Improving the performance of a traffic system by fair rerouting of travelers

Some traffic management measures route drivers towards socially-desired paths in order to achieve the system optimum: the traffic state with minimum total travel time. In previous attempts, the behavioral response to route advice is often not accounted for since some drivers are advised to take significantly longer paths for the system’s benefit. Hence, these drivers may not comply with such advice and the optimal state will not be achieved. In this paper, we propose a social routing strategy to approach the optimal state while accounting for fairness in the resulting state. This routing strategy asks travelers to take a limited detour in order to improve efficiency. We show that the best possible paths (in terms of efficiency) to be proposed by a service adopting this strategy can be found by solving a bilevel optimization problem with a non-unique lower-level solution. We use techniques from parametric analysis to show that the directional derivative of the lower-level link flows however exists. This derivative is the optimal solution of a quadratic optimization problem with a suitable route flow solution as parameter. We use the derivative in a descent algorithm to solve the bilevel problem. Numerical experiments in a realistic environment show that the routing strategy only asks a small fraction of the drivers to take a limited detour and thereby substantially improves the performance of the traffic system

Variations in Urban Traffic

The traffic network is operating close to capacity in many urban traffic networks. Unexpected and small fluctuations in traffic flow can then result in a significant disruption in the level of service. In fact, accumulated local and short-term fluctuations pose a serious risk to actors operating in the urban traffic domain who aim for decisions with stable performance under all conditions. Robust decisions anticipate the uncertainty in the sense that the potential effects of local, yet natural fluctuations are incorporated during the decision-making process. This thesis investigates urban traffic variations on different scales and explores the potential of using volume predictions to improve pro-active decision-making processes