IET Intelligent Transport Systems最新文献

An adaptive transit signal priority strategy is presented in this paper with the objective of passenger delay minimization at isolated intersections serving conflicting bus rapid transit (BRT) routes. The proposed passenger-based adaptive signal priority for BRT systems (PASPB) is designed to optimize both green times and phase sequences at the start of each cycle and for a prespecified decision horizon. Since a public transportation (PT) vehicle travel time model capable of estimating the dwell time at stops with multiple loading areas has not yet been developed, PT vehicle dwell time is modeled in this study by analyzing the cases of passenger service by single or double PT vehicles. The problem is formulated as a mixed-integer nonlinear program (MINLP) and at each execution, the optimization is conducted by genetic algorithm. The model is deployed to a real-field intersection with conflicting BRT routes under the SUMO microsimulation environment. The results show that PASPB outperforms the SYNCHRO optimal solution and phase insertion strategy regarding PT passenger delay. Besides, the sensitivity analysis proves that at high demand levels of the PT system or general traffic, PASPB presents the best performance in terms of general traffic, PT, and total passenger delay compared to other models.

Electronic toll collection system (ETCS) devices on vehicles communicate wirelessly with road head devices at toll stations. ETCS is preferred in countries because it is more convenient and efficient than manual toll collection. However, the scattered and constant innovations in this sector require a bridge between worldwide ETCS usage and advancements and a single page to grasp this sector's developments to ensure further developments. Following a systematic review, this study examined ETCS worldwide and technology adaption considering the scopes, limitations, usages, and related technology of the ever-developing electronic toll collection system. Existing ETCS technologies have been investigated and synchronized with ingrowing tech disparity to overcome the research synchronization gap. The study also included a bibliometric assessment to identify the important areas of research, technologies most used, degree of knowledge generated, growth/trend of the researches, most well-known authors, keywords, countries, documents, source of publication, and so on considering time-to-time changes. The study provided bibliometric solutions to the gap of lack of synchronization of research worldwide and even ETCS implementation difference within a country, which complicated collaboration with the central system and linked organizations and made it difficult for users to adapt the system.

In order to enhance the performance of safety and fuel economy of connected hybrid electric vehicles (CHEVs), a novel distributed hierarchical platoon control scheme of CHEVs is proposed. First, the non-linear dynamic model of CHEVs platooning is established to accurately depict the multi-process coupling characteristics of CHEVs. Then, a distributed hierarchical control framework for CHEVs platooning is proposed, which is consisted of a upper model predictive control (MPC) law and a lower energy management control law. The upper MPC control law is built to produce the desired accelerations of every vehicle in the platoon and the lower fuzzy-based energy management control law is constructed to ensure the engine maintain at the rang of optimum working point and the motor work with the high efficiency of CHEVs platooning. Finally, the results manifest that the effectiveness of proposed platoon control scheme for CHEVs.

Connected and automated vehicles (CAVs) are being developed and designed to operate on existing roads. Their safe and efficient operation during roadworks, where traffic management measures are often introduced, is crucial. Two alternative measures are commonly applied during roadworks on motorways: (i) closing one or multiple lanes (ii) narrowing one or all lanes. The former can cause delays and increased emissions, while the latter can pose safety risks. This study uses a VISSIM-based traffic microsimulation to compare the effectiveness of these two strategies on traffic efficiency and safety, considering various market penetration rates (MPR) of CAVs. The model was calibrated and validated with the data collected from M1 motorway in the United Kingdom. Results show that average delays per vehicle-kilometre-travelled decreased from 102.7 to 2.5 s (with lane closure) and 23.6 to 0.6 s (with narrow lanes) with 0% and 100% CAV MPR, respectively. Moreover, safety in narrow lanes improved by 4.8 times compared to 1.5 times improvement in lane closure with a 100% CAV MPR; indicating that narrow lanes would result in better safety performance. These findings could assist transport authorities in designing temporary traffic management measure that results in better CAV performance when navigating through roadworks.

Marine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well-known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in-depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area.

The ability to predict the trajectory of an autonomous vehicle accurately is crucial for safe and efficient navigation. However, predicting diverse and multimodal futures can be challenging. Recent approaches such as attention and graph neural networks have achieved state-of-the-art performance by considering agent interactions and map contexts. This study focused on multi-agent prediction using an agent-centric approach with transformers. This enables parallel computation and a comprehensive understanding of the environment. Two main features are introduced: an adaptive receptive field (ARF) that captures the relevant surroundings for each agent, and perception encoding, which serves as spatial context embeddings. The ARF adapts to the agent's velocity and rotation, focusing attention ahead at high speeds or to the sides when it is slower. Perception encoding divides agents or lanes into levels and encodes the information of each level. This approach enables the efficient encoding of complex spatial relationships. The proposed method combines these advances with transformer modelling for multi-agent trajectory prediction while ensuring real-time prediction capabilities. The approach is evaluated on the Argoverse benchmark and better performance than the state-of-the-art baseline is achieved. By addressing challenges such as multimodal outputs and robustness, the study enhances the safety and efficiency of autonomous driving systems by more accurately predicting trajectories.