A philosophical context for methods to estimate origin-destination trip matrices using link counts.

This paper creates a philosophical structure for classifying methods which estimate origin-destination matrices using link counts. It is claimed that the motivation for doing so is to help real-life transport planners use matrix estimation methods effectively, especially in terms of trading-off observational data with prior subjective input (typically referred to as 'professional judgement'). The paper lists a number of applications that require such methods, differentiating between relatively simple and highly complex applications. It is argued that a sound philosophical perspective is particularly important for estimating trip matrices in the latter type of application. As a result of this argument, a classification structure is built up through using concepts of realism, subjectivity, empiricism and rationalism. Emphasis is put on the fact that, in typical transport planning applications, none of these concepts is useful in its extreme form. The structure is then used to make a review of methods for estimating trip matrices using link counts, covering material published over the past 30 years. The paper concludes by making recommendations, both philosophical and methodological, concerning both practical applications and further research

A philosophical context for methods to estimate origin-destination trip matrices using link counts.

This paper creates a philosophical structure for classifying methods which estimate origin-destination matrices using link counts. It is claimed that the motivation for doing so is to help real-life transport planners use matrix estimation methods effectively, especially in terms of trading-off observational data with prior subjective input (typically referred to as 'professional judgement'). The paper lists a number of applications that require such methods, differentiating between relatively simple and highly complex applications. It is argued that a sound philosophical perspective is particularly important for estimating trip matrices in the latter type of application. As a result of this argument, a classification structure is built up through using concepts of realism, subjectivity, empiricism and rationalism. Emphasis is put on the fact that, in typical transport planning applications, none of these concepts is useful in its extreme form. The structure is then used to make a review of methods for estimating trip matrices using link counts, covering material published over the past 30 years. The paper concludes by making recommendations, both philosophical and methodological, concerning both practical applications and further research

Measuring and Understanding Throughput of Network Topologies

High throughput is of particular interest in data center and HPC networks. Although myriad network topologies have been proposed, a broad head-to-head comparison across topologies and across traffic patterns is absent, and the right way to compare worst-case throughput performance is a subtle problem. In this paper, we develop a framework to benchmark the throughput of network topologies, using a two-pronged approach. First, we study performance on a variety of synthetic and experimentally-measured traffic matrices (TMs). Second, we show how to measure worst-case throughput by generating a near-worst-case TM for any given topology. We apply the framework to study the performance of these TMs in a wide range of network topologies, revealing insights into the performance of topologies with scaling, robustness of performance across TMs, and the effect of scattered workload placement. Our evaluation code is freely available

Methods to estimate link level travel based on spatial effects

Annual Average Daily Traffic (AADT) is used in several planning, roadway design, operational and safety analyses by transportation planners and engineers. Existing methods are very complex and do not adequately address the modeling needs. Errors and inaccuracies in a traditional four-step method get carried to later steps often resulting in incorrect estimates of travel demand. The primary focus of this research is to develop a systematic and simplified methodology to estimate link level travel on roadways. The proposed methodology involves scientific principles and statistical techniques, but bypasses the tedious four-step method. Two spatial methods, first one based on “spatial proximity” and second one based on “spatial weighting”, are proposed to estimate link level travel. While the former method investigates to identify ideal “proximal” distance to capture spatial data, the later method involves application of “spatial weights” that decrease with an increase in distance to integrate spatial data from multiple buffer bandwidths. Generalized Estimating Equations (GEE) models are developed for both the methods using Poisson and Negative Binomial distributions with and without network characteristics to facilitate transportation planning and analysis. Validation of the developed models is carried out using Chi-Square Statistic test. The goodness of fit statistics indicates that Negative Binomial models performed better than Poisson models. Models with network characteristics performed better than models without network characteristics. Model validation results indicate that link level travel can be accurately estimated using both the spatial methods