Demand management/estimation in large-scale traffic networks
The aim of this study is set to develop demand management and demand estimation tools for large-scale traffic networks. Two objectives are accomplished in this project where we focus on developing demand management and demand estimation strategies for large-scale traffic networks by incorporating the macroscopic fundamental diagram-based traffic dynamics.
Demand management and estimation for large-scale traffic networks is an essential requirement for the elimination of traffic congestion in urban networks. Macroscopic traffic models based on the macroscopic fundamental diagram (MFD) play a vital role on this regard, enabling analytically tractable approaches for complex problems in demand management and estimation. Hence in this research we focus on developing demand management and demand estimation strategies for large-scale traffic networks by incorporating the MFD-based traffic dynamics.
The proposed demand management strategy focuses on redistributing the demand by limited schedule changes. It introduces a city-scale, realistic, day-to-day traffic assignment model to evaluate equilibrium conditions for a two-region urban network with constrained system optimal conditions. Two macroscopic fundamental diagram-based models are jointly involved in this study to explore the system performance and limited cooperation of travellers.
The demand estimation method presents a pragmatic way to estimate origin-destination flows on complex large-scale traffic networks combined with urban roads and freeways. A two-level hierarchical method is proposed for demand estimation, where the first level estimates regional demands combining complex traffic systems following a system of system approach. The second level estimates link-level origin-destination flows from the regional demand profiles following matrix balancing principles.
Traffic congestion is the result of traffic demand exceeding the roadway supply capacity, which could be identified by low speeds, longer travel times and lengthy vehicle queues. The economic and population growth, enhancement in communal needs and lifestyles are major factors which contribute to high travel demands and traffic congestion.
Even though economic activities influence the traffic congestion, the growth and stability of an economy are at the mercy of traffic congestion. Department of Infrastructure and Regional Development identified that the congestion cost of Australia was $16.5 billion in 2015 and expected to reach between $ 27.7 -37.3 billion by 2030 if major policy changes were not introduced, which will be a higher burden for the Australian economy. Further, Australian Automobile Association finds that major cities in Australia such as Sydney, Melbourne, and Brisbane are facing rapid population booms due to urban sprawl, which may result to increase congestion and may cause traffic gridlock in the near future unless decisive action is taken.
The introduction of novel traffic monitoring and management techniques are an attractive avenue to manage the traffic congestion as building new infrastructure is not a justifiable solution. Demand management strategies and traffic control strategies are the broader categories of methods to reduce traffic congestion found in the literature. Although there are numerous traffic control strategies in literature, less work has been focused on demand management strategies.
After a careful review of the existing literature, we break down traffic demand management strategies into two categories:
- Strategies focused on reducing demand or shifting the travellers to other transport modes such as public transport, ridesharing, parking restrictions etc.
- The strategies focused on redistribution of demand over space and time such as route guidance, congestion pricing, peak-hour pricing, flexible working hours, etc.
We see that a vast number of studies were focused on spatial redistribution of demand and less attention was given to temporal redistribution of demand although such strategies have a very high potential in mitigating traffic congestion. At the same time, we see that the successful implementation of any demand management strategy often relies on accurate demand estimates. Nevertheless, existing demand estimation techniques face challenges in scalability to large scale networks.
Given the above research gaps, this study will focus on developing demand management and demand estimation techniques for large scale traffic networks. Our study will build upon macroscopic traffic models based on the macroscopic fundamental diagram (MFD) to understand the complex interaction of large-scale traffic networks. MFD enables analytically tractable approaches for complex problems in demand management and estimation.
The aim of this study is set to develop demand management and demand estimation tools for large-scale traffic networks. Two objectives are accomplished in this project where we focus on developing demand management and demand estimation strategies for large-scale traffic networks by incorporating the MFD-based traffic dynamics.
A thorough review of the literature shows that there are no promising methods to demand management and demand estimation in large scale traffic networks. The existing traffic demand management (TDM) strategies often face incompliance problems as they require substantial schedule changes. Hence, there is a need for developing TDM strategies which target to achieve system optimum conditions by limited schedule changes to travellers.
On the other hand, there exist demand uncertainties due to technology penetration of advanced traveller information systems and incompliance of travellers to TDM strategies. The existing demand estimation methods suffer challenges in scalability to large-scale networks and reliability in estimates. Thus, there is a need to develop computationally feasible methods to estimate demand using analytically tractable macroscopic traffic models.
Two objectives are formulated to bridge the research gap.
Develop a method to manage the demand in a large-scale traffic network by limited schedule changes.
Develop a robust demand estimation method applicable to large-scale and complex traffic networks.
This PhD was completed in October 2022.