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Technology Overview
Urban congestion is a huge problem all over the world. Congestion not only causes inconvenience for commuters, but also substantial social problems and large economic losses. Large urban cities are continuously looking into innovative technologies to ease congestion.
One aspect of Dynamic Mobility Management research focuses on designing intelligent information systems so that transport service providers (such as taxis and all forms of public transport) can be empowered to provide higher quality services. This would contribute towards easing congestion and achieving a car-lite society in urban cities.
Our current focus is on taxi fleet operation, and we aim to deliver a series of innovative software tools and applications that empower drivers and fleet operators. They will be provided with relevant information to make decisions that will dynamically balance taxi supply and demand, and improve customer service level along with driver income. The applications leverage on vehicle location data and video footages to infer taxi demand at targeted and defined zones and taxi queues, and provide recommendations to the drivers in the vicinity to pick up passengers. A simulation tool is used to measure the effectiveness of the recommendation.
A parallel research track that we are also working on is to facilitate and to streamline the service mode of taxi ride-sharing, both for increasing fleet service capacity during peak demand and for lowering commuter costs when taxis are used as bridging services to access public transport.
Features & Specifications
Taxis or mobility-on-demand services are an important part of transport services that would convince people to abandon private transport. Yet in face of the latest innovations such as Uber, Lyft, Grab or many other ride-hailing services, taxis lag far behind in terms of technology sophistication and service standards. Recognising the limitations of taxi fleet operations, we seek to introduce technologies that would uniquely suit the needs of taxi fleet operators, and to up the game of taxi industry such that better services can be delivered in a competitive manner.
- Driver Guidance System
- Taxi demands are inferred using both historical and real-time data
- Drivers are provided with individualised guidance to relevant high demand locations using real-time data, delivered either via smartphones or operator-owned terminals
- Taxi Simulator with Massive Scale
- Capable of simulating tens of thousands of independent taxis, each with its own strategies, which can be derived from historical data
- Used to evaluate effectiveness of the driver guidance system and other operation policies
- Ride Sharing for Last Mile and Bridging Service
- Real-time clustering of passengers for ride-sharing to optimise the balance between passenger service, detour distance and feasible fare
- Enables evaluation of the effectiveness of ride-sharing mode under various demand scenarios such as crowd dispersion at high volume events and last mile connection service
Potential Applications
The technologies and applications can be applied to taxi drivers and fleet operators, transport agencies and large venue operators.
- Infer taxi demand to provide real-time guidance to roaming drivers, to empower them with information to improve service and income levels
- Simulate fleet operation scenarios for policy evaluation
- Enable ride sharing services to ease last mile congestion at high demand locations
Customer Benefits
- Taxi drivers are empowered with relevant information to serve more customers, resulting in improved customer service and higher income opportunity
- Large venue operators can pre-emptively plan and leverage on ridesharing and bridging services to mitigate congestion and improve egress efficiency during large events
- Policy & decision makers such as fleet operators and transport agencies can plan and evaluate new policies, and visualise impact of proposed policies using the taxi simulator prior to implementation
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