A novel curvature-compensated bandgap reference (BGR) using a four-input current feedback amplifier is presented in this paper. High-order curvature compensation terms (T•ln(T) dependence) are produced by the VBE’s difference of bipolar transistors (BJT) whose currents are biased at proportional to absolute temperature (PTAT) and complementary to absolute temperature (CTAT) currents, which can diminish the temperature-dependent nonlinearity of traditional BGR. A four-input current feedback summation amplifier innovatively incorporates an additional input pair to complete the summation of the first-order and high-order curvature compensation terms. Combined with an auto-zero technique built on a new timing constraint, the proposed BGR greatly enhances the BGR’s accuracy and suppresses the low-frequency noise. Implemented in a standard 180nm CMOS process, the proposed BGR achieves the average temperature coefficient (TC) of 3.5 ppm/°C and the worst case is 5.08 ppm/°C from -40 °C to 125 °C with merely one-point 4-bit trimming. Measurement shows a 3σ initial inaccuracy of ±0.14% without trimming, and ±0.05% is achieved with trimming. The low-frequency noise (0.01Hz to 10Hz) is 6.8 µVRMS.

Lane-changing (LC) critically affects traffic efficiency and safety, making it a key focus in autonomous driving strategy development. In the human-machine co-driving phase, assisted driving systems must integrate driver habits to enable effective driver-vehicle collaboration. To this end, this paper proposes an LC strategy for autonomous electric vehicles (EVs) that integrates driver habits and vehicle dynamic characteristics. It solves two crucial issues: 1) how to guarantee drivers’ LC habits in the proposed strategy, and 2) how to maximize the utilization of electric vehicle (EV) dynamics on the LC performance. In the lane-changing decision (LCD) stage, we estimate the LC probability to obtain a range of LC starting positions that align with driver habits, and we select one to enhance the EV performance. In addition, in the lane-changing implementation (LCI) stage, we propose an anthropomorphic EV control to ensure the LC trajectory is consistent with driver habits, while the EV dynamics are optimized with different trajectory objectives. The simulation results show the driver’s LCD is dependent on the longitudinal position difference between the preceding vehicles in the original and target lanes, and the LCD predicted accuracy reaches 95.2%. In addition, the proposed LCI can meet the differentiated LC demands, as the LCI strategies focusing on economy, comfort, and efficiency can reduce the SOC consumption by 28.6%, the wheel angular velocity by 94.4%, and the LC duration by 70.0%, respectively. Besides, the robustness of the strategy is verified by the relatively stable performance under SOCs and environment temperatures. Thus, this paper has the potential to clarify the LC optimization requirements for autonomous EVs and assist in the electrification and intelligent development of transportation systems.

Accurate prediction of passenger demand is essential for the efficient operation and management of metro systems. In practical scenarios, strategies to enhance metro service quality often require passenger demand information on multiple fronts, such as inflow to a station, outflow from a station, as well as transition flow between entry/exit stations. While predictions for a single type of passenger demand have been extensively studied, limited attention was paid to jointly predicting multiple demands. This problem is challenging due to the complex relationships among multiple demands (e.g., inflow is only correlated with historical inflow, while the outflow is not only correlated with outflow but also determined by the inflow) and the imbalanced training issue of multiple prediction tasks. To address these challenges, this paper proposes a deep multi-task learning (MTL) model called MTLMetro to co-predict multiple demands in metro systems. More specifically, we deploy the message-passing schemes in graph neural networks (GNNs) as the knowledge-sharing mechanisms in the MTL model to capture the inherent relationships among multiple demands. To balance the training of multiple tasks, we introduce a novel weighting scheme named dynamic weight average (DWA), which can dynamically adapt relative weight for each task. In addition, the partial observability problem of transition flow is also considered in MTLMetro in an end-to-end manner. Empirical evaluation on a real-world dataset demonstrates MTLMetro’s superior performance across the different demand prediction tasks when compared to several benchmarks. Further ablation experiments verify the effectiveness of the proposed modules and the weighting method.

The efficient operation of urban bus networks largely depends on optimised scheduling conducted before the one-day operation, crucially relying on reliable origin-destination (OD) information. Passengers travel on direct and transfer trips due to complex infrastructure and services in bus networks. These two differential behaviours necessitate a model that captures topological differences to accurately predict the OD matrix. Responding to this need, we propose a graph-based deep learning model, termed the Direct-Transfer Heterogeneous Graph Network (DT-HGN). This model is designed to predict the OD matrix whilst expressly distinguishing direct and transfer passenger behaviour. DT-HGN articulates direct and transfer trips as distinct graphs, each characterised by its unique adjacency matrix. The model’s architecture embraces two principal blocks: a Spatio-Temporal (ST) construct and an Auto-Encoder (AE) component. The ST-block applies a Gated Recurrent Unit model and a Graph Convolutional Network to discern features of direct and transfer trips, considering both temporal and spatial dimensions. Conversely, the AE-block utilises a heterogeneous graph convolutional network to transmute the two heterogeneous graphs into latent features. Our real-world validation process, executed over a two-month period on an urban bus network, attests to DT-HGN’s robust ability in accurate OD matrix prediction, outperforming contemporaneous state-of-the-art models. This study addresses the crucial need for a comprehensive network-level OD matrix and provides a new perspective for optimising the entire public transport network by accurately depicting station-to-station demand. The approach extends beyond the limitations of traditional bus lines, allowing for a more comprehensive analysis and improvement of urban public transport systems.

The tap-on smart-card data provides a valuable source to learn passengers’ boarding behaviour and predict future travel demand. However, when examining the smart-card records (or instances) by the time of day and by boarding stops, the positive instances (i.e. boarding at a specific bus stop at a specific time) are rare compared to negative instances (not boarding at that bus stop at that time). Imbalanced data has been demonstrated to significantly reduce the accuracy of machine-learning models deployed for predicting hourly boarding numbers from a particular location. This paper addresses this data imbalance issue in the smart-card data before applying it to predict bus boarding demand. We propose the deep generative adversarial nets (Deep-GAN) to generate dummy travelling instances to add to a synthetic training dataset with more balanced travelling and non-travelling instances. The synthetic dataset is then used to train a deep neural network (DNN) for predicting the travelling and non-travelling instances from a particular stop in a given time window. The results show that addressing the data imbalance issue can significantly improve the predictive model’s performance and better fit ridership’s actual profile. Comparing the performance of the Deep-GAN with other traditional resampling methods shows that the proposed method can produce a synthetic training dataset with a higher similarity and diversity and, thus, a stronger prediction power. The paper highlights the significance and provides practical guidance in improving the data quality and model performance on travel behaviour prediction and individual travel behaviour analysis.

Traffic missing data imputation is a fundamental demand and crucial application for real-world intelligent transportation systems. The wide imputation methods in different missing patterns have demonstrated the superiority of tensor learning by effectively characterizing complex spatiotemporal correlations. However, interval-wise missing volume scenarios remain a challenging topic, in particular for long-term continuous missing and high-dimensional data with complex missing mechanisms and patterns. In this paper, we propose a customized tensor decomposition framework, named the data fusion CANDECOMP/PARAFAC (DFCP) tensor decomposition, to combine vehicle license plate recognition (LPR) data and cellphone location (CL) data for the interval-wise missing volume imputation on urban networks. Benefiting from the unique advantages of CL data in the wide spatiotemporal coverage and correlates highly with real-world traffic states, it is fused into vehicle license plate recognition (LPR) data imputation. They are regarded as data types dimension, combined with other dimensions (different segments, time, days), we innovatively design a 4-way low-n-rank tensor decomposition for data reconstruction. Furthermore, to deal with the diverse disturbances in different data dimensions, we derive a regularization penalty coefficient in data imputation. Different from existing regularization schemes, we further introduce Bayesian optimization (BO) to enhance the performance in the non-convexity of the objective function in our regularized hyperparametric solutions during tensor decomposition. Numerical experiments highlight that our proposed method, combining CL and LPR data, significantly outperforms the imputation method using LPR data only. And a sensitivity analysis with varying missing length and rate scenarios demonstrates the robustness of model performance.

Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

Electric vehicles (EVs) endow great potentials for future transportation systems, while efficient charge scheduling strategies are crucial for improving profits and mass adoption of EVs. Two critical and open issues concerning EV charging are how to minimize the total charging cost (Objective 1) and how to minimize the peak load (Objective 2). Although extensive efforts have been made to model EV charging problems, little information is available about model properties and efficient algorithms for dynamic charging problems. This paper aims to fill these gaps. For Objective 1, we demonstrate that the greedy-choice property applies, which means that a globally optimal solution can be achieved by making locally optimal greedy choices, whereas it does not apply to Objective 2. We propose a non-myopic charging strategy accounting for future demands to achieve global optimality for Objective 2. The problem is addressed by a heuristic algorithm combining a multi-commodity network flow model with customized bisection search algorithm in a rolling horizon framework. To expedite the solution efficiency, we derive the upper bound and lower bound in the bisection search based on the relationship between charging volume and parking time. We also explore the impact of demand levels and peak arrival ratios on the system performance. Results show that with prediction, the peak load can converge to a globally optimal solution, and that an optimal look-ahead time exists beyond which any prediction is ineffective. The proposed algorithm outperforms the state-of-the-art algorithms, and is robust to the variations of demand and peak arrival ratios.

A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

With the development of modern wireless communication technology, especially the vehicle infrastructure integration (VII) technology, vehicles' information such as identification, location, and speed can be readily obtained at upstream cross-section. This information can be used to support traffic signal timing optimization in real time. A dynamic predictive traffic signal control framework for isolated intersections is proposed in a cross-sectional VII environment, which has the ability to predict vehicle arrivals and use this to optimize traffic signals. The proposed dynamic predictive control framework includes a dynamic platoon dispersion model (DPDM) which uses the vehicles' speed data from the cross-sectional VII environment, as opposed to traditional vehicle passing/existing data, to predict the arriving flow distribution at the downstream stop-line. Then, a dynamic programming algorithm based on the exhaustive optimization of phases (EOP) is proposed working in rolling optimization (RO) scheme with a 2s time horizon. The signal timings are continuously optimized by regarding the minimization of intersection delay as the optimization objective, and setting the green time duration of each phase as a constraint. In the end, the proposed dynamic predictive control framework is tested in a simulated cross-sectional VII environment and a case study carried out based on a real road network. The results show that the proposed framework can reduce the average delay and queue length by up to 33% and 35%, respectively, compared with the traditional full-actuated control.

As the development of traffic detection technology, recent research is directed to a new generation of signal control systems supported by new traffic data. One of these directions is dynamic predictive control by incorporating short-term prediction capability. This paper focuses on investigating dynamic platoon dispersion models which could capture the variability of traffic flow in a cross-sectional traffic detection environment. The dynamic models are applied to predict the evolution of traffic flow, and further used to produce signal timing plans that account not only for the current state of the system but also for the expected short-term changes in traffic flows. We investigate factors affecting model accuracy, including time-zone length, position of upstream traffic detection equipment, road section length, traffic volume, turning percentages, and computation time. The impact of these factors on the model's performance is illustrated through a simulation analysis, and the computation performance of models is discussed. The results show that both the dynamic speed-truncated normal distribution model and dynamic Robertson model with dynamics outperform their respective static versions, and that they can be further applied for dynamic control.

The seven regular papers and one short paper in this special issue focus on artificial transportation systems and simulation.