Figure - available from: Autonomous Agents and Multi-Agent Systems
This content is subject to copyright. Terms and conditions apply.
Source publication
The rapid evolution of technology in connected automated and autonomous vehicles offers immense potential for revolutionizing future intelligent traffic control and management. This potential is exemplified by the diverse range of control paradigms, ranging from self-routing to centralized control. However, the selection among these paradigms is be...
Similar publications
The autonomous vehicles (AVs) are an intelligent mode of transport which can perceive their surroundings and perform autonomous actions without human control. The advanced driver assistance systems (ADAS) technology is considered as the future of transportation systems. The main aim of autonomous driving is to implement a safe transport system for...
Citations
... Road networks are complex systems where drivers adopt selfish behaviours aimed at minimising their own travel costs, for instance time, disregarding the impact of such behaviours on the onset of congestion. Macroscopic traffic assignment (TA) models have been used by transport planners to model driver behaviour since the middle of the twentieth century, however, the topic continues to attract significant attention from the research community [1][2] [3]. In TA models the choice of congestion functions is a fundamental issue. ...
... System-optimal is the flow pattern under which the global cost of all drivers is minimised. To quantify the difference in cost between these routing patterns and highlight the loss of performance on the network, the price of anarchy (POA) is used as a metric to compare the two [3] [25]. ...
... In other works, exponential functions are used as an alternative to BPR as they possess a similar shape [15], and in some studies they fit traffic traffic with greater accuracy [3]. In this work, the form of exponential function used is: ...
The ability to build accurate traffic assignment models on large-scale major road networks is essential for effective infrastructure planning. Static traffic assignment models often utilize standard formulations of congestion functions which suffer from various inaccuracies. Conversely, newer approaches in the literature rely on inverse optimisation to provide enhanced accuracy but incur significantly heavy computational costs. The work in this article develops density-based congestion function fitting in order to compute traffic assignment patterns. Computational efficiency makes the method amenable to be used on real-world networks at national scale. The methodology is applied on the motorway network connecting the primary metropolitan areas in England using Motorway Incident Detection and Automatic Signalling system data. The results demonstrate that the use of density-based congestion functions provides significant improvement in terms of computational runtime in the order of 11,000 times (22 secs vs 68 hours). Correspondingly, prediction error from this method (3.9 to 6.9% for time prediction and 10.4 to 10.7% for flow prediction) slightly outperforms the state-of-the-art Inv-Opt method (5.3 to 8.8% for time prediction and 10.5 to 11% for flow prediction). The increased accuracy provides greater confidence in modelling results for applications such as cost-benefit analysis and price of anarchy calculations.
