IET Intelligent Transport Systems最新文献

The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents’ activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra-similarities, inter-differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.

Drawing inspiration from the state-of-the-art object detection framework YOLOv8, a new model termed adverse weather net (ADWNet) is proposed. To enhance the model's feature extraction capabilities, the efficient multi-scale attention (EMA) module has been integrated into the backbone. To address the problem of information loss in fused features, Neck has been replaced with RepGDNeck. Simultaneously, to expedite the model's convergence, the bounding box's loss function has been optimized to SIoU loss. To elucidate the advantages of ADWNet in the context of adverse weather conditions, ablation studies and comparative experiments were conducted. The results indicate that although the model's parameter count increased by 18.4%, the accuracy for detecting rain, snow, and fog in adverse weather conditions improved by 22%, while the FLOPs (floating point operations) decreased by 5%. The results of the comparison experiments conducted on the WEDGE dataset show that ADWNet outperforms other object detection models in adverse weather in terms of accuracy, model parameters and FLOPs. To validate ADWNet's real-world efficacy, data was extracted from a car recorder under adverse conditions on highways, visual inference was conducted, and its accuracy was demonstrated in interpreting real-world scenarios. The config files are available at https://github.com/Xinyun-Feng/ADWNet.

The UK rail network is subject to costly disruption due to the operational effects of adhesion variation between the wheel and rail. Causes of this are often environmental introduction of contaminants that require a wide-scale approach to risk mitigation such as defensive driving or rail-head maintenance. It remains an open problem to monitor the real-time status of the network to optimise resources and approaches in response to adhesion problems. This article presents an on-vehicle monitoring method designed to estimate the coefficient of friction by processing data from on-board sensors of typical rail passenger vehicles. This approach uses a multi-body physics analysis of a target vehicle to create estimators for both creep force and creep, allowing a curve fitting approach to estimate the coefficient for friction from the creep curves.

Cycling is increasingly promoted worldwide, but many urban areas lack satisfactory cycling environments. Assessing these environments is crucial, but existing methods face data challenges for large urban networks. This study proposes a data-driven framework using dockless shared bicycle data to efficiently evaluate large-scale cycling environments. First, critical cycling behaviour features that reflect cyclists’ perceptions are identified applying the fuzzy C-means and random forest model. Then, a distribution-oriented evaluation method is developed, ensuring the incorporation of cyclist heterogeneity and quantifying the quality differences among road segments by combining statistical analysis with a hierarchical clustering model. The evaluation framework is applied to Yangpu District, Shanghai, using Mobike data covering 114.9 km of cycling roads. Results show that indicators related to speed magnitude and fluctuation are critical, and an experimental study validates the effectiveness of the data-driven feature extraction method. A minimum trajectory sample size of 260 is required to account for cyclist heterogeneity for one road segment to be evaluated. Further analysis of lower-performing segments identifies vehicle-bicycle separation, on-street parking, and traffic volume as key influencing factors. The rationality of these findings further supports the reliability of the evaluation framework.

Driving while inattentive or fatigued significantly contributes to traffic accidents and puts road users at a significantly higher risk of collision. The rise in road accidents due to driver inattention resulting from distractive objects, for example, mobile phones, drinking, or tiredness, requires intelligent traffic monitoring systems to promote road safety. However, outdated detection technologies cannot handle the poor accuracy and the lack of real-time processing possibility especially when combined with the variations of driving environment. This paper introduces “ME-YOLOv8” which operates driver`s distraction and fatigue through a modified version of YOLOv8, which includes modules multi-head self-attention (MHSA) and efficient channel attention (ECA) modules applied, where the goal of MHSA is to improve the sensitivity of global features and the ECA attentions focus on critical features. Additionally, a dataset was created containing 3660 images covering multiple distracted and drowsy driver scenarios. The results reflect the enhanced detection capabilities of ME-YOLOv8 and demonstrate its effectiveness in real-time scenarios. This study demonstrates a significant advancement in the application of AI to public safety and highlights the critical role that state-of-the-art deep learning algorithms play in lowering the risks associated with distracted and tired driving.

Individual mobility is driven by activities and thus restricted geographically, especially for trip destination prediction in public transport. Existing statistical learning based models focus on extracting mobility regularity in predicting an individual's mobility. However, they are limited in modeling varied spatial mobility patterns driven by the same activity (e.g. an individual may travel to different locations for shopping). The paper proposes a deep learning model with activity, geographic and sequential (DeepAGS) information in predicting an individual's next trip destination in public transport. DeepAGS models the semantic features of activity and geography by using word embedding and graph convolutional network. An adaptive neural fusion gate mechanism is proposed to dynamically fuse the mobility activity and geographical information given the current trip information. Besides, DeepAGS uses the gated recurrent unit to capture the temporal mobility regularity. The approach is validated by using a real-world smartcard dataset in urban railway systems and comparing with state-of-the-art models. The results show that the proposed model outperforms its peers in terms of accuracy and robustness by effectively integrating the activity and geographical information relevant to a trip context. Also, we illustrate and verify the working mechanism of the DeepAGS model using the synthetic data constructed using real-world data. The DeepAGS model captures both the activity and geographic information of hidden mobility activities and thus could be potentially applicable to other mobility prediction tasks, such as bus trip destinations and individual GPS locations.

This study examines road user opinions regarding small modular autonomous electric vehicles, focusing on the differences between the elderly and non-elderly populations in Norway. The data allowed for a comparison between 193 respondents under 65 years old and 208 respondents over 65 years old. The results highlighted significant differences between the two groups about the vehicles, their usability, and the likeliness of using them as public transport if implemented in the future. Traffic safety and personal security were found to be decisive aspects, for respondents over 65 years old being more worried about safety and security than their counterparts. Trust that the authorities will ensure the safe implementation of such vehicles in the current transportation system was also significantly different between the two groups, with the younger generations having more trust in the authorities than the older group. The results shed light on road user opinions about a small modular transport mode, particularly on those over 65 years old, indicating a need for research efforts to better identify how this new form of public transport should be implemented in the future to improve the mobility of all travellers and meet the needs of the seniors.