Das, R. D., Ronald, N., Winter, S. (2014), Clustering based Transfer Detection with Fuzzy Activity Recognition from Smart-phone GPS Trajectories, 17th International IEEE Conference on Intelligent Transportation Systems. IEEE, Qingdao, China.
This paper introduces an adaptive clustering-based transfer detection framework. Existing transfer detection algorithms are based on a walking-based approach. But in a walking-based approach it is difficult to set a deterministic walking threshold. However during transfer people generally move slowly or wait for a while and thus the spatio-temporal points are located close to each other and tend to form clusters. To mitigate such problems an adaptive density-based fuzzy approach is proposed for detecting transfers and activities performed during transfers.
Please contact one of the authors for a copy.
Vishwanath, A.; Gan, H.-S.; Kalyanaraman, S.; Winter, S.; Mareels, I. (2014). Personalised Public Transportation: A New Mobility Model for Urban and Suburban Transportation. Available from IBM’s Research Library.
This paper explores a new vision for urban and suburban transportation, termed Personalised Public Transportation, which builds upon recent trends in vehicle sharing, electric vehicles, mobile payments and cloud computing. The goal is to build on the best of the worlds of private and public transportation. Private transportation offers ownership, comfort and convenience, but is higher cost, and subject to externalities (traffic jams, pollution, etc.). Public transportation is efficient, cheaper and has lower energy/Carbon footprint, but has a last-mile problem (access) and low spatio-temporal coverage in suburbia. We envisage a future model of leasing public transportation via a service similar to cell-phone services, where the user pays for convenience and sharing of a network. We describe the key design features inherent to this mobility model. The vehicular platform allows the entire fleet to be operated and managed via a cloud computing service in order to maximise convenience and minimise cost. An optimisation formulation to quantify the benefits of Personalised Public Transportation shows that it is a promising approach for transforming future generations of transportation into sustainable ecosystems.
Rigby, M.; Winter, S. (2014). Enhancing Launch Pads for Decision Making in Intelligent Mobility On-Demand (Extended Abstract), IEEE Intelligent Transportation Systems Conference. IEEE, Qingdao, China.
Interacting for shared mobility is a complex spatio-temporal task. Traditional approaches rely on the full disclosure of inherently private trip information to perform ride matching. Such a requirement however creates a rigid architecture with location privacy and service knowledge issues. Catering for these complexities, we extend previous work on an intuitive interface concept, launch pads, to address individual route choice by enhancing the visualization in a third dimension. This representation provides a client with a more detailed pick-up choice set. To examine the value of this enhancement, we implement a multi-agent simulation and observe a client agent’s responses to 3D launch pads visualized according to three different fare models. Results show that a client’s flexibility in space is dependent on the fare model chosen and by using the visualization they can increase their utility.
iMoD is well-represented at this week’s IEEE Intelligent Transport Systems Conference in Qingdao, China.
Rahul Deb Das will present his work on automated detection of mode transfers based on GPS data. A poster by Michael Rigby on visualising pickup locations for ridesharing will also appear. Joint work by Stephan Winter with Iven Mareels (Dean, Melbourne School of Engineering) and IBM Research on personalized (leased) public transportation will also be presented.
Please feel free to contact Rahul or Stephan to organise a meeting about the iMoD project.