Flexi-Bus

Team Name

Detour Labs

Team Members

Project Description

This project focuses on utilising AI to optimise the routing of buses that have a fixed final destination, such as a train station. Using data such as vehicle registrations, Bus Stop data, Travel and Activity data, and Train Service Passenger Count data to understand transport trends and patterns, an AI service will be able to provide riders with the most efficient route.

Data Story

Problem Statement
The prevalence of privately own vehicles coupled with the underutilization of bus services in Victoria has become critical challenge that defeats the Victorian Government’s commitments under the transport sector emissions reduction pledge. Bus transportation systems often struggle with aligning with peak office hours and convenient pick-ups for riders. This deficiency leads to lower rider satisfaction, increased dependence on private vehicles, increased travel times where riders have to walk long distances to catch buses, and overfilled car parks at train stations due to private car dependence. Additionally, infrastructure decisions, such as where to place extra bus stops, are a challenge if they are not people focused and are based on generalized data, leading to below-expectation services and poor accessibility.

Approach
Our approach to this predicament therefore involves developing an AI-driven application that adjusts bus routes in real time to pick up riders from nearby locations while ensuring that the bus still arrives at its fixed destination, such as a train station, on time. In this solution, there are two categories of buses, that is, those that will still follow the prescribed route from VicRoads, and those that will make optimised detours to pick up nearby riders. For the latter, the following proposals will apply to ensure an efficient, convenient and desirable outcome.
Dynamic routing:
A button will be introduced at various bus stops to enable riders that a travelling to a fixed destination such as a train station to request a bus. The request will be processed by the AI and the best positioned rerouted to make the pickup. If the calculations show that ETA will be affected, then that particular bus will not make any more detours till its destination.
On the other hand, if multiple riders all heading to various destinations are present at one or more bus stops, a screen will be available at the bus stop for riders to indicate which major destination they are heading to. In this way, the appropriate busses will be rerouted.
Data-driven monitoring:

Various datasets such as the VISTA and Metro Bus Routes data, Train Service Passenger counts data will enable the AI system to identify high-demand areas, calculate the best pick pickup points, and adjusts routes in real-time to balance rider demand, convenience and timely arrival at the set destination.

Infrastructure and Resources
The following infrastructure and resources will need to be available for the proper implementation of this solution. That is,
Mobile app Integration: an AI will have to be integrated into the PTV app so as to leverage the already existing schedules and frameworks on both the driver and rider ends. However, rides can only be requested at the station and this is only for updates.

Bus stop screen display and button: This will be used to request rides and specify the set destination required.
Policy: Incentives should be provided for early adoption of the system such as reduced fares and loyalty programs.
Interactive maps: Providing a maps application or integration that will show real time updates of but routes, locations and ETAs.

Outcome
The proposed solution results in a more efficient bus transportation system that enhances rider satisfaction by reducing wait times and travel times by improving access to bus services. It also aids in infrastructure planning by pinpointing areas where additional bus stops may be needed based on real-world travel data, ensuring better service coverage and accessibility. The result of this implementation is reduced traffic congestion, lower emissions and enhanced accessibility as riders move away from private transportation.