IET Intelligent Transport Systems最新文献

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.

The distribution of public electric vehicle (EV) charging infrastructure is a widespread approach for promoting EV adoption and decarbonising transportation. A significant amount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale. This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingham and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Nottingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode district (ZIP code) are examined: the percentage of apartment buildings/floor area ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in districts located near urban centres or those with high availability of amenities but also ensuring that users without home charging options are not left behind.

The potential of passively generated big data sources in transport modelling is well-recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground-truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent-based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation-based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Commuting traffic associated with the “9 to 5” workday shaped the morning and evening peaks across the world. The COVID-19 pandemic led to unprecedented changes in travel behaviour such as an increase in cyclists and telecommuting, where employees worked from home during lockdown periods. Transport modellers, planners and policy makers need to know whether the 9 to 5 has returned, or we have entered a “New-normal” of more flexible working arrangements and increased cycling, key for delivering sustainability targets. In this research, the unsupervised machine learning technique k-means clustering investigates temporal patterns across the day and week, comparing the pre- and post-pandemic era across both motorised vehicles and bicycles. Results show that the total daily traffic flow has returned to pre-pandemic volumes, but more spread across the day. Mondays and Fridays have less-pronounced peaks compared to pre-pandemic, having implications for air quality modelling and assessment, traffic management and transport planning. Meanwhile, cycling has increased in volume and the time-of-day people are travelling has changed. Policy makers need to consider whether the additional capacity on the road, brought about by reduced peak traffic, could be reallocated to make roads safer for and reduce delay to cyclists, contributing towards net zero goals.

At present, the mainstream studies on route selection optimization at the railway station rarely considered the overall punctuality of the operation plans and the seizing route resource between shunting operation and train running, which can endanger the running safety and reduce the efficiency at the station. Therefore, this paper proposes an optimization method for the route selection under the integration of dispatching and control at the railway station. Firstly, the station-type data structure, the route occupation conflict, and the operation task order were defined. Then, a 0-1 programming model was constructed to minimize the total delay time and shorten the total travel time of all operations. Finally, a two-stage solution algorithm based on depth-first search algorithm and genetic algorithm was designed, and two actual cases of a technical station in China were designed. The instance verification results show that the algorithm can find the satisfactory route scheme in 250 iterations; different delay factors and travel coefficients will get different route schemes, which can provide decision support for dispatchers and operators to select routes. Through comparative analysis of algorithms, it is found that the two-stage algorithm has higher solving efficiency than the individual depth-first search algorithm and individual genetic algorithm.

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.

Continuous-flow intersections (CFI), also known as displaced left-turn (DLT) intersections, aim to improve the efficiency and safety of traffic junctions. A CFI introduces additional cross-over intersections upstream of the main intersection to split the left-turn flow from the through movement before it arrives at the main intersection which decreases the number of conflict points between left-turn and through movements. This study develops and examine a two-step optimization model for CFI traffic signal control design and demonstrates its performance across more than 300 different travel demand scenarios. The proposed model is compared against a state-of-practice CFI signal control model as a benchmark. Microsimulation results suggest that the proposed model reduces average delay by 17% and average queue length by 32% for a full CFI compared with the benchmark signal control model.

With the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)-bi-directional long short-term memory (BiLSTM) interval prediction method for medium and long-term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN-BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

The co-phase traction power supply system (TPSS) with hybrid energy storage system (HESS) and photovoltaic (PV) is proposed to eliminate the neutral section and improve the regenerative braking energy (RBE) utilization. Although the integration of HESS and PV facilitates the energy saving and cost reduction of the co-phase TPSS, the high cost and configuration of HESS should be considered, which is the key to affect the optimal operation strategy of co-phase TPSS. Here, the optimal operation strategy of co-phase TPSS with HESS and PV is proposed to design the HESS configuration, recycle RBE and improve power quality. The proposed model aims to minimize the total system cost, including HESS investment cost, electricity cost and operation and maintenance cost. Moreover, the proposed model is formulated as a mixed integer linear programming by employing linearization approaches. Finally, case studies verify that the 29.2% cost reduction rate is achieved and the three-phase voltage unbalance meets the standard requirements.