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The simplest and most desirable method of traffic signal control is precalculated regulation, when the parameters of the traffic light object operation are calculated in advance and activated in accordance to a schedule. This work proposes a method of forming a signal plan that allows one to calculate the control programs and set the period of their activity. Preparation of initial data for the calculation includes the formation of a time series of daily traffic intensity with an interval of 15 minutes. When carrying out field studies, it is possible that part of the traffic intensity measurements is missing. To fill up the missing traffic intensity measurements, the spline interpolation method is used. The next step of the method is to calculate the daily set of signal plans. The work presents the interdependencies, which allow one to calculate the optimal durations of the control cycle and the permitting phase movement and to set the period of their activity. The present movement control systems have a limit on the number of control programs. To reduce the signal plans' number and to determine their activity period, the clusterization using the $k$-means method in the transport phase space is introduced In the new daily signal plan, the duration of the phases is determined by the coordinates of the received cluster centers, and the activity periods are set by the elements included in the cluster. Testing on a numerical illustration showed that, when the number of clusters is 10, the deviation of the optimal phase duration from the cluster centers does not exceed 2 seconds. To evaluate the effectiveness of the developed methodology, a real intersection with traffic light regulation was considered as an example. Based on field studies of traffic patterns and traffic demand, a microscopic model for the SUMO (Simulation of Urban Mobility) program was developed. The efficiency assessment is based on the transport losses estimated by the time spent on movement. Simulation modeling of the multiprogram control of traffic lights showed a 20% reduction in the delay time at the traffic light object in comparison with the single-program control. The proposed method allows automation of the process of calculating daily signal plans and setting the time of their activity.

The problem of application of miscroscopic traffic models for the analysis of large network segments is discussed with an example of discrete flow with safe distance. A concept of integral charasteristics of network segments is introduced, a method for obtaining such characteristics via microscopic traffic flow models is presented. Said method is applied to a circular unidirectional interchange, obtained characteristics analysed.

We present a simulation methodology for the prediction of ЃgunexpectedЃh bottlenecks, i.e., the bottlenecks that occur suddenly and unexpectedly for drivers on a highway. Such unexpected bottlenecks can be either a moving bottleneck (MB) caused by a slow moving vehicle or a motionless bottleneck caused by a stopped vehicle (SV). Based on simulations of a stochastic microscopic traffic flow model in the framework of KernerЃfs three-phase traffic theory, we show that through the use of a small share of probe vehicles (FCD) randomly distributed in traffic flow the reliable prediction of ЃgunexpectedЃh bottlenecks is possible. We have found that the time dependence of the probability of MB and SV prediction as well as the accuracy of the estimation of MB and SV location depend considerably on sequences of phase transitions from free flow (F) to synchronized flow (S) (F→S transition) and back from synchronized flow to free flow (S→F transition) as well as on speed oscillations in synchronized flow at the bottleneck. In the simulation approach, the identification of F→S and S→F transitions at an unexpected bottleneck has been made in accordance with Kerner's three-phase traffic theory. The presented simulation methodology allows us both the prediction of the unexpected bottleneck that suddenly occurs on a highway and the distinguishing of the origin of the unexpected bottleneck, i.e., whether the unexpected bottleneck has occurred due to a MB or a SV.

This article proposes the sumo-atclib framework, which provides a convenient uniform interface for testing adaptive control algorithms with different limitations, for example, restrictions on phase durations, phase sequences, restrictions on the minimum time between control actions, which uses the open source microscopic transport modeling environment SUMO. The framework shares the functionality of controllers (class TrafficController) and a monitoring and detection system (class StateObserver), which repeats the architecture of real traffic light objects and adaptive control systems and simplifies the testing of new algorithms, since combinations of different controllers and vehicle detection systems can be freely varied. Also, unlike most existing solutions, the road class Road has been added, which combines a set of lanes, this allows, for example, to determine the adjacency of regulated intersections, in cases when the number of lanes changes on the way from one intersection to another, and therefore the road graph is divided into several edges. At the same time, the algorithms themselves use the same interface and are abstracted from the specific parameters of the detectors, network topologies, that is, it is assumed that this solution will allow the transport engineer to test ready-made algorithms for a new scenario, without the need to adapt them to new conditions, which speeds up the development process of the control system, and reduces design overhead. At the moment, the package contains examples of MaxPressure algorithms and the Q-learning reinforcement learning method, the database of examples is also being updated. The framework also includes a set of SUMO scripts for testing algorithms, which includes both synthetic maps and well-verified SUMO scripts such as Cologne and Ingolstadt. In addition, the framework provides a set of automatically calculated metrics, such as total travel time, delay time, average speed; the framework also provides a ready-made example for visualization of metrics.

In this paper, we propose an adaptive traffic signal control method invariant to the configuration of the traffic signal. The proposed method uses one neural network model to control traffic signals of various configurations, differing both in the number of controlled lanes and in the used traffic light control cycle (set of phases). To describe the state space, both dynamic information about the current state of the traffic flow and static data about the configuration of a controlled intersection are used. To increase the speed of model training and reduce the required amount of data required for model convergence, it is proposed to use an “expert” who provides additional data for model training. As an expert, we propose to use an adaptive control method based on maximizing the weighted flow of vehicles through an intersection. Experimental studies of the effectiveness of the developed method were carried out in a microscopic simulation software package. The obtained results confirmed the effectiveness of the proposed method in different simulation scenarios. The possibility of using the developed method in a simulation scenario that is not used in the training process was shown. We provide a comparison of the proposed method with other baseline solutions, including the method used as an “expert”. In most scenarios, the developed method showed the best results by average travel time and average waiting time criteria. The advantage over the method used as an expert, depending on the scenario under study, ranged from 2% to 12% according to the criterion of average vehicle waiting time and from 1% to 7% according to the criterion of average travel time.

The purpose of the study is to simulate the dynamics of traffic flows on city road networks as well as to systematize the current state of affairs in this area. The introduction states that the development of intelligent transportation systems as an integral part of modern transportation technologies is coming to the fore. The core of these systems contain adequate mathematical models that allow to simulate traffic as close to reality as possible. The necessity of using supercomputers due to the large amount of calculations is also noted, therefore, the creation of special parallel algorithms is needed. The beginning of the article is devoted to the up-to-date classification of traffic flow models and characterization of each class, including their distinctive features and relevant examples with links. Further, the main focus of the article is shifted towards the development of macroscopic and microscopic models, created by the authors, and determination of the place of these models in the aforementioned classification. The macroscopic model is based on the continuum approach and uses the ideology of quasi-gasdynamic systems of equations. Its advantages are indicated in comparison with existing models of this class. The model is presented both in one-dimensional and two-dimensional versions. The both versions feature the ability to study multi-lane traffic. In the two-dimensional version it is made possible by introduction of the concept of “lateral” velocity, i. e., the speed of changing lanes. The latter version allows for carrying out calculations in the computational domain which corresponds to the actual geometry of the road. The section also presents the test results of modeling vehicle dynamics on a road fragment with the local widening and on a road fragment with traffic lights, including several variants of traffic light regimes. In the first case, the calculations allow to draw interesting conclusions about the impact of a road widening on a road capacity as a whole, and in the second case — to select the optimal regime configuration to obtain the “green wave” effect. The microscopic model is based on the cellular automata theory and the single-lane Nagel – Schreckenberg model and is generalized for the multi-lane case by the authors of the article. The model implements various behavioral strategies of drivers. Test computations for the real transport network section in Moscow city center are presented. To achieve an adequate representation of vehicles moving through the network according to road traffic regulations the authors implemented special algorithms adapted for parallel computing. Test calculations were performed on the K-100 supercomputer installed in the Centre of Collective Usage of KIAM RAS.