SwePub - sökning: WFRF:(Ma Xiaoliang)

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1.	Chi, Pengnan, et al. (författare) Difforecast : Image Generation Based Highway Traffic Forecasting with Diffusion Model 2023 Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 608-615 Konferensbidrag (refereegranskat)abstract Monitoring and forecasting of road traffic conditions is a common practice for real traffic information system, and is of vital importance to traffic management and control. While dynamic traffic patterns can be intuitively represented by space-time diagrams, this study proposes a new concept of space-time image (ST-image) to incorporate physical meanings of traffic state variables. We therefore transform the forecasting problem for time-series traffic states into a conditional image generation problem. We explore the inherent properties of the ST images from the perspectives of physical meaning and traffic dynamics. An innovative deep learning based architecture is designed to process the ST-image, and a diffusion model is trained to obtain traffic forecasts by generating the future ST-images based on the historical patterns.
2.	Chi, Pengnan, et al. (författare) Short-Term Traffic Prediction on Swedish Highways: A Deep Learning Approach with Knowledge Representation 2023 Konferensbidrag (refereegranskat)abstract Accurate prediction of highway traffic is of vital importance to proactive traffic monitoring, operation and controls. In the data mining of highway traffic, abstracting temporal knowledge is often prioritized than exploring topological relationship. In this study, we propose a deep learning model, called Knowledge-Sequence-to-Sequence (K-Seq2Seq), to solve the short-term highway traffic prediction problem in two stages: representing temporal knowledge and predicting future traffic. Through computational experiment in a road section of a Swedish motorway, we show that our model outperforms the conventional Seq2Seq model significantly, more than 20% when predicting information of longer time step.
3.	Deng, Qichen, et al. (författare) A fast algorithm for planning optimal platoon speeds on highway 2014 Ingår i: Elsevier IFAC Publications / IFAC Proceedings series. - : IFAC Papers Online. - 1474-6670. - 9783902823625 ; 19, s. 8073-8078 Tidskriftsartikel (refereegranskat)abstract 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.
4.	Eilers, S., et al. (författare) COMPANION-Towards Co-operative Platoon Management of Heavy-Duty Vehicles 2015 Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. - : IEEE. - 9781467365956 ; , s. 1267-1273 Konferensbidrag (refereegranskat)abstract 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.
5.	Grumert, Ellen, 1983-, et al. (författare) Analysis of a cooperative variable speed limit system using microscopic traffic simulation 2015 Ingår i: Transportation Research Part C. - : Elsevier. - 0968-090X .- 1879-2359. ; 52, s. 173-186 Tidskriftsartikel (refereegranskat)abstract 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, 1983-, et al. (författare) Characteristics of variable speed limit systems 2018 Ingår i: European Transport Research Review. - : SPRINGER HEIDELBERG. - 1867-0717 .- 1866-8887. ; 10:2 Tidskriftsartikel (refereegranskat)abstract 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.
7.	Grumert, Ellen, 1983-, et al. (författare) Effects of a Cooperative Variable Speed Limit System on Traffic Performance and Exhaust Emissions 2013 Ingår i: TRB 92nd Annual Meeting Compendium of Papers. Konferensbidrag (refereegranskat)abstract 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.
8.	Grumert, Ellen, 1983-, et al. (författare) Evaluation of Four Control Algorithms Used in Variable Speed Limit Systems 2016 Ingår i: TRB 95th Annual Meeting Compendium of Papers. - Washington : Transportation Research Board. Konferensbidrag (refereegranskat)abstract Control algorithms used for deciding on the speed limits in variable speed limit systems are crucial for the performance of the system. Today, many of the control algorithms used are based on fixed thresholds in speed and/or flow for lowering and increasing the speed limit. The algorithms are not necessarily reflecting the conditions on the road, which might lead to low traffic efficiency. Our hypothesis is that by use of a simple and efficient control algorithm that is better in reflecting the conditions on the road, both traffic efficiency and traffic safety could be increased. In this study, four control algorithms used in variable speed limit systems, and fulfilling the above criteria, are evealuted through microscopic traffic simulation. Performance indicators related to traffic safety, traffic efficiency and environmental impacts are presented. The results show that the design of, and the objective with, the control algorithm have a great impact on the performance. Moreover, the time needed for incident detection, the duration of and the size of the speed limit reduction and the location of the congestion are of importance for the performance of the control algorithms. These results will be of importance for design and implementation of future efficient variable speed limit systems.
9.	Guan, Wei, et al. (författare) Advanced Dynamic Simulations in Transportation 2015 Ingår i: Discrete dynamics in nature and society. - : Hindawi Publishing Corporation. - 1026-0226 .- 1607-887X. ; 2015 Tidskriftsartikel (refereegranskat)
10.	Hong, Beichuan, 1989-, et al. (författare) Modeling of dynamic NOx emission for nonroad machinery : a study on wheel loader using engine test data and on-board measurement 2016 Konferensbidrag (refereegranskat)abstract 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.
11.	Hong, Beichuan, 1989-, et al. (författare) Path optimization for a wheel loader considering construction site terrain 2018 Ingår i: 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, Suzhou, China, 26-30 June 2018. - Changshu, China : IEEE. ; , s. 2098-2103 Konferensbidrag (refereegranskat)abstract 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.
12.	Hong, Beichuan, et al. (författare) Path Planning for Wheel Loaders : a Discrete Optimization Approach 2017 Konferensbidrag (refereegranskat)
13.	Hong, Beichuan, 1989-, et al. (författare) Quantification of Emissions for non-road Machinery in Earthwork : Modeling and Simulation Approaches 2017 Konferensbidrag (refereegranskat)abstract 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.
14.	Huang, Zhen, et al. (författare) A numerical optimization approach for calibration of dynamic emission models based on aggregate estimation of ARTEMIS 2010 Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. ; , s. 1221-1226 Konferensbidrag (refereegranskat)abstract 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.
15.	Huang, Zhen, et al. (författare) Integration of Emission and Fuel Consumption Computing with Traffic Simulation using a Distributed Framework 2009 Ingår i: 2009 12TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC 2009). - NEW YORK : IEEE. - 9781424455188 ; , s. 154-159 Konferensbidrag (refereegranskat)abstract 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.
16.	Ji, Qingyuan, et al. (författare) GraphPro : A Graph-based Proactive Prediction Approach for Link Speeds on Signalized Urban Traffic Network 2022 Ingår i: Conference Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 339-346 Konferensbidrag (refereegranskat)abstract This paper proposes GraphPro, a short-term link speed prediction framework for signalized urban traffic networks. Different from other traditional approaches that adopt only reactive inputs (i.e., surrounding traffic data), GraphPro also accepts proactive inputs (i.e., traffic signal timing). This allows GraphPro to predict link speed more accurately, depending on whether or not there is a contextual change in traffic signal timing. A Wasserstein generative adversarial network (WGAN) structure, including a generator (prediction model) and a discriminator, is employed to incorporate unprecedented network traffic states and ensures a high level of generalizability for the prediction model. A hybrid graph block, comprised of a reactive cell and a proactive cell, is implemented into each neural layer of the generator. In order to jointly capture spatio-temporal influences and signal contextual information on traffic links, the two cells adopt several key neural network-based components, including graph convolutional network, recurrent neural architecture, and self-attention mechanism. The double-cell structure ensures GraphPro learns from proactive input only when required. The effectiveness and efficiency of GraphPro are tested on a short-term link speed prediction task using real-world traffic data. Due to the capabilities of learning from real data distribution and generating unseen samples, GraphPro offers a more reliable and robust prediction when compared with state-of-the-art data-driven models.
17.	Jin, Junchen, et al. (författare) A Decentralized Traffic Light Control System Based on Adaptive Learning 2017 Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 50:1, s. 5301-5306 Tidskriftsartikel (refereegranskat)abstract 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.
18.	Jin, J., et al. (författare) A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework 2022 Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16185-16196 Tidskriftsartikel (refereegranskat)abstract Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.
19.	Jin, Junchen, et al. (författare) A group-based traffic signal control with adaptive learning ability 2017 Ingår i: Engineering applications of artificial intelligence. - : Elsevier. - 0952-1976 .- 1873-6769. ; 65, s. 282-293 Tidskriftsartikel (refereegranskat)abstract 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.
20.	Jin, Junchen, et al. (författare) A Learning-based Adaptive Group-based Signal Control System under Oversaturated Conditions 2016 Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 49:5, s. 291-296 Tidskriftsartikel (refereegranskat)abstract 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.
21.	Jin, Junchen, et al. (författare) A Learning-based Adaptive Signal Control System with Function Approximation 2016 Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 49:3, s. 5-10 Tidskriftsartikel (refereegranskat)abstract 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.
22.	Jin, Junchen, et al. (författare) A multi-criteria intelligent control for traffic lights using reinforcement learning 2018 Ingår i: Advanced Concepts, Methodologies and Technologies for Transportation and Logistics. - Cham : Springer Verlag. ; , s. 438-451 Bokkapitel (refereegranskat)abstract 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.
23.	Jin, Junchen, et al. (författare) A multi-objective agent-based approach for road traffic controls: application for adaptive traffic signal systems 2017 Annan publikation (övrigt vetenskapligt/konstnärligt)abstract 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.
24.	Jin, Junchen, et al. (författare) A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System 2019 Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1524-9050 .- 1558-0016. ; 20:10, s. 3900-3912 Tidskriftsartikel (refereegranskat)abstract 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 constrained Markov decision process (CMDP) model is used to represent agent decision making in the context of multi-objective policy goals, where the policy goal with the highest priority becomes the single optimization objective and the other goals are transformed as constraints. A reinforcement learning-based computational framework is developed for control applications. To implement the multi-objective decision model, a threshold lexicographic ordering method is introduced and integrated with the learning-based algorithm. Moreover, a two-stage hybrid framework is established to improve the learning efficiency of the model. While the proposed approach is potentially applicable for different road traffic operations, this paper applies the framework for traffic signal control in a network of Stockholm based on traffic simulation. The computational results show that the proposed control approach can handle a complex case of multiple policy requirements. Meanwhile, the agent-based intelligent control has shown superior performance when compared to other optimized signal control methods.
25.	Jin, Junchen, et al. (författare) A multi-objective multi-agent framework for traffic light control 2017 Ingår i: 2017 11TH ASIAN CONTROL CONFERENCE (ASCC). - : IEEE. ; , s. 1199-1204 Konferensbidrag (refereegranskat)abstract 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.

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