International Journal of Transportation Science and Technology最新文献

Mobility as a service (MaaS) is an innovative and value-laden method to operate on-demand mobility services within the existing transport infrastructures. A big challenge for the transport system is that the transport infrastructure cannot be restructured as quickly as a digital platform like cloud computing infrastructure services, which is a constraint that limits the balance between mobility demand and supply in real-time. The ownership of mobility services needs to be redefined to establish legal binds between service vendors and commuters, further demonstrating the capacity to balance the demand and supply of mobility services in real-time. The ownership of a mobility service is defined as the commuter's right to use the mobility service provided by the service vendor under agreed terms and conditions. This is facilitated by MobiToken in this study. An eMarket-place is proposed for commuters, mobility service vendors and other stakeholders to exchange MobiToken, where the marketplace demonstrates the potential use of blockchain tools, methods and algorithms, where a specialised capability for reliably link stakeholders is proposed through a nonrevertible blockchain dataset. The bidden price of MobiToken reflects the demand for mobility services in the real world, where congestion charges are incorporated within the bidden price. The performance of the preliminary platform is evaluated with three testing scenarios, and the results of performance testing indicate the stability and reliability of the Raft consensus algorithm for the blockchain database.

Subgrade is an important construction element of the railroad track. In the process of long-term operation, the subgrade is loaded by trains and exposed to natural climatic factors, such as wind and seismic loads, moisture caused by atmospheric precipitations and groundwater, exposure to positive and negative temperatures. At the same time, the subgrade must provide reliability and stable properties of the railway track because its renovations are the most expensive ones among those for the railway track in general. Due to this fact, national regulatory documents for the construction of the subgrade impose strict requirements on its deformity for the entire duration of modern railways operation. One of the ways to solve this problem on the stage of construction designing is computer predictive modelling of structural behavior that considers the changes of its elements and properties of construction materials under the long-term train impact taking into consideration the varying natural climatic factors.

In this review, we analyze accumulated scientific and technical experience in formulating optimal approaches to the determination of initial data for computer simulation of repeated impact of the train loads for predicting railway track subgrade deformation considering engineering-geological conditions and natural climatic factors. We describe the stages of creating the subgrade computer modelling taking into account the analysis of trains load impact, the properties of model’s structural layers, and the change of soil properties under long-term loads.

With the development of highways, new technologies should be continuously introduced to improve highway traffic safety. Digital twin (DT) has been an emerging field of research in recent years. To develop a digital twin management system, a data model is essential. In the field of highway operational risk management (HORM), however, the development of data models is still in its infancy. Motivated by the concept of linked data, in this paper, we attempt to propose an information model for HORM. The main achievements of this paper include data architecture, identification and classification code methods, data interaction method, and the developed system. Based on data needs analysis, the highway information model architecture for risk management is defined as five layers: basic highway products, traffic sensors and equipment, traffic rules, traffic flow, and weather. Furthermore, according to the concepts of semantic data, these five layers can be classified into three categories: highway product data, topology data, and sensor data. Although the Industry Foundation Classes (IFC) standard and Brick schema were first proposed and applied in the building domain, some of their entities and relationships can also be applied to highways. To this end, we defined some new classes, a specific ontology, and an integrated framework for HORM. Finally, a case study was carried out. Applying such information model to highways has broad potential. It changes the file-based exchange method to the data-based one, which can promote highway data exchange and applications. The proposed information model could be of great significance for HORM.

Although multiple studies have modeled and predicted the potential effects of shared autonomous vehicles (SAVs), the research on the adoption of SAVs by riders with actual ridership experience is still limited. In addition, the increasing tendency towards operating SAV technology requires understanding its efficiency while integrating it into the existing transportation network infrastructure. This study aims to identify the factors affecting the user's willingness to ride the SAVs based on the data collected from a comprehensive survey distributed among users and non-users of a self-driving pilot project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) in Arlington, Texas. Using structural equation modeling (SEM), we identify the effects from vehicle ownership, RAPID usage, existing modes of transportation, RAPID service attributes (comfort and safety), and sociodemographic variables on individuals' willingness to use SAVs in the future. Results indicate that most riders of the RAPID service are young Asian individuals and students from low-income households with limited or no access to a private vehicle. Furthermore, SEM results show that RAPID usage directly impacts willingness to use SAVs, implying that people start developing trust for the technology with an increase in the frequency of using the service. Our model suggests that sociodemographic attributes of the SAV riders indirectly influence the willingness to use SAVs through the mediators, including RAPID usage, existing modes of transportation, and vehicle ownership. This study provides crucial insights about individual travel behavior after integrating SAVs into existing transportation infrastructure to assist policymakers and transportation planners in developing AV-related policies.

“Metro economy” has led to intensive land development along the metro lines. However, engineering activities associated with land development inevitably disturb the service environment, causing severe settlement of adjacent metro tunnel structures in soft soil areas. As a reference point for pre-treating this type of tunnel settlement, a method is proposed to classify settlement risk sections along metro lines based on a land-use development simulation and corresponding uncertainty analysis. First, the land-use development along the metro line was simulated by Artificial Neural Network–Cellular Automata (ANN-CA). Second, the land-use development process was considered a random event rather than a deterministic prediction as in a typical ANN-CA, with its probability quantified based on the cells’ conversion probability. The classification of the surrounding land-use development probability was used to allocate the settlement risk sections of metro lines. This method was applied to the Han–You section of the Nanjing Metro Line 2. The predicted settlement risk sections corresponded suitably with the actual settlement troughs, demonstrating the effectiveness of this method. Thus, this method provides a novel consideration for the pre-treatment of metro tunnel settlement from the perspective of interactions between the metro line and surrounding land development.

While numerous studies have investigated attitudes towards self-driving cars in general, less research attention has been focused on individuals' comfort with the presence (or absence) of third-party human supervision of this automation, and its potential correlates. In the present study we perform a secondary analysis of pre-existing data from The European Commission’s Eurobarometer 92.1, a large-scale European survey (n = 27565) of expectations and concerns of connected and automated driving. By comparing responses to three levels of human supervision in self-driving cars, we aim to identify changes in the importance of predictors of comfort with automation. We find considerable heterogeneity in both individual attitudes, as well as in country-level attitudes in our descriptive analysis. We find a trend of decreasing comfort as external human supervision is reduced, although this effect differs between countries. We then investigate potential drivers of self-reported comfort with varying levels of external human supervision in a regression framework. Gender differences get stronger with decreasing supervision, suggesting a possible resolution to conflicting evidence in previous studies. Following this, we fit an ordinal random forest model to derive variable importance metrics, which enable us to compare the changing role predictor variables might play in shaping self-reported comfort, depending on varying levels of third-party supervision. Data privacy is highlighted as an important variable, regardless of level of supervision. Our findings provide confirmation for previous literature in a large sample, while also uncovering a number of novel associations, providing guidance for future policy-making and research efforts.