IET Intelligent Transport Systems最新文献

Demand-responsive transit has gradually attracted attention in recent years for its flexibility, efficiency, and ability to meet the diverse travel demands of passengers. To improve the operational efficiency of demand-responsive transit (DRT) with dynamic demand, this study innovatively investigates the DRT scheduling problem from multiple perspectives, such as multi-vehicle, non-fixed stop, and dynamic demand, and constructs a two-phase DRT vehicle scheduling model. In the first phase, a static scheduling model is established with the objective of minimizing vehicle setup cost, operation cost, and CO₂ emission cost according to passenger travel satisfaction. In the second phase, a dynamic scheduling model is constructed with the objective of minimizing the increased vehicle operation cost in response to dynamic demand and the penalty cost of violating the time window and rejecting passengers. In addition, in the first static phase, an improved heuristic algorithm is used to obtain optimal routes based on passengers’ subscriptions, while in the second phase, an insertion algorithm is designed to solve the dynamic scheduling model based on the previous schedule. Finally, cases are applied to a realistic network in Chaoyang District, Beijing, China, to verify the effectiveness of the proposed scheduling model. The results demonstrate that dynamic scheduling can enable more passengers to be served with a slight increase in total vehicle operating costs. Besides, the introduction of the non-fixed stop service model can significantly reduce total travel time by up to 8.8% compared with the fixed stop service. The proposed models and solution algorithms in this study are practical for real-world applications.

The lateral motion control of an in-wheel motor drive electric vehicle (IWMD-EV) necessitates an accurate measurement of the vehicle states. However, these measured states are always affected by delays due to sensor measurements, communication latencies, and computation time, which results in the degradation of the controller performance. Motivated by this issue, a novel reaching law based predictive sliding mode control (NRL-PSMC) is proposed to maintain the lateral motion control of the IWMD-EV subjected to unknown time delay. Initially, a PSMC framework is built, in which a predictor integrating with the sliding mode control is designed to eliminate the effect of time delay and generate the virtual control signals. Further, to alleviate the chattering phenomenon, a novel-reaching law is developed, enabling the vehicle to track the desired states effectively. Subsequently, a dynamic control allocation technique is presented to optimally allocate the virtual control input to the actual control input. The accurate estimation of the aforementioned unknown delay is realized through a delay estimator. Finally, simulation and hardware-in-the-loop experiments are performed for three specific driving manoeuvres, and the results demonstrate the effectiveness of the proposed controller design.

In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert-based way, a novel sequence-to-sequence transfer transformer network to automatic flight plan generation with enhanced operational acceptability is presented. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences. The approach leverages deep neural networks to autonomously learn air transport specialized knowledge and human expert insights from industry legacy data. Moreover, a multi-head attention mechanism is adopted to model the complex correlation between airport pairs. Besides, we introduce an innovative waypoint embedding layer to learn effective embeddings for waypoint sequences. Additionally, an extensive flight plan dataset is constructed utilizing real-world data in China spanning from July to September 2019. Employing the proposed model, rigorous training and testing procedures are conducted on this dataset, yielding remarkably favourable outcomes based on automatic evaluation metrics that are BLEU and METEOR, which outperform other popular approaches. More importantly, the proposed approach achieves high performance in the operational validation and visualization, showing its application potential for real-world air traffic operation.

An object detection framework using thermal infrared (TIR) cameras is proposed to meet the needs of an advanced driver assistance system (ADAS) operating at night-time and in low-visibility conditions. The proposed detection framework, referred to as TIR-YOLO-ADAS, is an improvement of YOLOX for TIR object detection in ADAS. First, to address the disadvantages of TIR objects, the part of the attention mechanism is designed to enhance the discriminative ability of feature maps in the spatial and channel dimensions. Second, a focal loss function is used as the confidence loss function to enable the framework to focus on detection tasks of difficult, misclassified targets in the process of network training. The results of the ablation experiment on the Forward-looking infrared (FLIR) thermal ADAS dataset indicate that the proposed framework significantly improves the performance of TIR object detection. Comparative experimental results further show that TIR-YOLO-ADAS performs favourably when compared with three representative detection algorithms. To evaluate the practicality and feasibility of the proposed framework in various applications, a qualitative assessment in real road scenarios was conducted. The experimental results confirm that the proposed framework performs promisingly and could be integrated into vehicle platforms as an ADAS module.

Accurately predicting the future trajectory and behavior of traffic participants is crucial for the maneuvers of self-driving vehicles. Many existing works employed a learning-based “encoder-interactor-decoder” structure, but they often fail to clearly articulate the relationship between module selections and real-world interactions. As a result, these approaches tend to rely on a simplistic stacking of attention modules. To address this issue, a trajectory prediction network (Q-EANet) is presented in this study, which integrates GRU encoders, MLPs and attention modules. By introducing a new explanatory rule, it makes a contribution to interpretable modeling, models the entire trajectory prediction process via an implicit social modeling formula. Inspired by the anchoring effect in decision psychology, the prediction task is formulated as an information query process that occurs before traffic participants make decisions. Specifically, Q-EANet uses GRUs to encode features and utilizes attention modules to aggregates interaction information for generating the target trajectory anchors. Then, queries are introduced for further interaction. These queries, along with the trajectory anchors with added Gaussian noise, are then processed by a GRU-based decoder. The final prediction results are obtained through a Laplace MDN. Experimental results on the several benchmarks demonstrate the effectiveness of Q-EANet in trajectory prediction tasks. Compared to the existing works, the proposed method achieves state-of-the-art performance with only simple module design. The code for this work is publicly available at https://github.com/Jctrp/socialea.

The integrated design of traffic signal control (TSC) and reversible lane (RL) is an effective way to solve the problem of tidal congestion with uncertainty at isolated intersections, because of its advantage in making full use of temporal-spatial transportation facilities. Considering the contradiction between the dynamic TSC scheme and the fixed RL scheme in one period, a two-stage optimization method based on improved mean-standard deviation (MSD) model for isolated intersections with historical and real-time uncertain traffic flow is proposed. In the first stage, applying the same-period historical data of multiple days, a robust optimal traffic signal control model with reversible lane based on MSD model (MSD-RTR model) is put forward to obtain the fixed RL scheme and the compatible initial TSC scheme. A double-layer nested genetic algorithm (DN-GA) is designed to solve this model. In the second stage, applying real-time period data and multi-day same-period historical data, a robust optimal dynamic traffic signal control model based on MSD model (MSD-RDT model) is put forward to obtain the dynamic TSC scheme. Three modes which reflect the different weights of historical period and real-time period in this MSD-RDT model are presented to improve the model stability, and a multi-mode genetic algorithm (MM-GA) is designed. Finally, a case study is presented to demonstrate the efficiency and applicability of the proposed models and algorithms.

Implementing reliable lane changes is crucial for reducing collisions and enhancing traffic safety. However, existing research lacks comprehensive investigation into the optimal path for maintaining driving quality, and little attention has been given to determining the appropriate lane changing time point. This paper addresses these gaps by presenting a novel hierarchical strategy. First, a synthesized safety distance for lane changing, which considers variable execution duration, is designed to reduce collision risk. Next, a hierarchy of optimization control strategies is proposed to obtain the optimal path. An upper neural network-fuzzy control algorithm is established to identify an appropriate lane-changing time point. Additionally, a lower neural network-improved firefly algorithm is formulated to optimize the preliminary safety path based on multiple driving criteria. Furthermore, the dynamics characteristics of the vehicle are incorporated into the model predictive control algorithm to ensure the vehicle follows the optimal path. Finally, the feasibility of the proposed hierarchical control strategy is validated through typical lane-changing scenarios conducted on the Carsim–Simulink platform.

It is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two-layer energy-efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real-time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods.

Numerous efforts have been made to address the section-level travel speed prediction problem. However, section-level predictions can hardly be used for fine-grained applications, such as lane management and lane-level navigation. The main reason for this is that significant speed heterogeneity exists among the lanes within one section. Thus, this study proposes a three-dimensional (3D) dual attention convolution-based deep learning model for predicting the lane-level travel speed. 3D convolutions are designed to learn high-dimensional spatiotemporal traffic flow features, that is, the relationships between different sections, lanes, and periods. Dual attention modules are used to focus on the traffic flow propagation patterns and to explain the model's mechanisms. To evaluate the proposed model, an indicator is introduced to assess the spatio-temporal learning ability, based on targeting the lane-level case. Evaluation experiments are conducted based on loop detector data in Shanghai, China. The results show that high accuracy is obtained by the proposed model, with a 2.9 km/h mean absolute error, thereby outperforming several existing methods. Finally, an in-depth analysis is provided regarding the attention coefficients and interpretation of real-world lane-level traffic flow propagation patterns, so as to gain insights into the model's mechanism when capturing dynamic lane-level traffic flow.

In urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two-objective train operation optimization based on eco-driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware-in-the-loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow.