Assessing the Value of TMCs and Methods to Evaluate the Long Term Effects of ITS: Measuring Congestion, Productivity and Benefi t Flow from Implementation

The study carries out an evaluation of TMCs (traffic management centers) using three methodologies; case studies, performance based regressions and time series analysis. The study is an extension of previous work that assessed the contribution of different types of intelligent transportation investments and initiatives. However, this research sought to distinguish the separate contributions of the ITS investments from the synergies of integration under a traffic management center. Secondly, the research investigated the time dimension of benefits where we investigated if there was an 'S' curve effect in which a change in the network due to an ITS investment or the introduction of a TMC lead to benefits distributed over time. The distribution was important to evaluating ITS investments. If one measured the impact of the investment too soon, in the disequilibrium period, it would underestimate the true contribution of the investment or change in process or management strategy. We found the institutions that affect TMC operations with their designation of responsibilities, who can do what, when and where, requires change before the TMC can be an effective addition to the management of the transportation network. TMCs represent an integration of hardware and people and that process and management were most important in ensuring the TMCs had added value. Our performance related regressions used levels and changes in congestion (measured by a congestion index) and changes in VMT for autos and trucks. We found that for auto VMT ramp meters were more important than CMSs in improving the system. This was, more VMT can be obtained from the system, holding congestion constant, with ramp meters. We found that TMCs had no statistical impact on auto VMT. In the case of truck VMT, the results were just the reverse; CMSs appeared to be more important than ramp meters in improving system efficiency when efficiency was measured by extracting more truck VMT from the system, holding congestion constant. As with the auto results, TMCs were not significant in the analysis. The regression using the congestion index found ramp meters appear to be 4 times as effective as CMSs. TMCs as before were not statistically significant in affecting congestion. Overall the model did not have a lot of explanatory power in sorting out the differences in congestion among counties or what the underlying contribution is of ITS relative to investments. But it is evident that among conventional congestion relief measures maintaining infrastructure (roads) is more effective than expanding capacity. It also appears that ramp meters and CMSs, indicators of improved network management are more effective in reducing congestion than are expanding the network.