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.

Optimal Sensor Locations for Freeway Bottleneck Identification

“…A study by Fujito et al (2006) determined that the actual location of sensors is more important for the estimation of congestion along a transportation corridor than uniform spacing. Empirical analysis showed that results varied according to the positions in which sensors were removed.…”

Optimal sensing of urban road networks with large-scale ridesourcing vehicles

“…Later, researchers proposed different optimization-based frameworks to find the optimal sensor placement considering the travel time estimation and traffic OD flow count (Yang and Zhou 1998, Ban et al 2009, Zhou and List 2010, Li and Ouyang 2011. Besides, the reliability of sensor location on the travel time estimation (Fujito et al 2006, Ban et al 2007 and OD coverage (Fei et al 2007(Fei et al , 2013 has also been investigated. With a limited budget for fixedlocation sensors, complete coverage on every road segment can hardly be achieved.…”

Impact of Accurate Detection of Freeway Traffic Conditions on the Dynamic Pricing: A Case Study of I-95 Express Lanes

“…For instance, Chaudhuri et al [ 11 ] studied the spacing of loop sensors and found that the spacing between sensors significantly impacts the accuracy of travel-time estimates. Fujito et al [ 12 ] investigated the impact of sensor spacing on estimating traffic performance measures. They concluded that the actual sensor locations influence travel time estimation more than the spacing between them.…”

“…After the first implementations of HOT lanes in California [6], several studies had included evaluations and innovations of this concept [6][7][8][9][10]. Multiple research efforts have investigated the impact of sensor density (spacing) [11][12][13]. An older study analyzed the reliability and health of the installed sensors [14].…”