DeepSignal-4B-V1 (GGUF)

This repository provides a GGUF model file for local inference (e.g., llama.cpp / LM Studio). It is intended for traffic-signal-control analysis and related text-generation workflows. For details, check our repository at AIMSLaboratory/DeepSignal.

Files

• DeepSignal-4B_V1.F16.gguf
• config.json

Quickstart (llama.cpp)

llama-cli -m DeepSignal-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)."

You need to input the {scene} (total number of phases, which phases controls which lanes/directions and current phase ID/number, etc) and {state} (number of queing vehicles per lane, throughout vehicles per lane during the current phase, etc)

Evaluation (Traffic Simulation)

Performance Metrics Comparison by Model

Model	Avg Saturation	Avg Queue Length	Max Saturation	Max Queue Length	Avg Congestion Index
Qwen3-30B-A3B	0.1550	5.5000	0.1550	5.4995	0.1500
DeepSignal-4B (Ours)	0.1580	5.5500	0.1580	5.5498	0.1550
LightGPT-8B-Llama3	0.1720	6.1000	0.1720	6.1000	0.1950
SFT	0.1780	6.2500	0.1780	6.2500	0.2050
Last Round GRPO	0.1850	6.4500	0.1850	6.4500	0.2150
Qwen3-4B	0.1980	7.2000	0.1980	7.1989	0.2450
Max Pressure	0.2050	7.8000	0.2049	7.7968	0.2550
GPT-OSS-20B	0.2250	8.5001	0.2250	8.4933	0.3050

Congestion Level Distribution by Model (%)

Model	Light congestion	Smooth	Very smooth
DeepSignal-4B (Ours)	0.00	12.00	88.00
GPT-OSS-20B	2.00	53.33	44.67
LightGPT-8B-Llama3	0.00	21.00	79.00
Max Pressure	0.00	36.44	63.56
Qwen3-30B-A3B	0.00	10.00	90.00
Qwen3-4B	2.33	32.00	65.67
Qwen3-4B-SFT	0.00	23.33	76.67