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New approaches to control stochastic non-linear time-variant processes include the application of machine learning techniques. One of the problems with learning- based controllers is their reliability in a wide area of process parameters as the controller is trained using a limited set of representative scenarios, either chosen by the designer or taken from historic records. Thus, reliable controller behavior can be guaranteed only in scenarios applied during controller training. Due to the very larger number of random variables and possible scenarios, not all variations can be applied in the controller training process using simulators to guarantee good controller behavior when applied in a real system. One case is traffic control (signal programs, variable speed limit, ramp metering) having large travel patterns variety. The concept of Structured Simulations Framework (SSF) can cover most probable learning scenarios. Thus, applying SSF enables a systematic controller training approach by complementing existing scenarios with synthesized ones that evoke or replicate substantial aspects of real traffic. Such training is necessary to ensure reliable learning-based controllers. This paper discusses the concept of applying SSF to ensure the reliability of learning-based controllers and proposes the application in traffic control for the case of variable speed limits on motorways.

This paper focuses on the method used to calibrate a simulation of a Variable Speed Limit Control (VSLC) system implemented in a Brazilian highway with a high share of trucks and fleet heterogeneity. To achieve a validated model, crucial parameters, such as the car-following and the lane change rules, have to be adjusted, and there must be a metric for comparison, which is presented in this work. The VSLC is added to the validated model and has to be adjusted to the local situation considering the flow and average speed throughout the day. The implementation of VSLC aims to reduce congestion and homogenization of the vehicles’ average speed throughout the day. The microscopic traffic simulator VISSIM and MATLAB were applied to implement and evaluate the calibrated VSLC proving its effectiveness. As a result, a valid VISSIM simulation model, parameters for VSLC calibration, and an improvement of the average speed up to 5.9% were obtained.

Intelligent Transportation Systems (ITS) offer a solution for alleviating congestion in urban areas based on its traffic control service. It is part of the build + ITS approach where new infrastructure with different ITS services is applied to improve traffic throughput, reduce vehicle emissions, increase the Level of Service (LoS), and improve the usage of existing vehicles with the Mobility as a Service (MaaS) concept. The latter enables the cities to offer mobility users concepts of car-sharing, ride-sharing, and tailored multi-modal travel patterns. The city or urban road infrastructure can be divided into urban motorways and networks of signalized intersections. Traffic control approaches related to Variable Speed Limit (VSL) control, ramp metering, lane change recommendation, and Adaptive Traffic Signal Control (ATSC) are applied to manage the urban road infrastructure.

U uvjetima povećane prometne potražnje na urbanim autocestama javlja se potreba za uvođenjem upravljanja prometom s ciljem prevencije i smanjenja posljedica nastajanja prometnih zagušenja. Jedno od rješenja iz područja inteligentnih transportnih sustava je sustav za upravljanje ograničenjem brzine na urbanim autocestama. Noviji pristup u upravljanju promjenjivim ograničenjem brzine na urbanim autocestama koristi algoritme zasnovane na učenju. U radu je opisano upravljanje prometom na urbanim autocestama zasnovano na upravljanju promjenjivim ograničenjem brzine, analizirana je mogućnost primjene učenja s posebnim naglaskom na algoritmu Q-učenja u upravljanju promjenjivim ograničenjem brzine te je izrađeno okruženje za simulaciju takvog upravljanja korištenjem mikroskopskog simulatora VISSIM i programskog paketa MATLAB.

Urban motorways serve the traffic demand from adjacent dense populated urban regions and transit traffic. Combinations of these two traffic demand types generate periodically traffic congestions during the peak hours. The capacity drop effect is the most common cause of congestions. It occurs when the current traffic flow reached its maximal capacity of the observed urban motorway section. In order to alleviate the capacity drop effect and enable better level of service on motorways new traffic control methods are introduced. One of the used traffic control methods is variable speed limit control (VSLC). VSLC conducts homogenization and reduction of vehicle speeds what consequently decreases time needed to create a critical shockwave, which will induce the capacity drop. This paper presents a short overview of the latest VSLC algorithms originally designed for mitigation of traffic congestions and their effects on the motorway mainline. Firstly, simple reactive VSLC algorithms are introduced. Then approaches based on model based optimization are described. Possible coordination between VSLC and other motorway management strategies is also described in this paper.

Za provjeru novih rješenja upravljanja cestovnom mrežom koriste se simulacije. Takva provjera se uvijek radi prije implementacije novih rješenja u realni upravljački sustav. Odabir odgovarajućeg kvalitetnog simulacijskog modela je bitan radi dobre simulacijske provjere rješenja. Iz tog razloga sve češće se za modeliranje cestovnih mreža koriste modeli drugog reda. U radu je napravljen pregled postojećih modela cestovnih mreža uz naglasak na modele drugog reda. Detaljnije je opisan AW-Rascle model drugog reda i razrađen je postupak implementacije istog u programski paket Matlab.