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China is faced with a serve aging crisis, and the seniors’ long-distance travel is becoming more challenging with the rapid development of high-speed trains. This paper studies the transfer feasibility for elderly passengers passing through specific barriers at high-speed train stations. Questionnaires were distributed at Beijing High-Speed Train Station, with 716 valid samples involved. This paper divides the transfer procedure into seven phases, and proposes various points of interest at each phase with different barriers. These points of barriers are categorized into three major different types: Serious AND Common, Serious BUT NOT Common, and Common BUT NOT Serious. The quantitative relationships between each point of barriers and old travelers’ information have been explored using decision tree and binary logistics regression. As a conclusion, we suggest some recommendations for improving the transfer environment, long-distance mobility, as well as wellbeing of the older adults at high-speed train stations. To the best of our knowledge, we are the first to study the transfer feasibility at high speed train stations for seniors both qualitatively and quantitatively, especially dealing with the complicated transfer procedure. [ABSTRACT FROM AUTHOR]

• The effectiveness of existing traffic measures in Beijing is investigated. • A survey on drivers' opinions and attitudes towards the traffic situation is given. • Most respondents think congestion is still serious in Beijing. • The perceived impact of congestion on personal car use is relatively low. • The symbolic meaning of personal cars has large impact on willingness to change. Facing serious challenges of traffic congestion and air pollution, Beijing has implemented a series of traffic polices. In this paper, we first investigate the effectiveness of existing traffic measures in Beijing and analyse the underlying factors from the perspective of drivers, which provide insights for the traffic regulations in other cities worldwide. While the car ownership restriction has effectively limited the total number of vehicle in Beijing, the effect of the car use restriction only lasts for a short period. A survey on drivers' opinions and attitudes towards the traffic situation and a potential measure, i.e., tradable driving credit (TDC), is conducted. Although traffic data shows that congestion improved and traffic was slightly congested in recent years in Beijing, most respondents believe congestion is still serious in Beijing. However, they think the impact of traffic congestion on their personal car use is relatively low, which could partly explain why so many travellers still depend on driving given the relative convenient transit facilities in Beijing. In addition, a large proportion of respondents treat personal cars as a representation of social status and think driving bring them pleasures, which indicates that it may be hard to further decrease the share of car trips in Beijing. According to their attitudes and social-economical characters, respondents' willingness on their changes of travel behaviours under TDC is investigated. The results show that individuals' social-economic characters such as income level, education and family size and the symbolic meaning of personal cars have significant influence on respondents' willingness to switch to other transport modes. [ABSTRACT FROM AUTHOR]

• A graphical method for intuitively illuminate fundamental driving habits of drivers. • Describe the basic and internal driving patterns of drivers based on big data. • A good application in the analysis of driving safety. Due to differences in driving skills and personal characteristics among drivers, the behaviors of drivers when faced with various driving environments differ, causing different levels of driving safety concerns. In past research, the measurement of safety-related driving behavior mostly focused on classification, while few studies were concerned with individual driving behavior characteristics. However, it is important for drivers to recognize and correct their dangerous behaviors and optimize their driving. This paper presents a graphical method for modeling individual driving behaviors, and the results can be used in driving safety analysis. Based on the assumption that drivers have specific driving habits, typical driving patterns during driving are first detected and extracted. These typical driving patterns are then sorted according to their frequencies, forming a driving behavior graph that can directly illustrate each driver's behavior features. Furthermore, a quantitative analysis method for evaluating driving safety based on the behavior graph is provided. To verify the proposed method, a case study focusing on vehicles' longitudinal motion was conducted using GPS data collected from Beijing taxis. The results demonstrated that the graphical method can describe the individual features of a driver's longitudinal acceleration behavior and distinguish differences among drivers. The development of this method can help understand the individual features of driving behaviors and further support measures to optimize driving safety. [ABSTRACT FROM AUTHOR]

Automated Fare Collection (AFC) systems in rail transit services collect enormous amounts of detailed data on on-board transactions. A better understanding of travelers' commuting and transfer behavior based on those massive volumes of AFC data would enable the rail service operators to evaluate their service quality and optimize operation strategies. This paper proposes an efficient and effective data mining procedure to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines in urban rail transit networks. A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. Using up to 28 million AFC smart card transaction data, we match and analyze travelers' trip chains to investigate the commuting trip patterns in terms of spatio-temporal distribution characteristics. An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. Further, the results indicated by stronger association rules (2-frequent itemset and 3-frequent itemset) also provide a better understanding of transfer behaviors, like the frequent transfers among different service lines, and potentially vulnerable stations in the network. The research outcomes can be used to assist rail transit service operators in developing optimal operation strategies like timetabling design to enhance the transfer performance between different rail lines. • An efficient and effective data mining procedure is developed to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines. • An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. • A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. • Experimental results indicated by strong association rules provide a better understanding of transfer coordination in urban rail transit networks. [ABSTRACT FROM AUTHOR]

• The motility framework is employed to understand travel behaviour from a subjective perspective. • Sustainable travel behaviour predictors are compared across three heterogeneous urban contexts. • Perceived functional suitability of a sustainable travel mode varies across different urban contexts. • Good access to public transport (PT) is likely to increase cycling, PT, and reduce car using. Drawing on the conceptualisation of motility as the capacity to be mobile, this paper employs statistical and GIS-based analyses to explore the associations between travel mode choice and mobility-related attitudes, skills and opportunities to access transport modes. The study builds on survey data and spatial data from three urban contexts of Beijing, Gothenburg and Malmo to analyse both individual-level and contextual factors influencing sustainable travel behaviour. The results indicate that despite varying contexts, the three dimensions of attitude, skills and access significantly explain individuals' travel behaviour and their choice to travel by public transport, bicycle or car. Among the studied travel modes, cycling appears to be a competitive mode when the travel distances are within 5 km. In all three urban contexts, individuals who have greater environmental awareness are more likely to travel by public transport or cycling if the physical conditions facilitate using these modes. Good access to public transport is likely to increase the usage of both cycling and public transport and reduce car use. Favourable conditions for cycling within 2 km and 5 km radius can positively encourage people to use a bicycle as a feeder mode for public transport. Overall, our findings demonstrate that for mobility policies to increase individuals' motility in relation to sustainable travel modes and encourage a travel behaviour shift towards using alternatives to cars, planners need to take more holistic approaches and design policies that deal with the three motility dimensions in an integrated manner and avoid focusing on a single dimension in isolation. [ABSTRACT FROM AUTHOR]