Reliability of stochastic capacity estimates
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917287839137792 |
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| author | Mikolasek, Igor |
| author_facet | Mikolasek, Igor |
| contents | Stochastic traffic capacity is used in traffic modelling and control for unidirectional sections of road infrastructure, although some of the estimation methods have recently proved flawed. However, even sound estimation methods require sufficient data. Because breakdowns are rare, the number of recorded breakdowns effectively determines sample size. This is especially relevant for temporary traffic infrastructure, but also for permanent bottlenecks (e.g., on- and off-ramps), where practitioners must know when estimates are reliable enough for control or design decisions. This paper studies this reliability along with the impact of censored data using synthetic data with a known capacity distribution. A corrected maximum-likelihood estimator is applied to varied samples. In total, 360 artificial measurements are created and used to estimate the capacity distribution, and the deviation from the pre-defined distribution is then quantified. Results indicate that at least 50 recorded breakdowns are necessary; 100-200 are the recommended minimum for temporary measurements. Beyond this, further improvements are marginal, with the expected average relative error below 5 %. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19370 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reliability of stochastic capacity estimates Mikolasek, Igor Applications Methodology Stochastic traffic capacity is used in traffic modelling and control for unidirectional sections of road infrastructure, although some of the estimation methods have recently proved flawed. However, even sound estimation methods require sufficient data. Because breakdowns are rare, the number of recorded breakdowns effectively determines sample size. This is especially relevant for temporary traffic infrastructure, but also for permanent bottlenecks (e.g., on- and off-ramps), where practitioners must know when estimates are reliable enough for control or design decisions. This paper studies this reliability along with the impact of censored data using synthetic data with a known capacity distribution. A corrected maximum-likelihood estimator is applied to varied samples. In total, 360 artificial measurements are created and used to estimate the capacity distribution, and the deviation from the pre-defined distribution is then quantified. Results indicate that at least 50 recorded breakdowns are necessary; 100-200 are the recommended minimum for temporary measurements. Beyond this, further improvements are marginal, with the expected average relative error below 5 %. |
| title | Reliability of stochastic capacity estimates |
| topic | Applications Methodology |
| url | https://arxiv.org/abs/2602.19370 |