Monthly Archives: March 2015

Simulating Demand-responsive Transportation: A Review of Agent-based Approaches

Nicole Ronald, Russell Thompson, Stephan Winter (2015), Simulating Demand-responsive Transportation: A Review of Agent-based Approaches. Online first (16 Mar 2015), Transport Reviews.

In light of the need to make better use of existing transport infrastructure, demand-responsive transportation (DRT) systems are gaining traction internationally. However, many systems fail due to poor implementation, planning, and marketing. Being able to realistically simulate a system to evaluate its viability before implementation is important. This review investigates the application of agent-based simulation for studying DRT. We identify that existing simulations are strongly focused on the optimisation of trips, usually in favour of the operator, and rarely consider individual preferences and needs. Agent-based simulations, however, permit incorporation of the latter, as well as capture the interactions between operators and customers. Several areas of future research are identified in order to unify future research efforts.

This paper is available from the authors or online at http://www.tandfonline.com/doi/full/10.1080/01441647.2015.1017749. Taylor and Francis have kindly made a limited number of free copies available.

A Simulation Study on Automated Transport Mode Detection in Near-Real Time using a Neural Network

Das, R.D., Ronald, N., and Winter. S. (2015), A Simulation Study on Automated Transport Mode Detection in Near-Real Time using a Neural Network. In Proceedings of Research@Locate’15, Brisbane, Australia.

Detecting transport modes in near-real time is important for various context-aware location based services and understanding urban dynamics. In this paper we present a simulated study on detecting transport modes in near-real time using a neural network. We have shown how detection accuracy will vary with different temporal window sizes and different combination of modes. Since in urban environment transport modes move slowly due to traffic, considering movement attributes or kinematics alone for mode detection is not sufficient. That is why we investigated how spatial information can improve mode detection accuracy. The model has achieved 82%-95% accuracy and proves its efficacy over other detection models.

This paper is available online in the Research@Locate proceedings.