IET Intelligent Transport Systems最新文献

Spread of parking difficulty can be regarded as a special cascading failure process of urban parking systems. A comprehensive understanding of this process can be greatly helpful to build a more robust parking system. Parking network, a specified complex network, is proposed to model, simulate, and analyse the failure process of urban parking systems in this paper. This model is applied to the analysis of parking systems in an abstract city grid and the downtown area of Luohu, Shenzhen. The results demonstrate that the parking network can capture subtle variations among various parking cruising behaviours or strategies from a network perspective. To enhance the utility of the parking network, an auxiliary indicator named “Parking Difficulty Index” is introduced to help assess the failure degree of urban parking system, estimate the optimal timing for parking guidance intervention, and evaluate the effectiveness of various guidance strategies in mitigating parking difficulties.

In the era of data-driven transportation development, traffic forecasting is crucial. Established studies either ignore the inherent spatial structure of the traffic network or ignore the global spatial correlation and may not capture the spatial relationships adequately. In this work, a Dynamic Spatial-Temporal Network (DSTN) based on Joint Latent Space Representation (JLSR) is proposed for traffic forecasting. Specifically, in the spatial dimension, a JLSR network is developed by integrating graph convolution and spatial attention operations to model complex spatial dependencies. Since it can adaptively fuse the representation information of local topological space and global dynamic space, a more comprehensive spatial dependency can be captured. In the temporal dimension, a Stacked Bidirectional Unidirectional Gated Recurrent Unit (SBUGRU) network is developed, which captures long-term temporal dependencies through both forward and backward computations and superimposed recurrent layers. On these bases, DSTN is developed in an encoder-decoder framework and periodicity is flexibly modeled by embedding branches. The performance of DSTN is validated on two types of real-world traffic flow datasets, and it improves over baselines.

Deep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross-modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two-branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency.

Quantifying the complexity of traffic scenarios not only provides an essential foundation for constructing the scenarios used in autonomous vehicle training and testing, but also enhances the robustness of the resulting driving decisions and planning operations. However, currently available quantification methods suffer from inaccuracies and coarse-granularity in complexity measurements due to issues such as insufficient specificity or indirect quantification. The present work addresses these challenges by proposing a comprehensive entropy-based model for quantifying traffic scenario complexity across multiple dimensions based on a consideration of the essential components of the traffic environment, including traffic participants, static elements, and dynamic elements. In addition, the limitations of the classical information entropy models applied for assessing traffic scenarios are addressed by calculating magnitude entropy. The proposed entropy-based model is analyzed in detail according to its application to simulated traffic scenarios. Moreover, the model is applied to real world data within a naturalistic driving dataset. Finally, the effectiveness of the proposed quantification model is illustrated by comparing the complexity results obtained for three typical traffic scenarios with those obtained using an existing multi-factor complexity quantification method.

Predicting and understanding travellers’ mode choices is crucial to developing urban transportation systems and formulating traffic demand management strategies. Machine learning (ML) methods have been widely used as promising alternatives to traditional discrete choice models owing to their high prediction accuracy. However, a significant body of ML methods, especially the branch of neural networks, is constrained by overfitting and a lack of model interpretability. This study employs a neural network with feature selection for predicting travel mode choices and Shapley additive explanations (SHAP) analysis for model interpretation. A dataset collected in Chengdu, China was used for experimentation. The results reveal that the neural network achieves commendable prediction performance, with a 12% improvement over the traditional multinomial logit model. Also, feature selection using a combined result from two embedded methods can alleviate the overfitting tendency of the neural network, while establishing a more robust model against redundant or unnecessary variables. Additionally, the SHAP analysis identifies factors such as travel expenditure, age, driving experience, number of cars owned, individual monthly income, and trip purpose as significant features in our dataset. The heterogeneity of mode choice behaviour is significant among demographic groups, including different age, car ownership, and income levels.

Recent years have witnessed the proliferation of traffic accidents, which led wide researches on automated vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time-based frameworks cannot handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, a comprehensive driving risk management framework named RCP-RF is novelly proposed based on potential field theory under connected and automated vehicles environment, where the pedestrian risk metric is combined into a unified road-vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only $��O�\left(�N^2�\right)�$ of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state-of-the-art methods on real-world dataset NGSIM and real AV platform.

Speed curve planning is one of the most important functions of automatic train operation (ATO). To improve the real-time optimization capability and driver-friendliness of the existing ATO, an extended ATO framework considering both automatic driving and assisted driving is designed. A multi-objective optimization model based on quadratic programming is established considering energy-saving, punctuality, and comfort. However, due to the influence of the weight of multi-objectives, this method cannot directly obtain the speed curve satisfying the trip time constraint. Further, based on the analysis about the weight of multi-objects, a time-constrained quadratic programming algorithm is proposed. With the proposed method, the speed curve can be calculated in real-time both before operations and during operations. For the former, time-varying train mass and trip time are considered to guarantee an optimal solution. For the latter, deviations, delays, and maloperations on the way are corrected. Simulation experiments verify the solvability and real-time performance of the proposed method. In particular, compared with the dynamic programming and (mixed-integer linear programming) MILP method, the proposed method is more energy-efficient and easier to be followed by the driver. In addition, a prototype is developed for commercial tests on a Beijing subway line. The relevant performance is verified in commercial tests.

Accurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state-related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource-multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on-board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi-target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM-PHD-LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM-PHD-LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system.

Under the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand-responsive and station-station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

The sensor-enabled in-vehicle communication and infrastructure-centric vehicle-to-everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust-based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.