To promote the green and high-quality development of rural e-commerce logistics, we propose the Two-Echelon Location-Routing Problem with Fuzzy Demand (2E-LRP-FD) of the rural e-commerce logistics network. Considering fuzzy demand, government subsidies and simultaneous delivery, the objective function aims to maximise the profit of enterprises considering government subsidies. The fuzzy chance-constrained programming method is used to deal with the triangular fuzzy variables of pickup demands. Additionally, we present a two-stage Improved Non-Dominated Sorting Genetic Algorithm II (INSGA-II) that integrates stochastic simulation and a K-means clustering algorithm to effectively solve the problem. In the end, the numerical experiments of algorithm and model design are verified. The experimental results demonstrate that the proposed INSGA-II is significantly efficient and effective. Furthermore, we discuss the relationship between subsidy strategies and logistics enterprise profits. This research contributes valuable insights for the establishment of rural e-commerce logistics systems.

Exploring the degree to which bus stop times are affected by rainfall is necessary for a reasonable formulation of bus-scheduling management schemes under rainy conditions. Although numerous mathematical models have been proposed, the predictive accuracy of existing models is insufficient for the precise formulation of bus policies. This study considered linear bus stops in Shenyang as research targets, and based on field survey data, we analysed the bus dwell time and its influencing factors under varying degrees of rainfall. The Pearson correlation analysis method and SPSS software were used to reveal the degree of influence of parameters, such as the number of passengers boarding and alighting buses, rainfall level, number of berthing spaces, load rate and presence of signalised intersections, on the bus stop time under rainfall conditions. Support vector machine, k-nearest neighbour and backpropagation (BP) prediction models were established, and the BP neural network model, having the best prediction effect, was optimised using a genetic algorithm (GA). The constructed GA-BP prediction model was more realistic than the BP prediction model and can be used to predict bus dwell times under rainfall conditions. The study findings will facilitate bus punctuality and improve customer appeal for bus services.

Noise pollution from the streets is a critical problem for those living or working near them. Although the traffic noise problem is not a new research topic, it is usually limited to providing average values. This paper aims to determine variations in the instantaneous noise level and its influencing factors using the experimental noise level and theoretical traffic flow using a discrete traffic flow model. The research results suggested that the noise level could be changed by properly managing traffic flow with existing traffic lights without changing the infrastructure. The results of this research may be useful for city transport traffic management institutions.

Work-related road deaths are the leading cause of occupational death. These traffic accidents contribute to at least one quarter all work-related deaths. Key risk factors associated with driving for work are driver fatigue and speeding. Driver fatigue is the growing problem of the new era. Due to traffic exposure, commercial vehicles are identified as a particularly risky category. According to traffic accident data, depending on the country, the percentage of traffic accidents caused by driver fatigue ranges up to 40%. In this paper, we used a unique procedure for identifying fatigue based on eleven factors, using expert knowledge, budget allocation and the composite rank method. The case study was realised in the Republic of Serbia, which is a country with a huge professional drivers deficiency problem. The main objective of this paper is to present an approach to reducing work-related road deaths to reach vision zero, based on a model for identifying commercial vehicle driver fatigue before the drivers start their shift. The advantage of this model is that it does not distract the driver in any way while driving and is based on objective data. It does not require recording the driver with a camera or hooking up to an electrode to record heart or brain activity.

Regarding “carbon peaking and carbon neutrality goals”, with the transportation sector as a key area of carbon emissions, the development of low-carbon transportation is imminent. Urban bus route scheduling is pivotal in realizing carbon emission reduction in transportation, and this paper focuses on the achievement of optimal bus-stop layouts for increased convenience for residents. To realise carbon reduction benefits, this paper focuses on achieving the minimum personal bus trip average carbon emission, passenger trip costs and bus operation costs, while reducing the impact on other bus stops and routes by proposing bus stop planning and layout method under the micro-community scale. Through the simulated annealing algorithm, the optimised bus stop can optimise the average carbon emission of the residents’ personal trip by 36.87%, while the probability of residents choosing low-carbon trip increased by 4.94%, choosing medium-carbon trip increased by 1.48% and choosing high-carbon trip decreased by 10.84%, realising a substantial carbon reduction benefit. Furthermore, this paper introduces the emotional coefficient of the residents’ public transport trip to determine the effect of travel, waiting and connecting times thereof. Accordingly, new methods and ideas are presented for urban bus stop planning, and the process toward ‘carbon neutrality’ and ‘carbon peaking’ is accelerated.

The study explores the relationship between subway passenger satisfaction and passenger travel behaviour characteristics from the perspective of subway passengers. This study takes Qingdao as an example and designs a questionnaire that includes the basic personal information and travel behaviour characteristics of passengers and evaluates their subway satisfaction. A total of 6340 valid questionnaires were obtained through the combination of online surveys and on-site random surveys. By using the fuzzy synthetic evaluation, the overall score of passenger satisfaction with the Qingdao subway is determined. According to the relationship between passenger satisfaction and travel behaviour characteristics, the chi-square test is used to select the correlation variables group. The results show that there is no significant correlation among the satisfaction of subway passengers, the main means of transportation and the availability of private cars; the frequency of taking the subway is related to the satisfaction of subway passengers; and the purpose of travelling and the reasons for choosing the subway are significantly related to the satisfaction of subway passengers. Finally, based on the analysis of the differences in satisfaction under different conditions, some suggestions were proposed to improve the satisfaction of subway passengers.

Multi-intersection cooperative control for arterial or network scenarios is a crucial issue in urban traffic management. Multi-agent reinforcement learning (MARL) has been recognised as an efficient solution and shows outperformed results. However, most existing MARL-based methods treat intersection equally, ignoring different importance of each intersection, such as high traffic volume, connecting multiple main roads, serving as entry or exit point for highways or commercial areas, etc. Besides, learning efficiency and practicality remain challenges. To address these issues, this paper proposes a novel importance-aware MARL-based method named IALight for traffic optimisation control. First, a normalised traffic pressure is introduced to ensure our state and reward design can accurately reflect the status of intersection traffic flow. Second, a reward adjustment module is designed to modify the reward based on intersection importance. To enhance practicality and safety for real-world applications, we adopt a green duration optimisation strategy under a cyclic fixed phase sequence. Comprehensive experiments on both synthetic and real-world traffic scenarios demonstrate that the proposed IALight outperforms the traditional and deep reinforcement learning baselines by more than 20.41% and 17.88% in average vehicle travel time, respectively.

The purpose of this research paper is to develop a comprehensive assessment model to examine how different forms of airport ownership affect the design and construction of airport passenger terminals. The study emphasises the development and justification of the research framework through an extensive literature review and theoretical foundation. The significance of this research lies in the need for a thorough understanding of the relationship between airport ownership forms and the execution of passenger terminal projects. The literature review identifies gaps in existing research and underscores the necessity of a new research framework. The chosen research approach, which includes interviews and case studies, is carefully justified. The paper systematically outlines the key elements of the research framework, linking them to relevant literature and theoretical concepts. By providing a step-by-step development of the research framework, this paper lays the groundwork for future investigations in this area. The conclusion summarises the key points presented in the article and emphasises the importance of the developed research framework in guiding further research efforts.

Nowadays, in terms of complex and random incidents for locomotive operation, the prevention and control for every tiny and possible influencing factor is not only costly, but also brings great psychological burden to locomotive drivers. Firstly, 68 sets of data of railway locomotive operation accidents happened in recent two years were collected and compiled. Secondly, the system theory process analysis (STPA) method was adopted to extract 68 accident chains based on those data. Then, the complex network theory and PageRank algorithm were utilised to calculate the importance of every node in directed-weighted network formed by those accident chains. The results showed that the importance of human factors is significantly higher than other layers including environment, facility and management. Especially, no effective control behaviour (H7) and false control behaviour (H10) are the top two important causative nodes among all human factors. Besides, being forced to stop (D39) and overrunning of signal (D42) are the top two important causative nodes among unsafe events. For those nodes with high value of PageRank, some targeted security measures should be adopted, so as to save risk management investment and improve the overall safety level of the locomotive operation system.

Congested urban traffic substantially contributes to air pollution in cities. While waiting at bus stops, passengers may be exposed to increased contamination caused by vehicles, including particulate matter (PM). The modern bus stop layout, position and design ignore air quality and allow excessive exposure to pollution. Particulate matter seriously harms the environment, threatening human health and severely damaging all living organisms. The research purpose is to monitor particle emissions at the bus station in the city of Žilina (Slovakia), amassing data on exhaust emissions released from buses at the station premises. As moving or running-engine vehicles incessantly produce atmospheric emissions, we measure air quality during peak hours at the bus station. The results indicate a direct interconnection between passing vehicles and produced particle emissions, when multiple times higher emission levels are revealed. During the morning rush hour, the particulate matter exceeded 360% for PM_2.5 and 420% for PM₁₀. The research showed PM released directly from the buses tends to accumulate in covered premises of the bus station, severely damaging the health of passengers and staff. Our study warns about possible risks of deteriorating human health as waiting passengers unknowingly inhale contaminated particles. Our results indicate the largest emission producers and suggest remedial measures.

This study evaluated the efficiency of the logistics industry in Shijiazhuang City by using the DEA-BCC and Malmquist index models to analyse efficiency changes from 2010 to 2019 and compared them with seven logistics hub cities in the eastern region. The results indicate that Shijiazhuang’s logistics efficiency is high, with leading technology and management levels in the eastern region. Additionally, the Tobit regression model was used to explore factors affecting Shijiazhuang’s logistics efficiency, finding that economic development and locational advantages positively influence logistics efficiency, whereas industrial structure has a negative impact. Based on these findings, it is recommended that Shijiazhuang City enhance its logistics efficiency by improving logistics infrastructure, developing multimodal transport, leveraging locational advantages, elevating economic levels and openness, advancing logistics informatisation and nurturing high-quality logistics talent.

Non-motorised travel and public transportation travel are recognised as low-carbon travel modes, in contrast to car travel, which is considered a non-low-carbon option. Based on this, the paper proposes a stratified assessment method for the urban low-carbon travel potential. The proportion of the motorised travel population that could potentially shift to non-motorised travel within the entire travel population is defined as the urban Tier 1 low-carbon travel potential. Meanwhile, the proportion of the car travel population that could potentially shift to public transportation travel within the entire travel population is defined as the urban Tier 2 low-carbon travel potential. This method holistically presents the potential for improvement in urban traffic carbon emission control. This method considers distance as a primary negative factor affecting the residents’ willingness to engage in non-motorised travel compared to motorised travel. Additionally, it recognises connection, delay and transfer as the main negative factors influencing the residents’ willingness for public transportation travel over car travel. By comparing the actual travel distances of residents and the actual intensity of connection, delay and transfer in public transportation travel modes with the assumed maximum acceptable distances and intensity for residents, the method identifies the number of people who could potentially shift to corresponding levels of low-carbon travel in hypothetical scenarios. Based on this, the corresponding low-carbon travel potential values are calculated. The method then further analyses the trend of these values as the residents’ acceptable thresholds for non-motorised travel distances and acceptable intensity for public transportation travel connection, delay and transfer change. A relationship curve is fitted, which intriguingly exhibits a reverse “S” shape, allowing for the identification of the “rapid release zone” and “key points” on the curve. These insights are essential for effectively targeting interventions to increase the adoption of low-carbon travel modes. This paper takes the cities of Shanghai and Wuhan in China as examples, conducting a stratified assessment of the low-carbon travel potential for both cities based on 19,732 daily travel origin– destination (OD) survey samples from residents. Additionally, the low-carbon travel potential of the two cities is visualised by district, enabling an analysis of the characteristics of low-carbon travel potential in each city and a comparison of the differences in low-carbon travel potential between them.

This paper investigates the impact of the COVID-19 pandemic on travel modes choice behaviour using a case study from Wuhan, China. A SP-experiment based survey was conducted in Wuhan, based on which an MNL model and a latent class MNL model were established, respectively. The model estimation results show the following conclusions. First, the attributes that are normally believed to significantly affect the residents’ travel mode choice behaviour turned out to be insignificant during the COVID-19 pandemic. Second, attributes such as age, gender, driving license, income trend, use frequency of public transit, currently most-frequent-used mode, household size, monthly household income, distance from metro station to home, number of confirmed/deaths cases, vaccination are significantly affecting the respondents’ travel preferences. Third, the outbreak of the COVID-19 pandemic leads to a decline in the residents’ preferences toward public transit, but the promotion of vaccines can lead residents to return to the public transit system. Fourth, the respondents were divided into three latent classes: high-susceptible, medium-susceptible and low-susceptible classes. These conclusions are believed to provide a reference for the investigation of impact of the COVID-19 pandemic or other similar public health events on the transportation system, and also offer supports for policy-making to effectively deal with such pandemics.

As artificial waterway transportation systems, canals played a crucial role in the initial stages of the Industrial Revolution, facilitating faster, more convenient and economically viable mass transportation of goods, thus becoming indispensable components in certain regions’ urbanisation and industrialisation processes. This study employs the bibliometric analysis tool CiteSpace to investigate 212 papers on canal transportation from the Web of Science over the past three decades. The objective of this research is to elucidate the knowledge structure through visual representations of collaboration networks, co-citation networks, keyword co-occurrence and clustering patterns. In the findings, we establish author, institution and country co-authorship networks to ascertain the distribution of core journals by determining journal co-citation networks. The literature co-citation network reveals the main research themes and knowledge structure of canal transportation. Influential authors are identified through author co-citation networks, while research hotspots and frontiers are discovered through keyword co-occurrence networks. This study offers a comprehensive and informative perspective on current trends and research developments in canal transportation. Additionally, we propose future research directions with potential prospects to propel the advancement of this field further comprehensively.