This study explores the potential impact of per capita gross domestic product (GDP) changes on the adoption of autonomous vehicles (AVs). The level of adoption of AVs is anticipated to influence the benefits of future mobility, prompting numerous studies that forecast the market share of AVs using various methods. The influence of changes in the per capita GDP on vehicle ownership is crucial in assessing the challenges associated with reducing dependence on AVs in the future. This phenomenon, known as the hysteresis effect, implies that AV adoption estimates may differ when the GDP is rising as opposed to when it is falling. This research examines the effect of rising and falling GDP per capita on the anticipated AV diffusion in Hungary, utilising a scenario-based method to account for the variation in adoption rates in the literature. The study findings indicate that declines in GDP in the past will impact AV ownership, leading to a shift in future adoption patterns. The AV market is projected to reach saturation in the 2070s and the 2090s in favourable and moderate scenarios, respectively, while a pessimistic state would delay this outcome until after the year 2100.

Transportation, which is a significant facilitator of global trade and development, faces a serious problem with respect to sustainability. Firstly, there is the need to minimise greenhouse gas emissions while maintaining profitability and social responsibility. Transportation will be totally decarbonised by consistently moving towards a more sustainable, diverse and resilient range of transportation modes with advanced vehicle technologies. However, what impact this will have on the economic performance of transport service providers remains a big question. The aim of this study is to examine the short-run relationship between environmental sustainability in road freight transportation and the economic performance of the road freight transport sector in the European Union using an autoregressive conditional heteroscedasticity (ARCH) model. The analysis was conducted using annual data spanning from 2008 to 2021. The results indicate that energy taxes on transport and storage, biodiesel consumption and vehicle capacity utilisation have a positive and significant impact on freight transport performance (FTP). The findings suggest that policymakers could use energy taxes and incentives to promote the use of biodiesel in the transportation sector to increase FTP. Additionally, efforts to improve vehicle capacity utilisation could significantly increase FTP and have positive environmental implications such as reducing traffic congestion and emissions.

Postal service providers can reorganise the last-mile delivery process within the scope of universal service and apply some of the flexible models for the organisation of the delivery. In this paper, the question of the selection of Flexible Last-Mile Delivery Models (FLMDMs) is treated using multicriteria decision-making. We have identified four different sustainable last-mile delivery models with an emphasis on the number of delivery workers. One postal service provider from Europe was selected, where the proposed FLMDMs were tested. The proposed last-mile delivery models are ranked using Multiple Criteria Decision Analysis (MCDA) techniques. In this context, MCDA techniques are used to make a comparative assessment of alternatives. The obtained results suggest the AB delivery model as the optimal choice for the last-mile delivery and complete allocation of the number of delivery workers.

In this paper, the author investigated the stated preference survey in transport modelling. The research was conducted to ensure that the best fractional orthogonal design of stated preference paired comparison survey would not increase the error or uncertainty in transport-related decision modelling. The research was conducted based on artificial Monte Carlo simulated respondents, and the results were assessed with standard mathematical-statistical tools. Although the assessment should have resulted in 0% errors, according to our 2,000 sample, a minor 5% of errors occurred. The problem to be investigated in this paper is that the best-designed survey could have some errors.

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm.

A macroscopic fundamental diagram (MFD) is an important basis for road network research. It describes the functional relationship between the average flow and average density of the road network. We proposed an MFD estimation method based on the traffic flow condition. Firstly, according to statistical theories, the road network data are divided into three traffic flow conditions (free flow, chaotic and congested) bounded by a 95% confidence interval of the maximum traffic capacity of each intersection in the road network. Then, in each condition, we combined principal component analysis and the Jolliffe B4 method to reduce dimension for extracting critical intersections. Finally, the full-scale dataset of the road network was reconstructed to estimate the road network MFD. Through numerical simulation and empirical research, it is found that the root mean square error and absolute percentage error between estimated MFD and true MFD considering the traffic flow condition are smaller than those without considering the traffic flow condition. The MFD estimation and the division of the traffic states of the road network were completed at the same time. The proposed method effectively saves the time cost of road network research and is highly accurate.

Urban rail transit plays a very important role in cities’ social and economic development. To ensure the safe and stable operation of urban rail transit operation equipment and facilities, it is necessary to monitor a large number of safety hazard statuses and data and improve the over-centralisation of traditional monitoring. This paper designs a scheme for storing, validating and monitoring the safety hazard status of urban rail transit operation equipment and facilities based on blockchain technology. The safety hazards of equipment and facilities during the operation stage of urban rail transit are listed using the literature analysis method and the case study method. The European RAMS (reliability, availability, maintainability and safety) standard method is used to determine the safety hazard status of equipment and facilities by availability index. Based on the features of the consensus mechanism, smart contract and other features of blockchain technology, this paper designs an overall scheme for storing, verifying and monitoring the safety hazard status of equipment and facilities. This scheme provides a practical operation method for evaluating the safety hazard status of rail transit equipment and facilities, which is conducive to the safety rectification of the entire urban rail transit.

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking.

Electric buses (EBs) have attracted more and more attention in recent years because of their energy-saving and pollution-free characteristics. However, very few studies have considered the impact of stochastic traffic conditions on their operations. This paper focuses on the departure interval optimisation of EBs which is a critical problem in the operations. We consider the stochastic traffic conditions in the operations and establish a departure interval optimisation model. The objective function aims at minimising passenger travel costs and enterprise operation costs, including waiting time costs, congestion costs, energy consumption costs and operational fixed costs. To solve this problem, a genetic algorithm (GA) based on fitness adjustment crossover and mutation rate is proposed. Based on the Harbin bus dataset, we find that improved GA performance is 4.481% higher, and it can solve the models more accurately and efficiently. Compared with the current situation, the optimisation model reduces passenger travel costs by 20.2% and helps improve passenger travel quality. Under stochastic traffic conditions, total cost change is small, but passenger travel costs increase significantly. This indicates the high impact degree of random traffic conditions on passenger travel. In addition, a sensitivity analysis is conducted to provide suggestions for improving the EBs operation and management.

The Guangzhou Metro Authority implemented health condition registration and temperature checks to curb the spread of the virus during the COVID-19 pandemic. However, it is important to investigate how these measures may have impacted the get-through efficiency and whether they caused the increased crowding at entrances and the station hall. To address these questions, simulation models based on the T Station were developed using AnyLogic. The model compared the get-through efficiencies with and without the anti-epidemic measures, while also analysing the risk of crowding at entrances and within the station hall after their implementation. Results revealed an increase in the number of passengers unsuccessfully passing through the check-in gate machines from 15% to 53% within 5 minutes, and 10% to 45% within 10 minutes when the anti-epidemic measures were in place. It was also observed that some entrances experienced significant crowding. Three measures were simulated to find effective ways to increase the get-through efficiency and mitigate the crowding – increasing the distance between security and health checks, utilising automatic infrared thermometers, and arranging volunteers or staff to assist with the registration process. The results demonstrated that using automatic infrared thermometers instead of handheld forehead thermometers proved to be effective in improving passenger efficiency and alleviating crowding at entrances and within the station hall.

In the future, mixed traffic flow will consist of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs). Effective traffic management is a global challenge, especially in urban areas with many intersections. Much research has focused on solving this problem to increase intersection network performance. Reinforcement learning (RL) is a new approach to optimising traffic signal lights that overcomes the disadvantages of traditional methods. In this paper, we propose an integrated approach that combines the multi-agent advantage actor-critic (MA-A2C) and smart navigation (SN) to solve the congestion problem in a road network under mixed traffic conditions. The A2C algorithm combines the advantages of value-based and policy-based methods to stabilise the training by reducing the variance. It also overcomes the limitations of centralised and independent MARL. In addition, the SN technique reroutes traffic load to alternate paths to avoid congestion at intersections. To evaluate the robustness of our approach, we compare our model against independent-A2C (I-A2C) and max pressure (MP). These results show that our proposed approach performs more efficiently than others regarding average waiting time, speed and queue length. In addition, the simulation results also suggest that the model is effective as the CAV penetration rate is greater than 20%.

In order to avoid the congestion in front of the entrance gate units, it is necessary to analyse and optimise the queuing situation at the planning and design stage. The security inspection area and the ticket-checking area were jointly considered, and a queuing congestion analysis method was proposed. Firstly, the research problem was stated. Then, the problem of calculating the number of passengers in each subarea at any time was transformed into the problem of calculating the transit time of each passenger in each subarea. The transit time was divided into basic transit time and additional transit time. Based on the velocity-density relationship, a quantisation method for basic transit time was proposed related to passenger arrival time. The additional transit time was determined by the moment when the passengers left the subarea according to the sequence of arrival of passengers, the number of queuing passengers in the subarea and the congestion of the subarea to be entered. Finally, the queuing situation of passengers in each subarea at any moment was obtained through passenger flow recursion. Examples showed that the proposed method can deal with multiple working conditions and avoid the tedious and time-consuming scene construction process of the microsimulation software.