| 1. |
- Ji, Qingyuan, et al.
(författare)
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GraphPro : A Graph-based Proactive Prediction Approach for Link Speeds on Signalized Urban Traffic Network
- 2022
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Ingår i: Conference Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 339-346
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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.
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| 2. |
- Jin, Junchen, et al.
(författare)
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A Decentralized Traffic Light Control System Based on Adaptive Learning
- 2017
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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.
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| 3. |
- Jin, Junchen, et al.
(författare)
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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.
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| 4. |
- Jin, Junchen, et al.
(författare)
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A Learning-based Adaptive Group-based Signal Control System under Oversaturated Conditions
- 2016
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 49:5, s. 291-296
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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.
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| 5. |
- Jin, Junchen, et al.
(författare)
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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.
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| 6. |
- Jin, Junchen, et al.
(författare)
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A multi-criteria intelligent control for traffic lights using reinforcement learning
- 2018
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Ingår i: Advanced Concepts, Methodologies and Technologies for Transportation and Logistics. - Cham : Springer Verlag. ; , s. 438-451
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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.
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| 7. |
- Jin, Junchen, et al.
(författare)
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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.
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| 8. |
- Jin, Junchen, et al.
(författare)
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A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System
- 2019
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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.
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| 9. |
- Jin, Junchen, et al.
(författare)
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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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| 10. |
- Jin, Junchen, et al.
(författare)
-
A non-parametric Bayesian framework for traffic-state estimation at signalized intersections
- 2017
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 11. |
- Jin, Junchen, et al.
(författare)
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A non-parametric Bayesian framework for traffic-state estimation at signalized intersections
- 2019
-
Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 498, s. 21-40
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 12. |
- Jin, Junchen, et al.
(författare)
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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation
- 2017
-
Ingår i: Advances in Engineering Software. - : Elsevier. - 0965-9978 .- 1873-5339. ; 114, s. 348-360
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 13. |
- Jin, Junchen, et al.
(författare)
-
Adaptive group-based signal control by reinforcement learning
- 2015
-
Ingår i: Transportation Research Procedia. - : Elsevier. - 2352-1465. ; , s. 207-216
-
Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 14. |
- Jin, Junchen, et al.
(författare)
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Adaptive Group-Based Signal Control Using Reinforcement Learning with Eligibility Traces
- 2015
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. - : IEEE conference proceedings. - 9781467365956 - 9781467365956 - 9781467365956 - 9781467365956 ; , s. 2412-2417
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 15. |
- Jin, Junchen
(författare)
-
Advance Traffic Signal Control Systems with Emerging Technologies
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Nowadays, traffic congestion poses critical problems including the undermined mobility and sustainability efficiencies. Mitigating traffic congestions in urban areas is a crucial task for both research and in practice. With decades of experience in road traffic controls, there is still room for improving traffic control measures; especially with the emerging technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and Big Data. The focus of this thesis lies in the development and implementation of enhanced traffic signal control systems, one of the most ubiquitous and challenging traffic control measures.This thesis makes the following major contributions. Firstly, a simulation-based optimization framework is proposed, which is inherently general in which various signal control types, and different simulation models and optimization methods can be integrated. Requiring heavy computing resources is a common issue of simulation-based optimization approaches, which is addressed by an advanced genetic algorithm and parallel traffic simulation in this study.The second contribution is an investigation of an intelligent local control system. The local signal control operation is formulated as a sequential decision-making process where each controller or control component is modeled as an intelligent agent. The agents make decisions based on traffic conditions and the deployed road infrastructure, as well as the implemented control scheme. A non-parametric state estimation method and an adaptive control scheme by reinforcement learning (RL) are introduced to facilitate such an intelligent system.The local intelligence is expanded to an arterial road using a decentralized design, which is enabled by a hierarchical framework. Then, a network of signalized intersections is operated under the cooperation of agents at different levels of hierarchy. An agent at a lower level is instructed by the agent at the next higher level toward a common operational goal. Agents at the same level can communicate with their neighbors and perform collective behaviors.Additionally, a multi-objective RL approach is in use to handle the potential conflict between agents at different hierarchical levels. Simulation experiments have been carried out, and the results verify the capabilities of the proposed methodologies in traffic signal control applications. Furthermore, this thesis demonstrates an opportunity to employ the systems in practice when the system is programmed on an intermediate hardware device. Such a device can receive streaming detection data from signal controller hardware or the simulation environment and override the controlled traffic lights in real time.
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| 16. |
- Jin, Junchen, et al.
(författare)
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An intelligent control system for traffic lights with simulation-based evaluation
- 2017
-
Ingår i: Control Engineering Practice. - : Elsevier. - 0967-0661 .- 1873-6939. ; 58, s. 24-33
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 17. |
- Jin, Junchen, et al.
(författare)
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Heavy-duty vehicle platoons in real traffic: simulation modeling and analysis
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 18. |
- Jin, Junchen, et al.
(författare)
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Hierarchical multi-agent control of traffic lights based on collective learning
- 2018
-
Ingår i: Engineering applications of artificial intelligence. - : Elsevier. - 0952-1976 .- 1873-6769. ; 68, s. 236-248
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 19. |
- Jin, Junchen, et al.
(författare)
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Implementation and Optimization of Group-based Signal Control in Traffic Simulation
- 2014
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Ingår i: 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - 9781479960781 ; , s. 2517-2522
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 20. |
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| 21. |
- Jin, Junchen, et al.
(författare)
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PRECOM : A Parallel Recommendation Engine for Control, Operations, and Management on Congested Urban Traffic Networks
- 2021
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; , s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a parallel recommendation engine, PRECOM, for traffic control operations to mitigate congestion of road traffic in the metropolitan area. The recommendation engine can provide, in real-time, effective and optimal control plans to traffic engineers, who are responsible for manually calibrating traffic signal plans especially when a road network suffers from heavy congestion due to disruptive events. With the idea of incorporating expert knowledge in the operation loop, the PRECOM system is designed to include three conceptual components: an artificial system model, a computational experiment module, and a parallel execution module. Meanwhile, three essential algorithmic steps are implemented in the recommendation engine: a candidate generator based on a graph model, a spatiotemporal ranker, and a context-aware re-ranker. The PRECOM system has been deployed in the city of Hangzhou, China, through both offline and online evaluation. The experimental results are promising, and prove that the recommendation system can provide effective support to the current human-in-the-loop control scheme in the practice of traffic control, operations, and management.
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| 22. |
- Johansson, Ingrid, et al.
(författare)
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Look-ahead speed planning for heavy-duty vehicle platoons using traffic information
- 2016
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 23. |
- Johansson, Ingrid, 1990-, et al.
(författare)
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Look-ahead speed planning for heavy-duty vehicle platoons using traffic information
- 2017
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Ingår i: Transportation Research Procedia. - : Elsevier. - 2352-1465. ; 22, s. 561-569
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 24. |
- Ma, Xiaoliang, et al.
(författare)
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Multi-criteria analysis of optimal signal plans using microscopic traffic models
- 2014
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Ingår i: Transportation Research Part D. - : Elsevier. - 1361-9209 .- 1879-2340. ; 32, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Increasing concerns on environment and natural resources, coupled with increasing demand for transport, put lots of pressure for improved efficiency and performance on transport systems worldwide. New technology nowadays enables fast innovation in transport, but it is the policy for deployment and operation with a systems perspective that often determines success. Smart traffic management has played important roles for continuous development of traffic systems especially in urban areas. There is, however, still lack of effort in current traffic management and planning practice prioritizing policy goals in environment and energy. This paper presents an application of a model-based framework to quantify environmental impacts and fuel efficiency of road traffic, and to evaluate optimal signal plans with respect not only to traffic mobility performance but also other important measures for sustainability. Microscopic traffic simulator is integrated with micro-scale emission model for estimation of emissions and fuel consumption at high resolution. A stochastic optimization engine is implemented to facilitate optimal signal planning for different policy goals, including delay, stop-and-goes, fuel economy etc. In order to enhance the validity of the modeling framework, both traffic and emission models are fine-tuned using data collected in a Chinese city. In addition, two microscopic traffic models are applied, and lead to consistent results for signal optimization. Two control schemes, fixed time and vehicle actuated, are optimized while multiple performance indexes are analyzed and compared for corresponding objectives. Solutions, representing compromise between different policies, are also obtained in the case study by optimizing an integrated performance index.
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| 25. |
- Ma, Xiaoliang, et al.
(författare)
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Prediction of arterial travel time considering delay in vehicle re-identification
- 2017
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Ingår i: Transportation Research Procedia. - : Elsevier. - 2352-1465. ; 22, s. 625-634
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 26. |
- Ma, Xiaoliang, et al.
(författare)
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Prediction of arterial travel time considering delay in vehicle re-identification
- 2016
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 27. |
- Sederlin, Michael, et al.
(författare)
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A Hybrid Modelling Approach for Traffic State Estimation at Signalized Intersections
- 2021
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Ingår i: 2021 IEEE Intelligent Transportation Systems Conference (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3604-3609
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Konferensbidrag (refereegranskat)abstract
- Traffic state estimation is an important part of the traffic control process and aims to creates an accurate understanding of the current situation in traffic system. Bayesian Filtering is a statistical modelling framework that is useful in representing traffic state update as well as the relation between traffic state and detection data. This study develops a hybrid approach and uses non-parametric Gaussian Process (GP) to model the state-space transition of traffic system. Through representing the system models as either fully data-driven GP or as a hybrid model using a parametric mean function fusing the conventional principle of traffic flow with the data-driven approach, the requirement of an analytical model can be removed or relaxed. The computational results show that the proposed approach for lane based TSE can capture both short-term fluctuations and larger demand changes. In particular, the Bayesian nature of the GP models offer relative ease in quantifying the model uncertainties in combination with a conventional traffic flow model.
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| 28. |
- Tang, Q., et al.
(författare)
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Left turn phasing type determination at isolated intersections solved by a genetic algorithm
- 2019
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Ingår i: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781538670248 ; , s. 4236-4241
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Konferensbidrag (refereegranskat)abstract
- A left turn could be treated as a permitted or protected left turn. Good left turn treatment could improve the efficiency at intersections. This paper aims to solve the left turn phasing type problem with the goal of minimizing the total delay. The determination of left turn phasing types extends the lane-based signal optimization method by introducing the decision variables of left turn phasing type indicators. This problem is solved with a genetic algorithm with penalty functions. It is found that permitted left turns contribute to total delay reduction.
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| 29. |
- Wang, Jiaxi, et al.
(författare)
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Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm
- 2016
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Ingår i: Computational Intelligence and Neuroscience. - : Hindawi Limited. - 1687-5265 .- 1687-5273.
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Tidskriftsartikel (refereegranskat)abstract
- The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.
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| 30. |
- Zhang, Tong, et al.
(författare)
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Link speed prediction for signalized urban traffic network using a hybrid deep learning approach
- 2019
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Konferensbidrag (refereegranskat)abstract
- Predicting traffic speed is of importance in transportation management. Signalized road networks manifest highly dynamic speed patterns that are challenging to model and predict. We propose a hybrid deep-learning-based approach for link speed prediction, aiming at capturing heterogeneous spatiotemporal correlations between road intersections. After transforming original road networks and intersections into graphs, this approach leverages a layered graph convolution network structure to model traffic speed variations at both intersection and road network levels. The two levels are combined through a fully connected neural layer. Neural spatiotemporal attention mechanisms are applied to modulate the most relevant periodical traffic information during signal cycles. The proposed approach was evaluated using real-world speed data collected in Hangzhou City, China. Experiments demonstrate that the proposed approach can offer a scalable and effective solution for predicting short-term speed for signalized road networks.
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| 31. |
- Zhang, Zhiguo, et al.
(författare)
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A Meta-Graph Deep Learning Framework for Forecasting Air Pollutants in Stockholm
- 2023
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Ingår i: 2023 IEEE World Forum on Internet of Things. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Forecasting air pollution is an important activity for developing sustainable and smart cities. Generated by various sources, air pollutants distribute in the atmospheric environment due to the complex dispersion processes. The emerging sensor and data technologies have promoted the development of data-driven approaches to replace conventional physical models in urban air pollution forecasting. Nevertheless, it is still challenging to capture the intricate spatial and temporal patterns of air pollutant concentrations measured by heterogeneous sensors, especially for long-term prediction of the multi-variate time series data. This paper proposes a deep learning framework for longer-term forecast of air pollutants concentrations using air pollution sensing data, based on a conceptual framework of meta-graph deep learning. The key modules in the framework include meta-graph units and fusion layers, which are designed to learn temporal and spatial correlations respectively. A detailed case was formulated for forecasting air pollutants in Stockholm using air quality sensing data, meteorological data and so on. Experiments were conducted to evaluate the performance of the proposed modelling framework. The computational results show that it outperforms the baseline models and conventional deterministic dispersion models, demonstrating the potential of the framework to be deployed for the real air quality information systems in Stockholm.
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