| 1. |
- Zhang, Wei, et al.
(författare)
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Coordination for heavy-duty vehicle platoon formation considering travel time variance
- 2015
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Konferensbidrag (refereegranskat)abstract
- Forming a platoon has the potential to reduce the overall drag, providing economic and ecological benefitssuch as reduced energy consumption, increased safety and a more efficient utilization of roadinfrastructure. Previous research on platoon coordination has mainly focused on local control of platoonformation at highway on‐ramps and off‐ramps, or large‐network coordination strategy based on real‐timevehicle‐to‐vehicle communication. The platoon scheduling problem, however, has been barely explored.This study investigates the optimization of platoon scheduling problem, which is defined as theminimization of the total cost of all vehicles, including travel cost, early or late penalty and fuelconsumption. The travel cost is modelled as driver wage of certain travel time, which is comprised ofrecurrent travel time and non‐recurrent delay. Non‐recurrent delay is a random variable independent ofdeparture time. If the actual arrival time is earlier than the preferred arrival time, an early penalty isincurred. Otherwise a late penalty, which has a greater weight coefficient than early penalty, is incurred.Fuel consumption is a nonlinear function of travel time and platooning state. All vehicles in the platoonexcept the leader will experience an air‐drag reduction. The fuel cost caused by air drag only composes partof the total fuel consumption, from the perspective of energy conservation. For this nonlinear stochasticprogramming problem, a solution is proposed for the platoon‐or‐not‐platoon dilemma. Moreover, theoptimal departure time of the platoon is given when it is more beneficial to form a platoon than drivingindividually. Several numerical examples are presented to study the influences of different unit costparameters, together with various assumptions of the distribution of non‐recurrent delay, on the optimaldeparture time. The model enables the operator to predict the expected cost of platooning and scheduleadjustment and make a reasonable decision.
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| 2. |
- Chi, Pengnan, et al.
(författare)
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Difforecast : Image Generation Based Highway Traffic Forecasting with Diffusion Model
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 608-615
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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.
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| 3. |
- Chi, Pengnan, et al.
(författare)
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Short-Term Traffic Prediction on Swedish Highways: A Deep Learning Approach with Knowledge Representation
- 2023
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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.
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| 4. |
- 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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| 5. |
- Jin, J., et al.
(författare)
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A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
- 2022
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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
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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.
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| 6. |
- Jin, Junchen
(författare)
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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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| 7. |
- 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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| 8. |
- Johansson, Ingrid
(författare)
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Simulation Studies of Impact of Heavy-Duty Vehicle Platoons on Road Traffic and Fuel Consumption
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The demand for road freight transport continues to grow with the growing economy, resulting in increased fossil fuel consumption and emissions. At the same time, the fossil fuel use needs to decrease substantially to counteract the ongoing global warming. One way to reduce fuel consumption is to utilize emerging intelligent transport system (ITS) technologies and introduce heavy-duty vehicle (HDV) platooning, i.e. HDVs driving with small inter-vehicle gaps enabled by the use of sensors and controllers. It is of importance for transport authorities and industries to investigate the effects of introducing HDV platooning. Previous studies have investigated the potential benefits, but the effects in real traffic, both for the platoons and for the surrounding vehicles, have barely been explored. To further utilize ITS and optimize the platoons, information about the traffic situation ahead can be used to optimize the vehicle trajectories for the platoons. Paper I presents a dynamic programming-based optimal speed control including information of the traffic situation ahead. The optimal control is applied to HDV platoons in a deceleration case and the potential fuel consumption reduction is evaluated by a microscopic traffic simulation study with HDV platoons driving in real traffic conditions. The effects for the surrounding traffic are also analysed. Paper II and Paper III present a simulation platform to assess the effects of HDV platooning in real traffic conditions. Through simulation studies, the potential fuel consumption reduction by adopting HDV platooning on a real highway stretch is evaluated, and the effects for the other vehicles in the network are investigated.
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| 9. |
- Liang, Xinyue, et al.
(författare)
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AVIATOR: fAst Visual Perception and Analytics for Drone-Based Traffic Operations
- 2023
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2959-2964
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Konferensbidrag (refereegranskat)abstract
- Drone-based system is an emerging technology for advanced applications in Intelligent Transport Systems (ITS). This paper presents our latest developments of a visual perception and analysis system, called AVIATOR, for drone-based road traffic management. The system advances from the previous SeeFar system in several aspects. For visual perception, deep-learning based computer vision models still play the central role but the current system development focuses on fast and efficient detection and tracking performance during real-time image processing. To achieve that, YOLOv7 and ByteTrack models have replaced the previous perception modules to gain better computational performance. Meanwhile, a lane-based traffic steam detection module is added for recognizing detailed traffic flow per lane, enabling more detailed estimation of traffic flow patterns. The traffic analytics module has been modified to estimate traffic states using lane-based data collection. This includes detailed lane-based traffic flow counting as well as traffic density estimation according to vehicle arrival patterns per lane.
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| 10. |
- Ma, Xiaoliang, Docent, et al.
(författare)
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DigiWays: A Digitalisation Testbed for Sustainable Traffic Management on Swedish Motorways
- 2023
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Ingår i: 2023 IEEE World Forum on Internet of Things: The Blue Planet: A Marriage of Sea and Space, WF-IoT 2023. - : Institute of Electrical and Electronics Engineers Inc..
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Konferensbidrag (refereegranskat)abstract
- Motorway traffic management system plays important roles for modern Intelligent Transport Systems (ITS). The Swedish motorways near large cities such as Stockholm are equipped with a large number of sensors for traffic monitoring and advanced traffic management purposes. This paper introduces our recent experiments of digitalising motorway traffic system using vehicle-to-everything (V2X) communication and other sensors deployed for measuring road traffic and road-side air pollutants. In addition to the deployment of V2X testbed, a Cyber-Physical system (CPS) framework is presented to integrate the deployed sensors with the computational models for estimation and prediction of traffic and road-side environmental conditions. A digital twin of motorway traffic flow is established using traffic flow models of different levels. The computation in experiment of the cyber space shows that traffic states can be estimated using V2X sensing data by applying the model-based estimation approach.
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