کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
292740 1363106 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Path sets size, model specification, or model estimation: Which one matters most in predicting stochastic user equilibrium traffic flow?
ترجمه فارسی عنوان
اندازه مجموعه مسیر، مشخصات مدل، یا برآورد مدل: کدام یک بیشتر در پیش بینی جریان ترافیک تعادلی کاربر تصادفی مورد استفاده قرار می گیرد؟
کلمات کلیدی
تخصیص ترافیک تصادفی؛ تخصیص ترافیک مبتنی بر مسیر؛ نسل راه؛ پارامتر پراکندگی؛
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی هوافضا
چکیده انگلیسی

This study aims to make an objective comparative analysis between the relative significance of three crucial modelling aspects involved in the probabilistic analysis of transport networks. The first question to address is the extent to which the size of generated path sets can affect the prediction of the static flow in the path-based traffic assignment paradigm. The importance of this question arises from the fact that the need to generate a large quantity of paths may be perceived by analysts as a preventative reason as to the application of path-based stochastic traffic assignment (STA) models for large-scale networks. A simulated path generation algorithm, which allows the number of generated paths to be under modeller's control, is applied. Findings show that the size of the generated path sets does not substantially affect the flow prediction outcome in this case study.Further investigations with respect to the relative importance of STA model estimation (or equivalently, parameter calibration) and model specification (or equivalently, error term formulation) are also conducted. A paired combinatorial logit (PCL) assignment model with an origin–destination-specific-parameter, along with a heuristic method of model estimation (calibration), is proposed. The proposed model cannot only accommodate the correlation between path utilities, but also accounts for the fact that travelling between different origin–destination (O–D) pairs can correspond to different levels of stochasticity and choice randomness. Results suggest that the estimation of the stochastic user equilibrium (SUE) models can affect the outcome of the flow prediction far more meaningfully than the complexity of the choice model (i.e., model specification).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Traffic and Transportation Engineering (English Edition) - Volume 3, Issue 3, June 2016, Pages 181–191
نویسندگان
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