Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
Abstract
:1. Introduction
- State improvement: We have edited this to reflect the perspective of the people who use it to make traffic light control decisions. The state collects information regarding the waiting time, divided into ten intervals, and the vehicle’s position.
- Reward improvement: We have modified the calculation of the reward values, where the waiting time for each lane is calculated, and the highest waiting time is selected from the lanes on each arm that use the same traffic signals.
2. Fundamental Principles
2.1. Reinforcement Learning
- Environment: The specific environment in which we desire to operate.
- Agent: The worker who acts.
- Action: The possible actions involved can be conducted in that specific environment.
- State: The decision-making process uses the current state or situation of the environment to make decisions.
- Reward: The reward value is a measure of the performance of an action.
- Policy: Rules or methods for determining the best choice of action based on the current state.
2.2. Deep Reinforcement Learning
2.3. Epsilon-Greedy Policy
2.4. Fuzzy Logic
3. System Design
3.1. Environment
3.2. State
3.3. Action
- Green light for north–south.
- Green light for north–south left turn.
- Green light for east–west.
- Green light for east–west left turn.
3.4. Reward
3.5. Agent
3.6. Necessary Information
4. Simulation Design
4.1. Network Setting
Algorithm 1. Training. |
Input: number of hidden layers, number of nodes in hidden layers, batch size, learning rate, training epochs, memory size, discount factor, number of states, number of actions, max steps, total cars generated, total episodes Output: traffic light control model and episode stats 1: set: episode = 0 2: while current episode < total episodes do 3: calculate the epsilon value 4: set: step = 0 5: while step < max steps do 6: get current state 7: calculate the reward 8: add sample data to memory 9: if random number 0.0 to 1.0 < epsilon value then 10: get random action 11: else 12: get action based on model prediction 13: end if 14: if the phase has changed then 15: set yellow phase 16: set yellow duration 17: while yellow duration > 0 do 18: step = step + 1 19: yellow duration = yellow duration − 1 20: end while 21: end if 22: set green phase based on action 23: get green duration based on fuzzy 24: set green duration 25: while green duration > 0 do 26: step = step + 1 27: green duration = green duration − 1 28: end while 29: end while 30: save episode stats 31: for epoch in range training epochs do 32: get samples from batch = batch size 33: train 34: end for 35: episode = episode + 1 36: end while 37: save model |
4.2. Environment Setting
- Car-Following Models: The models define the distance and speed of vehicles according to changing traffic conditions.
- Lane-Changing Models: The models determine the lane-changing behavior of vehicles based on rules such as gap and safety considerations.
5. Methodology
6. Experimental Results
- Traditional traffic light control is a method that is currently used worldwide, in which a green light signal is given for 10 s in a sequence and repeated for each arm, as shown in Figure 5. It is called the traditional method in the experimental results.
- Traffic light control using a prototype Deep Q-network with fuzzy logic is used for comparison. In the experimental results, it is called the original DQN with the fuzzy logic method [29].
- Traffic light control using the proposed method.
- The average total waiting time (WTAVG) is the average accumulated waiting time of all vehicles in the outgoing lane per step.
- The average total waiting time for vehicles to make a left turn (WTLAVG) is the average accumulated waiting time for all vehicles in the outgoing lane to make a left turn per step.
- The average total waiting time per car (WTCAVG) is the average accumulated waiting time per vehicle in the outgoing lane per step.
6.1. All-4 Training Results
6.2. All-4
- The average total waiting time for vehicles to make a right turn (WTRAVG) is the average accumulated waiting time for all vehicles in the outgoing lane to make a right turn per step.
6.3. Left-Hand Traffic
6.4. Three Lanes One Arm
6.5. Three Lanes Two Arms
6.6. Three Lanes Three Arms
- Total ambulance waiting time (WTATotal) is the accumulated waiting time for all ambulances.
6.7. Ambulance Prioritization System
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Level | Waiting Time (s) |
---|---|
1 | 0 |
2 | 1–6 |
3 | 7–12 |
4 | 13–18 |
5 | 19–24 |
6 | 25–30 |
7 | 31–36 |
8 | 37–42 |
9 | 43–48 |
10 | More than 48 |
Category | Necessary Information | Conditions | Cost |
---|---|---|---|
Data collection devices | Number and position of vehicles | Installation of sensors, cameras, or detection devices | Cost for installation, maintenance, and time required for data processing |
Processing systems and servers | Data storage, processing, model training | Data management, data processing, setting up servers, and security | Cost for hardware, software, and time required for system development and management |
Traffic signal control systems | Current signal, phase duration, and control. | Integration with existing traffic control systems or new system installation | Cost for integration or installation and system updates |
Parameter | Value |
---|---|
Hidden layers | 2 |
Nodes in hidden layers | 100 |
Batch size | 64 |
Learning rate | 0.001 |
Training epochs | 500 |
Memory size | 50,000 |
Discount factor | 0.75 |
Number of states (Input) | 80 |
Number of actions (Output) | 4 |
Parameter | Value |
---|---|
Max steps (s) | 5400 |
Total cars generated | 1000 |
Total episodes | 500 |
GP | |||
---|---|---|---|
Index | Membership Function | Description | Range |
1 | vl | Very Low | 0–5 |
2 | l | Low | 0–10 |
3 | m | Medium | 5–15 |
4 | h | High | 10–20 |
5 | vh | Very High | 15 onwards |
RP | |||
---|---|---|---|
Index | Membership Function | Description | Range |
1 | vl | Very Low | 0–10 |
2 | l | Low | 0–30 |
3 | m | Medium | 15–45 |
4 | h | High | 30–60 |
5 | vh | Very High | 45 onwards |
GP | RP | |||||
vl | l | m | h | vh | ||
vl | vvs | vvs | vvs | vvs | vvs | |
l | s | s | vs | vs | vs | |
m | m | m | m | s | s | |
h | l | l | m | m | s | |
vh | vvl | vl | l | m | m |
Green Duration | |||
---|---|---|---|
Index | Membership Function | Description | Range |
1 | vvs | Very very short | 0–5 |
2 | vs | Very short | 0–10 |
3 | s | Short | 5–15 |
4 | m | Medium | 10–20 |
5 | l | Low | 15–25 |
6 | vl | Very low | 20–30 |
7 | vvl | Very very low | 25 onwards |
Metrics | Method | ||
---|---|---|---|
Traditional | Original DQN with Fuzzy Logic [29] | Proposed Method | |
WTAVG (s) | 43.57 | 16.14 | 13.16 |
WTLAVG (s) | 5.12 | 6.59 | 3.93 |
WTCAVG (s) | 9.46 | 4.71 | 3.71 |
Metrics | Method | ||
---|---|---|---|
Traditional | Original DQN with Fuzzy Logic [29] | Proposed Method | |
WTAVG (s) | 43.45 | 16.22 | 12.51 |
WTRAVG (s) | 4.94 | 6.52 | 3.53 |
WTCAVG (s) | 9.44 | 4.64 | 3.66 |
Average Total Waiting Time(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy Logic [29] | Proposed Method | |
E-3 | 66.51 | 17.32 | 13.73 |
N-3 | 59.48 | 17.79 | 14.53 |
S-3 | 56.59 | 18.18 | 15.09 |
W-3 | 88.50 | 18.05 | 14.36 |
Average total waiting time for vehicles to make a left turn (s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy logic [29] | Proposed Method | |
E-3 | 5.05 | 6.93 | 4.07 |
N-3 | 5.02 | 6.96 | 4.09 |
S-3 | 5.00 | 7.23 | 4.70 |
W-3 | 5.01 | 7.37 | 4.26 |
Average total waiting time per car (s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy logic [29] | Proposed Method | |
E-3 | 11.07 | 4.84 | 3.85 |
N-3 | 10.61 | 4.82 | 3.90 |
S-3 | 10.60 | 4.90 | 3.96 |
W-3 | 11.73 | 4.89 | 3.91 |
Average Total Waiting Time(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy Logic [29] | Proposed Method | |
E-3 S-3 | 75.85 | 19.53 | 16.75 |
E-3 W-3 | 104.60 | 19.93 | 14.97 |
N-3 S-3 | 72.83 | 19.81 | 15.85 |
N-3 W-3 | 100.23 | 21.57 | 16.17 |
N-3 E-3 | 83.61 | 19.39 | 16.03 |
S-3 W-3 | 94.58 | 19.57 | 15.68 |
Average total waiting time for vehicles to make a left turn(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy logic [29] | Proposed Method | |
E-3 S-3 | 5.10 | 8.29 | 4.88 |
E-3 W-3 | 5.11 | 8.38 | 4.42 |
N-3 S-3 | 5.14 | 8.30 | 4.54 |
N-3 W-3 | 5.06 | 9.21 | 4.76 |
N-3 E-3 | 5.03 | 8.22 | 4.43 |
S-3 W-3 | 5.02 | 8.07 | 4.62 |
Average total waiting time per car(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy logic [29] | Proposed Method | |
E-3 S-3 | 11.44 | 5.17 | 4.08 |
E-3 W-3 | 12.86 | 5.15 | 3.96 |
N-3 S-3 | 11.37 | 5.16 | 4.08 |
N-3 W-3 | 12.47 | 5.31 | 4.11 |
N-3 E-3 | 12.02 | 5.11 | 4.13 |
S-3 W-3 | 12.16 | 5.10 | 4.02 |
Average Total Waiting Time(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy Logic [29] | Proposed Method | |
E-3 S-3 W-3 | 122.02 | 22.48 | 17.37 |
E-3 S-3 N-3 | 95.74 | 21.09 | 17.86 |
E-3 N-3 W-3 | 118.49 | 22.38 | 16.90 |
S-3 N-3 W-3 | 113.16 | 21.20 | 17.51 |
Average total waiting time for vehicles to make a left turn(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy logic [29] | Proposed Method | |
E-3 S-3 W-3 | 5.06 | 9.63 | 4.80 |
E-3 S-3 N-3 | 5.05 | 8.70 | 4.69 |
E-3 N-3 W-3 | 5.02 | 9.11 | 4.71 |
S-3 N-3 W-3 | 5.00 | 9.21 | 4.67 |
Average total waiting time per car(s) | |||
Lane | Method | ||
Traditional | Original DQN with Fuzzy logic [29] | Proposed Method | |
E-3 S-3 W-3 | 13.39 | 5.40 | 4.25 |
E-3 S-3 N-3 | 12.41 | 5.28 | 4.28 |
E-3 N-3 W-3 | 13.46 | 5.38 | 4.17 |
S-3 N-3 W-3 | 12.94 | 5.27 | 4.22 |
Average Total Waiting Time(s) | ||||
Safety Duration (s) | Detection Distance(m) | |||
150 | 200 | 250 | 300 | |
3 | 12.87 | 12.98 | 13.14 | 13.53 |
5 | 14.17 | 13.23 | 13.48 | 13.92 |
Total ambulance waiting time(s) | ||||
Safety Duration (s) | Detection Distance(m) | |||
150 | 200 | 250 | 300 | |
3 | 0.24 | 0.24 | 0.00 | 0.00 |
5 | 2.96 | 1.20 | 0.20 | 0.16 |
With the Ambulance Prioritization System | ||
Metrics | Original DQN with Fuzzy Logic [29] | Proposed Method |
WTAVG (s) | 17.01 | 13.14 |
WTATotal (s) | 0.04 | 0.00 |
Without an ambulance prioritization system | ||
Metrics | Original DQN with Fuzzy logic [29] | Proposed Method |
WTAVG (s) | 16.14 | 13.16 |
WTATotal (s) | 65.24 | 67.60 |
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Meepokgit, T.; Wisayataksin, S. Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic. Appl. Sci. 2024, 14, 7908. https://doi.org/10.3390/app14177908
Meepokgit T, Wisayataksin S. Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic. Applied Sciences. 2024; 14(17):7908. https://doi.org/10.3390/app14177908
Chicago/Turabian StyleMeepokgit, Teerapun, and Sumek Wisayataksin. 2024. "Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic" Applied Sciences 14, no. 17: 7908. https://doi.org/10.3390/app14177908