An analysis of four methodologies for estimating highway capacity from ITS data

Abstract

With the recent advent of Intelligent Transportation Systems (ITS), and their associated data collection and archiving capabilities, there is now a rich data source for transportation professionals to develop capacity values for their own jurisdictions. Unfortunately, there is no consensus on the best approach for estimating capacity from ITS data. The motivation of this paper is to compare and contrast four of the most popular capacity estimation techniques in terms of (1) data requirements, (2) modeling effort required, (3) estimated parameter values, (4) theoretical background, and (5) statistical differences across time and over geographically dispersed locations. Specifically, the first method is the maximum observed value, the second is a standard fundamental diagram curve fitting approach using the popular Van Aerde model, the third method uses the breakdown identification approach, and the fourth method is the survival probability based on product limit method. These four approaches were tested on two test beds: one is located in San Diego, California, U.S., and has data from 112 work days; the other is located in Shanghai, China, and consists of 81 work days. It was found that, irrespective of the estimation methodology and the definition of capacity, the estimated capacity can vary considerably over time. The second finding was that, as expected, the different approaches yielded different capacity results. These estimated capacities varied by as much as 26 % at the San Diego test site and by 34 % at the Shanghai test site. It was also found that each of the methodologies has advantages and disadvantages, and the best method will be the function of the available data, the application, and the goals of the modeler. Consequently, it is critical for users of automatic capacity estimation techniques, which utilize ITS data, to understand the underlying assumptions of each of the different approaches.

Development of Capacity Estimation Models for Multi-lane Roads under Heterogeneous Traffic Conditions

“…5min, 15-min, 60-min, etc.) and the total period of flow observation are important factors that affect the estimated capacity [32]. The maximum capacity method is shown in Equation 3.…”

“…Van Aerde proposed a four-parameter model to estimate capacity which has more degrees of freedom to capture a range of behaviours on different facility types [33]. This model is designed specifically to be calibrated using Intelligent Transportation Systems (ITS) data such as inductive loop data, radar and video detector data [32]. Among other capacity estimation methodologies, two methods that are commonly used are the Breakdown capacity methodology and the Product Limit Method (PLM) for capacity estimation.…”

“…Based on this finding other researchers have developed the PLM to estimate the capacity distribution function from empirical data. Li & Laurence relate the PLM to the flows that cause breakdown in flow and the flows that do not cause breakdown to develop the capacity distribution function [32]. The drawback with these methods and also the Van Aerde method is that they are data intensive methods.…”

Effect of Median Application on Section Road Performance Based on Degree of Saturation and Speed at Road of Small Town in Lombok, Indonesia

“…Road segment capacity is the maximum stable traffic movement on a road cross-section under geometrics, direction separators, traffic composition, and environmental conditions [10] [17].…”