Abstract
This paper presents an analysis of the effects of cognitive agents employing selfish routing behavior in traffic networks with linear latency functions. Selfish routing occurs when each agent traveling on a network acts in a purely selfish manner, therefore the Braess Paradox is likely to occur. The Braess Paradox describes a situation where an additional edge with positive capacity is added to a given network, which leads to higher total system delay. By applying the concept of cognitive agents, each agent is able to make a range of non-selfish and selfish decisions. In addition, each agent has to cope with uncertainty in terms of travel time information associated with the traffic system, a factor in real-world traffic networks. This paper evaluates the influence of travel time uncertainty, and possible non-selfish decisions of the agents on overall network delay. The results indicate that both non-selfish behavior and uncertainty have an influence on overall travel delay. In addition, understanding the influence of cognitive agents on delay can help to better plan and influence traffic flows resulting in “closer to optimal” flows involving overall lower delays.
Highlights
The shortest path problem is well studied in the literature and has been applied in many different types of applications in the field of Geographic Information Science and Technology that range from habitat connectivity to vehicle routing
This aspect in itself is not new, but what is new is the fact that we model agents where they are provided traffic information where there is a degree of uncertainty as to its accuracy
The computational results are given in respective tables, elaborating on the effect of cognitive agents with respect to the experiment settings
Summary
The shortest path problem is well studied in the literature and has been applied in many different types of applications in the field of Geographic Information Science and Technology that range from habitat connectivity to vehicle routing. Due to the fact that (near) real-time information is integrated into the navigation process, the separation between “planning” and “execution of movements”, described as wayfinding and locomotion in [1], appears to be diminishing. Based on the chosen cost function the navigation system responds with the minimum cost route from the current location to the destination. This planning process is valid for non-dynamic traffic situations but does not consider traffic as a “living” system, that displays certain dynamics. These algorithms try to “react” to dynamic conditions in the road network, and identify one shortest path for exactly one agent navigating in the network in a given situation—i.e., network status within the time-frame of the shortest path traversal)
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