DeepSignal (GGUF)

This repository provides GGUF checkpoints for local inference with traffic-signal-control models from AIMSLaboratory/DeepSignal.

Models

This repository currently contains:

• DeepSignal-Phase-4B-V1: next signal-phase prediction
• DeepSignal_CyclePlan-4B-V1: cycle-level green-time allocation for all phases in the upcoming signal cycle

Model Files

Filename	Task	Quantization	Size	Notes
DeepSignal-Phase-4B_V1.F16.gguf	Phase prediction	F16	~8 GB	Existing phase model
DeepSignal_CyclePlan-4B-V1-F16.gguf	Cycle planning	F16	~7.5 GB	Highest-fidelity CyclePlan checkpoint
DeepSignal_CyclePlan-4B-V1-Q8_0.gguf	Cycle planning	Q8_0	~4.0 GB	Balanced quality / speed
DeepSignal_CyclePlan-4B-V1-Q4_K_M.gguf	Cycle planning	Q4_K_M	~2.4 GB	Recommended for local inference

DeepSignal_CyclePlan-4B-V1

DeepSignal_CyclePlan-4B-V1 is a traffic signal cycle planning model. It takes the predicted traffic state for the next cycle and outputs the green-time allocation for each phase while respecting phase-specific minimum and maximum green constraints.

Recommended Prompt Format

System prompt

You are a traffic signal timing optimization expert.
Please carefully analyze the predicted traffic states for each phase in the next cycle, provide the timing plan for the next cycle, and give your reasoning process.
Place the reasoning process between <start_working_out> and <end_working_out>.
Then, place your final plan between <SOLUTION> and </SOLUTION>.

Input JSON format

Wrap the input with 【cycle_predict_input_json】...【/cycle_predict_input_json】 tags. The core field is prediction.phase_waits, an array of per-phase objects:

• phase_id: phase index
• pred_saturation: predicted saturation for the next cycle
• min_green: minimum allowed green time in seconds
• max_green: maximum allowed green time in seconds
• capacity: reference capacity used to compute pred_saturation

Quickstart with llama.cpp

Q4_K_M is the recommended local default:

llama-cli -m DeepSignal_CyclePlan-4B-V1-Q4_K_M.gguf \
  --ctx-size 8192 \
  --temp 0.2 \
  -p 'You are a traffic signal timing optimization expert.
Please carefully analyze the predicted traffic states for each phase in the next cycle, provide the timing plan for the next cycle, and give your reasoning process.
Place the reasoning process between <start_working_out> and <end_working_out>.
Then, place your final plan between <SOLUTION> and </SOLUTION>.

【cycle_predict_input_json】{
  "prediction": {
    "as_of": "2026-02-22T10:00:00",
    "phase_waits": [
      {"phase_id": 0, "pred_saturation": 0.80, "min_green": 20, "max_green": 60, "capacity": 100},
      {"phase_id": 1, "pred_saturation": 0.55, "min_green": 15, "max_green": 45, "capacity": 80},
      {"phase_id": 2, "pred_saturation": 0.42, "min_green": 15, "max_green": 35, "capacity": 70}
    ]
  }
}【/cycle_predict_input_json】

Task (must complete):
Mainly based on prediction.phase_waits pred_saturation, output the final green-light time for each phase in the next cycle (unit: seconds) while satisfying all hard constraints.'

Expected Output

The final answer should contain a machine-readable plan inside <SOLUTION>...</SOLUTION>, for example:

[
  {"phase_id": 0, "final": 31},
  {"phase_id": 1, "final": 24},
  {"phase_id": 2, "final": 18}
]

Download Example

huggingface-cli download AIMS2025/DeepSignal DeepSignal_CyclePlan-4B-V1-Q4_K_M.gguf --local-dir .

DeepSignal-Phase-4B-V1

DeepSignal-Phase-4B-V1 is designed for next signal-phase prediction. Given the current traffic scene and state at an intersection, it predicts which signal phase to activate next and for how long.

llama-cli -m DeepSignal-Phase-4B_V1.F16.gguf -p "You are a traffic management expert. You can use your traffic knowledge to solve the traffic signal control task.
Based on the given traffic scene and state, predict the next signal phase and its duration.
You must answer directly, the format must be: next signal phase: {number}, duration: {seconds} seconds
where the number is the phase index (starting from 0) and the seconds is the duration (usually between 20-90 seconds)."

Evaluation (Traffic Simulation)

Performance Metrics Comparison by Model (Phase) *

Model	Avg Saturation	Avg Cumulative Queue Length (veh⋅min)	Avg Throughput (veh/5min)	Avg Response Time (s)
GPT-OSS-20B (thinking)	0.380	14.088	77.910	6.768
DeepSignal-Phase-4B (thinking, Ours)	0.422	15.703	79.883	2.131
Qwen3-30B-A3B	0.431	17.046	79.059	2.727
Qwen3-4B	0.466	57.699	75.712	1.994
Max Pressure	0.465	23.022	77.236	**
LightGPT-8B-Llama3	0.523	54.384	75.512	3.025***

*: Each simulation scenario runs for 60 minutes. We discard the first 5 minutes as warm-up, then compute metrics over the next 20 minutes (minute 5 to 25). We cap the evaluation window because, when an LLM controls signal timing for only a single intersection, spillback from neighboring intersections may occur after ~20+ minutes and destabilize the scenario. All evaluations are conducted on a Mac Studio M3 Ultra. **: Max Pressure is a fixed signal-timing optimization algorithm (not an LLM), so we omit its Avg Response Time; this metric is only defined for LLM-based signal-timing optimization. ***: For LightGPT-8B-Llama3, Avg Response Time is computed using only the successful responses.

Performance Metrics Comparison by Model (CyclePlan) *

Model	Format Success Rate (%)	Avg Queue Vehicles	Avg Delay per Vehicle (s)	Throughput (veh/min)	Avg Response Time (s)
DeepSignal_CyclePlan-4B-V1 F16 (thinking, Ours)	100.0	3.504	27.747	8.611	4.351
GLM-4.7-Flash (thinking)	100.0	7.323	29.422	8.567	36.388
DeepSignal_CyclePlan-4B-V1 Q4_K_M (thinking, Ours)	98.1	4.783	29.891	7.722	1.674
Qwen3-30B-A3B	97.1	6.938	31.135	7.578	7.885
LightGPT-8B-Llama3	68.0	5.026	31.266	7.380	167.373
GPT-OSS-20B (thinking)	65.4	6.289	31.947	7.247	4.919
Qwen3-4B (thinking)	54.1	10.060	48.895	7.096	122.333

*: Each simulation scenario runs for 60 minutes. We discard the first 5 minutes as warm-up, then compute metrics over the next 20 minutes (minute 5 to 25). All evaluations are conducted on a Mac Studio M3 Ultra.

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). Commercial use is strictly prohibited.