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.
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.
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.
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.
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):
Stephan Winter has been invited to give a keynote at the International Workshop on Computational Transportation Science, in conjunction with the ACM SIGSPATIAL Conference in November in San Francisco Bay Area, CA. He will present results of the project to an international audience.
Stephan Winter, in his other role as Fellow of the Melbourne Networked Society Institute, chaired a panel of speakers on an event of the Melbourne Knowledge Week, on 21 October. This public forum brought together a panel of experts to discuss how the networked society is transforming our cities, and to critically examine how urban connectedness can increase productivity, sustainability and liveability. Intelligent mobility on demand provided an appropriate background.
The group submitted five abstracts for poster presentations in the Disrupting Mobility Summit: A global summit investigating sustainable futures to be held in November, Cambridge MA. All five were accepted. This summit is an interactive forum for leading executives, government representatives, and academics to discuss sustainable futures of transportation. It will bring together around 350 mobility experts from different continents. The program will tackle current trends in mobility by attracting thought leaders from companies, governments and academia. More details about the summit can be found here.
Here is the list of the posters we will present at the summit:
R. Kutadinata, R. D. Das, C. Duffield, S. Jain, 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
Ronald, R. Thompson, R. Kutadinata, S. Winter, “Optimizing shared on-demand passenger and goods mobility.”
Navidikashani, S. Winter, N. Ronald, R. Kutadinata, “Disruptive effects of demand responsive transport systems on mobility.”
Wang, N. Ronald, R. Kutadinata, S. Winter, “How much is trust: The cost and benefit of ridesharing with friends.”
S. Jain, N. Ronald, R. Thompson, R. Kutadinata, S. Winter, “Exploring susceptibility of shared mobility in urban space.”
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.
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.