Buying into the Bypass: Allowing Trucks to pay to use the Ramp Meter Bypasses

People make their route choices based on the delays they experience but not on the delays they impose on others. Moreover different travelers have different values of time. Road Pricing can be seen as the means to optimize the use of a roadway by charging each traveler the cost he imposes on others. This paper analyzes the opening of an HOV ramp meter bypass to trucks that pay a toll. Trucks are similar to HOV as both have a higher value of time than a single occupant car. Thus, by saving time for these vehicles the system stands to gain. The toll to be set was estimated under three scenarios ­ user benefit maximization, profit maximization and system benefit maximization. A queue was simulated, and based on the decision criteria the optimal toll was determined. It is found that to maximize the system welfare, the high Value of Time vehicles like trucks should be allowed to use the bypass for free, but that raises some equity and operational issues. However a toll that allows trucks to use the bypass improves the welfare over simply prohibiting the trucks from the bypass.Trucks, HOV, Ramp Meter, Pricing .

Freeway Origin Destination Matrices: Not as Simple as They Seem

Travel demand can be elegantly represented using an Origin-Destination (OD) matrix. The link counts observed on the network are produced by the underlying travel demand. One could use these counts to reconstruct the OD matrix. An offline approach to estimate a static OD matrix over the peak period for freeway sections using these counts is proposed in this research. Almost all the offline methods use linear models to approximate the relationship between the on-ramp and off-ramp counts. Previous work indicates that the use of a traffic flow model embedded in a search routine performs better than these linear models. In this research that approach is enhanced using a microscopic traffic simulator, AIMSUN, and a gradient based optimization routine, MINOS, interfaced to estimate an OD matrix. This approach is an application of the Prediction Error Minimization (PEM) method. The problem is non-linear and non-smooth, and the optimization routine finds multiple local minima, but cannot guarantee a global minima. However, with a number of starting seed matrices, an OD matrix with a good fit in terms of reproducing traffic counts can be estimated. The dominance of the mainline counts in the OD estimation and an identifiability issue is indicated from the experiments. The quality of the estimates improves as the specification error, introduced due to the discrepancy between the traffic flow model and the real world process that generates the on-ramp and off-ramp counts, reduces.travel demand, OD estimation, simulation, optimization