Purpose. There is a current need for innovation in research on the fear of crime to move on from general and static representations and instead approach it as a dynamic phenomenon experienced in everyday life, to inform or evaluate situational interventions.Methods. This study presents a novel approach to fear of crime research using the framework of routine activities theory and environmental criminology to present it as a specific event characterized by spatial, temporal, and personal variables. We suggest and illustrate a new experience sampling approach to data collection, captured via a mobile phone application.Results. By studying the fear of crime in the environment where it occurs, and focusing on a microscale geography with the additional dimension of time, new insight into fear of crime can be attained. Results from a data collection pilot demonstrate significant spatiotemporal variation in individuals' fear of crime levels and hence illustrate the viability of such approaches.Conclusions. We argue that this new insight can lead to the development of situational interventions which target fear of crime hot spots as they move about in place and time, allowing limited resources to be allocated more efficiently to enhance perceptions of safety.

Summary. This study investigates the Information Process Space (IPS) of pedestrians, which has been widely used in microscopic pedestrian movement simulation models. IPS is a conceptual framework to define the spatial extent within which all objects are considered as potential obstacles for each pedestrian when computing where to move next. The particular focus of our study was identifying the size and shape of IPS by examining observed gaze patterns of pedestrians. A series of experiments was conducted in a controlled laboratory environment, in which up to 4 participants walked on a platform at their natural speed. Their gaze patterns were recorded by a head-mounted eye tracker and walking paths by laser-range-scanner-based tracking systems at the frequency of 25Hz. Our findings are threefold: pedestrians pay much more attention to ground surfaces to detect immediate potential environmental hazards than fixating on obstacles; most of their fixations fall within a cone-shape area rather than a semicircle; and the attention paid to approaching pedestrians is not as high as that paid to static obstacles. These results led to an insight that the structure of IPS should be re-examined by researching directional characteristics of pedestrians' vision. Key words. Pedestrian vision, collision avoidance, Information Process Space BackgroundFollowing the trend of sustainable development, pedestrian-oriented planning has started attracting much attention in several discourses; transport studies, urban planning and architecture. A recent trend in this subject is to develop predictive models of pedestrian movement. The subject of how people move around encompasses a huge variety of activities, ranging from migration and commuting movement between cities to how they manoeuvre themselves in crowds. This paper focuses on pedestrian movement at the smallest scale, where the individual pedestrians' movement patterns, more specifically how they avoid bumping into each other or how they avoid obstacles, are analyzed. At this "microscopic" level there has been a recent surge of studies that utilize disaggregated models that represent pedestrians' dynamics as a series of interactions between individual pedestrians' behaviour [1][2][3][4][5][6][7][8][9][10].

Urban passenger transport significantly contributes to global greenhouse gas emissions, especially in developing countries owing to the rapid motorization, thus making it an important target for carbon reduction. This article established a method to estimate and analyze carbon emission from urban passenger transport including cars, rail transit, taxis and buses. The scope of research was defined based on car registration area, transport types and modes, the stages and energy record of rail transit. The data availability and gathering were fully illustrated. A city level emission model for the aforementioned four modes of passenger transport was formulated, and parameters including emission factor of electricity and fuel efficiency were tailored according to local situations such as energy structure and field survey. The results reveal that the emission from Beijing's urban passenger transport in 2012 stood at 15 million tonnes of CO2, of which 75.5% was from cars, whereas car trips sharing constitutes only 42.5% of the total residential trips. Bus travel, yielding 28.6 g CO2, is the most efficient mode of transport under the current situations in terms of per passenger kilometer (PKM) emission, whereas car or taxi trips emit more than 5 times that of bus trips. Although a decrease trend appears, Beijing still has potential for further carbon reduction in passenger transport field in contrast to other cities in developed countries. Development of rail transit and further limitation on cars could assist in reducing 4.39 million tonnes CO2 emission.

1 149 Manuscript 1 7683 1 Corresponding author. To reduce passenger interactions improvement on platform designs is needed. Present procedures use the Level of Service (LOS) based only on average values and therefore is not possible to identify which piece of space reached the highest interaction. This paper explores a new method to classify the interaction between passengers boarding and alighting through laboratory experiments under controlled conditions. The experiments were based on observation at two stations operated by London Underground Limited, which included platform edge doors and a semi-circular space defined as platform conflict area. Results were expressed according to the types of queues, formation of lanes, density by layer, and distance between passengers. The Level of Interaction (LOI) was a more precise indicator compared to the LOS. The density by layer followed a logarithmic distribution, reaching almost four times the overall density. Further research needs to be conducted to measure the passenger space on the platform.