Path-Based Stochastic Traffic Assignment: An Investigation on the Effect of Choice-Sets Size, Model Specification and Model Calibration on Prediction of Static Flow

This research aims to make a comprehensive and objective comparison and assessment between the relative significance of three crucial issues in the context of probabilistic network analysis and prediction of static stochastic pattern of flow. In recent years, there has been a growing recognition towards the advantage of path-based methods of stochastic traffic assignment (STA). It has been well established that explicit treatment of path-flow variables allows application of more advanced random utility models in analysis of transportation networks. This research is intended to investigate the question that to what extent the size of generated path sets can affect prediction of flows. A simulated path generation algorithm was applied to an illustrative network and the investigations showed that the size of generated path sets only slightly affects the flow prediction. Further investigations have also been conducted with respect to the relative importance of model estimation and model specification in STA. In the literature of univariate STA, there has been considerable attention to utilization of sophisticated choice models to address different problems in this context including the problem of path overlapping. However, in most of these researches the problem of estimating the single calibration parameter of these models has been neglected and the models were studied using pre-specified parameters. A structured-parameter paired combinatorial logit (PCL) assignment model along with a practical and heuristic method of calibration is proposed in this study. The proposed model not only can represent the correlation among path utilities, but also considers the fact that traveling between different origin-destination (O-D) pairs may correspond to different levels of stochasticity. Results showed that calibration of stochastic user equilibrium (SUE) models could more meaningfully affect the prediction of flow than selection (specification) of the choice model.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ADB30(2) Network Equilibrium Modeling. Alternate title: Path-Based Stochastic Traffic Assignment: Investigation of the Effect of Choice-Set Size, Model Specification, and Model Calibration on Prediction of Static Flow.
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Haghani, Milad
  • Shahhoseini, Zahra
  • Sarvi, Majid
• Conference:
  • Transportation Research Board 93rd Annual Meeting
  • Location: Washington DC
  • Date: 2014-1-12 to 2014-1-16
• Date: 2014

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 17p
• Monograph Title: TRB 93rd Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Calibration; Heuristic methods; Logits; Origin and destination; Stochastic processes; Traffic assignment; Traffic flow
• Uncontrolled Terms: Path based algorithms; Stochastic user equilibrium models
• Subject Areas: Highways; Planning and Forecasting; I71: Traffic Theory; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01514303
• Record Type: Publication
• Report/Paper Numbers: 14-0578
• Files: TRIS, TRB, ATRI
• Created Date: Feb 12 2014 12:32PM