Abstract
To date the resilience of transport networks has not been effectively modelled by taking into account the traffic dynamics along with individual drivers' learning process and irrational behaviours. This study proposes an agent-based day-to-day dynamic model with bounded rationality to capture traffic evolution and drivers' inertial behaviours when transport networks suffer from local capacity degradation, and variable message signs are incorporated into the proposed model to improve the resilience, which is indicated by the rapidity of recovering to a new approximation equilibrium after disruptions. We employ a small network as a numerical study to conduct resilience analysis, and variable message signs with different compliance rates are utilized to induce traffic flows for alternative routes when a given link of the network is subject to mild (25%), moderate (50%), severe (75%) capacity reduction. The results show that variable message signs can apparently improve the resilience of the network in most of cases, and a larger compliance rate of variable message signs does not necessarily lead to better rapidity of recovery for approximation equilibrium. This study may provide an insight into the resilience analysis and improvement of transport networks under different levels of disruptions, which fully takes into account the individual drivers' day-to-day learning process, behavioural inertial and the control mechanism of variable message signs with different compliance rates.
Highlights
Reliable and resilient transportation networks are one of the backbones underpinning the prosperity of our society and economy [1], [2]
WORK Resilience of transport networks has been a central concern for transportation management, but few studies are able to effectively model the resilience by considering drivers’ learning process and irrational behaviors at agent level
This study proposes an agent-based day-to-day (ABDTD) dynamic model with bounded rationality to analyze the resilience of the transport network suffering from different levels of disruptions, and variable message signs (VMS) with different compliance rates (CR) is incorporated into the ABDTD model to improve rapidity of recovery of the network
Summary
Reliable and resilient transportation networks are one of the backbones underpinning the prosperity of our society and economy [1], [2]. As an important lifeline infrastructure to secure the normal operations of cities, if transport networks suffer from internal or external disruptions and cannot recover to normal state timely, the huge amount of loss of life and economic loss will be incurred. Transport networks are frequently exposed to a variety of disasters, such as Indonesian tsunami, Hurricane Katrina, the Haiti earthquake, floods in north-eastern Australia and Brazil and so on. All examples above highlight the importance and urgency of understanding and analyzing resilience of transport networks. Resilience of transport networks have been a primary focus of planning and management of transportation
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