Recently, most urban arterial roads are equipped with intelligent transportation systems devices capable of reporting the collected data on high temporal and spatial resolution, which gives us the opportunity to overcome traditional signal timing planning flaws. Improperly scheduled signal timing plans are one of the main reasons for reduced efficiency of traffic signals at coordinated urban arterials. Also, by evaluating existing occupancy-based measures, it was found that they can be used to estimate capacity utilization, including oversaturated conditions. This study proposes three TSPMs: ‘‘queued volume in volume to capacity ratio’’ (QViV/C), ‘‘cycle utilization’’ (CLU), and ‘‘volume-occupancy capacity utilization’’ (VOCU) to overcome the limitations of Therefore, if only V/C or GOR are used to monitor signal performance, the understanding of utilized capacity will be incomplete or misleading. In addition, because of a lack of scientific attention, GOR has often been misinterpreted as some similar, yet different TSPM. On the other hand, GOR ‘‘overlooks’’ cases when a high number of vehicles travel without stopping or fewer vehicles depart the signal with significant startup delays and similar. Similarly, the amount of time in the cycle utilized by volume (characterized with the first and last vehicle arrival time) is not revealed within V/C.
#FREEWAY EXPRESS DOWNLOAD FREE#
When V/C is used to observe capacity utilization, it is not known whether the capacity is utilized from interrupted flow, free flow, or a combination of two. The former is known as ‘‘volume over capacity ratio’’ (V/C), whereas the latter is known as ‘‘green occupancy ratio’’ (GOR). A fundamental measure for capacity utilization is the ‘‘degree of saturation’’ (DS), and it has both volume-based and occupancy-based formulations.
One of the specific objectives of signal operations is related to the measurement Moreover, it has been found that the accuracy of individual sensors and the number of sensors utilized to estimate traffic conditions are critical for accurate toll calculations.Įmerging high-resolution datasets allow for developing traffic signal performance measures (TSPMs) which can provide a better understanding of traffic signal operations. The results have shown that the tolls increase when sensors erroneously report higher than actual traffic demand. We implemented the methodology in a case study of ELs on Interstate-95 in Southeast Florida. The paper’s methodology relies on applying the dynamic toll pricing algorithm implemented in the field and utilizing the fundamental speed-volume relationship to ‘simulate’ the sensors’ reported data. This research fills this gap by examining the effects of sensor failure and faulty detection scenarios on ELs tolls calculated by a dynamic pricing algorithm. However, no previous research has been conducted to explicitly investigate the impact of sensor failures and erroneous sensors’ data on toll computations. Thus, the operation of dynamic pricing critically depends on the accuracy of data collected by such traffic sensors. The primary inputs for the typical dynamic pricing algorithms are vehicular volumes and speeds derived from the data collected by sensors installed along the ELs.
Dynamic toll pricing schemes effectively achieve reliable travel time on ELs.
Express lanes (ELs) implementation is a proven strategy to deal with freeway traffic congestion.
0 kommentar(er)
