Gender can determine mobility habits and patterns in everyday life. Yet, different mobility needs of men and women are often ignored in the urban transport systems analysis and planning. Different attitudes and perceptions can determine the transport mode choice as well as the basic characteristics of a person’s trip. Integrated transport and urban planning require taking into account these differences for reasons of inclusivity. This paper identifies gender differences in travel perceptions, attitudes and behaviours using inferential statistical analysis of questionnaire surveys contacted in the city of Thessaloniki, Greece, and discusses the policy directions to enhance gender mobility equity. The results are cross-checked with international literature to explore the role of local culture in explaining gender gaps in urban mobility.

The main purpose of the article is to study the infrastructure of multimodal transportation in the context of changes in transport and logistics routes. The object of research is the infrastructure of multimodal transportation. The main issue of the study is to determine ways to develop the infrastructure of multimodal transportation in the context of changing transport and logistics routes. For this, a modern methodology of functional modeling was used. As a result, a model was obtained that, in a graphical language, represents the main aspects of the development of the multimodal transportation infrastructure in the context of changes in transport and logistics routes. The focus of the study falls on Ukraine and its transport infrastructure. The article explores the development of multimodal transportation infrastructure in the context of changing transport and logistics routes. Showing the loss of the size of cargo transshipment by the seaports of the annexation of the area. The features of the functioning of seaports in an aggressive environment are described. Empirically, the potential volume of cargo transshipment and the volume of exports by seaports for a single country under different conditions have been established. Empirically established the volume of losses of container handling of goods. The study is limited by taking into account the multimodal transportation infrastructure of only one country taken.

Autonomous vehicles (AV) have the potential to improve mobility, enhance traffic safety, and provide societal benefits. To date, users’ attitudes toward the adoption of AVs have been mainly extracted from questionnaire surveys among participants that were not directly exposed to AVs. Thus, there is a need to (a) record users' opinions toward AVs before and after they are exposed to the technology, and (b) investigate how demographic factors affect these perceptions and attitudes. This paper compared the attitudes of drivers (N=101) toward AVs before and after being exposed to an interactive, high-fidelity driving simulator that replicated the AV user experience. The analysis examined differences within and between age groups (younger, middle-aged, and older adults) with respect to Intention to Use, Barriers, and Acceptance. Investigation of the gender-related impacts was also performed and documented. The results provided evidence that the perceptions and attitudes of AVs from older and middle-aged adults significantly improved after driving simulator exposure. Older participants and females showed the greatest positive changes in Intention to Use, Barriers, and Acceptance. The study further showcased the value of exposing users to an AV driving simulator as an efficient way to promote users’ acceptance of AV technology.

Between 2018 and 2020, in the approximately 2,600 km of Italian road tunnels, 2,899 people were injured and 60 died in 1,885 road accidents. The accident frequency was lower than on open roads, while the injury/fatality rate was higher. Using the recursive partitioning and regression trees method (rpart), we developed two accident models useful for predicting the probability of involvement of "vehicle type" in short and long tunnels. Variables such as the type of accident, the circumstances, the type of road, the carriageway, the time of the accident, the journey purpose (whether work-related or not), and the length of the tunnel defined the nodes and paths of the regression tree associated with a vehicle type involved. The “road type” was the best predictors for short tunnels while the “journey purpose” was the best predictor for long tunnels. The most important result of the study refers to the similarity between the probability of an accident in short and long tunnels for a specific segment of road users: commuting and non-commuting car drivers and drivers of heavy goods vehicles on-duty. The study showed that this road user segment in short tunnels has an accident probability half that observed in long tunnels.

Driving cycle is as representation of traffic behaviour in an area or city. It plays a fundamental role in the design of vehicles and to test the performance of the vehicles. This paper studies a driving cycle development method based on k-means clustering and driving cycle prediction based on Long Short-Term Memory (LSTM) by Recurrent Neural Network (RNN). The objectives of this paper are to develop a Kuala Terengganu Driving Cycle (KTDC) by using k-means clustering, to develop a prediction of future KTDC, and lastly to analyse the energy consumption and emissions of KTDC. Firstly, the driving data is collected in five different routes in Kuala Terengganu city at go-to-work times. Then the data is divided into micro-trips and the driving features are extracted. The features are used to develop a driving cycle using k-means clustering approach. The prediction is developed after the training of neural networks by using LSTM network approach. Finally, the energy consumption and emissions of KTDC is analysed by using AUTONOMIE software.

Almost 60,000 km of high-speed lines are currently in operation worldwide, and around 90,000 km are under construction or planned. A conventional network exists in countries where the high-speed rail (HSR) network is implemented. One of the most important operating models is that which considers the use of HSR-type rolling stock also on conventional networks to produce high-quality services. The problem of designing services that operate on the two types of network is faced at a heuristic level by the different companies that manage railway services. it is therefore useful to have a design model that allows to obtain the optimal solution, with unchanged infrastructural resources. The method used is developed starting from the analysis of the services currently operating on the two networks, building a synthetic graph composed of the macro-links representative of the services existing on the two networks, optimizing the number of stops and the stop time on each macro-link. On the synthetic graph it is possible to obtain the optimal design solution by applying Bellman's theorem. The method is applied to the Italian HSR and conventional networks by studying a new service, referring to the Southern Italy. The results are interesting because compared with the services currently available, they make it possible to optimize travel times, significantly reducing. The lesson that can be drawn is that as all countries build new HSR sections, they can increase user utility by offering high-quality services that use the two networks in an integrated way.

This work attempts to develop a universal model for predicting micro-and macro-level accident frequency. The study implies that the number of accidents may vary depending on the type of roadway and the characteristics of its small segments, both at the group and individual levels. A multilevel model has been designed to address the nested link between individual road segments and multiple road classifications. The multilevel analysis allows one to investigate the hierarchical nature of road accident parameters at the micro and macro levels to comprehend the risk of specific road segments within distinct roadway categories. To accomplish this, a case study of fifty-seven roads has been selected, covering five main categories: motorway, expressway, primary arterial, secondary main roads, and minor roads. In addition, each roadway has been subdivided into several flexible-length segments, each of which is identified by its traffic and geometrical characteristics. The outcome confirms that the accident frequency per road segment is more likely to vary between roads with distinct categories. Moreover, road types with low design requirements are more hazardous than well-designed roads, even with lower speed restrictions and traffic numbers.

Traffic accidents are becoming a global issue, causing enormous losses in both human and financial resources. According to a World Health Organization assessment, the severity of road accidents affects between 20 and 50 million people each year. This study intends to examine significant factors that contribute to road traffic accident severity. Seven machine learning models namely, Naive Bayes, KNN, Logistic model tree, Decision Tree, Random Tree, and Logistic Regression machine learning models were compared to the J48 pruned tree model to analyze and predict accident severity in the road traffic accident. To compare the effectiveness of the machine learning models, ten well-known performance evaluation metrics were employed. According to the experimental results, the J48 pruned tree model performed more accurately than the other seven machine learning models. According to the analysis, the number of casualties, the number of vehicles involved in the accident, the weather conditions, and the lighting conditions of the road, is the main determinant of road traffic accident severity.

The Trans Java Toll Road infrastructure is an integral part of the development initiatives within Central Java Province. By the end of 2018, the province witnessed a significant rise in economic activities due to the operation of several cross-Java toll roads. Therefore, this research explores the impact of road length, investment, human development index, and labor on the economy in the city area due to the operational influence of toll roads. The sample of this study was 15 cities and regencies in Central Java Province. The period studied is from 2018 to 2022. The analysis utilizes multiple regression methods, including panel and logistics regression using Eviews and Stata software. The investment analysis results have a negative coefficient with no significant effect. High foreign investment can undermine economic growth due to weak infrastructure support, low-quality human resources, and rising inflation. Therefore, for every 1% increase in investment, the gross regional domestic product will decrease by 4%. However, road length, labor, and human development index variables significantly affect the Economy in Central Java Province. Road infrastructure’s impact on the economy indicates that an increase in road length per thousand population substantially affects the gross regional domestic product.

Climate change is related with weather extremes, which may cause damages to infrastructure used by freight transport services. Heavy rainfall may lead to flooding and damage to railway lines, roads and inland waterways. Extreme drought may lead to extremely low water levels, which prevent safe navigation by inland barges. Wet and dry periods may alternate, leaving little time to repair damages. In some Western and Middle-European countries, barges have a large share in freight transport. If a main waterway is out of service, then alternatives are called for. Volume- and price-wise, trucking is not a viable alternative. Could railways be that alternative? The paper was written after the unusually long dry summer period in Europe in 2022. It deals with the question: If the Rhine, a major European waterway becomes locally inaccessible, could railways (temporarily) play a larger role in freight transport? It is a continuation of our earlier research. It contains a case study, the data of which was fed into a simulation model. The model deals with technical details like service specification route length, energy consumption and emissions. The study points to interesting rail services to keep Europe’s freight on the move. Their realization may be complex especially in terms of logistics and infrastructure, but is there an alternative?