Category Archives: Publications

A Continuous Representation of Ad Hoc Ridesharing Potential

M. Rigby; S. Winter; A. Krüger, “A Continuous Representation of Ad Hoc Ridesharing Potential,” in IEEE Transactions on Intelligent Transportation Systems , vol.PP, no.99, pp.1-11
doi: 10.1109/TITS.2016.2527052

Abstract:

Interacting with ridesharing systems is a complex spatiotemporal task. Traditional approaches rely on the full disclosure of a client’s trip information to perform ride matching. However during poor service conditions of low supply or high demand, this requirement may mean that a client cannot find any ride matching their intentions. To address this within real-world road networks, we extend our map-based opportunistic client user interface concept, i.e., launch pads, from a discrete to a continuous space–time representation of vehicle accessibility to provide a client with a more realistic choice set. To examine this extension under different conditions, we conduct two computational experiments. First, we extend our previous investigation into the effects of varying vehicle flexibility and population size on launch pads and a client’s probability of pick-up, describing the increased opportunity. Second, observing launch pads within a real-world road network, we analyze aspects of choice and propose necessary architecture improvements. The communication of ride share potential using launch pads provides a client with a simple yet flexible means of interfacing with on-demand transportation.

A context-sensitive conceptual framework for activity modeling

Das, Rahul Deb and Winter, Stephan (2016) “A context-sensitive conceptual framework for activity modeling,” Journal of Spatial Information Science: Issue. 12, pp. 45-85

Abstract:

Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place “along the way,” such as “reading on the bus,” are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., “holidaying in a country,” could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts.

Creating a synthetic population: A comparison of tools

S. Jain, N. Ronald, S. Winter, “Creating a synthetic population: A comparison of tools”, in Proceedings of 3rd Conference of Transportation Research Group, Dec. 2015

Shubham Jain will present his work titled “Creating a synthetic population: A comparison of tools” in Conference of Transportation Research Group in Kolkata, India. In his MPhil research, he plans to use population characteristics and current activity-travel pattern of Melbourne from population census data and existing, self-completed household travel survey (VISTA) in conjunction with insights from usage pattern of some of the existing collaborative transport services in different regions of the world to identify demand of these services here. This kind of demand modelling requires microdata of population at household and personal level as a key input. Unfortunately due to privacy constraints, such data is often not available. To fulfill this lack of data, population can be synthesised to represent actual demographics of study area as per population census. This paper presents the process of creating a synthetic population for a metropolis for the case of Greater Melbourne using 2011 Population Census. Microdata was created for households and persons in Greater Melbourne at statistical area level 1 (SA1). PopSynWin (developed by University of Illinois at Chicago in 2008), which is based on Iterative Proportional Fitting (IPF) algorithm, and PopGen (Population Generator, developed by Arizona State University in 2009), which is based on Iterative Proportional Update (IPU) algorithm, were used as tools for this purpose, generating two different synthetic populations. Microdata within these synthetic populations were aggregated to validate against actual aggregate census data to evaluate its representativeness to original population. Finally, both the generated populations were compared in various ways to use the more accurate of them for further work.

Exploring co-modality using on-demand transport systems

N. Ronald, J. Yang, and R. Thompson, “Exploring co-modality using on-demand transport systems,” in Proceedings of the 9th International Conference on City Logistics, Jun. 2015

This paper extended the previous DRT model to include shared trips between passengers and parcels, in this case home-delivered takeaway.

This paper is available from one of the authors.

Personalizing Public Transportation

A. Vishwanath, H.-S. Gan, S. Kalyanaraman, S. Winter, I. Mareels, “Personalizing Public Transportation”, in IEEE Intelligent Transportation Systems Magazine, accepted 22 June 2015.

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.

This paper is available from one of the authors.

Enhancing launch pads for decision-making in intelligent mobility on-demand

Rigby, M. and Winter. S. (2015), Enhancing launch pads for decision-making in intelligent mobility on-demand. To appear in Journal of Location Based Services.

Interacting with an application for shared mobility is a complex spatio-temporal task, considering the degrees of freedom in planning and preferences together with the dynamics of supply. Traditional approaches also rely on the disclosure of inherently private, discrete information from both vehicle and client to perform ride matching. Catering for both aspects, we have previously suggested an intuitive interface concept, launch pads. In this paper we extend launch pads by enhancing the visualisation in a third dimension. This representation provides a client with a more detailed choice set which should lead to improved decision-making. To examine the value of this enhancement, we implement a multi-agent simulation and observe a client agent’s responses to 3D launch pads visualised according to three different fare models. Results show that a client’s flexibility in space is dependent on the fare model chosen, and it is this offering which can increase a client’s utility.

This paper is available from http://dx.doi.org/10.1080/17489725.2015.1027752 or by contacting one of the authors.

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.

A comparison of constrained and ad-hoc demand-responsive transportation systems

Ronald, N., Thompson, R.G., and Winter, S. A comparison of constrained and ad-hoc demand-responsive transportation systems, Proceedings of the 94th Annual Meeting of the Transportation Research Board, Washington, DC, January 2015.

Planning public transport services for areas of low population density is important to enable those without convenient travel options to travel. In these areas, transit vehicles frequently travel with low numbers or even no passengers on board, therefore incurring more cost to the transport providers. Demand-responsive transportation (DRT) services are a potential efficient mobility solution to this problem.

The choice of DRT scheme is important as different types of schemes might produce different performances in the same area with the same demand. While many DRT schemes have some constraints, for example, a fixed route or a fixed time, these impose constraints on users who are already constrained, for example, due to lack of access to a car or limited times to undertake activities. Removing the fixed constraint on time leads to evaluating the performance of an ad-hoc system.

matsim-ymThe aim of this paper is to investigate the change in performance between two different DRT schemes — a fixed-time but flexible route scheme and a completely ad-hoc scheme — using MATSim, a large-scale agent-based transport simulation, and real data from an existing fixed-time DRT service in rural Victoria, Australia. Experimentation showed that the schemes produced different outcomes for the operator and passengers, however the optimization algorithm is less important in areas of low demand. Higher levels of demand lead to extensive vehicle travel for an ad-hoc service, while altering the headways between fixed-time services could achieve a middle ground for operators and passengers.

This work is the first step towards developing a decision-support tool to evaluate different DRT schemes, in particular integrated with other modes of transport.

Please email the authors for a copy.

Clustering based Transfer Detection with Fuzzy Activity Recognition from Smart-phone GPS Trajectories

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.