces pressure into state and reward design and gets significant improvement from LIT and IntelliLight. Efficient-XLight @efficient rethinks the calculation of pressure and introduce efficient pressure as an effective state representation, improving the control performance of MPLight @mplight and CoLight @colight. QL-XLight @ql rethinks queue length and uses it both as state and reward, getting improvement from CoLight @colight. FRAP @frap develops a special network structure to construct phase features and capture phase competition relations. CoLight @colight adopts a graph attention network @gats to realize intersection cooperation. AttendLight @attend adopts the attention network to handle the different topologies of intersections. DemoLight @demolight introduces imitation learning @imitation, HiLight @hilight adopts hierarchical RL @hierarchical, and MetaLight @metalight introduces meta-learning @meta.

Dynamic phase duration and cycle traffic control
------------------------------------------------

Most RL methods use fixed action duration (denoted as $t_{duration}$) and select the green phase for the next state. Under this circumstance, the duration of each phase can only be multiple of the fixed $t_{duration}$ and can't realize dynamic duration. Several studies can realize dynamic traffic light duration but are not adaptive and flexible enough. Liang et al. @liang develop a model that can change the duration of a traffic light in a cycle control, but the duration of one and only one phase in the next cycle is the current duration added or subtracted by 5 seconds. IPDALight @ipdalight dynamically adjusts the phase duration according to the real-time traffic condition. PRGLight @prglight uses dynamic phase duration according to real-time traffic state and predicted traffic state. However, both IPDALight and PRGLight apply the same duration for all the intersections in the multi-intersection scenario which ignores the difference in intersections and lacks flexiblility. Moreover, the reported performance of IPDALight and PRGLight cannot outperform AttentionLight @ql. There are several studies that concentrate on cycle traffic signal control in the traditional TSC field @mp2015 [@mp2020]. However, in RL based TSC field, few studies concentrated on cycle traffic control @liang due to the performance limitation.

Preliminary
===========

In this paper, we consider multi-intersection TSC, in which each intersection is controlled by an RL agent. Agent $i$ views the environment as its observation and takes an action to control the signal of intersection $i$. The goal of the agent $i$ is to take an optimal action to maximize its cumulative reward. To illustrate the definitions clearly, we use the intersection of four approaches as an example.

![Illustration of a traffic network.<span data-label="fig:inter"></span>](fig/inter_2.png){width="1\linewidth"}

#### Traffic network

The traffic network is described as a directed graph in which nodes represent intersections and edges represent roads. Each road consists of several lanes, which are the basic unit of vehicle movement and determines the way each vehicle passes through an intersection, such as turning left, going straight, and turning right. An incoming lane is where vehicles enter the intersection, and an outgoing lane is where vehicles leave the intersection. We denote the set of incoming lanes and outgoing lanes of intersection $i$ as $\mathcal{L}_i^{in}$ and $\mathcal{L}_i^{in}$ respectively.

#### Traffic movements

Each traffic movement is defined as traffic traveling across an intersection towards a certain direction, i.e., left, straight, and right. According to the traffic rules of some countries, vehicles that turn right can pass regardless of the signal but must stop at a red light. As shown in Figure \[fig:inter\], each intersection has twelve traffic movements and eight of which are used to construct signal phases.

#### Signal phase

Each signal phase is a set of permitted traffic movements, denoted by $d$, and $\mathcal{D}_i$ denotes the set of all the phases at intersection $i$. As shown in Figure \[fig:inter\], twelve traffic movements can be organized into four phases(d) or eights phases (e). We use a four-tuple $<A, B< C, D>$ to denote 4-phase, and a eight-tuple $<A, B< C, D, E, F, G, H>$ to denote 8-phase.

#### Phase duration

The duration of each phase is the time that the phase spends in the green signal, denoted by $t_{duration}$. It also represents the action duration of some RL agents.

#### State representation

State representations of each intersection are lane-based, such as number of vehicles(denoted as $x(l), l\in\mathcal{L}_i^{in}$) and queue length(denoted as $q(l), l\in\mathcal{L}_i^{in}$). Traffic movement pressure is the difference of average queue length between upstream and downstream, which is calculated by above-mentioned queue length.

  Notation                Meaning
  ----------------------- -------------------------------------------
  $\mathcal{L}_i^{in}$    set of incoming lanes of intersection $i$
  $\mathcal{L}_i^{out}$   set of exiting lanes of intersection $i$
  $l,m,k$                 lanes
  $d$                     a signal phase
  $\mathcal{D}_i$         set of all phases at intersection $i$
  $x(l)$                  number of vehicles on lane $l$
  $q(l)$                  queue length on lane $l$

  : Summary of notation<span data-label="tab:notation"></span>

Method
======

In this section, we first propose a multi-level traffic signal control framework DynLight which divides the TSC into two levels: phase selection and duration selection. The state, action, reward, and network design of DynLight are further described in detail. Next, we develop a model with a cyclical phase structure that is suitable for deployment based on DynLight. Finally, we develop the adjusted average time as a fair evaluation metric.

Multi-level traffic signal control
----------------------------------

We divided the progress of traffic signal control into two levels: phase selection and duration selection. Phase selection and duration selection are also called phase action and duration action of each agent. In the stage of phase selection, a phase $\hat{d}$ is determined to be actuated according to the traffic condition. In the stage of duration selection, a phase duration $\hat{t}$ is further determined for phase $\hat{t}_{duration}$ according to the traffic condition. We refer to the multi-level traffic signal control framework as DynLight. The DynLight is formally summarized in Algorithm \[alg:dyn\].

\[htb\]

**Parameters**: Intersection number $n$; current phase at intersection $i$ $\hat{d}^i$; duration of phase $\hat{d}^i$ $\hat{t}^i_{duration}$;current phase time at intersection $i$ $hat{t}^i$.

\[1\] Select phase $\hat{d}_i$ for intersection $i$; Select duration $\hat{t}_{duration}^i$ for phase $\hat{d}_i$; $t^i=t^i+1$ Select phase $\hat{d}_i$ for intersection $i$; Select duration $\hat{t}_{duration}^i$ for phase $\hat{d}_i$; $t^i=0$

Network design of DynLight
--------------------------

DynLight consists of two complete control approaches: phase selection and duration selection. The phase selection is realized with an optimization method: Max-QueueLenght (M-QL) @ql, which selects the phase with the maximum queue length. The duration selection is realized with a deep Q-network @DQN2015 which outputs the Q-values of each duration action. Before designing the network of DynLight, we first fully describe the state, action, and reward.

-   **State**. The queue length on each entering lane is used for M-QL to determine the phase. The number of vehicles on each segment of incoming lanes $x(l)_k, k=1,2,3,4$ is used for duration selection. In this paper, each lane is divided into 100-meter long segments, and we denote the segment on lane $l$ nearest to the intersection as the first segment $x(l)_1$.

-   **Action**. At time $t$, each agent first chooses a phase $\hat{d}$ as its phase action from action set $A$, indicating the traffic signal should be set to phase $\hat{d}$. Next, each agent chooses a duration $\hat{t}$ as the duration of $\hat{d}$, indicating the traffic signal of phase $\hat{d}$ will last $\hat{t}$. In this paper, each agent has four permissible phase actions, correspondingly four phases in Figure \[fig:inter\], and each agent has seven permissible duration actions, denoted as $\{10, 15, 20, 25, 30, 35, 40\}$.

-   **Reward**. Negative intersection queue length is used as the reward. The reward for the agent that is controlling intersection $i$ is denoted by: $$r_i = -\sum q(l), l\in \mathcal{L}_i^{in}$$ in which $q(l)$ is the queue length at lane $l$. Intuitively, by maximizing the reward, the agent is trying to minimize the average travel time in the system. In addition, M-QL can optimize the reward.

### Network design

The network of DynLight mainly consists of three stages (see Figure \[fig:dyn\] ):

-   Phase feature selection. After the phase $\hat{d}$ is determined by M-QL, the feature of phase $\hat{d}$ will be extracted from the feature of all the phases according to the phase composition(see Figure \[fig:inter\] (c) and (d)).

-   Feature fusion. The features of phase $\hat{d}$ are embedded and fused. In this paper, we use addition to fuse the features.

-   Predict Q-values. The feature of phase $\hat{d}$ is further embedded to get Q-values. Dueling block is used to accelerate learning efficiency.

The illustration of DynLight is shown in Figure \[fig:dyn\].

![Network design of DynLight.<span data-label="fig:dyn"></span>](fig/network.png){width="1\linewidth"}

Cyclical phase selection
------------------------

In real-world deployment, the phase is usually required in a fixed cyclical order. To describe the cyclical phase selection, we define fixed cyclical control.

**Definition 1** (Fixed Cyclical control). A policy that cyclically actuates a set of phases in order.

Due to the property of DynLight that determines the phase and duration at a different level, we can replace the M-QL with a fixed cyclical control policy that actuates the phase with a fixed cyclical order. Because the fixed phase order cannot support the optimization of reward, we cannot directly learn the RL model with the fixed cyclical phase order. Under fixed phase order, the duration action is required and highly influences the control performance.

To solve this problem, we first well train DynLight, next replacing M-QL with fixed cyclical control. We refer to DynLight with cyclical phase selection as DynLight-C.

Adjusted average travel time
----------------------------

With the problem of average travel time and throughput, we propose adjusted average travel time that extends the testing time of each episode to enable all the vehicles can pass through the traffic network. In this way, with the same throughput, the adjusted average travel time can fully evaluate the model performance.

Experiments
===========

We conduct comprehensive experiments on CityFlow[^2] which is open-source and supports large-scale traffic signal control @cityflow. CityFlow has been wildly used by multiple RL methods such as MPLight @mplight, CoLight @colight, and HiLight @hilight. The simulator provides the environments observations to the TSC methods and executes the actions from the control methods.

Datasets
--------

Each traffic dataset consists of two parts: traffic network dataset and traffic flow dataset. The traffic network dataset describes the lanes, roads, intersections, and signal phases. The traffic flow dataset describes how vehicles travel across the network, denoted as $(t,u)$, in which $t$ is the time when the vehicle enters the traffic network, and $u$ is the pre-defined route from its original location to its destination. After the traffic data is fed into the simulator, each vehicle starts moving according to the pre-defined route $u$ at time $t$.

We use two groups (JiNan and HangZhou) of datasets consisting of seven(three from JiNan, two from HangZhou) real-world traffic flow datasets from China. The average arrival rate of the five datasets are different from each other as shown in Table \[tab:dataset\].

-   **JiNan datasets** The traffic network has 12($3\times4$) intersections. Each intersection is four-way, with two 400-meter road segments (East-West) and two 800-meter road segments (South-North).

-   **HangZhou datasets** The traffic network has 16($4\times4$) intersections. Each intersection is four-way, with two 800-meter road segments (East-West) and two 600-meter road segments (South-North).

  Dataset             Arrival rate(vehicles/s)
  ------------------ --------------------------
  $D_{JiNan_1}$                 1.75
  $D_{JiNan_2}$                 1.21
  $D_{JiNan_3}$                 1.53
  $D_{HangZhou_1}$              0.83
  $D_{HangZhou_2}$              1.94

  : Average arrival rate of the two datasets.<span data-label="tab:dataset"></span>

Evaluation metric
-----------------

Instead of using average travel time and throughput as evaluation metrics in most studies @frap [@colight; @mplight], we adopt adjusted average travel time in this article which is proposed to evaluate model performance.

\[htb\]

  ------------------- ---------- ---------- ---------- ---------- ----------
                                                                  
                          1          2          3          1          2
  FixeTime             $585.76$   $411.63$   $465.64$   $611.65$   $834.99$
  M-QL                 $288.84$   $248.36$   $254.74$   $300.54$   $468.28$
  Efficient-MP         $286.37$   $247.75$   $252.47$   $299.73$   $459.81$
  FRAP                 $331.97$   $272.23$   $282.67$   $326.12$   $524.32$
  CoLight              $283.14$   $257.44$   $261.23$   $311.54$   $477.77$
  Efficient-MPLight    $279.42$   $250.59$   $253.53$   $301.26$   $417.60$
  AttentionLight       $270.14$   $247.16$   $250.61$   $300.22$   $417.62$
  DynLight             $255.16$   $237.44$   $238.82$   $286.69$   $399.04$
  DynLight-C           $293.62$   $258.06$   $269.12$   $343.99$   $465.05$
  ------------------- ---------- ---------- ---------- ---------- ----------

\[htb\] ![image](fig/overall_2.png){width="1\linewidth"}

Compared methods
----------------

We compare our proposed methods with the following baseline methods, including traditional and RL TSC methods. For a fair comparison, the phase number is set as four, the action interval is set as 15-second for all the baseline methods. Each episode is a 60-minutes simulation, and we adopt one result as the average of the last ten episodes of testing. Each reported result is the average of three independent experiments.

**Traditional Methods**

-   **FixedTime** @fixedtime: a policy uses a fixed cycle length with pre-defined phase split among all the phases.

-   **Max-QueueLength** @ql: a policy selects the phase with maximum queue length.

-   **Efficient-MaxPressure** @efficient: a policy selects the phase with maximum efficient pressure.

**RL Methods**

-   **FRAP** @frap: uses a modified network to construct phase features and capture phase competition relations among all the traffic movements.

-   **CoLight** @colight: uses a graph attention network @gats to realize intersection cooperation and has shown superior performance in large-scale TSC.

-   **Efficient-MPLight** @efficient: uses FRAP @frap as the base model and introduces efficient pressure as an effective state representation. It has shown superior performance than FRAP @frap and MPLight @mplight.

-   **AttentionLight** @ql: uses self-attention @attention to learn phase correlation and adopts queue length as the state and reward.

**Our Proposed Methods**

-   **DynLight**: a multi-level traffic signal control method that uses M-QL for phase selection and a deep RL network for duration selection.

-   **DynLight-C**: with well pre-trained DynLight, the M-QL is replaced with a fixed cyclical control policy to realize cyclical phase structure for real-world deployment.

Overall performance
-------------------

Table \[tab:overall\] and Figure \[fig:overall\] demonstrate the overall performance under JiNan and HangZhou real-world datasets with respect to the adjusted average travel time.

DynLight consistently outperforms all other previous methods among JiNan and HangZhou real-world datasets. The performance difference is significant. DynLight achieves a new state-of-the-art performance for traffic signal control.

Cyclical phase structure significantly influences the model performance. The comparison of DynLight and DynLight-C indicates that phase selection is essential for traffic signal control. In addition, DynLight-C demonstrates high performance and is a promising solution for traffic signal control.

The performance of DynLight and DynLight-C demonstrates that the network of DynLight can well learn a policy to determine the phase duration. The importance of phase duration is further emphasized for traffic signal control.

Action study
------------

How DynLight performs under different phase action sets and duration action sets is further studied. With the phase action set as 4-phase, the duration action sets are configured with different range and resolution:

-   Config1. The duration actions are configured as $set\{10, 20, 30, 40\}$.

-   Config2. The duration actions are configured as $set\{10, 15, 20, 25, 30, 35, 40\}$.

-   Config3. The duration actions are configured as $set\{10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40\}$.

-   Config4. The duration actions are configured as $set\{10, 15, 20\}$.

-   Config5. The duration actions are configured as $set\{10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60\}$.

Experiments are conducted on JiNan and HangZhou datasets, and Figure \[fig:daction\] demonstrates the performance of DynLight under different duration action sets. The configuration of duration action significantly influences the performance of DynLight. We select $set\{10, 15, 20, 25, 30, 35, 40\}$ as the default duration action set.

![Model performance under different duration action sets.<span data-label="fig:daction"></span>](fig/dactions_0.png){width="1\linewidth"}

With the duration action as $set\{10, 15, 20, 25, 30, 35, 40\}$, the phase action sets are configured as 4-phase and 8-phase. Figure \[fig:paction\] demonstrates the model performance under 4-phase and 8-phase. DynLight has better performance under 8-phase than 4-phase. For fair comparison in baseline, the default phase action of DynLight is set as 4-phase.

![Model performance under different phase action sets.<span data-label="fig:paction"></span>](fig/pactions_0.png){width="1\linewidth"}

State representation study
--------------------------

The performance of DynLight under different state representations is further studied. We choose some typically used state representations for comparison:

-   Number of vehicles (NV): total number of vehicles on each entering lanes of the intersection.

-   Traffic movement pressure (TMP): we adopt the traffic movement pressure from Efficient-MPLight @efficient which is the difference of average queue length between upstream and downstream.

-   Queue length (QL): queuing vehicle number on each entering lanes of the intersection.

-   Number of vehicles under segmented roads (NV-S): the number of vehicles on each segment of incoming lanes $x(l)_k$, $k=1,2,3,4$, under which each road is divided into 100-meter long segments, and we denote the segment on lane $l$ nearest to the intersection as the first segment $x(l)_1$.

Figure \[fig:state\] demonstrates the model performance under four state representations. DynLight performs best under NV-S. Under the framework of DynLight, the model performs better under NV than TMP and QL although TMP and QL have been considered more effective in Efficient-XLight @efficient and QL-XLight @ql. In addition, even under NV, DynLight consistently outperforms previous methods under JiNan and HangZhou datasets. NV-S is more effective than NV under DynLight to determine an optimal phase duration. We choose NV-S as one of the default state representations.

![Model performance under different state representations.<span data-label="fig:state"></span>](fig/states_0.png){width="1\linewidth"}

Phase control study
-------------------

As an optimization method, Efficient-MP can also be used by DynLight to determine the phase. When Efficient-MP is used, the reward is set as pressure to be consistent with Efficient-MP. With only change phase selection policy and reward, Figure \[fig:trad\] demonstrates the model performance under JiNan and HangZhou real-world datasets. DynLight has better performance under M-QL than Efficient-MP. We finally choose M-QL as the default phase selection policy for DynLight.

![DynLight performance under different phase selection policies.<span data-label="fig:trad"></span>](fig/trad_0.png){width="1\linewidth"}

Model generalization
--------------------

Model generalization is an essential property of RL models. An idea RL model should be resilient to different traffic conditions after training in a traffic situation. In addition, the over-fitting problem is addressed by model generalization.

We train DynLight on each dataset and transfer them to other datasets. Same to the training process of baseline models, each transfer result reports the average of three independent experiments, and each experiment shows the average result of the final 10 episodes. Experiments are conducted under JiNan and HangZhou real-world datasets. The transferability of each mode that trained on dataset $i$ and transferred on dataset $j$ is calculated as follows: $$E_i^j = \frac{t_{transfer}^{j}}{t_{train}^{i}} -1$$ in which $t_{train}^i$ represents the training result (average travel time) on datasets $i$, $t_{transfer}^j$ represents the transfer result on dataset $j$.

Figure \[fig:transfer\] demonstrates the transferability of DynLight. DynLight can learn better at JiNan datasets than HangZhou datasets, and they all show high transferability.

![The transfer performance comparison, the smaller the better.<span data-label="fig:transfer"></span>](fig/transfer_0.png){width="1\linewidth"}

Model deployment
----------------

DynLight and DynLight-C fully consider deployment issues. If the phase is allowed in arbitrary order, DynLight shows state-of-the-art performance and high transferability. If the phases are required in cyclical order, DynLight-C supports cyclical phase structure and demonstrates superior performance than FRAP @frap.

The demonstrated performance of DynLight and DynLight-C indicates they are promising solutions for real-world traffic signal control. The comparison of DynLight-C and FixedTime further addresses the importance of dynamic duration. As shown in Figure \[fig:deploy\], DynLight-C significantly outperforms FixedTime among JiNan and HangZhou datasets.

![Performance comparison of FixedTime and DynLight-C. <span data-label="fig:deploy"></span>](fig/deploy_0.png){width="1\linewidth"}

Conclusion
==========

In this paper, we rethink the logic of traffic signal control and propose multi-level traffic signal control which first select phase next select duration. The phase selection is realized by an optimization method: M-QL and the duration selection is realized by a DQN. In addition, we propose a more fair evaluation metric as adjusted average travel time and use it for model comparison. Comprehensive experiments under real-world datasets demonstrate that DynLight outperforms all the previous methods. With well pre-trained DynLight, the phase selection is replaced with a cyclical phase selection that can also demonstrate high performance. Our proposed DynLight not only achieves state-of-the-art performance but is also flexible enough for deployment. The phase duration is essential for traffic signal control.

In future research, we will consider more complex network and RL techniques for traffic signal control. More efficient state representation is also under consideration.

Acknowledgments
===============

[^1]: Jianming Deng is the corresponding author.

[^2]: http://cityflow-project.github.io

{'source': 'arXiv_out/2204'}