This study introduces an advanced software platform and process for the quantitative national economic evaluation of high-speed maglev systems, overcoming limitations of traditional methods through parameter variation experiments and automated solution search. Utilising the adapted German standardised evaluation, this research demonstrates how integrated modelling, evaluation and optimisation software can deeply analyse the impact of various variables and parameters on economic outcomes. By employing an optimisation algorithm, the software not only determines critical evaluation parameters to ensure benefits exceed costs but also deduces optimised model variables. The macroeconomic benefit-cost ratio guides the optimal design concept, with the research finding a critical value for ensuring economic feasibility. The proposed solution achieves a 22% improvement in this ratio (1.106 vs. 0.909) compared to the existing Hefei-Wuhu route, highlighting its potential for large-scale maglev implementation. Future development directions include integration with micro-simulation systems, support for random behaviour, sensitivity analysis, data-driven machine learning and enhanced user interface design for broader applicability. The findings underscore the software’s capability to provide robust, data-driven insights for economic feasibility studies of high-speed maglev systems, presenting a significant step forward in infrastructure project evaluation.

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

The concept of risk analysis is especially important because it examines and analyses in a detailed manner the factors that affect the normal functioning of a system. In this paper, the level crossing is considered as one system, composed of several elements. The failures of those elements were analysed with the aim of showing which are the most frequent and most critical failures. A multi-methodological approach was used in the analysis. The failure modes and effects analysis (FMEA) method was used to determine risk factors, after that a multi-criteria model was created in a fuzzy environment, and as output, it gave a ranking list of critical failures in the system. Through the discussion of the results, a comparison of the basic model with two other similar ones was made, and the comparative results were analysed. The main aim of this paper is to present one of the possible ways to analyse the risk of the system of level crossings with the aim of improving traffic safety at the crossing.

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

In China and other developing countries, some bicycle riders exhibit retrograde behaviour, which affects the riding safety of normal cyclists. The effect of retrograde behaviour on visual search and cycling behaviours of normal cyclists is investigated and quantified in this study. First, cyclists are instructed to wear an SMI iView ETG head-mounted mobile eye tracker and a mobile phone equipped with a Global Positioning System real-time location monitoring function to cycle on a road to obtain the times of fixation, saccade and blink, as well as the pupil diameter, gaze position and velocity in normal and retrograde conditions. Subsequently, the effect of retrograde behaviour on the attention of normal cyclists is analysed using three indexes: proportion of fixation time, coefficient of variation of pupil diameter and area of interest. Then, the effect of cycling behaviour is analysed using three indexes: the cycling trajectory, the velocity at three stages and the coefficient of variation of velocity. Finally, polynomial regression analysis is performed to analyse the visual and cycling behaviour impact indexes under the retrograde condition. The results show that retrograde behaviour significantly affects the vision and cycling behaviour of normal cyclists and that the two indexes are positively correlated.

Vehicle trajectory prediction plays a critical role before the decision planning of autonomous vehicles in complex and dynamic traffic environments. It helps autonomous vehicles better understand the traffic environments and ensure safe and efficient tasks. In this study, a hierarchical trajectory prediction method is proposed. The graph attention network (GAT) model was selected to estimate the interactions of surrounding vehicles. Considering the behaviour of surrounding agents, the future trajectory of the target vehicle is predicted based on the long short-term memory network (LSTM). The model has been validated in real traffic environments. By comparing the accuracy and real-time performance of target vehicle trajectory prediction, the proposed model is superior to the traditional single trajectory prediction model. The results of this study will provide new modelling ideas and a theoretical basis for the vehicle trajectory prediction in urban traffic environments.

Airport clusters are of great significance to the sustainable development of the civil aviation transportation industry. The study utilises common frontier and super-efficiency DEA methods to assess the efficiency of China’s six major airport groups. It then employs the Malmquist index method to analyse changes in airport productivity. The results highlight regional disparities in airport efficiency. The East China Airport Group and the Southwest Airport Group consistently demonstrate excellent efficiency values, while the North China Airport Group and the Northeast Airport Group have significant room for improvement. Most airports within the groups operate at low and ineffective levels, with efficiency initially increasing and then decreasing. Moreover, the technology gap ratio (TGR) for each airport group somewhat shows a downward trend. The Malmquist index indicates that the overall factor productivity of each airport has generally remained stable, with efficiency growth primarily dependent on scale efficiency. On average, technical efficiency has increased by 1.5%. However, in terms of technological changes, most airports have experienced technological regression, indicating insufficient focus on technological improvement. Therefore, it is crucial to prioritise technological innovation and enhance management efficiency to achieve efficiency improvements in airport clusters. It is necessary to formulate strategies accurately based on the specific conditions of different regions, promote coordinated development, foster regional exchanges and cooperation, address regional disparities, ensure sustainable development of China’s airport clusters, and establish a world-class airport cluster.

This study investigates the factors that drive users to sustain their usage of shared electric scooter (e-scooter) services in Taiwan, distinguishing itself from the conventional focus on predicting consumers’ initial adoption and behavioural intentions. It employs subjective rating questions, incorporating constructs related to user acceptance, attitudes and user experience (UX). Through hierarchical regression analysis of quantitative survey data, the study identifies key factors such as users’ modes of transportation, environmental attitudes, acceptance of shared services, attitudes towards private scooters, UX, total usage instances and age. However, reliance on private scooters as a mode of transportation and frequent usage of shared e-scooters negatively impact the sustained usage of these services. The research further highlights early development challenges in shared vehicle services, including concerns over personal data security, user-unfriendly system designs, lack of convenience, inadequate parking infrastructure and ineffective financial incentives. Based on these findings, the study provides recommendations for service providers and government entities to enhance service design and proactively address these challenges. Implementing these recommendations is expected to mitigate the impact of these challenges and potentially improve user acceptance, UX, and the overall sustainability of shared vehicle services.

This study intended to explore college students’ cognition and attitudes towards connected and autonomous vehicles (CAVs) in China. A comprehensive questionnaire was designed and distributed in Mainland China, and after collecting and processing the data, Bayesian multivariate analysis was presented to evaluate the six dimensions of cognition, consciousness, safety, privacy, liability, education and acceptance. By analysing each dimension, the results show that gender and status are significant for consciousness, safety, privacy and education, but location plays a significant role in safety and liability. It is found that each dimension reveals a specific thought of college students, and the potential users’ cognition and attitude should be paid more attention to. Some empirical suggestions are presented to enhance the systematic improvement of CAVs and possible ethics issues.

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

Vehicle turn-in rate is a critical and widely adopted input for expressway rest area design and operation. With the implementation of expressway ETC gantries, the ERA turn-in rate can be further estimated by measuring the travel speed distribution via ETC gantry data. This paper proposed an adaptive density peak clustering Gaussian mixture model (ADPC-GMM) for ERA turn-in rate estimation. The ADPC algorithm is applied to generate the GMM’s inputs accommodating to the traffic characteristic of ERA expressway segments and GMM would further provide the turn-in rate estimation results. To validate the model precision, the turn-in rate data of four selected ERAs in Sichuan, China, as well as the ETC gantry data of their corresponding expressway sections are obtained. According to the estimation results, the MAE and RMSE are 0.0228 and 0.0267 for the passenger car scenario and 0.0264 and 0.0356 for the commercial truck scenario, respectively. These results are also at the lowest level compared with the results acquired from ordinary GMM, K-Means and DBSCAN algorithms. The proposed method has good applicability for vehicle turn-in rate estimation and can be deployed at different ERAs, especially those ERAs without traffic monitoring.

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

This study investigates the overtaking lane-changing (OLC) behaviour in expressway interchange weaving areas, aiming to analyse these behaviours’ causes and potential impacts. Field data are utilised to analyse the statistical characteristics of lane-changing points and spatio-temporal utilisation in weaving areas. A modified NS model, which considers the distribution pattern of vehicle speeds, and a rigid lane-changing rule based on Gaussian distribution are proposed. Additionally, a cellular automaton simulation model is constructed to quantify the influence of OLC behaviour on traffic efficiency and spatio-temporal utilisation based on simulated data. The findings indicate that the imbalanced distribution of lane-changing points and spatio-temporal utilisation in weaving segments, caused by rigid lane-changing behaviour, is an objective factor that triggers OLC behaviour. When the traffic volume in weaving areas ranges from 500 to 1,100 pcu/5 min and the proportion of OLC behaviour is between 0.35 and 0.7, the behaviour will significantly enhance the average vehicle speeds of the outermost lane of the main road and normal rigid lane-changing (NRLC) vehicles, with increases of up to 48% and 51%, respectively. Moreover, OLC behaviour also improves the balance of spatio-temporal utilisation in weaving areas and reduces the average spatio-temporal utilisation. This study clarifies the positive impact of OLC behaviour on expressway interchange weaving areas and provides new research ideas for enhancing the efficiency of these areas.

Cost-benefit analysis (CBA) is the universally applied tool to assess economic viability in assisting decisions on transport investments. Its framework is heavily influenced by the numerous variables it considers through estimating and valuing the intervention’s effects. This paper – utilising the authors’ previously implemented CBA test environment – comprehensively analyses the sensitivity of significant variables of three typical CBA models for transport interventions (road, rail and urban) to understand the prevailing appraisal approach better and to help focus on further methodological improvements. Morris and Sobol methods were selected to study the global sensitivity of and the relations between the input parameters of the models. The sensitivity test of the three analysed models provided similar results regarding which variables are most influential in CBAs. Input variables such as the investment cost, the economic discount rate, forecasted GDP changes and specific elasticities to these GDP changes often have a firm but mostly linear effect. Value of time, vehicle operating cost and mode choice-related parameters such as car availability, car occupancy rate, level of service indicators (e.g. frequency of service) and potential to induce travel demand (proxied by a ‘no travel’ parameter) are inputs with considerable linear effects and greater interactive effects.

The transportation sector wields substantial influence on society, encompassing economic, social and environmental dimensions of sustainability. Recognising environmentally conscious actions initiated by individuals, particularly at grassroots levels, fosters the development of a pro-environmental social identity. The article aims to analyse the transportation systems from a bottom-up perspective within a municipality. Consequently, three objectives are proposed for this research paper: investigate citizen behaviour regarding transportation, assess the strengths and weaknesses of communities based on citizen perspectives and generate ideas for improving transit through responsible management principles using a bottom-up approach. It has been determined that private car is the most commonly used mode of transportation. The number of cars is the only variable that influences the choice of transportation. A significant positive relationship has been identified between the number of cars and car travels, while a negative relationship has been observed between the number of cars and travels by transit, pedestrian or bicycle. In addition to this, other significant relationships were determined. Regarding the second objective, the majority of the interviewees perceive that the commune lacks any significant strengths. In terms of enhancement opportunities, respondents express a desire for improvements in pedestrian and cyclist infrastructure, transit facilities and the addition of more lanes and roads.

In this paper, we consider the problem of minimising the cost of data transmission as a function of the capacity of telecommunication links. To solve this problem, we first formulated a mathematical model, and then we designed and developed a software that enables the optimisation of the given or randomly generated telecommunications network. Declarative programming is a good choice for optimisation problems because it is enough to specify only the relations that must be satisfied, without giving any effective procedure for finding the values for the decision variables. To test the application, we developed a software that randomly generates a telecommunications network that meets the given requirements. This enables us to test the application on an arbitrary number of different telecommunication networks with different numbers of nodes and links, and analyse the impact of changing network parameters on the flow and results of the optimisation. As telecommunications networks operate in conditions of uncertainty, the subject of special analysis was the potential failure of some of the network links. The paper presents and thoroughly analyses the optimisation results for several selected networks, as well as summary results for a number of telecommunications networks.

Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness. This study investigates terminal layout characteristics, specifically decentralised layouts, to predict and analyse arrival flight taxi-in times. We develop a surface traffic flow calculation method considering arrival and departure flights, eliminating fixed thresholds. We introduce runway-crossing operations for decentralised airports, creating new prediction variables. We consider factors like runway, aircraft type, airline, taxi distance, and time periods. Gradient Boosting Regression Tree predicts taxi-in times, while Lasso analyses factor impact. Our approach yields highly accurate predictions for decentralised airports, with Surface traffic flow and Runway-crossing variables significantly influencing taxi-in times. This research informs airport managers in decentralised layouts, enabling tailored management strategies.

This study aims to quantitatively assess the adjustment effects of various visual guiding schemes on the abrupt change of vehicle trajectory. A driving simulation experiment was conducted using five simulated scenes: (1) baseline (actual situation), (2) pavement (road studs), (3) low position (flexible guideposts), (4) high position (warning alignment signs and retroreflective arches) and (5) multilayer (combination of all devices). Raw data, including vehicle positions, steering wheel angles and lateral offset, were collected. Based on these data, the gradual change degree of vehicle trajectory (G) and average steering wheel angle (SWAav) were computed to quantitatively evaluate the extent of vehicle trajectory deviation and the stability of steering wheel operations respectively. These two evaluation indicators were then translated into trajectory gradualness (TG) and operation stability (OS), respectively, to assess the adjustment effects of different visual guiding schemes. The study results demonstrate that road studs perform a certain degree of enhancement on operation stability (OS). Flexible guideposts exhibit the best effects on operation stability (OS). Additionally, the combination of warning alignment signs and retroreflective arches demonstrate the best regulation of trajectory gradualness (TG). Multilayer visual guiding system achieves the optimal trajectory gradualness (TG) and operation stability (OS).

The emergence of battery electric buses (BEBs) can alleviate environmental problems caused by tailpipe emissions in transit system. However, the high cost of on-board batteries and range anxiety hinder its further development. Recently, the advent of dynamic wireless power transfer technology (DWPT) has become a potential solution to promote the development of BEBs. Hence, this study focuses on the application of DWPT in flex-route transit system. A mixed integer non-linear model is proposed to simultaneously optimise the bus routing and the selection of corresponding bus types considering the constraints of passengers’ travel time, battery size and bus capacity. The objective is to minimise both transit agency cost and passengers’ travel time cost. A tangible hybrid variable neighbourhood search (HVNS) consisting of simulated annealing (SA) and variable neighbourhood search (VNS) is developed to solve the proposed model efficiently. Compared with GAMS (DICOPT solver) and VNS, the proposed algorithm can considerably improve computational efficiency. The results suggest that the proposed model can effectively determine the BEBs’ routing and bus type for flex-route transit system powered by DWPT through a case study in Xi’an China. A comparative analysis shows the proposed model takes 12.97% less total cost than the alternative model with terminal charging technology (TCT).

Weaving sections on roads are crucial areas with high concentrations of mandatory lane changes, which can increase the likelihood of traffic accidents. Speed is a key factor in determining traffic safety, and the development of an accurate speed prediction method is essential for improving safety in weaving sections. While current methods are effective in predicting speeds in straightforward situations, they face challenges in more complex scenarios such as weaving sections. This study presents a refined traffic speed prediction approach specifically designed for weaving sections in order to tackle the aforementioned issue. Initially, novel variables were formulated to capture the unique traffic flow attributes present in weaving sections, which distinguish them from standard road segments. Subsequently, supplementary empirical variables that are known to impact speed were incorporated. We conducted a variable importance assessment to ascertain the extent and direction of each variable’s contribution. Lastly, variables with significant positive effects were chosen as inputs for three machine learning algorithms: Random Forest (RF), Backpropagation Neural Network optimised with Genetic Algorithm (BPNN-GA) and Support Vector Regression (SVR). This method was evaluated using aerial footage from five distinct weaving sections in China, maintaining an approximately 3 km/h prediction error. In addition, the study also finds that the speed distribution in weaving sections is negatively correlated with the number of lane-changing. Vehicles experience deceleration at both on-ramp and off-ramp, with a more significant deceleration occurring at the on-ramp. Speed is significantly affected by short length, number of lanes and proportion of large vehicles. The proposed method can be embedded into intelligent traffic systems for safe speeds of autonomous vehicles in weaving sections. Reconstructing spatiotemporal patterns of traffic congestion, predicting traffic accidents and implementing active traffic management strategies in weaving sections could be investigated in the future.

The popularisation of autonomous vehicles will give rise to a new business model called shared autonomous vehicle (SAV). SAVs may attract a large number of passengers and lead to a decline in the share of buses, which can be interpreted by exploring passengers’ travel behaviour when confronting the SAV and bus modes. Thus, this paper addresses the SAV and bus passengers’ travel behaviour, aiming to examine the factors influencing travel behaviour and revealing the characteristics of SAV passengers. We classified passengers using latent class cluster analysis and modelled passengers’ travel behaviour based on confirmatory factor analysis and mixed logit model. The findings indicate a variation in travel preferences among different classes of travellers. Short-distance travellers pay less attention to travel time. Non-short-distance PT travellers are most likely to be affected by service attributes (waiting time, travel time and travel costs). Non-short-distance private car travellers are more likely to become early SAV adopters. Passengers travelling for short distances may be more likely to choose SAV, which reveals the potential of SAVs to become a first and last mile connection for public transport. Passengers lack trust in SAVs, which will affect their promotion.

The pedestrian cognitive load has an important effect on the pedestrian crossing decision making. Compared with young adults, old people are characterised by declining physical function and slower reaction ability, which makes them prone to traffic accidents when crossing the street. This study aims to compare the visual information-mental load correlation between elderly and young adults waiting at the signalised intersections and evaluate their cognitive load conditions. Therefore, two signalised intersections with different traffic scenes in the Nan’an District, Chongqing, China were selected. The eye-tracking, electrocardiographic and electrodermal activity data of young and old pedestrians were collected using eye-tracking and physiological instruments. The visual indexes (the total duration of fixations, the number of fixations, the average pupil diameter changing rate, the number of saccades, the average peak speed of saccades, the average amplitude of saccades and the total amplitude of saccades) and physiological indicators (the average growth rate of heart rate, the time-domain analysis indicator of HRV and HRV frequency domain analysis indicators, electrodermal response amplitude and rise time of the EDR amp.) were taken as inputs and outputs parameters, respectively. Then, the comprehensive cognitive load evaluation model for pedestrians was constructed when waiting to cross the street based on the data envelopment analysis method. And the cognitive load characteristic differences between the young adults and the elderly were compared. The results show that in the same crossing scene, compared with the young pedestrian, the elder pedestrian exhibited lower overall perceptual efficiency, lower fixation durations and higher cognitive loads. These results can provide certain references on improving the street crossing safety for the elderly pedestrian.

Expressway weaving areas meet dissipative structure characteristics. When traffic states reach a certain range, they exhibit self-organising criticality, and slight changes may trigger unpredictable congestion. This paper examines the correlation between the dissipative structure of the weaving area and key traffic parameters. The range of dissipative structure states in the weaving area is defined through the dissipative structure concept with three-phase traffic flow theory and real traffic data. Based on the fundamental diagram and measured traffic data, the weaving area dissipative structure model characterising the relationship between critical state changes in traffic volume is constructed and validated. Finally, the Cell Transmission Model simulation was used to examine the characteristic relationship between the weaving area dissipative structure state duration, the weaving area length and the weaving flow ratio. The results show that the length of the dissipative structure state is maintained when the traffic flow is self-organised into a free-flow or a congested state positively correlates with the length of the weaving area. Higher weaving flow ratios lead to shorter dissipative structure state durations during congestion formation, and the exact opposite during congestion evacuation. This paper is important for analysing the congestion mechanism and managing congestion.

In order to enhance the driving ability of autonomous vehicles on structured roads and enable them to plan safe and comfortable paths, we propose an obstacle avoidance path strategy for autonomous vehicles based on genetic algorithm. The use of Frenet-Serret enhances the adaptability of the algorithm in complex environments. In order to improve the generation and optimisation of obstacle avoidance trajectory, we establish an anti-collision model. When the vehicle faces a potential collision with an obstacle, the genetic algorithm quickly iterates and selects the first nine genes to generate the rough solution and convex space of the path. Combined with convex space, the quadratic programming method will numerically optimise the generated rough solution to generate an accurate path that satisfies the constraints. In addition, in order to ensure the safety and comfort in the process of obstacle avoidance, based on the dynamic constraints of the vehicle, the speed planning is used to determine the speed curve. We simulate in various scenarios involving moving obstacles. The real-time simulation based on the HIL platform proves that the proposed path planning strategy is effective in various driving scenarios.

Data collection technologies or data sources are critical for highway network management. However, due to the limitations on available management resources, determining the importance of these data sources is necessary to allocate these resources reasonably. This study proposes a complex network based method for evaluating the importance of multiple data sources in highway networks. This method includes mainly three steps. First, the business-data source relation will be identified and formulated for the highway network. Second, a business data source complex network is built from the previously identified business-data relationship. Third, an entropy weight method is used to compute and rank the importance of data source nodes by combining three indexes of degree centrality (DC), closeness centrality (CC) and structural holes (SC) computed based on the complex network. The proposed method is applied and illustrated using the highway network of Xuzhou City, Jiangsu Province, China. The results show that among the data sources, the most important data source is the continuous traffic survey station, followed by an automatic gantry-based station and vehicle detectors-based system. Discussions on the limitations, applications and future studies are provided for the proposed approach.

Predicting traffic speed accurately and in real-time is crucial for the development of smart transportation systems. Given the nonlinear and stochastic nature of vehicle data, integrating diverse spatio-temporal data sources with the Improved Particle Swarm Optimisation (IPSO) offers a promising approach to optimise the Long Short-Term Memory Neural Network (LSTM). Firstly, we enhance the optimisation capabilities of PSO by implementing nonlinear inertial weight and adaptive variation. Secondly, addressing the challenge of selecting the LSTM hyperparameters, the PSO algorithm effectively identifies global optimal solutions for hyperparameter optimisation, ensuring appropriate settings through iterative training. Subsequently, we conduct a case study using multi-source spatio-temporal traffic speed data, comparing our proposed IPSO-LSTM model with traditional neural network prediction models and advanced models. Results from the experiment demonstrate that the IPSO-LSTM model presented in this study addresses issues of parameter selection and inaccurate prediction encountered by traditional LSTM models in traffic state prediction. Moreover, it enhances the model’s ability to capture speed time series dynamics. Notably, in processing complex speed data, our model exhibits superior accuracy and stability in prediction.

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.

The level of greenhouse gas emissions is one of the most important issues today, both professionally and politically, because a lower level of greenhouse gas emission is mandatory for a sustainable economy. Besides industry and households, the transport sector is also responsible for these emissions. For this reason, it may be essential to set up a model with which the amount of CO2 emissions could be estimated or predicted. This article presents a model that examines the extent of economic development and CO2 emissions in European countries. The result is establishing a pattern requiring a longer time series. If the pattern is proven, a clear reassessment of the current relationship between economic development and environmental protection should be made.

Instrument flight procedures are essential and critical components of the global aviation system. They are designed for all phases of flight, i.e.  the standard instrument departures, standard instrument arrivals, instrument approaches and the en-route phase of flight. Instrument flight procedures are designed from various aeronautical data, information, dimensions, etc., which are named instrument flight procedure elements according to this paper. Development of air navigation systems affect design of instrument flight procedures and flexible use of airspace. The design process is carried out within a framework defined by international and national standards, organizational norms and economic aspects. Instrument flight procedure elements are a fundamental part of the process. Deviations of these elements from full compliance with international regulations can significantly and negatively affect air traffic safety. The objective of this paper was to investigate the basic prerequisites for statistical analysis of the design quality of instrument flight procedures, which have not been explored before. Six prerequisites were proposed for acquiring the data and preparing them for further statistical use.

The paper presents a simpler and more precise model of lumbar moment prediction based on single linear, or multiple linear regression with two predictors. The body mass index (BMI) as the predictor contains two of the most important static anthropometric measures, height and mass, whose separated role in lumbar moment prediction, as well as their mutual relations, have not been sufficiently investigated. This study analysed mass, height, age and BMI as lumbar moment predictors, on a sample of 50 Croatian male engine drivers. Two prediction models were compared: (1) multiple linear regression prediction with mass and height as predictors; (2) single linear regression with mass as the only predictor. Results confirmed the multiple regression model as the best one (R2= 0.9015 with standard error of prediction 1.26), having the mass of the best predictor. Surprisingly, the single regression model with mass as predictor explained only 3.6% of lumbar moment variance less than multiple regression model, with related standard error of prediction 1.46 (mean percentage value of the relative error was only 0.8% higher than at multiple regression model). The obtained findings suggest high prediction potential of mass and height that should be verified on various subject samples.

This paper focuses on daily freight train scheduling and dynamic railcar routing problems for rail freight transportation at the operational level.  Two mixed integer linear programming models that adopted different strategies were formulated based on a continuous two-layer time-space network. We simultaneously considered the benefits of railroad company and service quality when setting the objective function. By solving the  models, we can distribute the dynamic railcar flows to the train paths in the basic train timetable to obtain the daily train operation plan over a short time horizon (e.g. a day), which will be helpful for dispatchers to make decisions such as the empty railcar distribution and car routing (trip planning). Finally, we compared two models on a part of the Chinese railroad network. The results show that the second model can effectively improve the efficiency of railroad freight transportation.

Logistics is playing a significant role in supporting economic growth and material security during the epidemic period and it has been  experiencing a rapid development in recent years. With the issues of personalisation and cost, the economy and society ask for higher requirements for logistics storage systems. The rational design of the functional area layout is an essential step to improve the operational efficiency of the logistics warehousing system. In reality, due to warehouse design and equipment application, there has been a gradual increase in irregular warehouses. By taking an irregular warehouse as an example, combining the operation status quo, this paper clarifies the functional area settings and constructs a 0–1 integer planning model based on the grid and systematic layout planning method with constraints, such as the unique functional attributes of the grid. We optimised the genetic algorithm based on the warehouse irregularity factor and the grids factor, and then solve it through MATLAB. Finally, by using the Flexsim software, simulation metrics were selected for evaluation, the method feasibility is verified.

Aiming at two aircraft conflict scenario in the pre-tactical stage, by converting the uncertain flight trajectory of the target aircraft into a spatio-temporal trajectory under its performance constraints, a conflict detection model based on truncated normal distribution was proposed,
and influencing factors affecting the overall conflict probability were analysed by numerical simulation. For the conflict scenario, nonlinear  particle swarm optimisation (NPSO) algorithm was applied to solve the optimal separation configuration strategy for the ownship. The simulation results show that, in comparison to conventional PSO algorithm, the improved NPSO algorithm improves the optimal value by 14.88% and decreases the maximum velocity change by 19.84%. The simulation also shows that the algorithm can maintain the minimum interval requirements under different initial parameters, demonstrating its strong adaptability.

Traffic violations are a major cause of traffic accidents, yet current research falls short in comprehensively analysing these violations and the  named entity method fails to extract the name of traffic violation events from records, thereby lacking in providing guidance for managing urban traffic violations. By expanding the People’s Daily dataset from 71,456 words to 95,291 words, the BERT-CRF (Bidirectional Encoder Representations from Transformers-Conditional Random Field) model achieves an accuracy rate of 88.53%, a recall rate of 92.90% and an F1 score of 90.66%, successfully identifying event, time and location named entities within traffic violations. The data of traffic violations is then enhanced through forward geocoding and the Bayesian formula, and traffic violations are analysed from time, space, administrative region, gender and weather, to provide support for the dynamic allocation of law enforcement forces on traffic scenes and the precise management of
traffic violations.

With the continuous increase of urban vehicles, traffic congestion becomes severe in the metropolitan areas and higher car utilisation areas. The traffic signal timing scheme can effectively alleviate traffic congestion at intersections. We need to make a profound study in the traffic signal timing. An optimisation model is established, which not only takes the average delay time of vehicles, the number of vehicle stops and the traffic capacity, but also takes the exhaust emissions as the evaluation indexes. The model is too complex and involves too many variables to be solved by using multi-objective programming. Thus, the Harris Hawks Optimisation (HHO) with few parameters and high search accuracy was used to solve the model. To avoid the disadvantages of poor search performance and easy to fall into local optimisation of the Harris Hawks Algorithm, multi-strategy improvements were introduced. The experimental effects show that during the peak hours of traffic flow, the improved algorithm can reduce the average vehicle delay by 36.7%, the exhaust emission by 31.2% and increase the vehicle capacity by 41.6%. The above indicators have also been upgraded during the low peak stage.

The transportation industry is a key area for ecological civilisation construction and low-carbon development. As the core support of the  national integrated transportation system, the ecological development level of integrated transportation hub (ITH) is crucial for enhancing 
the sustainable development capacity of the national integrated transportation. An eco-efficiency evaluation index system of ITH is established  in this study and the eco-efficiencies of twenty international ITHs in China are comprehensively evaluated based on the super-efficient epsilon-based measure (EBM) model. Then the panel Tobit regression model is adopted to analyse the influencing factors of eco-efficiency. The results  show that the average eco-efficiency of ITHs in China during 2011–2021 declines first and then rises, with a relatively high level overall but not efficient yet, and there is an obvious gradient distribution characteristic in all eco-efficiencies. Among them, Guangzhou ranks first, followed by  Haikou, and Harbin ranks last. It is found that integrated transportation efficiency, urban green coverage, level of opening-up and economic development improve eco-efficiency significantly, while urbanisation rate, industrial structure and technology input have a negative impact. The  results are consistent with the actual situation, verifying the practicality of models, and can be used to promote the sustainable development of integrated transportation.

The current development of urban agglomeration greatly promotes the intercity connection and elevates the significance of intercity mobility system. However, intercity mobility often exhibits extreme spatiotemporal imbalances due to the diverse urban characteristics. This poses a huge challenge for traffic management and reveals the necessity on understanding the urban attractiveness for intercity mobility, which is represented as spatial interaction gravity in this study. While recent works have explored relevant aspects, they failed to provide insights into temporal variations in spatial interaction gravity or capture the determining factors from multiple perspectives. To fill this gap, this study proposed a two-phase framework to measure the urban spatial interaction gravity and developed determination approaches utilising the large-scale location-based services (LBS) dataset. Specifically, the inverse gravity model was adopted for the measure within multiple urban agglomerations and city sets during weekdays, weekends and holidays. Then, we developed the fitting equations of spatial interaction gravity by incorporating the correlated features associated with social, economic, network accessibility and land use. The findings present spatial interaction gravity across different periods and substantiate the distinct determination effects of features, with a high fitting accuracy. They provide promising supports for the intercity mobility prediction and pre-emptive traffic management.

With the emergence of novel transportation trends, regular buses have experienced a significant decline in passenger numbers. Consequently, it becomes imperative to conduct studies on passengers’ intentions. This particular investigation employed a meticulously designed survey questionnaire to gather data, and developed a new model that integrates the theory of planned behaviour, technology acceptance model and expectation confirmation theory. The primary aim was to explore the key factors that influence residents’ ongoing behavioural intentions towards regular public bus travel. Furthermore, a gender-based multi-group analysis was conducted to investigate the impact mechanism of gender differences on ongoing behavioural intentions. The new model demonstrates various degrees of positive or negative influences among the variables, thereby confirming its universal applicability. Moreover, the multi-group analysis reveals that compared to gender, travel satisfaction has a stronger impact on women’s intentions, while travel attitude has a stronger impact on men’s intentions to travel by certain mean of transport. Simultaneously, perceived behavioural control does not significantly affect persistent intention for women but has a significant positive impact on persistent intention for men. Furthermore, perceived ease of use does not significantly impact perceived usefulness for women but has a significant positive effect on perceived usefulness for men. These research findings bear great significance in promoting environmentally-friendly travel practices.

The main objective of the transport reliability and maintenance analysis is to improve the understanding of accidents through incident investigations. This research focuses on composite pneumatic tyres used in transportation engineering and presents both theoretical and experimental studies. The finite element method used for numerical simulation combined with pre-experimental measurements based on optimisation by material vibration response is presented for tyre material modelling. Piezoelectric vibration test was used for the pre-experimental test of the tyre quarters. The simulation results indicate that the pneumatic tyre with the recommended air pressure inflation shows the least amount of deformation. In comparison, pneumatic tyres with recommended and reduced air pressure inflation of 0.25, 0.5 and 0.75 bar are under research. Additionally, it was established that, when subjected to external forces that exceed the tyre’s maximum weight capacity, as determined by the manufacturer, the tyre exhibits significant stiffening and internal stress. The research suggests that this methodology can be used to obtain a realistic model of vehicle tyre dynamic processes and assess the impact on road traffic safety with different inflation pressures and loads.

This article deals with the highly topical issue of greening air transport chains. It is important to consider the environmental aspect of the current performance of air transport in regions with less intensive air transport chains, such as the Eastern Adriatic. The regional airports of Ljubljana, Zagreb and Belgrade are dependent on European air cargo hubs and at the same time have the task of connecting the national airports in Sarajevo, Podgorica, etc., which complicates the functioning of air transport chains regionally. A comprehensive consideration of air cargo chains is important in terms of price and transport time, but also in terms of the GHG footprint. The results show that an environmental assessment of air transport chains is necessary for a more comprehensive decision on sustainable supply chains. The study enriches the scientific understanding of air transport chains in the Eastern Adriatic region from the point of view of carbon footprint and energy efficiency of transport, and highlights the need to use the already developed IT tools in the assessment and modelling of transport chains when different options are presented to cargo owners. Integrating the above approaches and data into current business models enables the gradual regional decarbonisation of air transport.

The role of transportation is becoming increasingly important in the world economy, and road transport in particular plays a very important role in all types of transportation. For this reason, it is extremely important to monitor its performance regularly. Very often, this is done using Data Envelopment Analysis (DEA) performance evaluation models, and consequently, there are numerous articles in the literature on DEA evaluation of road transport systems. In this study, we first summarise these articles and classify them according to different characteristics (environmental, safety, economic, energy). Finally, we use them as a basis for developing a novel DEA framework, which is used for the evaluation of the efficiency and ranking of road transport systems that also takes into account undesirable outputs, i.e. environmental and safety outputs. As a case study, we evaluate 28 European countries from technical, safety and environmental aspects. The CCR and SBM models are used to evaluate the efficiency of these countries for the last two years of published data. The results show that Denmark ranks first and Cyprus last for both years. It was also found that safety efficiency is generally rated lower than other criteria. Finally, the results and reasons for the efficiency and inefficiency of specific decision-making units, i.e. countries, are discussed.

The article discusses the results of studies of railway line capacity relative to the application of additional block division using virtual blocks in the process of positioning a train reporting its position and train set integrity. The studies were conducted using the authors’ original simulation software enabling extensive parameterisation of infrastructure, including configuration of the train control system and signalling principles, by taking the actual characteristics of train movements into account based on data obtained from real-life measurements.

Autonomous vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of travelling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.

Non-synchronised timetables of the first hour trains can lead to longer waiting times for passengers wishing to transfer at the transfer station. This study aims to reduce the waiting time of passengers by synchronising the timetables of first hour trains using actual transfer times. The transfer times of the passengers are obtained from the observations and are used in this synchronisation study. The genetic and simulated annealing algorithms are implemented to solve the first train synchronisation model. Finally, a case study is conducted on a section of the Istanbul metro network to test the synchronisation model. The results show that the total waiting time of the first hour trains transfer passengers is reduced by 35% by applying the proposed model. Another result of the study shows that using the actual transfer time instead of the average transfer time of the passengers reduces the average waiting time of the passengers by 19%.

With the popularity of electric vehicles, they have become an indispensable part of traffic flow on the road network. This paper presents a reliability-based network equilibrium model to realise the traffic flow pattern prediction on the road network with electric vehicles and gasoline vehicles, which incorporates travel time reliability, electric vehicles’ driving range and recharge requirement. The mathematical expression of reliable path travel time is derived, and the reliability-based network equilibrium model is formulated as a variational inequality problem. Then a multi-criterion labelling algorithm is proposed to solve the reliable shortest path problem, and a column-generation-based method of the successive average algorithm is proposed to solve the reliability-based network equilibrium model. The applicability and efficiency of the proposed model and algorithm are verified on the Nguyen-Dupuis network and the real road network of Sioux Falls City. The proposed model and algorithm can be extended to other road networks and help traffic managers analyse traffic conditions and make sustainable traffic policies.

Effectively equilibrating passenger distribution on metro platforms and carriages is important for relieving local congestion. This paper explores the role of incentive mechanisms in encouraging passenger queuing behaviours. To quantitatively analyse passenger compliance with the policy, a questionnaire survey was conducted in Fuzhou, China. According to the preliminary analysis of the survey data, passengers have various moving distance preferences under the incentive scenarios, namely, no movement, smaller distance and greater distance. Additionally, this paper establishes a nested logit model that considers travel purposes and moving distances. The empirical results show that although monetary and point-system incentives can effectively enhance passenger compliance with transfer queue-positioning requirements, when the moving distance is very small, people pay less attention to rewards. Compared to those commuting on weekends, passengers commuting on weekdays comply with policies more strongly, and the effect of implementing incentive policies is better; however, the effect of those policies is reduced among those travelling for leisure. Meanwhile, when travelling for leisure, as the number of companions increases, people’s willingness to follow guidance on where to wait increases. According to the results, the implementation of incentive-based waiting encouragement policies during peak working days can result in good compliance.

At present, interest in the application of unmanned aerial vehicles (UAV) for delivery is growing. A new “multi-type of UAV collaborative delivery” mode has been proposed. Through a combination of large, medium and small UAVs, the delivery capabilities of the UAV logistics system are significantly improved. Sometimes there is high demand, resulting in planned delivery routes that are no longer feasible, and even cause a shortage of distribution centre capacity and drones. This study explores logistics delivery strategies to solve problems caused by high demand. In this study, a multitype and multidistribution UAV model was established with the objective of minimising the total cost of distribution by considering factors such as the UAV energy consumption, load and distribution centre conditions. An improved ant colony algorithm was designed and its effectiveness was verified through the variability of the calculation time and multiple calculation results of different-scale examples. Finally, the classic vehicle routing problem (VRP) case is used in three scenarios to analyse the UAV scheduling optimisation problem. The results indicate that assisted delivery can reduce costs by 3% while ensuring delivery timeliness. The results of this study can provide guidance and benchmarks for the application of UAVs in urban logistics delivery systems.

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Based on two-sided market theory, this paper has studied the pricing problem of ride-hailing platforms with a combination of inter-group network externality and inner-group network externality. Two scenarios of user structure are considered. In scenario 1, both travellers and drivers are single-homing. In scenario 2, travellers are single-homing while drivers are multi-homing. Moreover, time sensitive factors and driver’s commission rate are introduced to reflect the characteristics of transport industry. Finally, the impact of network externality, time sensitivity, driver’s commission rate and entry cost on ride-hailing platform pricing, user scale and profits are analysed. The results show that inter-group network externality and inner-group network externality have a negative effect on platform prices charged to both travellers and drivers. However, when travellers are multi-homing, the price charged to travellers is positive with respect to the inter-group network externality from drivers. In the relationship between travellers’ scale and inter-group network externality, inner-group network externality is positive. Further, in both scenarios, the network externalities from the two sides affect platform profits negatively.

Connected and autonomous vehicles (CAVs) are recognised as a technology trend in the transportation engineering arena. As one of the most popular capabilities of CAVs, trajectory planning attracts extensive attention and interest from both academia and the industry. Segmented trajectory planning is gaining popularity for its simplicity and robustness in computation and deployment. Constructive recommendations and guidelines can be provided by exploring the effects of segmented trajectories in different settings of CAVs and intersections. This research proposes a control strategy for segmented trajectory planning in a fixed signal timing environment. To test the effects of this control strategy, this research designs simplified fixed signalised intersection scenarios and implements segmented trajectory planning features of CAVs with different traffic demand scenarios, distances and speed limits. The results show that the proposed control strategy has stable superior performances in different traffic scenarios especially when the traffic volume is near capacity.

Tram signal priority control is a crucial approach for enhancing the reliability of tram operations and has been implemented in various cities. Nevertheless, unpredictable tram operations influenced by tram dwell time during station stops can cause signal priority control failure at intersections. It is challenging to precisely predict tram dwell time at stations that offer multiple lines. To address this issue, the proposed research presents a capped robust optimal control (CRC) technique for tram signal priority. This method entails considering the stochastic number of passengers boarding and alighting at stations with multiple lines. Furthermore, tram delay calculation models at intersections are established and integrated into an objective function. The main objective of this strategy is to enhance tram operation reliability and maximise tram operation efficiency while reducing the adverse impact of tram priority on other vehicles at the intersection. A case study was conducted to evaluate the effectiveness of the CRC method. The results indicate that the CRC technique significantly improves tram operation reliability and efficiency.