01551177

Component

Recently, researchers have postulated and confirmed the existence of the Macroscopic Fundamental Diagram (MFD) of urban traffic, which relates the average flow and density on a network. MFDs and analogous relationships can be used to describe the dynamics of large traffic networks and to develop network-wide control strategies that improve performance. However, these strategies typically require accurate estimates of the MFD and real-time traffic measurements to implement. This work compares the use of traffic state and MFD estimations from two methods to inform a simple perimeter boundary-flow control scheme using microsimulation. The first uses point estimates on links traditionally available from fixed detectors. The second uses trajectory information from GPS-enabled mobile probe vehicles that can be more spatially distributed. The authors find here that both methods can be adequately used to inform the perimeter flow control schemes. Furthermore, the performance of the network is remarkably consistent when reduced information—from a subset of detectors or probe vehicles—is used to inform the control scheme. Additionally, results suggest that accounting for uncertainty in the state estimates can improve network performance when very few probe vehicles or detectors are used to inform the control scheme. These results are very promising for the implementation of MFD-based network-wide control in practice.

This paper was sponsored by TRB committee AHB45 Traffic Flow Theory and Characteristics.

01550057

15-0621

English

19p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Freeway management systems; Macroscopic traffic flow; Microsimulation; Network links; Probe vehicles; Traffic data; Traffic estimation; Traffic measurement

Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I73: Traffic Control

Transportation Research Board Annual Meeting 2015 Paper #15-0621

TRIS, TRB, ATRI

Dec 30 2014 12:18PM