On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Ph.DDOCTOR OF PHILOSOPH

Literature Review of Advancements in Adaptive Ramp Metering

Over a period spanning more than 30 years, several ramp metering algorithms have been developed to improve the operation of freeways. Many of these algorithms were deployed in several regions of the world, and field evaluations have shown their significance to improve traffic conditions on freeways and ramps. Previous reviews of ramp metering algorithms focused more on the research outcomes and evaluations of traditional metering algorithms developed in the early stage of ramp metering research. The purpose of this paper is to cover the more recent developments in ramp metering in relation to the traditional metering strategies. Several local and coordinated ramp metering algorithms were reviewed. In summary, Asservissement Linéaire d’Entrée Autoroutière (ALINEA) was found to be the most widely deployed local ramp metering strategy. The algorithm is simple and implementation costs less than other strategies. It also guarantees the targeted performance goals provided that the on-ramp has sufficient storage. Several extensions were proposed in the literature to fine-tune its performance. Among the coordinated metering strategies, zone based metering is simple to implement and easy to re-configure. System-wide adaptive ramp metering (SWARM) algorithm is more sensitive to calibrate for accurate prediction of traffic states. Heuristic ramp-metering coordination (HERO) algorithm can be useful if both local and coordinated control are desired particularly if the local control is using ALINEA. Fuzzy logic based algorithms are gaining popularity because of the simplicity and the fast re-configuration capability. Advanced real time metering system (ARMS) seems theoretically promising because of its proactive nature to prevent congestion; however, its performance is highly dependent upon accurate predictions. Finally, some guidelines were proposed for future research to develop new proposals and to extend the existing algorithms for guaranteed performance solutions.Scopu

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

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

Computational intelligence-based traffic signal timing optimization

 Traffic congestion has explicit effects on productivity and efficiency, as well as side effects on environmental sustainability and health. Controlling traffic flows at intersections is recognized as a beneficial technique, to decrease daily travel times. This thesis applies computational intelligence to optimize traffic signals\u27 timing and reduce urban traffic