Author Archives: iMoD team

Predicting susceptibility to use demand responsive transport using demographic and trip characteristics of the population

S. Jain, N. Ronald, R. Thompson, and S. Winter, “Predicting susceptibility to use demand responsive transport using demographic and trip characteristics of the population”, Travel Behaviour and Society, 6:44-56, 2017


Shared transportation providing point-to-point services on demand, although not an unknown element in urban mobility, has started gaining more presence with the growth of information technology in the transport sector. These forms of transport modes will supplement or compete with the existing public and private transport. Their mixed reception in the past is a matter of concern especially before making investment decisions. To find feasible opportunities of implementation, an estimation of the demand patterns in the target city is desirable. This paper will provide and evaluate a methodology for this estimation that avoids ambivalent and expensive user preference surveys. Demand patterns are caused by the spatial variation of demographic characteristics, and travel behavior over the city. Usability patterns of the proposed services can be learned from the experience of similar services operating elsewhere. Variations of the identified favorable characteristics can be found out in the target city using travel surveys of a population sample. The resulting spatial patterns can be used to find the more favorable areas for implementation of such transport modes. The methodology can be validated by applying it on the existing transport modes in the target city, which will also help in understanding the nature of competition among the proposed and existing transport modes. As the review of operating services is generic, it can be used in conjunction with respective travel surveys in different places. Similarly, a review can be done for any proposed transport mode and provided methodology can be applied for exploring demand patterns.

Comparing demand responsive and conventional public transport in a low demand context

Navidikashani, Z., Ronald, N., & Winter, S. (2016, March). Comparing demand responsive and conventional public transport in a low demand context. In 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)


Considering the sprawl of the cities, the conventional public transport (CPT) with fixed route and fixed schedule becomes less efficient and desirable every day. However, the emerging technologies in computation and communication are facilitating more adaptive types of public transport, such as Demand Responsive Transport (DRT) systems, which operate according to the demand. It is crucial to study the feasibility and advantages of these systems before implementation to prevent failure and financial loss. In this work, a realistic model is provided by incorporating a dynamic routing algorithm into an agent-based traffic simulation to compare DRT and CPT. This model provides a high spatial and temporal granularity, which makes it possible to analyze the results on an individual level. The results showed that replacing CPT with DRT will improve the mobility by decreasing the perceived travel time by passengers and without any extra cost under certain circumstances.

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


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


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.

International Workshop on Computational Transportation Science

Stephan Winter is co-chairing this year’s International Workshop on Computational Transportation Science, October 31st, 2016, San Francisco Bay Area, California, USA.

The workshop is aiming to address the prominence of connected automated vehicles technologies in the global auto industry’s near-term growth strategies, of big data analytics and unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. In particular, the following themes are encouraged:

  • Collaborative transport, including collaborative multi-modal transport
  • Computational and artificial intelligence aspects of assisted driving, collaborative transport or multi-modal transport
  • Crowd sourcing and participatory sensing in transport
  • Cameras as sensors for trajectory acquisition and event recognition
  • Computer Vision-based information extraction from image sequences
  • Context aware analysis of movement data
  • New processing frameworks for handling masses of transport data (e.g. Hadoop)
  • Uncertain information in collaborative transport and assisted travelling
  • Mechanism design for collaborative behavior
  • Data mining and statistical learning for travel information
  • Human-computer interfaces in intelligent transportation applications
  • Privacy, security, and trust in transportation information
  • Novel applications targeted to health, mobility, livability and sustainability

The workshop is now accepting paper submission until 2 September 2016.

2016 iMod final workshop, 24 Mar 2016

On Thursday 20 March 2016, the fourth and final annual iMoD workshop took place at the University of Melbourne.

As last time, the researchers in the group presented their latest works to the industry partners, including the FMS Victoria project.

In this workshop, the discussion focused on wrapping up the project within this year – how we should deliver our results to the industry partners to benefit their day-to-day operations the most. The industry partners attending the workshop provided useful feedback in this regard.

However, the research students will continue working on their topics, which are still within the umbrella of this project: comparison study between DRT and conventional public transport; social-network based ride-sharing; and understanding mobility from GPS traces.

Best poster at Disrupting Mobility


All 5 posters from our group presented at the summit, while the editor was assessing for the award.

We (Kutadinata, Das, Duffield, Jain, Kotagiri, Kulik, Navidikashani, Rigby, Ronald, Thompson, Wang and Winter, with Kelly and Wallace (Monash University)) have won the Best Poster Award at last week’s Disrupting Mobility, a Global Summit Investigating Sustainable Futures held in Cambridge, MA. Our awarded poster, Shared, Autonomous, Connected and Electric Urban Transport, showed results of various aspects of the ongoing ARC Linkage Project Integrating Mobility on Demand in Urban Transport Infrastructures.

Click on the following list to view the presented posters (as PDF files):

  1. R. Kutadinata, R. D. Das, C. Duffield, S. Jain, R. Kelly, R. Kotagiri, L. Kulik, Z. Navidikashani, M. Rigby, N. Ronald, R. Thompson, M. Wallace, Y. Wang, S. Winter, “Shared, autonomous, connected and electric urban transport.” – the big picture of the Linkage Project
  2. N. Ronald, R. Thompson, R. Kutadinata, S. Winter, “Optimizing shared on-demand passenger and goods mobility.”
  3. Z. Navidikashani, S. Winter, N. Ronald, R. Kutadinata, “Disruptive effects of demand responsive transport systems on mobility.”
  4. Y. Wang, N. Ronald, R. Kutadinata, S. Winter, “How much is trust: The cost and benefit of ridesharing with friends.”
  5. S. Jain, N. Ronald, R. Thompson, R. Kutadinata, S. Winter, “Exploring susceptibility of shared mobility in urban space.”