Effect of Sensor Spacing on Performance Measure Calculations

Abstract

State and local departments of transportation (DOTs) increasingly deploy road detectors, such as inductive loops, to monitor congestion on their road networks. As deployment increases, the operating and maintenance cost associated with these detector systems will become issues for many state DOTs. Agencies will need to decide where to add new detectors and which detectors should continue receiving maintenance, given their resource constraints. For data collected from these sensors to remain meaningful, traffic data quality should not be adversely affected in these decisions. The needed traffic data quality depends on the data's intended purposes. An empirical study was conducted to address the impact of sensor spacing along freeway corridors on the computation of performance measures such as travel time index. The scenario that has the smallest sensor spacing (greater density of sensors) is considered to capture actual traffic conditions most closely. If sensor spacing is increased, how will the quality of the traffic data be affected? The results showed that when sensors were deleted relative to the baseline sensor spacing condition, the congestion measure statistic varied. This congestion measure did not become “worse” as more sensors were deleted. Instead, sometimes one spacing pattern overestimated the measure, and at other times another spacing pattern underestimated this measure. The analysis showed that the location of the sensors is important in the estimation of congestion for the corridor.