MRT Information System

A terminal-based MRT route planning and visualisation tool for Singapore's rail network. It parses real station, line, and fare data into a weighted graph, computes routes using Dijkstra-style search, and renders the resulting path with live Turtle graphics - complete with line colours, interchanges, and station labels.

Features

#	Feature	Description
1	Shortest-distance route	Finds and visualises the route minimising total km travelled
2	Estimated travel time	Computes journey duration based on per-line speeds
3	Fare calculator	Looks up Adult / Student / Senior fare based on distance slabs
4	Last train feasibility	Checks whether a journey starting at a given time can be completed before the last train on each line/interchange
5	First train timing	Reports the earliest possible departure time from the start station
6	Minimum travel-time route	Runs a separate search optimising for time rather than distance
7	Stations reachable within a time limit	Walks the shortest path and lists every station reachable within a user-given number of minutes
8	Interchange detection	Lists every line change along the computed route

All options are driven from a single interactive menu, and any route-drawing option opens a Turtle window showing the path, colour-coded by line, with interchange stations displayed cleanly.

How it Works

Data model - load_mrt_data() parses a semicolon-delimited station/line file into three structures:

• stations: station -> codes, lines served and neighbour edges
• code_to_name: station code (e.g. NS1) -> full station name
• lines: line name -> list of connected station pairs

Station lookup — match_station() resolves user input against station codes, exact names, and name prefixes, verifying interactively when multiple stations match.

Pathfinding — graph_builder() turns the parsed data into an ordered list. Routes are computed with a priority-queue search (a_star / a_star_min_time) with heuristic h(n) = 0, which makes it functionally Dijkstra's algorithm run once over distance weights and once over time weights (computed from each line's given speed).

Fares — load_fare() parses tiered fare tables per passenger type, and get_fare() matches total route distance to the correct fare category.

Timetable checks — first/last train times are parsed per edge and per direction, with check_last_train() walking the route leg-by-leg to verify feasibility against boarding and interchange cut-off times.

Visualisation — draw_path() lays the route out left-to-right, shifting vertically at each interchange, and draws it with Turtle, colouring each segment by line.

Supported Lines

Line	Colour	Speed (km/h)
North-South	Red	41
East-West	Green	43
Circle	Orange	36
Downtown	Blue	38
North East	Purple	34
Thomson-East Coast	Brown	40

Getting Started

File paths are resolved relative to the script's own location, so MRT.txt and FareStructure.txt must sit alongside dec_mini_project.py

Prerequisites

• Python 3.x ( Turtle graphics ships with the standard library)

Run it

git clone https://github.com/AMGhalcyon/mrt-information-system.git
cd mrt-information-system/source
python dec_mini_project.py

You will be dropped into an interactive menu:

MRT INFORMATION SYSTEM
1. Shortest-distance route
2. Estimated travel time
3. Fare between stations
4. Last train feasibility
5. First train timing
6. Path with Minimum Travel Time
7. Stations reachable within time limit on shortest path
8. Interchange stations on route
0. Exit

Enter a station name, prefix, or code (e.g. Jurong East, jur, or NS1) when prompted — the system will clarify for you if there's more than one match.

Data File Format

MRT.txt (semicolon-delimited, one connection per line):

No.;Station Pair;Line;Distance(km);First Train;Last Train;First Train (Opp);Last Train (Opp)
1;NS1 Jurong East<->NS2 Bukit Batok;North-South;2.3;0530;2359;0530;2359

FareStructure.txt (grouped by passenger type, each with distance-band slabs):

Adult Fare
Up to 3.2 km;120
3.2 - 4.2 km;130
Over 40.2 km;250

Built With

• Python - entirety of the codebase
• heapq - priority-queue-based Dijkstra search
• turtle - route visualisation

Notes

This project was built as an exercise to understand graph algorithms - the core shortest-path search was developed with AI assistance while the author was learning the technique, and the visualisation was similarly AI-assisted for display polish. Core data modelling, parsing, dare/timetable logic, and the menu system are original.

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An exploration of graph-based pathfinding applied to a real-world transit network