An Empirical Analysis of Vehicle Time Headways on Rural Two-lane Two-way Roads

Rossi, Riccardo; Gastaldi, Massimiliano · 2012 · OpenAlex-citations

DOI: 10.1016/j.sbspro.2012.09.802

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the lack of empirical data regarding vehicle time headway distributions on rural two-lane two-way roads in Italy, a gap that hinders accurate traffic engineering applications such as capacity analysis, level of service assessment, and micro-simulation modeling. The study aims to characterize these distributions by analyzing real-world traffic data to identify probability density functions (pdfs) that best fit observed headways under varying traffic conditions. The researchers conducted an empirical analysis using data collected from four Automatic Traffic Recorder (ATR) sites equipped with inductive loop detectors on rural roads in the Province of Venice, Northern Italy. The data covered various time periods, capturing traffic volumes ranging from 100 to 1,200 vehicles per lane per hour, with heavy vehicle percentages remaining low (under 6.1%). The methodology involved dividing observation periods into 15-minute sub-intervals to assume steady flow conditions. Traffic volumes were converted to passenger car equivalents (pce) using a factor of 2.0 for heavy vehicles. These data were then classified into eight flow rate ranges, from 0–200 up to 1,400–1,600 pce/hour. Using Stat::Fit® software, the authors estimated pdfs for each sample via the Maximum Likelihood method and evaluated their goodness-of-fit using the Kolmogorov-Smirnov test at a 0.05 significance level. To prevent over-sensitivity in large samples, random sub-samples of approximately 200 headways were used for testing. The analysis of 77 headway samples revealed that the Inverse Weibull distribution provided the best fit for the majority of flow rate ranges across all analyzed sections. This distribution was characterized by a peak in the left part of the positive domain and a long positive tail, with curves shifting as flow rates increased. However, at the highest flow rates (1,200–1,400 and 1,400–1,600 pce/hour), the Inverse Weibull distribution failed to fit the data effectively. In these high-volume scenarios, Pearson 5 and Log-Logistic distributions emerged as the statistically superior models. Other evaluated distributions, including Negative Exponential, Lognormal, Gamma, and Erlang, were less consistent or significant across the broader range of conditions. The significance of this study lies in providing a validated catalogue of headway pdfs specific to rural two-lane roads in the Italian context. By identifying the Inverse Weibull as the primary model for most conditions and Pearson 5/Log-Logistic for high-volume scenarios, the research offers precise parameters for traffic simulation software and operational analysis. This contributes to more accurate modeling of driver behavior, gap-acceptance, and traffic flow dynamics, addressing a previously underrepresented area in traffic engineering literature.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• following distance

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data