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• A method to assess the vulnerability of PTS comprising subsystem is proposed. • Critical areas of PTS are identified based on running timetable and position data. • Differences of vulnerability or critical area between subsystem and PTS are revealed. • A case of high-speed rail and air transport coupling system in China is researched. The public transport system (PTS) consists of different types of transport subsystems, such as high-speed rail (HSR), air transport (AT), conventional railway, expressway and waterway in a state, which exerts significant impacts on distribution of demographic and economic activities. And its interruption or failure may cause severe social and economic disruption. So the vulnerability analysis of the system attracts an increasing attention. However, most of previous researches mainly concentrate on the vulnerability modeling and analyzing of a single subsystem, neglecting the simultaneous interruption of components, and less considering the complementary effects of other subsystems from systematic perspective and the geographic interdependency among components. For trying to fill these gaps, this paper proposes a method to analyze the vulnerability of PTS composed of two or more transport subsystems and identity the critical area within the system based on the data of running timetable and position coordinates. Finally, taking HSR and AT coupling system in mainland China as a case study, we investigate the differences of vulnerability and critical areas between individual subsystem and overall system. Results reveal that the vulnerability variations and the critical areas distribution of the system are different from these of the subsystems. The research also finds that whether a component is critical depends not only on the system's feature, but also on the component's feature, whether an area is critical depends on economic development and population density of the area. These findings are beneficial to prioritize the plan, budget and maintenance of PTS, and prepare emergency response plans. [ABSTRACT FROM AUTHOR]

New and emerging technologies, such as connected sensors, smartphones and smart cards, offer new possibilities to collect rich real-time information about travelers. Moreover, smartphones also enable travelers to actively share information, for instance, about their intended travel plans. This type of information can be used to improve public transport disturbance management. In this paper, the potential gain of collecting different types of information about travelers is explored to support action decisions made by public transport actors, during unplanned disturbances. Based on interviews and workshops, the paper provides a mapping between different information types and possible action decisions that can be supported. Furthermore, based on a literature review focused on current and potential technical solutions, a guidance to which solutions support which type of action decisions, is also provided. Amongst others, the results show that automated fare collection, which is one of the most commonly implemented systems providing real-time information about the traveler, can support a large number of action decisions relevant in unplanned disturbance scenarios. The technical solution providing the most extensive information, and thereby providing the best support for the action decisions, involves smartphone apps delivering user-generated information. The drawback with this solution is that it might violate privacy, and that it typically relies on the travelers providing relevant information voluntarily. [ABSTRACT FROM AUTHOR]

This paper presents some results devoted to providing network analysis functionalities for vulnerability assessment in public transportation networks with respect to disruptive events and/or targeted attacks to stations. The results have been obtained on two public transportation networks: the bus network in Florence, Italy, and the transportation network in the Attika region, Greece. The analysis implements a topological approach, based on graph theory, using a multi-graph to model public transportation networks and analyse vulnerabilities with respect to the removal of one or more of their components. Both directed attacks and cascading failures are considered. While the first type of disruptive events is related to a static analysis, where nodes are removed according to a rank related to some centrality measures, the second type is related to a dynamic analysis, where a failure cascade is simulated making unavailable the node with the highest betweenness value. Vulnerability measures are computed as loss of connectivity and efficiency, with respect to both the two different types of disruptive events considered. This study allows to evidence potential vulnerabilities of the urban networks, that must be considered to support the planning process into the creation of resilient structures. [ABSTRACT FROM AUTHOR]

The structure and properties of urban public transport networks are important for understanding the mobility of public transport systems. However, most studies have ignored the impact of shared mobility and lacked the support of large-scale empirical data when exploring the mobility of urban public transport networks. Using multi-mode travel data, this study constructs a three-layer multiplex network and assigns weights to reflect the transport capacity of different modes (bus, metro, and shared bike) based on ridership and travel distances. The case study was conducted on the public transport system of Shenzhen, China. The results show that the degree distribution and edge weight distribution of the multiplex network exhibit distinct scale-free characteristics. The effect of the multiplex network model on single-layer networks suggests that cooperation between networks significantly improves the capacity and efficiency of urban public transport systems. Regression analysis reveals the effects of the urban population and land use on the demand for public transport services. The results show an intrinsic association between the public transport system and urban land use, which will facilitate the prediction of public transport demand and serve the sustainable development of cities. [ABSTRACT FROM AUTHOR]

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