A number of recent technological breakthroughs promise disrupting urban mobility as we know it. But anticipating such disruption requires valid predictions: disruption implies that predictions cannot simply be extrapolations from a current state. Predictions have to consider the social, economic and spatial context of mobility. This paper studies mechanisms to support evidence-based transport planning in disrupting times. It presents various approaches, mostly based on simulation, to estimate the potential or real impact of the introduction of new paradigms on urban mobility, such as ad-hoc shared forms of transportation, au-tonomously driving electrical vehicles, or IT platforms coordinating and integrating modes of transportation.
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.
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.
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):
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.
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.
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.
Shubham Jain will be attending CAITR (Conference of Australian Institutes of Transport Research) on 12-13 February, hosted by the Melbourne School of Engineering. He will be presenting work-in-progress on his MPhil thesis, focusing on the simulation of demand for demand-responsive transportation.
Mobility and accessibility is a problem for growing cities. To meet this challenge in a sustainable way, taking congestion, fuel consumption, and environmental impacts into consideration, new forms of transport need to be considered. One possible solution is Demand Responsive Transport System (DRTS) which provides flexible point-to-point service on casual requests. It operates at flexible routes and does not have pre-defined schedules. Before deploying a DRTS, we need to simulate the facility and we require to predict travel demand for it. Activity-based micro-simulation models for travel demand explicitly recognise that individuals and households are the actual decision makers, and that travel demand is derived from travellers’ desire to participate in spatially dispersed activities. This research attempts to predict travel demand for DRTS using activity based modeling. This paper presents early research and findings on generating synthetic population of city of Melbourne using PopGen and PopSynWin software and their comparison. Further research would assign travel diaries to synthetic population using Victorian Integrated Survey of Travel and Activity (VISTA) data and predict mode shift to DRTS.
Joann Yang, a research assistant in Infrastructure Engineering at the University of Melbourne, will also present a literature review on on-demand freight transportation. This is part of another paper in preparation for the upcoming City Logistics conference, which uses the demand-responsive transport simulations developed as part of the iMoD project to explore on-demand, shared passenger/goods travel.
Note that both Russell Thompson and Nicole Ronald are involved in organising CAITR, so will be present as well. We are looking forward to welcoming all the participating students, researchers, and practitioners from around Australia!