Dynamic Congestion Pricing Considering Capacity Variations and Advanced Travel Information Systems in Urban Networks

Traffic accidents reduce the capacity of transportation networks and increase the travel delays. Advanced traveler information systems (ATIS) provide the drivers with information about these accidents, so that the uncertainty about the route capacities decreases and drivers can choose better routes to their destinations. Congestion pricing is among the traffic management policies, whose performance is directly affected by the availability of information to drivers. In this paper, an analytical dynamic traffic assignment model is proposed which can take account of tolls charged to drivers. An iterative algorithm is also proposed to solve the model for large-scale networks. Moreover, the assignment model is used as the sub-model within an optimization model proposed for dynamic cordon pricing problem. To solve the pricing model, a solution algorithm is developed which works based on a grid search method. Two pricing strategies are considered: fixed tolls for all days within the analysis period, and variable tolls in response to the network conditions in each day. Also, depended on providing information, the drivers can be either aware or unaware of accident occurrence. The problem is solved for combinations of different states of pricing schemes (fixed or responsive) and information provision (no or full) for the network of Sioux-Falls. Numerical results show that providing information to drivers becomes more important than congestion pricing as the level of uncertainty increases, and especially when severe accidents occur. Besides, applying fixed tolls is no longer beneficial when drivers are equipped with ATIS. Applying responsive pricing and ATIS can reduce the total travel time of the network by 11.8 percent. Moreover, some results from a sensitivity analysis on accident severity and accident probability are reported, showing that as accident severity increases, fixed tolls become more useful for congestion reduction.