What does the system do?

It allocates and releases parking slots in real time against a normalized SQL backend, and exposes both live occupancy and historical analytics through a Streamlit dashboard.

Why a SQL backend rather than in-memory state?

Persisted state survives restarts and makes historical occupancy analysis possible, which an in-memory allocator cannot provide.

How is double allocation prevented?

Slot assignment and release run as database transactions, so concurrent entries cannot claim the same slot.

Was the work published?

Yes, it was published in IJCRT in September 2025 with Nithin K as lead author.

What are the system's limits?

It models a single operator-controlled lot. Supporting many independent hosts required the rebuild that became LumoroX Park.

Where can I read the code?

The repository is at github.com/Nithin69-k/Parking-Space-Management-System.

Lead Author · Full-stack · 2025

Parking Space Management System

Published real-time slot allocation research — the foundation of LumoroX Park.

PythonSQLStreamlitAnalytics

View code Evolved into: LumoroX Park

Parking Space Management System — Published real-time slot allocation research — the foundation of LumoroX Park.

Overview

a real-time parking slot allocation system on a normalized SQL backend with a Streamlit dashboard and historical analytics, published in IJCRT (Sep 2025).

Role: Lead Author · Full-stack
Year: 2025
Live link: Source on GitHub

Goals

Cut the capacity wasted by manual allocation and poor slot visibility.
Keep live slot state accurate and persisted, not held in memory.
Give operators historical occupancy analytics, not just a live view.
Document the approach to a publishable standard.

Approach & architecture

1
Normalized schema
A SQL backend modelled lots, slots, vehicles and sessions so state survives restarts and supports history queries.
2
Allocation logic
Slots are assigned and released transactionally, preventing double allocation under concurrent entries.
3
Streamlit dashboard
A live operator view of occupancy plus historical charts over the session table.
4
Write-up
Method and results were written up and peer-reviewed for publication.

Normalized SQL schema diagram with lots, slots, vehicles and sessions tables, a transactional check-in allocation flow, and a Streamlit dashboard reading live occupancy and historical analytics. — Normalized schema plus transactional allocation feeding a Streamlit operator view.

Tech stack

Python

SQL

Streamlit

Analytics

Parking operator dashboard showing total, occupied and free slot counts, a colour-coded slot layout grid and an hourly historical occupancy chart. — Operator dashboard — live slot state and historical occupancy analytics.

Results

IJCRT

peer-reviewed publication, September 2025

Real-time

persisted slot state with historical analytics

→ LumoroX Park

engine rebuilt as a live marketplace

Parking Space Management System

Overview

Goals

Approach & architecture

Normalized schema

Allocation logic

Streamlit dashboard

Write-up

Tech stack

Results

Frequently asked questions

More case studies

J.A.R.V.I.S — NIX

LumoroX Park

LumoroX