Congestion pricing is a tool intended to alleviate congestion. However, it is difficult to speak of traffic optimization in this case, firstly because real-time congestion pricing, although technically possible, does not allow road users to make rational travel decisions; secondly, due to the limitations of second-best solutions; and thirdly, because the goals of congestion pricing can be divergent. The article presents three main theoretical approaches to the goals of congestion pricing found in economic literature.
Relationships between capacity, traffic volume, and traffic density
Road infrastructure capacity, which is the maximum number of vehicles that can pass through a cross-section of a road in a given time interval, is a measure of road infrastructure supply (Q). On the demand side, it corresponds to traffic volume. Actual capacity—due to various traffic restrictions, including traffic regulations—does not always correspond to design capacity.
Road traffic is generated by the demand for transport services, but actual road traffic on a given road section does not fully correspond to the demand for travel on a given route. In a situation of road congestion, part of the demand is waiting to enter the route; therefore, there are more people wanting to use the road section than its capacity allows. In economic literature, following W. S. Vickrey, this situation is described as queuing behind the bottleneck. Two basic traffic parameters necessary for analyzing the issue of road congestion are traffic volume and traffic density.
Traffic volume (F) is the number of vehicles that pass through a cross-section of a road or exit a road in a given time interval. The most popular measure of traffic volume is the Annual Average Daily Traffic (AADT), which is the average number of vehicles per day that cross a road section. Average daily traffic fluctuates depending on the season; on most roads, it is higher in the summer season than in the winter. Furthermore, a distinction is made between (1) peak hour traffic, i.e., increased demand for infrastructure capacity during commuting hours (commuter traffic), (2) off-peak traffic, and additionally (3) night-time traffic. Congestion pricing is treated as a tool for dealing with the problem of increased traffic during peak hours.
Traffic density (D) is the number of vehicles located on a given road section, for example, per 1 km. The significance of the “traffic density” parameter as a measure of traffic volume in congestion analysis boils down to the fact that traffic volume—also in mathematical terms—is a derivative of traffic density. An increase in traffic density also means an increase in traffic volume. However, this only occurs up to a certain traffic density value, at which traffic volume, as a derivative of traffic density, reaches its maximum (Fmax). After exceeding this point, an increase in traffic density means a decrease in traffic volume. More vehicles enter the route than exit it (Figure 1). An auxiliary traffic parameter is travel speed and travel time on a given route/road section. On an empty road, speed depends on the technical parameters of the road or regulations relating to traffic on that road. Such traffic is referred to as free flow traffic. However, from a certain point (marked as F* on the chart for the corresponding traffic volume), road users begin to hinder each other and the speed decreases. After the traffic volume exceeds the maximum level (Fmax), the speed decreases radically, and cars move by stopping every moment (stop and go traffic).
This relationship causes that after road traffic exceeds the Fmax volume on the supply side (capacity), we are dealing with inverse supply of road infrastructure. The more vehicles on a given road section, the lower the possible traffic volume.
The article comes from the “Logistyka” magazine 4/2009.