Excessive speed is one of the main causes of fatal crashes worldwide. One speed reduction measure is dynamic speed feedback signs (DSFS), whose main purpose is to make drivers aware of their excessive speed and thus influence their behaviour in a way that they reduce their driving speed. The objective of this review is to discuss the benefits of implementing DSFS in different settings, identify the most effective placement and messaging strategies, analyse the public perception and temporal effect of DSFS, and identify potential locations where this device can be further deployed. The study includes 44 studies, of which 35 are journal publications, three are conference proceedings and six are technical reports. The identified studies are divided into six categories based on their topic: (1) operational benefits of DSFS; (2) safety benefits of DSFS; (3) public perception of DSFS; (4) position of DSFS installation, message type and triggering; (5) temporal effect of DSFS; and 6) effect of vehicle type. The results of this study provide information on the use of DSFS and as such are valuable to road authorities and researchers.

Vehicle speed is one of the main factors that influence the occurrence and severity of the consequences of road traffic accidents. Operating speed can be defined, among other things, as the actual speed at which the largest number of road users drive in conditions of free traffic flow. It can be measured on existing roads, however, on newly designed roads it can only be predicted. For this reason, many researchers have examined the correlation between the elements of the road as well as its surroundings and operating speed. By determining the correlation, models for predicting operating speed were created. As part of this paper, the most significant models for predicting operating speed were analysed. Of course, the largest number of models are stochastic, but in recent years, models based on artificial intelligence, more precisely on deep learning, have also been created. Accordingly, the goal of this paper is to review the model for predicting the operating speed of vehicles while identifying opportunities for further research and improvement in this area.

The COVID-19 pandemic has posed significant challenges to global public health organisations and governments, leading to countermeasures like hand sanitizer availability, social distancing, and mandatory face mask wearing, which have disrupted the public transportation sector and impacted the virus spread. Anticipating the effects of circumstances like a pandemic on mobility is essential for operators and managers of public transportation systems to effectively and safely manage the system. In this study, the measures taken during the pandemic, such as those mentioned above, were considered as indicators in the latent class model (LCM) for modal shifting. The model incorporates sociodemographic variables as covariates to understand their impact on modal shifting from public transport to private cars. An online survey with 53,973 valid responses was conducted in Istanbul, Turkiye. As a result of the LCM with covariates, two-latent-class model, the best fit among models ranging from two to six latent classes, emerged. Class-1 participants show increased sensitivity to the pandemic, shifting to private mode, while Class-2 participants are less concerned and tend to maintain their existing mode. The model suggests using LCM with covariates to estimate the modal shift from public transportation to private cars in any given situation.

With the development of high-speed railway (HSR) systems, high-speed rail express delivery (HSReD) is currently the growing trend in railway cargo transport. The decisions on line planning and freight flow allocation are two of the main problems for the practical operation of HSReD. This paper focuses on integrating the above issues, considering differentiated transportation modes and products. A collaborative optimisation model is developed to maximise the benefits of freight transport. Numerical experiments are conducted based on the Beijing-Shanghai HSR. The results show that the collaborative optimisation model gets a 7.96% higher freight-demand fulfilment rate and an 18.64% increase in the profit rate, compared with the two-stage model under the same network conditions and parameter settings. Some operational implications are also obtained based on the sensitivity analysis, which is potentially useful for optimising the daily operation management of HSReD.

Members of marketing airline alliances cooperatively book seats from the operating airline and compete with each other in the market. This paper models and discusses two types of bargaining pricing processes: representative-based and agent-based cooperative bargaining. It also considers the internal negotiation mechanism within the marketing airline alliance for representative-based bargaining. Using a cooperative bargaining approach, the effects of marketing airline mergers in code-share agreements with the operating airline are analysed. The performance of two sub-strategies under representative-based bargaining is compared with the non-cooperative case. The study concludes that representative-based bargaining without internal negotiation intensifies competition, while representative-based bargaining with internal negotiation has the opposite effect. Cooperative bargaining with internal negotiation benefits both the marketing airlines and the operating airline, whereas representative-based bargaining without internal negotiation may result in a total profit loss. The choice of which bargaining strategy to adopt depends on the bargaining power and the substitutability of different market airline brands. This research provides the basis and support for the formulation of pricing strategies in airline alliances' code-sharing.

Anticipating uncertainty in short-term traffic flow is crucial for effective traffic management within intelligent transportation systems. Various methods for predicting uncertainty have been proposed and implemented. However, conventional techniques struggle to provide accurate forecasts when confronted with sparse data. Hence, this study focuses on developing an uncertainty prediction model for short-term traffic flow under limited data conditions. A novel grey model that considers the volatility of the traffic data is proposed, which extends the grey model (GM) by integrating two techniques: smooth pre-processing and background value construction. The performance of the proposed novel grey model is mainly illustrated by comparing the novel grey model with the traditional GM model. Our results, in terms of uncertainty quantification, demonstrate that the proposed model outperforms the GM model regarding mean kick-off percentage (KP), width interval (WI) and width amplitude.

This paper proposes an optimisation model for an urban rail transit line timetable considering headway coordination between the mainline and the depot during the transition period. The model accounts for the tracking operation scenario of trains inserted from the depot onto the mainline and related train operation constraints. The optimisation objectives are the number of trains inserted, maximum train capacity rate and average headway deviation. Second-generation non-dominated sorting genetic algorithm is designed to solve the model. A case study shows that optimisation achieves a total of 25 trains inserted, a maximum train capacity rate of 0.975 and an average headway deviation of 9.5 s, resulting in significant improvements in train operations and passenger satisfaction. Compared with the current train timetable before optimisation, the average dwell time and the maximum train capacity rate at various stations have been reduced after optimisation. The proposed model and approach can be used for train timetabling optimisation and managing the operations of urban rail transit lines.

The examination of highway travel behaviour during the COVID-19 pandemic can provide valuable insights into the impacts of the pandemic and associated policies on human mobility patterns. This paper proposes a comprehensive examination, measurement and characterisation approach in the perspective of network and community structure. To capture the changes in travel behaviour, four stages were defined based on four consecutive Augusts from 2019 to 2022, during which varying levels of restrictions were implemented. The findings reveal interesting trends in travel patterns. In 2020, after the clearance of pandemic cases, there was a remarkable increase of over 10% in highway trips. However, in 2021, with the emergence of COVID-19 variants, there was a significant decline of over 30% in highway trips. By employing complex network analysis, key metrics of the primary network, including link weight, node flux and network connectivity, exhibited a notable decrease during the pandemic. These changes in network properties also reflect the spatial heterogeneity of highway travel demand. Moreover, the outcomes of community detection shed light on the evolution of the highway community structure, highlighting the efficacy of a community-collaboration strategy for highway management during public emergency events, as it fosters strong local interaction within the community.

This study introduces a holistic framework for optimising road-rail intermodal hub locations based on real regional freight data and railway station information. The primary objective is to enhance railway transportation capacity, thereby facilitating the development of a low-carbon transport system. Research begins by scrutinising the freight landscape in the region, focusing on transport volume, freight intensity, goods types and average delivery distances. Subsequently, data mining techniques, including DBSCAN clustering and frequent itemset mining, are employed to uncover freight demand hotspots across both spatial and temporal dimensions. Based on these findings, a mathematical model for hub location selection is constructed, along with criteria for goods categories suitable for rail transportation. Ultimately, using the Beijing-Tianjin-Hebei region as a case study, 12 road-rail intermodal hubs are identified, along with the main cargo types best suited for rail transport within their respective service areas. This transition is expected to result in an annual reduction of 470,000 tons of regional carbon emissions. The proposed method framework provides valuable guidance and practical insights for the optimisation of freight structures in various regions. Furthermore, it aligns with contemporary environmental and sustainability objectives, contributing to the broader goal of establishing low-carbon transport systems.

A novel control method called dynamic straight-right lane (DSRL) control design is proposed for signalised intersections. This design aims to utilise the resources of the right-turn lane to increase the capacity for straight-through traffic while minimising the impact on right-turn vehicles. In this paper, an alternative approach to DSRL control design for T-shaped intersections is proposed. By redesigning the spatial and temporal allocation at the entrance, this design ensures the safety of lane change manoeuvres and reduces the design threshold for T-shaped intersections. To facilitate the implementation of the DSRL control design, a cellular automata model is constructed. Additionally, a case study is conducted, leading to the identification of the optimal design parameters for DSRL control. The proposed DSRL control design is compared with two conventional control designs, namely dedicated right-turn lane control design and static straight-right lane control design, in various geometric and traffic demand scenarios. The findings reveal that the T-shaped intersection, when equipped with a dedicated right-turn lane control design, can achieve a maximum delay optimisation rate of 91% by adopting the DSRL control design. Similarly, the T-shaped intersection, with a static straight-right lane control design, can attain a maximum delay optimisation rate of 84% when employing the DSRL control design.

Existing tracking algorithms mostly rely on model-driven approaches, which can be prone to inaccuracies due to unpredictable human behaviours. This article aims to address the issue of transient errors in tracking port container trucks (PCTrucks) when encountering obstructions. A data-driven algorithm for predicting vehicle trajectories is proposed in this study. The approach involves preprocessing an extensive dataset of GPS information, training a DeepLSTM-Attention model, and integrating the proposed model with the population-based training (PBT) algorithm to optimise network hyperparameters. The objective is to enhance the accuracy of predicting trajectories for vehicles moving horizontally. The trajectory data used are collected from real-world port operations. This research is conducted across nine trajectory segments and benchmarked against traditional approaches like Kalman filtering, machine learning techniques such as support vector regression (SVR) and standard long short-term memory (LSTM) networks. The results demonstrate that the proposed prediction method, that is, DeepPBM-Attention, outperforms other techniques in several evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), F1 score and trajectory reconstruction error (TRE). Compared to LSTM networks, the performance of DeepPBM-Attention is improved by approximately 40%. The proposed data-driven trajectory prediction algorithm exhibits high accuracy and practicality, which can effectively be applied to the positioning prediction of horizontally moving vehicles in port environments.

Air traffic scenario evaluation can support the optimisation of traffic flow and airspace configuration to improve the safety of air traffic control. Since the air traffic scenario is influenced by the interaction of multiple factors, and real labelled data are lacking, the feature index selection and scenario evaluation are challenging endeavours. In this study, indicators were selected from three dimensions: airspace structure, traffic characteristics and meteorological conditions. The evaluation indicators were quantitatively screened according to information importance and overlap. Utilising the flow control and traffic flow information, the authors defined the free and saturated states of the state interval and developed a metric-based learning method to calibrate the state samples. A multilayer perceptron regression model was employed to establish the mapping relationship between the feature indicators and air traffic scenario. The evaluation accuracy of the sample set from three sectors in Shanghai exceeded 80%, which verified the effectiveness of the scenario evaluation model. This contribution holds practical significance in enhancing the safety of airspace operations.

According to the current research status of urban rail transit’s fully automatic operation (FAO), the train driving speed curves are usually obtained through simulation and calculation. The train driving speed curves obtained by this method not only have low efficiency but also are not suitable for complex road conditions. Inspired by AlphaZero, a reinforcement learning algorithm that utilised vast amounts of artificial data to defeat AlphaGo, an AI Go program, this paper investigates and analyses methods for rapidly generating a large number of speed curves and selecting those with superior performance for train operation. Firstly, we use the powerful third-party library in Python as the basis, combined with the idea of AlphaZero, to produce artificial speed curves for metro train driving. Secondly, we set relevant parameters with reference to expert experience to quickly produce massive reasonable artificial speed curves. Thirdly, we analysed relevant indicators such as energy consumption, running time error and passenger comfort to select some speed curves with better comprehensive performance. Finally, through the many observations with different running distances and different speed limits, we found that the speed curves produced and selected by our algorithm are more productive, diverse and conducive to the research of train driving operation than the actual data from traditional manual driving and ATO (automatic train operation) system.