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  • 1.
    Deng, Qichen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A fast algorithm for planning optimal platoon speeds on highway2014In: Elsevier IFAC Publications / IFAC Proceedings series, ISSN 1474-6670, Vol. 19, p. 8073-8078Article in journal (Refereed)
    Abstract [en]

    To meet policy requirements on increased transport energy efficiency and reduced emissions, smart control and management of vehicles and fleets have become important for the development of eco-friendly intelligent transportation systems (ITS). The emergence of new information and communication technologies and their applications, particularly vehicle-to-vehicle and vehicle-to-infrastructure communication, facilitates the implementation of autonomous vehicle concepts, and meanwhile serves as an effective means for control of vehicle fleet by continuously providing support and guidance to drivers. While convoy driving of trucks by longitudinal automation could save 5-15% of fuel consumption due to the reduction of airdrag resistance, this study attempts to investigate the energy saving potential of truck platoons by intelligent speed planning. Assuming that real-time traffic information is available because of communication, an efficient speed control algorithm is proposed based on optimal control theory. The method is faster than the conventional dynamic programming approach and hence applied in the study to analyze energy saving potential of simple platoon operations including acceleration and deceleration. The numerical result shows significant improvement on energy saving due to speed planning during platooning. It can be further applied for more complex platooning operations.

  • 2.
    Deng, Qichen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Fast Algorithm for Planning Optimal Platoon Speeds on Highway2014In: Proceedings of the 19th IFAC World Congress, 2014, International Federation of Automatic Control , 2014Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    To meet policy requirements on increased transport energy eciency and reduced emissions, smart control and management of vehicles and eets have become important for the development of eco-friendly intelligent transportation systems (ITS). The emergence of new information and communication technologies and their applications, particularly vehicle to vehicle and vehicle-to-infrastructure communication, facilitates the implementation of autonomous vehicle concepts, and meanwhile serves as an eective means for control of vehicle eet by continuously providing support and guidance to drivers. While convoy driving of trucks by longitudinal automation could save 5-15% of fuel consumption due to the reduction of airdrag resistance, this study attempts to investigate the energy saving potential of truck platoons by intelligent speed planning. Assuming that real-time trac information is available because of communication, an ecient speed control algorithm is proposed based on optimal control theory. The method is faster than the conventional dynamic programming approach and hence applied in the study to analyze energy saving potential of simple platoon operations including acceleration and deceleration. The numerical result shows signicant improvement on energy saving due to speed planning during platooning. It can be further applied for more complex platooning operations.

  • 3. Eilers, S.
    et al.
    Mårtensson, Jonas
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biomedical and X-ray Physics.
    Pettersson, H.
    Pillado, M.
    Gallegos, D.
    Tobar, M.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Friedrichs, T.
    Borojeni, S. S.
    Adolfson, M.
    COMPANION-Towards Co-operative Platoon Management of Heavy-Duty Vehicles2015In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, IEEE , 2015, p. 1267-1273Conference paper (Refereed)
    Abstract [en]

    The objective of the EU project COMPANION is to develop co-operative mobility technologies for supervised vehicle platooning, in order to improve fuel efficiency and safety for goods transport. The potential social and environmental benefits inducted by heavy-duty vehicle platoons have been largely proven. However, until now, the creation, coordination, and operation of such platoons have been mostly neglected. In addition, the regulation and standardization of coordinated platooning, together with its acceptance by the end-users and the society need further attention and research. In this paper we give an overview over the project and present the architecture of the off-board and onboard platforms of the COMPANION cooperative platoon management system. Furthermore, the consortium reports on the first results of the human factors for platooning, legislative analysis of platooning aspects, clustering and optimization of platooning plans and prediction of congestion due to planned special events. Finally, we present the method of validation of the system via simulation and trials.

  • 4.
    Grumert, Ellen F.
    et al.
    Swedish Natl Rd & Transport Res Inst VTI, SE-58195 Linkoping, Sweden.;Linkoping Univ, Dept Sci & Technol, SE-60174 Norrkoping, Sweden..
    Tapani, Andreas
    Swedish Natl Rd & Transport Res Inst VTI, SE-58195 Linkoping, Sweden..
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Characteristics of variable speed limit systems2018In: European Transport Research Review, ISSN 1867-0717, E-ISSN 1866-8887, Vol. 10, no 2, article id 21Article in journal (Refereed)
    Abstract [en]

    The control algorithm used for deciding on the speed limit in variable speed limit systems is crucial for the performance of the systems. The algorithm is designed to fulfil the purpose of the variable speed limit system, which can be one or several of the following aspects: increasing safety, increasing efficiency and decreasing environmental impacts. Today, many of the control algorithms used in practice are based on fixed thresholds in speed and/or flow. Therefore, they are not necessarily reflecting the current traffic conditions. Control algorithms with a greater level of complexity can be found in the literature. In this paper, four existing control algorithms are investigated to conclude on important characteristics affecting the performance of the variable speed limit system. The purpose of the variable speed limit system and, consequently, the design of the control algorithm differ. Requirements of the investigated control algorithms are that they should be easy to interpret and the execution time should be short. The algorithms are evaluated through microscopic traffic simulation. Performance indicators related to traffic safety, traffic efficiency and environmental impacts are presented. The results show that the characteristics of the variable speed limit system and the design of the control algorithm will have effect on the resulting traffic performance, given that the drivers comply with the variable speed limits. Moreover, the time needed to trigger the system, the duration and the size of speed limit reductions, and the location of the congestion are factors of importance for the performance of variable speed limit systems.

  • 5. Grumert, Ellen
    et al.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Tapani, Andreas
    Analysis of a cooperative variable speed limit system using microscopic traffic simulation2015In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 52, p. 173-186Article in journal (Refereed)
    Abstract [en]

    Variable speed limit systems where variable message signs are used to show speed limits adjusted to the prevailing road or traffic conditions are installed on motorways in many countries. The objectives of variable speed limit system installations are often to decrease the number of accidents and to increase traffic efficiency. Currently, there is an interest in exploring the potential of cooperative intelligent transport systems including communication between vehicles and/or vehicles and the infrastructure. In this paper, we study the potential benefits of introducing infrastructure to vehicle communication, autonomous vehicle control and individualized speed limits in variable speed limit systems. We do this by proposing a cooperative variable speed limit system as an extension of an existing variable speed limit system. In the proposed system, communication between the infrastructure and the vehicles is used to transmit variable speed limits to upstream vehicles before the variable message signs become visible to the drivers. The system is evaluated by the means of microscopic traffic simulation. Traffic efficiency and environmental effects are considered in the analysis. The results of the study show benefits of the infrastructure to vehicle communication, autonomous vehicle control and individualized speed limits for variable speed limit systems in the form of lower acceleration rates and thereby harmonized traffic flow and reduced exhaust emissions.

  • 6. Grumert, Ellen
    et al.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Tapani, Andreas
    Effects of a Cooperative Variable Speed Limit System on Traffic Performance and Exhaust Emissions2013In: TRB 92nd Annual Meeting Compendium of Papers, 2013Conference paper (Refereed)
    Abstract [en]

    Variable Speed Limit Systems (VSLS) where variable message signs show speed limits based on traffic or road conditions exist on motorways in many countries. The purpose of the VSLS is to decrease the number of accidents while increasing efficiency of traffic system. Cooperative systems are a type of intelligent transport system that has received increasing interest lately. The central part of a cooperative system is communication between vehicles and/or vehicles and the infrastructure. In this paper, a cooperative systems extension of a VSLS is proposed and evaluated by means of microscopic traffic simulation. In the proposed cooperative VSLS, communication between the vehicles and the infrastructure is made available via a roadside unit communicating the speed limits to vehicles upstream on the road. Both aggregate and micro-scale emission models are used to estimate emission from vehicle states in traffic flow. The results of the study show that the cooperative VSLS has a potential to contribute to flow harmonization and to reduce environmental impacts. The emission estimates in the study are dependent on the emission models being applied.

  • 7. Guan, Wei
    et al.
    Yan, Xuedong
    Radwan, Essam
    Wong, Sze Chun
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Advanced Dynamic Simulations in Transportation2015In: Discrete dynamics in nature and society, ISSN 1026-0226, E-ISSN 1607-887X, Vol. 2015, article id 675263Article in journal (Refereed)
  • 8.
    Hong, Beichuan
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering.
    Path optimization for a wheel loader considering construction site terrain2018In: 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, Suzhou, China, 26-30 June 2018, Changshu, China: IEEE, 2018, p. 2098-2103Conference paper (Refereed)
    Abstract [en]

    Wheel loader is one of the most widely used heavy-duty vehicles for transporting building materials in construction site. Improvement of its efficiency is important for sustainable transport and construction operations. This paper proposes a path optimization approach that allows us to plan loader trajectory and corresponding vehicle motions in construction site when the topological relief information is available. Vehicle dynamics is modeled for 3D motions considering the power balance of vehicle propulsion. The path planning problem is then formulated using a framework of constrained optimal control where vehicle dynamics is incorporated as system constraints. In order to solve the problem, a discrete search method is developed based on the principle of dynamic programming (DP), in which the states of the forward and backward movement paths of wheel loader are explored in parallel. A numerical study is then presented to demonstrate the application of the proposed approach for optimizing the loader path using terrain information.

  • 9.
    Hong, Beichuan
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Path Planning for Wheel Loaders: a Discrete Optimization Approach2017Conference paper (Refereed)
  • 10.
    Hong, Beichuan
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Quantification of Emissions for non-road Machinery in Earthwork: Modeling and Simulation Approaches2017Conference paper (Refereed)
    Abstract [en]

    Earthwork, as an essential part of almost all heavy construction projects, is an energy consuming procedure and pollution source for both transport and construction sectors. Due to the increasing need and interest to achieve sustainable development in construction, the evaluation of emission and energy impact in earthwork is of high importance for improving the environmental sustainability. This paper proposes an approach to estimate emissions and fuel usage of construction equipment by using experimental data collected from a project mainly carried out in China. In the experiment, emissions and operational parameters of two loaders and two hauler trucks were measured and analyzed. Based on the power efficiency and other factors, different operation cycles are defined for wheel loader and trucks in the real measurement. Then, through establishing an estimation approach, the emission and fuel rates for different operational cycles are finally calculated. The results show that there are remarkable differences for emissions under different working conditions. In order to evaluate and reduce the emissions and fuel values of the whole earthwork project, a discrete-event simulation (DES) is developed and employed to simulate the earthwork scenarios in a detailed case study. The model provides a basis for the integration of the emission calculation with earthwork simulation. During the evaluation, an alternative plan has been proposed and analyzed for lowering the environmental impacts of the earthmoving operations.

  • 11.
    Hong, Beichuan
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. Wuhan University of Technology, China.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Chen, Hui
    Lv, Lin
    Modeling of dynamic NOx emission for nonroad machinery: a study on wheel loader using engine test data and on-board measurement2016Conference paper (Refereed)
    Abstract [en]

    Quantification of nonroad machinery emissions is of high importance for improving heavy construction processes especially concerning environmental sustainability. In comparison to the substantial research effort on modeling dynamic emissions for road transport, there is, however, lack of knowledge on how to quantify dynamic emissions during construction operations. This paper proposes an approach to model dynamic NOx emission for nonroad construction machinery using recent experimental data collected by a wheel loader operated in the Chinese environment. In the experiment, emissions were measured during different operational cycles for wheel loader and the data is used for both model calibration and validation. Starting from an initial emission map built from in-lab engine bench test, the model prediction of dynamic NOx emission is calibrated by three real-time engine performance parameters highly correlated to the NOx generation. Considering the characteristics of the nonroad equipment, a dynamic module is added to represent engine state transition due to frequent switching of an operational mode in construction activities, making the whole model more accurate in predicting instantaneous emission levels. Compared to the validation data randomly selected from three different cycle tests, the model shows good performance concerning prediction accuracy and with the capacity of handling drastic changes of the working condition of the machine. While the study focuses on the engine-out NOx emission the resulting methodology can be generalized for emission modeling of other nonroad construction machines.

  • 12.
    Huang, Zhen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301), Traffic and Logistics (closed 20110301).
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Integration of Emission and Fuel Consumption Computing with Traffic Simulation using a Distributed Framework2009In: 2009 12TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC 2009), NEW YORK: IEEE , 2009, p. 154-159Conference paper (Refereed)
    Abstract [en]

    Air quality and fuel efficiency has become important factors in decision-makings on urban traffic planning and management. To support the process simulation models have potential to play essential roles in evaluation of planning alternatives and control strategies. However, traffic and its environmental impacts are different processes and often require various levels of models. With concerns on high computing performance and rich functionalities, it may be not appropriate to model emission inventory within traffic simulation. In this paper, we present a distributed simulation approach, and an independent emission/energy computing platform is built to simulate, visualize and analyze online emission outputs, given a microscopic traffic simulation tool, KTH-TPMA. Two distributed computing frameworks, common objects request broker architecture (CORBA) and service oriented architecture (SOA), are adopted in the distributed software design and implementation. Several emission models are implemented and generally evaluated in microsimulation runs of two road networks.

  • 13.
    Huang, Zhen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Koutsopoulos, Haris
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics.
    A numerical optimization approach for calibration of dynamic emission models based on aggregate estimation of ARTEMIS2010In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010, p. 1221-1226Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose a numerical approach to calibrate dynamic emission models when on-road or in-lab instantaneous emission measurements are not fully available. Microscopic traffic simulation is applied to generate dynamic vehicle states in the second-by-second level. Using aggregate estimation of ARTEMIS as a standard reference, a numerical optimization scheme on the basis of a stochastic gradient approximation algorithm is applied to find optimal parameters for the dynamic emission model. The calibrated model has been validated on several road networks with traffic states generated by the same simulation model. The results show that with proper formulation of the optimization objective function the estimated dynamic emission model can reasonably capture the trends of online emissions of traffic fleets.

  • 14.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A Decentralized Traffic Light Control System Based on Adaptive Learning2017In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 5301-5306Article in journal (Refereed)
    Abstract [en]

    This paper proposes a decentralized traffic light control system in a multi-agent framework. Each signal controller at an intersection is modeled as an intelligent agent capable of making actions for signal operations according to received detection information. The controller agent works with a turning movement based phasing scheme. Duration of turning movement is determined by a multi-criteria reinforcement learning algorithm. In the design of agent, both traffic mobility and energy efficiency are taken into account. Then, a case study is carried out to assess the performance of the proposed decentralized signal control system. The simulation results outperforms an optimized vehicle-actuated control system by reducing average travel delay and average fuel consumption for vehicles. In particular, the decentralized control system is queue responsive and able to adapt to demand in its green time allocation.

  • 15.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. iTekn Solutions, Sweden.
    A group-based traffic signal control with adaptive learning ability2017In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 65, p. 282-293Article in journal (Refereed)
    Abstract [en]

    Group-based control is an advanced traffic signal strategy capable of dynamically generating phase sequences at intersections. Combined with the phasing scheme, vehicle actuated timing is often adopted to respond to the detected traffic. However, the parameters of a signal controller are often predetermined in practice, and the control performance may suffer from deterioration when dealing with highly fluctuating traffic demand. This study proposes a group-based signal control approach capable of making decisions based on its understanding of traffic conditions at the intersection level. In particular, the control problem is formulated using a framework of stochastic optimal control for multi-agent system in which each signal group is modeled as an intelligent agent. The agents learn how to react to traffic environment and make optimal timing decisions according to the perceived system states. Reinforcement learning, enhanced by multiple-step backups, is employed as the kernel of the intelligent control algorithm, where each agent updates its knowledge on-line based on a sequence of states during the process. In addition, the proposed system is designated to be compatible with the prevailing signal system. A case study was carried out in a simulation environment to compare the proposed control approach with a benchmark controller used in practice, group-based vehicle actuated (GBVA) controller, whose parameters were off-line optimized using a genetic algorithm. Simulation results show that the proposed adaptive group-based control system outperforms the optimized GBVA control system mainly because of its real-time adaptive learning capacity in response to the changes in traffic demand.

  • 16.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A Learning-based Adaptive Group-based Signal Control System under Oversaturated Conditions2016In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 49, no 5, p. 291-296Article in journal (Refereed)
    Abstract [en]

    The operation of traffic signal control is of significant importance in traffic management and operation practice, especially under oversaturated condition during the morning and afternoon peak hours. However, the conventional signal control systems showed the limitations in signal timing and phasing under oversaturated situations. This paper proposes a multi-agent adaptive signal control system in the context of group-based phasing techniques. The adaptive signal control system is able to acquire knowledge on-line based on the perceived traffic states and the feedback from the traffic environment. Reinforcement learning with eligibility trace is applied as the learning algorithm in the multi-agent system. As a result, the signal controller makes an intelligent timing decision. Feature-based function approximation method is incorporated into reinforcement learning framework to improve the learning efficiency as well as the quality of signal timing decisions. The learning process of the learning-based signal control is carried out with the aid of a microscopic traffic simulation model. A benchmarking system, an optimized group-based vehicle actuated signal control system, is compared with the proposed adaptive signal control systems. The simulation results show that the proposed adaptive group-based signal control system has the potential to improve the mobility efficiency under different congested situations.

  • 17.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A Learning-based Adaptive Signal Control System with Function Approximation2016In: IFAC Papers-Online, ISSN 2405-8963, Vol. 49, no 3, p. 5-10Article in journal (Refereed)
    Abstract [en]

    Traffic signal control plays a crucial role in traffic management and operation practice. In the past decade, adaptive signal control systems have shown the abilities to improve the effectiveness of the transportation system in many aspects. This paper proposes an adaptive signal control system in the context of group-based phasing techniques. The adaptive signal control system is modeled as a multi agent System capable of acquiring knowledge on-line based on the perceived traffic states and the feedback from the external environment,. Reinforcement learning is applied as the learning algorithm resulting in intelligent timing decisions. Feature based function approximation method is incorporated into the reinforcement learning framework for the purpose of improving learning efficiency as well as the quality of signal timing decisions. The assessment of such a learning-based signal control system is carried out by using an opensource microscopic traffic simulation software, SUMO. A benchmarking system, the optimized group-based vehicle actuated signal control system, compared with the learning-based signal control systems regarding mobility efficiency. The simulation results show that the proposed adaptive group based signal control system has the potential to improve the mobility efficiency regardless of the settings of traffic demands.

  • 18.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A multi-criteria intelligent control for traffic lights using reinforcement learning2018In: Advanced Concepts, Methodologies and Technologies for Transportation and Logistics, Springer Verlag , 2018, p. 438-451Chapter in book (Refereed)
    Abstract [en]

    Traffic signal control plays a crucial role in traffic management and operation practices. In the past decade, adaptive signal control systems, capable of adjusting control schemes in response to traffic patterns, have shown the abilities to improve traffic mobility. On the other hand, the negative impacts on environments by increased vehicles attract increased attentions from traffic stakeholders and the general public. Most of the prevalent adaptive signal control systems do not address energy and environmental issues. The present paper proposes an adaptive signal control system capable of taking multi-criteria strategies into account. A general multi-agent framework is introduced for modeling signal control operations. The behavior of each cognitive agent is modeled by a Constrained Markov Decision Process (CMDP). Reinforcement learning algorithms are applied to solve the MDP problem. As a result, the signal controller makes intelligent timing decisions according to a pre-defined policy goal. A case study is carried out for the stage-based control scheme to investigate the effectiveness of the adaptive signal control system from two perspectives, traffic mobility and energy efficiency. The control approach can be further applied to a large network in a decentralized manner. 

  • 19.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A multi-objective agent-based approach for road traffic controls: application for adaptive traffic signal systems2017Manuscript (preprint) (Other academic)
    Abstract [en]

    Agent-based approaches have gained popularity in engineering applications, but its potential for advanced traffic controls has not been sufficiently explored. This paper presents a multi-agent framework that models traffic control instruments and their interactions with road traffic. A multi-objective Markov decision process is applied to model agent operations, allowing agents to form a decision in the context of multiple policy goals. The problem is reformulated by a constrained Markov decision process (CMDP) to enhance the computational efficiency. In the study, the policy goal with the highest priority becomes the optimization objective, but the other objectives are transferred as constraints for optimization. A reinforcement learning based approach is developed with different function approximation methods used to enhance the control algorithm. For implementation of multi-objective control, a threshold lexicographic ordering method is introduced and integrated with the learning algorithm. While the multi-objective intelligent control method could be potentially applied for different road traffic controls, this paper demonstrates a case study on traffic signal control in a road network in Stockholm. Intersections are modeled as agents that can make intelligent timing decisions according to the detected traffic states and update their knowledge from system feedback. The evaluation results show the benefits offered by the control approach especially when multiple policy requirements are introduced.

  • 20.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    A multi-objective multi-agent framework for traffic light control2017In: 2017 11TH ASIAN CONTROL CONFERENCE (ASCC), IEEE , 2017, p. 1199-1204Conference paper (Refereed)
    Abstract [en]

    This paper introduces a multi-objective multi-agent framework for traffic light control. In particular, each agent in the proposed framework applies a multi-objective Markov decision process. For intelligent control, a reinforcement learning (RL) algorithm is enhanced with multiple-step backups and a function approximation approach to build the agent's knowledge. Moreover, a thresholded lexicographic ordering (TLO) action policy is integrated with the enhanced RL algorithm to solve the multi-objective control problem, which is reformulated by a constrained Markov decision process. A case study of three intersections is carried out and demonstrates the approach with a conventional stage-based phasing strategy using traffic simulation. The simulation experiments elaborate the benefits brought by MAMOD-TL system compared with optimized fixed-time controllers. More importantly, the Pareto optimality is approximately obtained by setting different control parameters for TLO action policy, which can be considered as a performance metric for decision makers.

  • 21.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    A non-parametric Bayesian framework for traffic-state estimation at signalized intersections2017Manuscript (preprint) (Other academic)
    Abstract [en]

    An accurate and practical traffic-state estimation (TSE) method for signalized intersections plays an important role for real-time operations to facilitate efficient traffic management. This paper presents a generalized modeling framework for estimating traffic states at signalized intersections. The framework is non-parametric and data-driven, without the requirement on explicit models of traffic. Additionally, in principle, any type of data source together with any type of signal controller can be incorporated with the proposed framework. The Bayesian filter (BF) approach is the core of the framework and introduces a recursive state estimation process. The required transition and measurement models of the BFs are trained using Gaussian process (GP) regression models with respect to a set of historical data. A Gaussian process model uses kernel functions to describe the proximity among data points, and the hyper-parameters adopted in the GP model are optimized according to the training data. In addition to the detailed derivation of the integration of BFs and GP regression models, an algorithm based on the extended Kalman filter is presented for real-time traffic estimation. The effectiveness of the proposed framework is demonstrated through several numerical experiments using data generated in microscopic traffic simulations. Both fixed-location data (i.e., loop detector) and mobile data (i.e., connected vehicle) are examined with the framework. As a result, the method performs well for the tested traffic conditions. In particular, the estimator provides a competitive estimation accuracy merely using the position information of a small portion of vehicles at the intersection. The approach is suitable for a short-term estimation requirement, which is normally a challenging task in traffic control and operations.

  • 22.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. Enjoyor Co Ltd, Hangzhou 310030, Zhejiang, Peoples R China.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    A non-parametric Bayesian framework for traffic-state estimation at signalized intersections2019In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 498, p. 21-40Article in journal (Refereed)
    Abstract [en]

    An accurate and practical traffic-state estimation (TSE) method for signalized intersections plays an important role in real-time operations to facilitate efficient traffic management. This paper presents a generalized modeling framework for estimating traffic states at signalized intersections. The framework is non-parametric and data-driven, without any requirement on explicit modeling of traffic flow. The Bayesian filter (BF) approach is the core of the framework and introduces a recursive state estimation process. The required transition and measurement models of the BFs are trained using Gaussian process (GP) regression models with respect to a historical dataset. In addition to the detailed derivation of the integration of BFs and GP regression models, an algorithm based on the extended Kalman filter is presented for real-time traffic estimation. The effectiveness of the proposed framework is demonstrated through several numerical experiments using data generated in microscopic traffic simulations. Both fixed-location data (i.e., loop detector) and mobile data (i.e., connected vehicle) are examined with the framework. As a result, the method shows good performance under the different traffic conditions in the experiment. In particular, the approach is suitable for short-term estimation, a challenging task in traffic control and operations.

  • 23.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Adaptive group-based signal control by reinforcement learning2015In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, p. 207-216Article in journal (Refereed)
    Abstract [en]

    Group-based signal control is one of the most prevalent control schemes in the European countries. The major advantage of group-based control is its capability in providing flexible phase structures. The current group-based control systems are usually implemented with rather simple timing logics, e.g. vehicle actuated logic. However, such a timing logic is not sufficient to respond to the traffic environment whose inputs, i.e. traffic demands, dynamically change over time. Therefore, the primary objective of this paper is to formulate the existing group-based signal controller as a multi-agent system. The proposed signal control system is capable of making intelligent timing decisions by utilizing machine learning techniques. In this regard, reinforcement learning is a potential solution because of its self-learning properties in a dynamic environment. This paper, thus, proposes an adaptive signal control system, enabled by a reinforcement learning algorithm, in the context of group-based phasing technique. Two different learning algorithms, Q-learning and SARSA, have been investigated and tested on a four-legged intersection. The experiments are carried out by means of an open-source traffic simulation tool, SUMO. Performances on traffic mobility of the adaptive group- based signal control systems are compared against those of a well-established group-based fixed time control system. In the testbed experiments, simulation results reveal that the learning-based adaptive signal controller outperforms group-based fixed time signal controller with regards to the improvements in traffic mobility efficiency. In addition, SARSA learning is a more suitable implementation for the proposed adaptive group-based signal control system compared to the Q-learning approach.

  • 24.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Adaptive Group-Based Signal Control Using Reinforcement Learning with Eligibility Traces2015In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, IEEE conference proceedings, 2015, p. 2412-2417Conference paper (Refereed)
    Abstract [en]

    Group-based signal controllers are widely deployed on urban networks in the Nordic countries. However, group-based signal controls are usually implemented with rather simple timing logics, e.g. vehicle actuated timing. In addition, group-based signal control systems with pre-defined signal parameter settings show relatively poor performances in a dynamically changed traffic environment. This study, therefore, presents an adaptive group-based signal control system capable of changing control strategies with respect to non-stationary traffic demands. In this study, signal groups are formulated as individual agents. The signal group agent learns from traffic environments and makes intelligent timing decisions according to the perceived system states. Reinforcement learning with multiple-step backups is applied as the learning algorithm. Agents on-line update their knowledge based on a sequence of states during the learning process rather than purely on the basis of single previous state. The proposed signal control system is integrated into a software-in-the-loop simulation (SILS) framework for evaluation purpose. In the testbed experiments, the proposed adaptive group-based control system is compared to a benchmark signal control system, the well-established group-based fixed-time control system. The simulation results demonstrate that learning-based and adaptive group-based signal control system owns its advantage in dealing with dynamic traffic environments in terms of improving traffic mobility efficiency.

  • 25.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering. iTekn Solutions, Stockholm, Sweden.
    Hierarchical multi-agent control of traffic lights based on collective learning2018In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 68, p. 236-248Article in journal (Refereed)
    Abstract [en]

    Increasing traffic congestion poses significant challenges for urban planning and management in metropolitan areas around the world. One way to tackle the problem is to resort to the emerging technologies in artificial intelligence. Traffic light control is one of the most traditional and important instruments for urban traffic management. The present study proposes a traffic light control system enabled by a hierarchical multi-agent modeling framework in a decentralized manner. In the framework, a traffic network is decomposed into regions represented by region agents. Each region consists of intersections, modeled by intersection agents who coordinate with neighboring intersection agents through communication. For each intersection, a collection of turning movement agents operate individually and implement optimal actions according to local control policies. By employing a reinforcement learning algorithm for each turning movement agent, the intersection controllers are enabled with the capability to make their timing decisions in a complex and dynamic environment. In addition, the traffic light control operates with an advanced phase composition process dynamically combining compatible turning movements. Moreover, the collective operations performed by the agents in a road network are further coordinated by varying priority settings for relevant turning movements. A case study was carried out by simulations to evaluate the performance of the proposed control system while comparing it with an optimized vehicle-actuated control system. The results show that the proposed traffic light system, after a collective machine learning process, not only improves the local signal operations at individual intersections but also enhances the traffic performance at the regional level through coordination of specific turning movements.

  • 26.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Implementation and Optimization of Group-based Signal Control in Traffic Simulation2014In: 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2014, p. 2517-2522Conference paper (Refereed)
    Abstract [en]

    Over the past decades, group-based control has become one of the most popular signal technologies being applied in many cities around the world. LHOVRA control is one of such group-based controls widely employed in Scandinavian countries. While several previous studies showed that group-based control outperforms stage-based control in many aspects, implementation and evaluation of signal controllers are complicated in a real application. In addition, little effort has been put in optimizing such group-based controllers in traffic management practice. This study implements generic group-based control in an object-oriented software framework, while a software-in-the-loop simulation is developed to integrate the signal controller with an open-source traffic simulator, SUMO. Also, stochastic optimization is applied to generate optimal signal parameters according to different settings of objective. In particular, part of the study is to improve the computational performance of the optimization process by parallelized simulation runs. Test-based experiments are finally carried out to evaluate traffic and optimize its impact on a small traffic network in Stockholm.

  • 27.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport Planning, Economics and Engineering.
    Johansson, Ingrid
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Heavy-duty vehicle platoons in real traffic: simulation modeling and analysisManuscript (preprint) (Other academic)
    Abstract [en]

    In freight transport systems, fuel consumption can be significantly reduced by means of heavy-duty vehicle (HDV) platooning on highways. An HDV platoon refers to a group of HDVs with small intermediate distances enabled by the HDVs being equipped by sensors and controllers. It is of importance for transport authorities and industries to explore the effects on overall traffic systems by introducing HDV platooning. Although previous studies have investigated the potential benefits of HDV platooning, the control performance and effects in real traffic have barely been explored. In the present study, a simulation platform has been developed to model and analyze the effects of HDV platoons in real traffic conditions. The simulation model is based on an open-source microscopic traffic simulator, SUMO, and calibrated using data collected by a motorway control system (MCS). The current model incorporates the vehicle dynamics of HDVs in the simulation, while an HDV in a platoon is controlled by a proportional-integral-derivative (PID) controller for its longitudinal behavior. Furthermore, the PID control parameters have been optimized for a driving cycle, according to predefined criteria, while taking vehicle dynamics and stability conditions into account. A case study has been carried out by adopting HDV platooning on a highway stretch in Sweden. The performance of the HDV platoons and effects on the other vehicles on the highway have been evaluated for different scenarios through multiple simulation runs. As a result, it is found that substantial fuel reductions have been achieved for HDVs if they form platoons in the evaluation cases. The analysis of the other vehicles shows only rather small effects when HDV platooning is implemented.

  • 28.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Johansson, Ingrid
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    MODELING AND ANALYSIS OF PID-CONTROLLED HEAVY-DUTY VEHICLE PLATOONS IN REAL TRAFFIC2017Conference paper (Refereed)
  • 29.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Kosonen, Iisakki
    A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation2017In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 114, p. 348-360Article in journal (Refereed)
    Abstract [en]

    Traffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this work, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and in application it is further enhanced by a sampling approach for initial population and utilizations of adaptive crossover and mutation probabilities. The final algorithm shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

  • 30.
    Jin, Junchen
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Kosonen, Iisakki
    An intelligent control system for traffic lights with simulation-based evaluation2017In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 58, p. 24-33Article in journal (Refereed)
    Abstract [en]

    This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control. It provides a convenient and economic approach to improve existing traffic light infrastructure. The control system is programmed on an intermediate hardware device capable of receiving messages from signal controller hardware as well as overriding traffic light indications during real-time operations. Signal control and optimization toolboxes are integrated into the embedded software in the FITS hardware device. A fuzzy logic based control has been implemented in FITS. In order to evaluate the effects of FITS system, this study attempts to develop a computational framework to evaluate FITS system using microscopic traffic simulation. A case study is carried out, comparing different commonly used signal control strategies with the FITS control approach. The simulation results show that the control system has the potential to improve traffic mobility, compared to all of the tested signal control strategies, due to its ability in generating flexible phase structures and making intelligent timing decisions. In addition, the effects of detector malfunction are also investigated in this study. The experiment results show that FITS exhibits superior performance than several other controllers when a few detectors are out-of-order due to its self-diagnostics feature.

  • 31.
    Johansson, Ingrid
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Jin, Junchen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Pettersson, Henrik
    Scania CV AB.
    Look-ahead speed planning for heavy-duty vehicle platoons using traffic information2016Conference paper (Refereed)
    Abstract [en]

    Freight transport is a fast increasing transportation mode due to the economic growth in the world. Heavy-duty vehicles (HDV) have considerably greater fuel consumption, thus making them a suitable target when new policies in road transport emphasize increased energy efficiency and mitigated emission impacts. Intelligent transportation systems, based on emerging V2X communication technology, open new possibilities for developing fuel-efficient driving support functions considering real traffic information. This indicates a large potential of fuel saving and emission reduction for freight transport. This paper studies a dynamic programming-based optimal speed planning considering a maximum acceleration model for HDVs. The optimal speed control is applied for the deceleration case of HDV platoons due to received information on traffic speed reduction ahead. The control can optimize fuel consumption as well as travel time, and theoretical results for the two cases are presented. For maximal fuel saving, a microscopic traffic simulation study is performed for single HDVs and HDV platoons running in real traffic conditions. The results show a decrease in fuel consumption of more than 80% compared to simulations without applying optimal control, while the fuel consumption of other vehicles in the simulation is not significantly affected.

  • 32.
    Johansson, Ingrid
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Jin, Junchen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Pettersson, Henrik
    Scania AB.
    Look-ahead speed planning for heavy-duty vehicle platoons using traffic information2017In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, p. 561-569Article in journal (Refereed)
    Abstract [en]

    Freight transport is a fast increasing transportation mode due to the economic growth in the world. Heavy-duty vehicles (HDV) have considerably greater fuel consumption, thus making them a suitable target when new policies in road transport emphasize increased energy efficiency and mitigated emission impacts. Intelligent transportation systems, based on emerging V2X communication technology, open new possibilities for developing fuel-efficient driving support functions considering real traffic information. This indicates a large potential of fuel saving and emission reduction for freight transport. This paper studies a dynamic programming-based optimal speed planning considering a maximum acceleration model for HDVs. The optimal speed control is applied for the deceleration case of HDV platoons due to received information on traffic speed reduction ahead. The control can optimize fuel consumption as well as travel time, and theoretical results for the two cases are presented. For maximal fuel saving, a microscopic traffic simulation study is performed for single HDVs and HDV platoons running in real traffic conditions. The results show a decrease in fuel consumption of more than 80% compared to simulations without applying optimal control, while the fuel consumption of other vehicles in the simulation is not significantly affected.

  • 33. Konsonen, Iisakki
    et al.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Traffic Signal Control with Autonomic Features2016In: Autonomic Road Transport Support Systems, Springer Publishing Company, 2016, p. 253-267Chapter in book (Refereed)
    Abstract [en]

    Inspired by diverse organic systems, autonomic computing is a rapidly growing field in computing science. To highlight this advancement, this chapter summarises the autonomic features utilised in a traffic signal control in the form of an operational control system, not simply a simulation study. In addition, the real-time simulation is used to refine the raw sensor data into a comprehensive picture of the traffic situation. We apply the multi-agent approach both for controlling the signals and for modelling the prevailing traffic situation. In contrast to most traffic signal control studies, the basic agent is one signal (head) also referred to as a signal group. The multi-agent process occurs between individual signal agents, which have autonomy to negotiate their timing, phasing, and priorities, limited only by the traffic safety requirements. The key contribution of this chapter lies not in a single method but rather in a combination of methods with autonomic properties. This unique combination involves a real-time microsimulation together with a signal group control and fuzzy logic supported by self-calibration and self-optimisation. The findings here are based on multiple research projects conducted at the Helsinki University of Technology (now Aalto University). Furthermore, we outline the basic concepts, methods, and some of the results. For detailed results and setup of experiments, we refer to the previous publications of the authors.

  • 34. Kosonen, Iisakki
    et al.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    An Agent-base Traffic Signal Control System: Autonomous Features2013Conference paper (Refereed)
  • 35.
    Lei, Wei
    et al.
    Wuhan University of Technology.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Chen, Hui
    Wuhan University of Technology.
    Assessment of Traffic Environment using Fine-tuned Dynamic Vehicle Emission Models2010In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2010, p. 1237-1242Conference paper (Refereed)
    Abstract [en]

    In order to assess environmental impacts of local traffic flow, a two-stage parameter tuning approach is proposed for recalibration of the Comprehensive Modal Emission Model (CMEM) using on-road emission measurements collected in Chinese cities. Based on the procedure comprising of grid search and nonlinear simplex optimization, the fuel- and emission-related parameters in the model are estimated to minimize the Mean Square Error (MSE) between model outputs and real measurements. In addition, a regression-based emission model is calibrated using the same data samples to compare performance. It is shown from the numerical results that the tuning process is able of improving the model prediction accuracy, especially concerning the CO emission, when comparing with the original CMEM model and the regression-based model. In addition, the emission models are, after the tuning process, applied together with a traffic simulation model to evaluate dynamic environmental effects of traffic in a case study.

  • 36.
    Liang, Kuo-Yun
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. Scania CV AB, Sweden.
    Deng, Qichen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Mårtensson, Jonas
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    The Influence of Traffic on Heavy-Duty Vehicle Platoon Formation2015Conference paper (Refereed)
    Abstract [en]

    Heavy-duty vehicle (HDV) platooning is a mean to significantly reduce the fuel consumption for the trailing vehicle. By driving close to the vehicle in front, the air drag is reduced tremendously. Due to each HDV being assigned with different transport missions, platoons will need to be frequently formed, merged, and split. Driving on the road requires interaction with surrounding traffic and road users, which will influence how well a platoon can be formed. In this paper, we study how traffic may affect a merging maneuver of two HDVs trying to form a platoon. We simulate this for different traffic densities and for different HDV speeds. Even on moderate traffic density, a platoon merge could be delayed with 20% compared to the ideal case with no traffic. 

  • 37.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    A computational model for driver-following behavior based on a neural-fuzzy system2007Report (Other academic)
  • 38.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    A neural-fuzzy framework for modeling car-following behavior2006In: 2006 IEEE International Conference on Systems, Man and Cybernetics, Proceedings, IEEE , 2006, p. 1178-1183Conference paper (Refereed)
    Abstract [en]

    A general framework is introduced to model driver behavior from real car-following data acquired on Swedish roads using an advanced instrumented vehicle. In early research, the data was classified into different car-following regimes based on fuzzy clustering methods and knowledge obtained from video analysis. In this paper, we propose a multi-regime framework based on the statistical property in each regime and mathematical models adopted in those regimes. This framework is an extension of TSK fuzzy inference system and can be expressed by a Neural-Fuzzy system. Genetic Algorithm (GA) is designed as the main learning method for this system. In practice, this model structure illustrates human knowledge of car-following in a more understandable manner and can be rather flexible as the regime parameters and model forms may vary according to the application context.

  • 39.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics.
    Driver Modeling based on computational intelligence approaches: exploaration and Modeling driver-following data collected by an instrumented vehicle2006Doctoral thesis, comprehensive summary (Other scientific)
    Abstract [en]

    This thesis is concerned with modeling of driver behavior based on data collected from real traffic using an advanced instrumented vehicle. In particular, the focus is on driver-following behavior (often called car-following in transport science) for microscopic simulation of road traffic systems. In addition, the modeling methodology developed can be applied for the design of human-centered control algorithms in adaptive cruise control (ACC) and other longitudinal active-safety technologies.

    Driver behavior is a constant research topic in the modeling of traffic systems and Intelligent Transportation Systems (ITS), which could be traced back to the work of GeneralMotor (GM) Co. in 1950’s. In the early time, researchers were only interested in the development of driver models fulfilling basic physical properties and producing reasonable flow dynamics on a macroscopic level. With the booming interest on driver modeling on a microscopic level and needs in ITS developments, researchers now emphasize modeling using microscopic data acquired from real world. To follow this research trend, a methodological framework on car-following data acquisition, analysis and modeling has been developed step by step in this thesis, and the basic idea is to build a computational model for car-following behavior by exploration of collected data. To carry out the work, different techniques within the field of modern Artificial Intelligence (AI), namely Computational Intelligence (CI)1, have been applied in the research subtasks e.g. information estimation, behavioral regime classification, regime model integration and model estimation. Therefore, a preliminary introduction of the CI methods being used in this thesis work is included in the text.

  • 40.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Optimal Controls of Fleet Trajectories for Fuel and Emissions2013In: Proceedings of the IEEE Intelligent Vehicle Symposium (IEEE IV13), IEEE , 2013, p. 1059-1064Conference paper (Refereed)
    Abstract [en]

    Increased demand for transport, coupled with energy, climate and environmental concerns, has put more and more pressure for improved performance on traffic systems. The recent development in vehicle-to-infrastructure (V2I) communication provides an effective means for continuous management of vehicle driving. This study presents an essential step of the work towards a dynamic fleet management system that takes advantages of real-time traffic information and communication. Based on the optimal control theory, a methodological approach is developed to control the environmental impacts of live vehicle fleets. In particular, vehicle trajectories that minimize local environmental objectives are derived by applying a discrete dynamic programming method. Numerical examples show that the method is promising for local V2I based traffic management applications and can be further extended for more complex optimal control problems in dynamic fleet management.

  • 41.
    Ma, Xiaoliang
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Towards Intelligent Fleet Management: Local Optimal Speeds for Fuel and Emissions2013In: Proceedings of the 16th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2013), Den Haag, Netherland: IEEE conference proceedings, 2013Conference paper (Refereed)
    Abstract [en]

    In order to fulfill the policy requirements on increased transport energy efficiency and reduced emission impacts, smart control and management of vehicles and fleets have become important for the evolution of green intelligent transportation systems (ITS). The emergence of new information and communication technologies (ICT) and their applications, especially vehicle-to-vehicle and vehicle-to-infrastructure (V2I) communication, serves as an effective means for continuous management of real traffic fleet by providing vehicle driving support and guidance, and therefore affecting driver behavior. This study presents a recent Swedish R&D project for developing a dynamic fleet management system that incorporates real-time traffic information, eco-driving guidance and automated vehicle control in real-time heavy vehicle platooning. In addition to a general illustration of the main objectives of the project, the paper presents a methodological approach to developed local fleet control strategies so that the fuel and emissions of the managed vehicle fleet can be reduced. Speed trajectories minimizing predefined objectives are derived by applying a discrete dynamic programming method, and an instantaneous emission estimator is used for predicting fuel and emissions. Numerical examples show that the method is promising for real-time fleet management applications with support of V2I communication while the computational efficiency of the method needs to be enhanced. The adaptive speed control approach is implemented in a microscopic traffic simulation environment for further evaluation.

  • 42.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Al Khoury, Fadi
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    Jin, Junchen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Prediction of arterial travel time considering delay in vehicle re-identification2017In: Transportation Research Procedia, ISSN 2324-9935, E-ISSN 2352-1465, Vol. 22, p. 625-634Article in journal (Refereed)
    Abstract [en]

    Travel time is important information for management and planning of road traffic. In the past decades, automated vehicle identification (AVI) systems have been deployed in many cities for collecting reliable travel time data. The fast technology advance has made the budget cost of such data collection system much cheaper than before. For example, bluetooth and WiFi-based systems have become economically a more feasible way for collecting interval travel time information in urban area. Due to increasing availability of such type of data, this paper aims to develop a travel time prediction approach that may take into account both online and historical measurements. Indeed, a statistical prediction approach for real-time application is proposed, modeling the deviation of live travel time from historical distribution estimated per time interval. An extended Kalman Filter (EKF) based algorithm is implemented to combine online travel time with historical patterns. In particular, the system delay due to vehicle re-identification is considered in the algorithm development. The methods are evaluated using Automated Number Plate Recognition (ANPR) data collected in Stockholm. The results show that the prediction performance is good and reliable in capturing major trends during congestion buildup and dissipation.

  • 43.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Al-Khoury, Fadi
    KTH, School of Information and Communication Technology (ICT).
    Jin, Junchen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Prediction of arterial travel time considering delay in vehicle re-identification2016Conference paper (Refereed)
    Abstract [en]

    Travel time is important information for management and planning of road traffic. In the past decades, automated vehicle identifi- cation (AVI) systems have been deployed in many cities for collecting reliable travel time data. The fast technology advance has made the budget cost of such data collection system much cheaper than before. For example, bluetooth and WiFi-based systems have become economically a more feasible way for collecting interval travel time information in urban area. Due to increasing availability of such type of data, this paper aims to develop a travel time prediction approach that may take into account both online and historical measurements. Indeed, a statistical prediction approach for real-time application is proposed, modeling the deviation of live travel time from historical distribution estimated per time interval. An extended Kalman Filter (EKF) based algorithm is implemented to combine online travel time with historical patterns. In particular, the system delay due to vehicle re-identification is considered in the algorithm development. The methods are evaluated using Automated Number Plate Recognition (ANPR) data collected in Stockholm. The results show that the prediction performance is good and reliable in capturing major trends during congestion buildup and dissipation. 

  • 44.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Behavior measurement, analysis and regime classification in car following2007In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 8, no 1, p. 144-156Article in journal (Refereed)
    Abstract [en]

    This paper first reports a data acquisition method that the authors used in a project on modeling driver behavior for microscopic traffic simulations. An advanced instrumented vehicle was employed to collect driver-behavior data, mainly car-following and lane-changing patterns, on Swedish roads. To eliminate the measurement noise in acquired car-following patterns, the Kalman smoothing algorithm was applied to the state-space model of the physical states (acceleration, speed, and position) of both instrumented and tracked vehicles. The denoised driving patterns were used in the analysis of driver properties in the car-following stage. For further modeling of car-following behavior, we developed and implemented a consolidated fuzzy clustering algorithm to classify different car-following regimes from the preprocessed data. The algorithm considers time continuity of collected driver-behavior patterns and can be more reliably applied in the classification of continuous car-following regimes when the classical fuzzy C-means algorithm gives unclear results.

  • 45.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Driver reaction delay estimation from real data and its application in GM-type model evaluation2006Conference paper (Refereed)
  • 46.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Dynamic car following data collection and noise cancellation based on the Kalman smoothing2005In: 2005 IEEE International Conference on Vehicular Electronics and Safety Proceedings, 2005, p. 35-41Conference paper (Refereed)
    Abstract [en]

    This paper will introduce a data collection method that we used in a project on modeling driver behavior in microscopic traffic simulation. A modern instrumented vehicle was employed to study a crucial element of driver behavior, that of car following, on Swedish roads. The collected car following data shows noisy patterns. To eliminate the measurement noise, Kalman smoothing algorithm is applied to the state-space formulation of the physical states (acceleration, speed and position) of tracked vehicles. The smoothed data shows clear car following patterns and has been further applied in our car following model calibration and validation study.

  • 47.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301). KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Estimation of driver reaction time from car-following data - Application in evaluation of general motor-type model2006In: Traffic Flow Theory 2006, 2006, no 1965, p. 130-141Conference paper (Refereed)
    Abstract [en]

    Driver behavior plays an important role in modeling vehicle dynamics in a traffic simulation environment. To study one element of general driver behavior, that of car following, an advanced-instrumented vehicle has been applied in dynamic data collection in real-traffic flow on Swedish roads. This paper briefly introduces the car-following data collection and smoothing methods. Moreover, spectrum analysis methods based on Fourier analysis of car-following data are introduced to estimate driver reaction times, a crucial parameter of driver behavior. A generalized general motor-type model was calibrated, an extension of the classic nonlinear general motor model, in a stable following regime based on estimated driver reaction times. The calibrated model was then evaluated by closed-loop simulations.

  • 48.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Predicting the effect of various ISA penetration grades on pedestrian safety by simulation2005In: Accident Analysis and Prevention, ISSN 0001-4575, E-ISSN 1879-2057, Vol. 37, no 6, p. 1162-1169Article in journal (Refereed)
    Abstract [en]

    Intelligent speed adaption (ISA) is one type of vehicle-based intelligent transportation systems (ITS), which warns and regulates driving speed according to the speed limits of the roads. Early field studies showed that ISA could reduce general mean speed levels and their variances in different road environments. This paper studies the effects of various ISA penetration grades on pedestrian safety in a single lane road. A microscopic traffic simulation tool, TPMA, was further developed and used to implement different ISA penetration grades. Momentary spot speed and traffic flow data are first logged in the traffic simulation for later prediction of pedestrian safety. Then a hypothetical vehicle-pedestrian collision model is extended from early researches in order to estimate two safety indicators: probability of collision, and risk of death. Finally, Monte Carlo method is applied iteratively to compute those safety indices. The computational result shows that raising ISA penetration in traffic flow will reduce both the probability of mid-block collision between vehicle and pedestrian and the risk of death in the collision accidents. Furthermore, the decrease of the risk of death will be more prominent than that of the collision probability according to this method.

  • 49.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Traffic and Logistics. KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Statistical analysis of driver behavioral data in different regimes of the car-following stage2007In: TRB 86th Annual Meeting Compendium of Papers CD-ROM, 2007Conference paper (Refereed)
  • 50.
    Ma, Xiaoliang
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Andreasson, Ingmar
    KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Traffic Research, CTR.
    Statistical analysis of driver behavioral data in different regimes of the car-following stage2007In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, no 2018, p. 87-96Article in journal (Refereed)
    Abstract [en]

    An instrumented vehicle has been used to study car-following behavior on Swedish motorways. In this study, the previous data collection and pre-processing work were briefly reviewed. To understand the driving behavior in the car-following stage more clearly, the collected time series were classified into a number of regimes using unsupervised fuzzy clustering methods. Then, the statistical relations between the driver acceleration response and the perceptual variables in each regime were analyzed using correlation and regression methods. It was found that regime classification helps discern the behavioral variance between those regime clusters. According to the data analysis, some of the car-following regimes, for example, opening and braking, can be described adequately in the statistical sense by a linear regression model (Helly's model). Therefore, a multiple regime car-following model with simple model forms, for example, linear models, has the potential to robustly represent the general car-following behavior in most regimes.

12 1 - 50 of 77
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