- Seattle
- Public Transportation
- 18 weeks
- B2C
- Python, R, Apache Spark, Tableau, PostgreSQL
Project Brief
Using data analytics, a large urban transportation agency intended to optimise bus routes, increase timely service, and improve user experience. The group aimed to use data-driven insights to enhance decision-making, reduce operating expenses, and grow membership.
Client Background
The agency ran a large network of bus lines over a huge metropolitan region. They encountered issues such as congestion in traffic, shifting passenger patterns, and ageing infrastructure. Traditional techniques of route scheduling and planning were no longer enough to fulfil the needs of an expanding and dynamic metropolis.
Key Challenges:
Solution:
1. Discovery and Planning
We began a detailed discovery phase, working with the agency's transportation planners and data analysts. We rigorously examined their current data sources, discovered gaps and inconsistencies, and created a detailed data analytics strategy.
2. Development
Our professional data analytics team processed and analysed massive volumes of transit data using Python and R. We used Apache Spark for distributed computing, which allowed us to handle enormous datasets effectively. We created prediction models utilising machine learning techniques to estimate rider demand and detect potential delays. We also created optimisation algorithms to identify the best bus routes based on real-time and historical data. We used Tableau to develop interactive dashboards that allowed the agency to examine and comprehend the data in an easy-to-use manner.
3. Implementation
We worked extensively with the agency's IT experts to integrate the data analytics platform into their existing systems. This entailed creating data pipelines to process real-time data from GPS trackers and fare collecting devices. We also gave personnel full training on how to utilise the dashboards and evaluate the data.
Tools & Technology Used
Python
Programming Language
Tableau
Data Visualization
Elasticsearch
Search
PostgreSQL
Database
Apache Spark
Data Processing
Features:
Real-Time Bus Tracking
View a live map of all buses in the network, including predicted arrival times and passenger counts.
On-Time Performance Dashboard
On Time Performance The dashboard measures on-time performance depending on route, time of day, and other factors to find areas for improvement.
Route Optimization
Route Optimisation is an algorithm that recommends best bus routes based on real-time and historical data, taking into account passenger demand, trip time, and traffic patterns.
Ridership Forecasting
Predictive models predict demand for different routes and times of day, allowing for timely scheduling modifications.
Delay Prediction
A system that predicts delays depending on real-time traffic and historical data, enabling proactive communications with passengers and timetable mo