Lagos, Nigeria · Open to Work

Onabanjo
Michael.

I'm focused on how transport actually works, especially the informal systems that move most people across African cities. A big part of my interest is figuring out how to capture real-time data from these systems and use it to make transport clean, safer and easier to navigate for the people who rely on it. At the core, I'm concerned with practical interventions, identifying key routes, integrating cleaner and electric vehicle technologies, and building the planning frameworks that let informal and multimodal systems operate more safely and efficiently.

I serve on the Editorial Board of the IEEE Intelligent Transportation Systems Society Podcast, where I conducted the interview in Episode 82 on ITS activities across Africa and contributed to Episode 84 on cybersecurity in intelligent transportation. I am also a research assistant and member at TRATSEDI, collecting and analysing driver behaviour data to support transport research in West Africa, and participated in the Transformative Transport Service Conference 2025 in Loughborough. Before that, I spent a year simulating traffic on actual Lagos infrastructure. My microsimulation of the Third Mainland Bridge was calibrated to approximately 117,000 vehicles per day and showed a 20.72% reduction in emissions under a 10% autonomous vehicle penetration scenario. More recently, this has extended into EV systems. As the founder of Electric Vehicle Demystify, i write technical content that focus on simplifying electric vehicle topics to the simplest form it can ever be. I hold a B.Eng in Electrical and Electronics Engineering from Olabisi Onabanjo University, where I majored in Control and Instrumentation Engineering, and completed my undergraduate internship as a Control Engineer at Tulip Cocoa Processing Limited, a facility where over 90% of machines and devices are automated.

Work I've built

Projects & Research

001
LIVE · AI AppDeployed
NaijaTrip AI — Nigeria Travel Intelligence
A travel intelligence app covering all 36 Nigerian states and the FCT, combining an LLM with live traffic, weather, exchange rate, and advisory feeds to deliver route briefs, fare estimates, and safety guidance in real time.
ReactGroq LLMNode/ExpressTomTom APIVercelPrompt Engineering
002
AI AppAV/EV
AV/EV Consultant — AI Advisory Platform for Mobility Practitioners
Closes the gap between emerging mobility technology and the people responsible for deploying it. Delivers on-demand, context-aware intelligence on AV and EV systems via a RAG pipeline — so practitioners and policymakers can move from question to informed decision without manual research overhead.
FastAPIStreamlitLangChainChromaDBRAGGroq LLMHuggingFaceSupabase
003
6
MLOpsProduction
Customer Satisfaction Prediction to Production
Predicts customer satisfaction in e-commerce and segments outcomes into actionable risk tiers — green for automated approval, yellow for proactive outreach, red for urgent intervention. Champion model CatBoost achieved ROC-AUC of 0.72, capturing 71% of dissatisfied customers before escalation.
PythonScikit-learnMLOpsCI/CDProduction
004
Deep RLLagos
Adaptive Traffic Signal Control with DQN
A Deep Q-Network agent trained on Lagos intersection data in SUMO. Compared against fixed-time controllers, the RL agent achieved a 24% reduction in average travel time — replacing static signal timing with responsive, data-driven urban control.
Deep RLDQNSUMOTraCILagos
005
ResearchLagos
Third Mainland Bridge — AV/HDV Simulation
Microsimulation of one of Africa's longest bridges, calibrated to ~117,000 vehicles/day. A 10% autonomous vehicle penetration scenario demonstrated a 20.72% reduction in traffic emissions — evidence-based intelligence for AV policy on critical Lagos infrastructure.
SUMOPythonAutonomous VehiclesEmissions
006
AVSimulation
AV Car-Following & Lane-Changing
Models how autonomous vehicles make longitudinal and lateral decisions — following distance, speed adaptation, and lane-change triggering — and studies the effect on corridor-level traffic flow and safety.
PythonAV ModellingCar FollowingLane Changing
007
LogisticsEDA
Delivery Logistics Analysis — Delhivery Network
Analyses Delhivery's logistics network to identify the key drivers behind delivery delays, surfacing patterns across trip types, geography, and time to give operations teams a clear basis for reducing late shipments.
PythonPandasSeabornEDASupply Chain
008
LogisticsAI App
Compliance QA Pipeline — Logistics Video Intelligence
Logistics operations lose significant value to compliance failures that go undetected until too late. This system watches live video feeds from logistics facilities and automatically flags violations in real time — turning a manual audit process into a continuous, automated one.
Azure AI VisionAzure OpenAIAzure AI SearchRAGFastAPIPython

Capabilities

What I actually know

Transport simulation & modelling

  • SUMO Microsimulation
  • Deep Reinforcement Learning
  • TraCI Python API
  • QGIS / GeoPandas
  • AV Behaviour Modelling
  • GTFS Data

Machine learning & AI engineering

  • TensorFlow / PyTorch
  • Scikit-learn
  • Stable-Baselines3 / DQN
  • Hugging Face Transformers
  • LLM APIs
  • Computer Vision / CNN

MLOps & cloud deployment

  • AWS / Azure / Vercel
  • DVC / MLflow / Kubeflow
  • GitHub Actions / CI-CD
  • Docker

Get in touch

Let's connect.

Open to ML engineering, transportation AI, and research roles. Available for remote opportunities and research collaborations.

If you're working on intelligent transportation systems, EV deployment, or African mobility — I'd like to hear from you.