Estimation of stochastic traffic capacity using extreme value theory and censoring: A case study in Salem, New Hampshire
Journal Title: Archives of Transport - Year 2018, Vol 4, Issue 48
Abstract
In this work, we introduce a method of estimating stochastic freeway capacity using elements of both extreme value theory and survival analysis. First, we define capacity data, or estimates of the capacity of the roadway, as the daily maximum flow values. Then, under a survival analysis premise, we introduce censoring into our definition. That is, on days when flows are sufficiently high and congestion occurs, corresponding flow maxima are considered true estimates of capacity; otherwise, for those days that do not observe high flows or congestion, flow maxima are deemed censored observations and capacities must be higher than the observations. By extreme value theory, the collection of flow maxima (block maxima) can be appropriately approximated with a generalized extreme value (GEV) distribution. Because of small sample sizes and the presence of censoring, a Bayesian framework is pursued for model fitting and parameter estimation. To lend credence to our proposed methodology, the procedure is applied to real-world traffic stream data collected by the New Hampshire Department of Transportation (NHDOT) at a busy location on Interstate I-93 near Salem, New Hampshire. Data were collected over a period of 11 months and raw data were aggregated into 15-minute intervals. To assess our procedure, and to provide proof of concept, several validation procedures are presented. First, using distinct training and validation subsets of our data, the procedure yields accurate predictions of highway capacity. Next, our procedure is applied to a training set to yield random capacities which are then used to predict breakdown in the validation set. The frequency of these predicted breakdowns is found to be statistically similar to observed breakdowns observed in our validation set. Lastly, after comparing our methodology to other methods of stochastic capacity estimation, we find our procedure to be highly successful.<br/><br/>
Authors and Affiliations
Eric M. LAFLAMME, Paul J. OSSENBRUGGEN
Keywords
Related Articles
- EP ID EP431370
- DOI 10.5604/01.3001.0012.8366
- Views 125
- Downloads 0
Development and implementation of technology to measure truck crossing time at international land ports of entry
Public and private stakeholders that operate land border crossings are increasingly concerned about long wait times for trucks crossing from Mexico into the United States. Long wait times are detrimental to the regional...
Optimisation Algorithm for the Profit from Operation of a Barge Train versus Settings of the Main Engine During Inland Navigation in Varying Water Resistance Conditions (Part 2)
The test calculations were carried out according to the profit optimisation algorithm of inland waterway transport means operation considering a division of the waterway into sectors detailed described in the first part...
Semi-Markov Model of the Operation and Maintenance Process of City Buses
This paper presents a model of the operation and maintenance process of the technical objects within the urban transport bus system. All the investigations have been presented on the basis of the selected real operation...
Implementation of Gravity Model to Estimation of Transportation Market Shares
The theoretical consideration presented in the paper is inspired by market gravity models, as an interesting attitude towards operations research on a market. The transportation market issues are emphasized. The mathemat...
The Concept of a Logistics Centre Model as a Nodal Point of a Transport and Logistics Network
The paper presents a concept of a logistics centre model. The model is based on defined output/input flows of goods. Therefore it is possible to determine and estimate the load of elements of the centre infrastructure be...