arXiv subjects

Igor Mikolasek

Publications and source records attributed to Igor Mikolasek.

3 recordsLinked to original sources

arXiv2026

Reliability of stochastic capacity estimates

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 %.

stat.AP↗

arXiv2025

New stochastic highway capacity estimation method and why product limit method is unsuitable

Kaplan-Meier estimate, commonly known as product limit method (PLM), and maximum likelihood estimate (MLE) methods in general are often cited as means of stochastic highway capacity estimation. This article discusses their unsuitability for such application as properties of traffic flow do not meet the assumptions for use of the methods. They assume the observed subject has a history which it went through and did not fail. However, due to its nature, each traffic flow measurement behaves as a separate subject which did not go through all the lower levels of intensity (did not "age"). An alternative method is proposed. It fits the resulting cumulative frequency of breakdowns with respect to the traffic flow intensity leading to the breakdown instead of directly estimating the underlying probability distribution of capacity. Analyses of accuracy and sensitivity to data quantity and censoring rate of the new method are provided along with comparison to the PLM. The results prove unsuitability of the PLM and MLE methods in general. The new method is then used in a case study which compares capacity of a work-zone with and without a traffic flow speed harmonisation system installed. The results confirm positive effect of harmonisation on capacity.

stat.AP↗

arXiv2024

Data Sharing at the Edge of the Network: A Disturbance Resilient Multi-modal ITS

Mobility-as-a-Service (MaaS) is a paradigm that encourages the shift from private cars to more sustainable alternative mobility services. MaaS provides services that enhances and enables multiple modes of transport to operate seamlessly and bringing Multimodal Intelligent Transport Systems (M-ITS) closer to reality. This requires sharing and integration of data collected from multiple sources including modes of transports, sensors, and end-users' devices to allow a seamless and integrated services especially during unprecedented disturbances. This paper discusses the interactions among transportation modes, networks, potential disturbance scenarios, and adaptation strategies to mitigate their impact on MaaS. We particularly discuss the need to share data between the modes of transport and relevant entities that are at the vicinity of each other, taking advantage of edge computing technology to avoid any latency due to communication to the cloud and privacy concerns. However, when sharing at the edge, bandwidth, storage, and computational limitations must be considered.

cs.DC↗