Computational Intelligence in Highway Management: A Review

Highway management systems are used to improve safety and driving comfort on highways by using control strategies and providing information and warnings to drivers. They use several strategies starting from speed and lane management, through incident detection and warning systems, ramp metering, weather information up to, for example, informing drivers about alternative roads. This paper provides a review of the existing approaches to highway management systems, particularly speed harmonization and ramp metering. It is focused only on modern and advanced approaches, such as soft computing, multi-agent methods and their interconnection. Its objective is to provide guidance in the wide field of highway management and to point out the most relevant recent activities which demonstrate that development in the field of highway management is still important and that the existing research exhibits potential for further enhancement

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

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

Fuzzy-Based Variable Speed Limits System Under Connected Vehicle Environment: A Simulation-Based Case Study in the City of Naples

This paper handles the problem of controlling speed limits on freeways in a connected traffic environment to reduce traffic congestion and improve both the operational and environmental performance of the road network. In order to achieve this objective, we present a Variable Speed Limit (VSL) system that utilizes fuzzy logic, which adjusts the speed limits that connected vehicles must comply with by leveraging traffic data such as vehicle flow, occupancy, and speed obtained from loop detectors installed along the road. To evaluate the effectiveness of the proposed Fuzzy-based VSL system and its potential benefits compared to the conventional rule-based VSL system in terms of traffic congestion and environmental impact, we conducted a simulation analysis using the microscopic traffic simulator, VISSIM. Specifically, three simulation scenarios are taken into account: i) no VSL, where the VSL system is not enabled; ii) Rule-based VSL system, where a typical a decision tree-based system is considered; iii) Fuzzy-based VSL system, where the herein proposed approach is appraised. The results demonstrate that the proposed approach enhances road efficiency by decreasing speed variation, increasing average speed and vehicle volume, and reducing fuel consumption

Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control

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

Ph.DDOCTOR OF PHILOSOPH