Kooperativno upravljanje priljevnim tokovima na urbanim autocestama zasnovano na strojnom učenju

To cope with today’s urban motorway congestions and the inability to increase motorway capacity in urban environments requires the implementation of advanced control methods. These methods are an integral part of Intelligent Transportation Systems (ITS). An ITS essentially integrates information and communication technology to solve the congestion problems. Ramp metering (RM) and Variable Speed Limit Control (VSLC) are some of the most widely used urban motorway traffic control methods. RM provide direct influence over the on-ramp flows by using specialized traffic lights, while the VSLC control speed of mainstream flow by using variable messaging signs. A dedicated algorithm for RM or VSLC uses sensory data form an urban motorway to compute actions that will have a positive impact on both types of traffic flow. This study will focus on the cooperation of an RM and a VSLC systems, and the integration of several different RM algorithms into a single algorithm called INTEGRA. The algorithm is created by using the Adaptive Neuro-fuzzy Inference System (ANFIS) as an instance of machine learning techniques. Furthermore, INTGERA is expanded in order to integrate its original functionality with a recurrent neural network for traffic demand prediction. As the final step, this doctoral thesis will provide evaluation of different criteria for learning dataset functional setup, based on which ANFIS neural network of INTEGRA will be learned. Results of all mentioned approaches will be compared and discussed in relation with other commonly used urban motorway control methods.Glavnina istraživanja u ovom doktorskom radu vezana je upravo za upravljanje priljevnim tokovima s posebnim naglaskom na kooperaciju s drugim sustavima upravljanja prometom, te primjeni strojnog učenja. Također, u kooperaciji s upravljanjem priljevnih tokova razmatrat će se druge upravljačke metode kao što su sustav zabrane prometovanja određenim prometnim trakama, te potpuno ili djelomično upravljanje vozilima opremljenim posebnim računalnim jedinicama. Od strane autora predložen je neuro-neizraziti okvir za učenje koji omogućuje integraciju različitih strategija upravljanja priljevnim tokovima. CTMSIM makro-simulacijski alat koji je izrađen u Matlab programskom okruženju korišten je u simulaciji odabranih metoda upravljanja prometom na urbanim autocestama. Simulator je proširen od strana autora kako bi podržao kooperativno upravljanje priljevnim tokovima, kao i sustav za promjenjivo ograničenje brzina vozila

Kooperativno upravljanje priljevnim tokovima na urbanim autocestama zasnovano na strojnom učenju

To cope with today’s urban motorway congestions and the inability to increase motorway capacity in urban environments requires the implementation of advanced control methods. These methods are an integral part of Intelligent Transportation Systems (ITS). An ITS essentially integrates information and communication technology to solve the congestion problems. Ramp metering (RM) and Variable Speed Limit Control (VSLC) are some of the most widely used urban motorway traffic control methods. RM provide direct influence over the on-ramp flows by using specialized traffic lights, while the VSLC control speed of mainstream flow by using variable messaging signs. A dedicated algorithm for RM or VSLC uses sensory data form an urban motorway to compute actions that will have a positive impact on both types of traffic flow. This study will focus on the cooperation of an RM and a VSLC systems, and the integration of several different RM algorithms into a single algorithm called INTEGRA. The algorithm is created by using the Adaptive Neuro-fuzzy Inference System (ANFIS) as an instance of machine learning techniques. Furthermore, INTGERA is expanded in order to integrate its original functionality with a recurrent neural network for traffic demand prediction. As the final step, this doctoral thesis will provide evaluation of different criteria for learning dataset functional setup, based on which ANFIS neural network of INTEGRA will be learned. Results of all mentioned approaches will be compared and discussed in relation with other commonly used urban motorway control methods.Glavnina istraživanja u ovom doktorskom radu vezana je upravo za upravljanje priljevnim tokovima s posebnim naglaskom na kooperaciju s drugim sustavima upravljanja prometom, te primjeni strojnog učenja. Također, u kooperaciji s upravljanjem priljevnih tokova razmatrat će se druge upravljačke metode kao što su sustav zabrane prometovanja određenim prometnim trakama, te potpuno ili djelomično upravljanje vozilima opremljenim posebnim računalnim jedinicama. Od strane autora predložen je neuro-neizraziti okvir za učenje koji omogućuje integraciju različitih strategija upravljanja priljevnim tokovima. CTMSIM makro-simulacijski alat koji je izrađen u Matlab programskom okruženju korišten je u simulaciji odabranih metoda upravljanja prometom na urbanim autocestama. Simulator je proširen od strana autora kako bi podržao kooperativno upravljanje priljevnim tokovima, kao i sustav za promjenjivo ograničenje brzina vozila

New concepts for urban highways control

In recent decades a significant increase in traffic demand has occurred. This trend is especially present in dense populated areas where daily traffic congestions during rush hours occur regularly. Congestions are significant in road traffic where they can simultaneously reduce public transportation level of service (LoS) also. As consequence, even more people are using their car additionally increasing the congestion problem. Classic solution for solving the road traffic congestion problem is infrastructure build up. Today’s dense urban areas mostly do not allow this approach because of the lack of available building space. More advanced road traffic control solutions from the domain of intelligent transportation systems (ITS) are being more and more applied to optimally use the existing infrastructure (Papageorgiou et al., 2003.). Such solutions include coordination between several consecutive crossroads, dynamic traffic assignment, driver informing systems, etc. One of the ITS application areas is related to urban highways which present a class of highways used as a city bypass or are just passing a dense urban are. Their main characteristic is that they have a larger number of on- and off-ramps often placed at small distances. Due to the small distance, mutual on-ramp influence can occur enlarging the problems of daily congestions and associated decrease of highway LoS. In order to prevent the appearance of traffic standstill or to reduce its duration control approaches as ramp metering and variable speed limit control (VSLC) are being applied (Hegyi et al., 2010.). In recent years, new cooperative concepts between several on-ramps and VSLC are used as a combined urban highway control system (Ghods et al., 2007.). This paper presents a new learning based cooperative ramp metering strategy in which several well-known ramp metering strategies (ALIENA, SWARM, HERO) are used to create a learning set for an ANFIS (Gregurić et al., 2013.) based control structure. Optimal ramp metering values are obtained for a wide range of traffic demand on the urban highway and belonging on-ramps. Optimal ramp metering values for specific traffic demand characteristics obtained from most suitable ramp metering strategies are integrated into only one control strategy. Thus, the need of applying several ramp metering strategies and switching between them is avoided. Additionally, cooperation between VSLC and vehicle control by an on-board unit is described and a discussion about possible implementation is given. Proposed cooperative urban highway management approach is tested in simulations using the city of Zagreb bypass as case study. For simulation, the macroscopic highway traffic simulator CTMsim (Kurzhanskiy et al., 2008.) is used. Used CTMsim simulator augmented to enable simulation of VSLC and cooperative ramp metering approaches

Kooperativno upravljanje priljevnim tokovima na urbanim autocestama zasnovano na strojnom učenju

To cope with today’s urban motorway congestions and the inability to increase motorway capacity in urban environments requires the implementation of advanced control methods. These methods are an integral part of Intelligent Transportation Systems (ITS). An ITS essentially integrates information and communication technology to solve the congestion problems. Ramp metering (RM) and Variable Speed Limit Control (VSLC) are some of the most widely used urban motorway traffic control methods. RM provide direct influence over the on-ramp flows by using specialized traffic lights, while the VSLC control speed of mainstream flow by using variable messaging signs. A dedicated algorithm for RM or VSLC uses sensory data form an urban motorway to compute actions that will have a positive impact on both types of traffic flow. This study will focus on the cooperation of an RM and a VSLC systems, and the integration of several different RM algorithms into a single algorithm called INTEGRA. The algorithm is created by using the Adaptive Neuro-fuzzy Inference System (ANFIS) as an instance of machine learning techniques. Furthermore, INTGERA is expanded in order to integrate its original functionality with a recurrent neural network for traffic demand prediction. As the final step, this doctoral thesis will provide evaluation of different criteria for learning dataset functional setup, based on which ANFIS neural network of INTEGRA will be learned. Results of all mentioned approaches will be compared and discussed in relation with other commonly used urban motorway control methods.Glavnina istraživanja u ovom doktorskom radu vezana je upravo za upravljanje priljevnim tokovima s posebnim naglaskom na kooperaciju s drugim sustavima upravljanja prometom, te primjeni strojnog učenja. Također, u kooperaciji s upravljanjem priljevnih tokova razmatrat će se druge upravljačke metode kao što su sustav zabrane prometovanja određenim prometnim trakama, te potpuno ili djelomično upravljanje vozilima opremljenim posebnim računalnim jedinicama. Od strane autora predložen je neuro-neizraziti okvir za učenje koji omogućuje integraciju različitih strategija upravljanja priljevnim tokovima. CTMSIM makro-simulacijski alat koji je izrađen u Matlab programskom okruženju korišten je u simulaciji odabranih metoda upravljanja prometom na urbanim autocestama. Simulator je proširen od strana autora kako bi podržao kooperativno upravljanje priljevnim tokovima, kao i sustav za promjenjivo ograničenje brzina vozila

IMPLEMENTATION AND MAINTENANCE OF TRAFFIC SIGNS IN A AROAD TRAFFIC

U ovom radu ćemo preko povijesnog razvoja prometnih znakova govorit o glavnim značajkama i elementima te isto tako o vrstama i prisutnosti pojedinih prometnih znakova u današnjem suvremenom cestovnom prometu. Isto tako opisati ćemo instaliranje i pozicioniranje određenih znakova te znakovnih stupova uz određene dijelove ceste, izvođenje tehničke inspekcije, održavanje i popravak prometnih znakova te mjerenje i načine mjerenja retrorefleksije prometnih znakova. Na kraju ćemo sagledati održavanje podataka unutar jedne agencije koje omogućava konstatno praćenje svih instalacija bilo novih ili promjenjenih na cestovnim prometnicama.In this thesis throughout the history and development of traffic signs we will be talking about main features, elements and the certan types of traffic signs in modern road traffic. Also we'll describe positioning and installation of signs and sign supports on the certan road and roadside areas, tehnical inspection procedures, sign maintaining and repairing, and measuring of a sign's retroreflectivity. At the end we will look at the record keeping within the agency, which helps with effective and efficient constant management of all instalations wether they are the new or the old one

Cooperative ramp metering simulation

The increase of vehicle numbers in recent decades resulted in road traffic congestion problems. Such congestions are a characteristic of densely populated urban areas and occur daily during morning and afternoon rush hours. Urban areas have been suffering from the lack of space needed to build new road infrastructure. The traffic congestion problem can be solved by applying new traffic control approaches from the domain of intelligent transportation systems (ITS). One of the applied methods from ITS is known as ramp metering and is used to increase the throughput of urban highways with many on- and off-ramps. Nowadays ramp metering is used in cooperation with additional control approaches like variable speed limit control (VSLC). Prior to implementation, such cooperative traffic control systems have to be tested in simulations using real world traffic data. One of the used simulators is CTMSIM which enables macroscopic simulation of highway traffic and local ramp metering approaches. In this paper the CTMSIM simulator is augmented to enable simulation of cooperative ramp metering algorithms, stand-alone VSLC, and cooperation between ramp metering and VSLC. Augmented simulator is tested using some limited available traffic data with the Zagreb bypass urban highway as a case study

The Use of Cooperative Approach in Ramp Metering

To ensure higher Level of Service (LoS) at urban motorways, new traffic control concepts are being applied since in most cases there is no available space for infrastructural build-up. For urban motorways, the mostly used control methods are ramp metering combined with additional control methods like variable speed limit control (VSLC). This paper gives a review of the current ramp metering approaches with special emphasis on cooperative control concepts between ramp metering, VSLC, prohibiting lane changes system and the vehicle itself. Additionally, a learning framework for ramp metering proposed by the authors is described. The CTMSIM Matlab based macroscopic motorway simulator with ramp metering control support is used for the simulation of selected ramp metering approaches. The simulator is also augmented to enable the development and implementation of cooperative ramp metering approaches. The Zagreb bypass is used as test case for evaluation of several different ramp metering algorithms

Spatial-Temporal Traffic Flow Control on Motorways Using Distributed Multi-Agent Reinforcement Learning

The prevailing variable speed limit (VSL) systems as an effective strategy for traffic control on motorways have the disadvantage that they only work with static VSL zones. Under changing traffic conditions, VSL systems with static VSL zones may perform suboptimally. Therefore, the adaptive design of VSL zones is required in traffic scenarios where congestion characteristics vary widely over space and time. To address this problem, we propose a novel distributed spatial-temporal multi-agent VSL (DWL-ST-VSL) approach capable of dynamically adjusting the length and position of VSL zones to complement the adjustment of speed limits in current VSL control systems. To model DWL-ST-VSL, distributed W-learning (DWL), a reinforcement learning (RL)-based algorithm for collaborative agent-based self-optimization toward multiple policies, is used. Each agent uses RL to learn local policies, thereby maximizing travel speed and eliminating congestion. In addition to local policies, through the concept of remote policies, agents learn how their actions affect their immediate neighbours and which policy or action is preferred in a given situation. To assess the impact of deploying additional agents in the control loop and the different cooperation levels on the control process, DWL-ST-VSL is evaluated in a four-agent configuration (DWL4-ST-VSL). This evaluation is done via SUMO microscopic simulations using collaborative agents controlling four segments upstream of the congestion in traffic scenarios with medium and high traffic loads. DWL also allows for heterogeneity in agents’ policies; cooperating agents in DWL4-ST-VSL implement two speed limit sets with different granularity. DWL4-ST-VSL outperforms all baselines (W-learning-based VSL and simple proportional speed control), which use static VSL zones. Finally, our experiments yield insights into the new concept of VSL control. This may trigger further research on using advanced learning-based technology to design a new generation of adaptive traffic control systems to meet the requirements of operating in a nonstationary environment and at the leading edge of emerging connected and autonomous vehicles in general