Concept: Train station
Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21(st) century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no “winner takes all”) unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities.
Suicides incur in all public transit systems which do not completely impede access to tracks. We conducted two studies to determine if we can reliably identify in stations people at risk of suicide in order to intervene in a timely manner. The first study analysed all CCTV recordings of suicide attempters in Montreal underground stations over 2 years to identify behaviours indicating suicide risk. The second study verified the potential of using those behaviours to discriminate attempters from other passengers in real time.
With an increasing proportion of the population living in cities, mass transportation has been rapidly expanding to facilitate the demand, yet there is a concern that mass transit has the potential to result in excessive exposure to noise, and subsequently noise-induced hearing loss.
We report the first demonstration of a microfluidic platform that captures the full physiological range of mass transport in 3-D tissue culture. The basis of our method used long microfluidic channels connected to both sides of a central microtissue chamber at different downstream positions to control the mass transport distribution within the chamber. Precise control of the Péclet number (Pe), defined as the ratio of convective to diffusive transport, over nearly five orders of magnitude (0.0056 to 160) was achieved. The platform was used to systematically investigate the role of physiological mass transport on vasculogenesis. We demonstrate, for the first time, that vasculogenesis can be independently stimulated by interstitial flow (Pe > 10) or hypoxic conditions (Pe < 0.1), and not by the intermediate state (normal living tissue). This simple platform can be applied to physiological and biological studies of 3D living tissue followed by pathological disease studies, such as cancer research and drug screening.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 2 years ago
We postulate that skepticism about climate change is partially caused by the spatial heterogeneity of climate change, which exposes experiential learners to climate heuristics that differ from the global average. This hypothesis is tested by formalizing an index that measures local changes in climate using station data and comparing this index with survey-based model estimates of county-level opinion about whether global warming is happening. Results indicate that more stations exhibit cooling and warming than predicted by random chance and that spatial variations in these changes can account for spatial variations in the percentage of the population that believes that “global warming is happening.” This effect is diminished in areas that have experienced more record low temperatures than record highs since 2005. Together, these results suggest that skepticism about climate change is driven partially by personal experiences; an accurate heuristic for local changes in climate identifies obstacles to communicating ongoing changes in climate to the public and how these communications might be improved.
The Barcelona subway system comprises eight subway lines, at different depths, with different tunnel dimensions, station designs and train frequencies. An extensive measurement campaign was performed in this subway system in order to characterise the airborne particulate matter (PM) measuring its concentration and investigating its variability, both inside trains and on platforms, in two different seasonal periods (warmer and colder), to better understand the main factors controlling it, and therefore the way to improve air quality. The majority of PM in the underground stations is generated within the subway system, due to abrasion and wear of rail tracks, wheels and braking pads caused during the motion of the trains. Substantial variation in average PM concentrations between underground stations was observed, which might be associated to different ventilation and air conditioning systems, characteristics/design of each station and variations in the train frequency. Average PM2.5 concentrations on the platforms in the subway operating hours ranged from 20 to 51 and from 41 to 91μgm(-3) in the warmer and colder period, respectively, mainly related to the seasonal changes in the subway ventilation systems. The new subway lines with platform screen doors showed PM2.5 concentrations lower than those in the conventional system, which is probably attributable not only to the more advanced ventilation setup, but also to the lower train frequency and the design of the stations. PM concentrations inside the trains were generally lower than those on the platforms, which is attributable to the air conditioning systems operating inside the trains, which are equipped with air filters. This study allows the analysis and quantification of the impact of different ventilation settings on air quality, which provides an improvement on the knowledge for the general understanding and good management of air quality in the subway system.
The equal headway instability-the fact that a configuration with regular time intervals between vehicles tends to be volatile-is a common regulation problem in public transportation systems. An unsatisfactory regulation results in low efficiency and possible collapses of the service. Computational simulations have shown that self-organizing methods can regulate the headway adaptively beyond the theoretical optimum. In this work, we develop a computer simulation for metro systems fed with real data from the Mexico City Metro to test the current regulatory method with a novel self-organizing approach. The current model considers overall system’s data such as minimum and maximum waiting times at stations, while the self-organizing method regulates the headway in a decentralized manner using local information such as the passenger’s inflow and the positions of neighboring trains. The simulation shows that the self-organizing method improves the performance over the current one as it adapts to environmental changes at the timescale they occur. The correlation between the simulation of the current model and empirical observations carried out in the Mexico City Metro provides a base to calculate the expected performance of the self-organizing method in case it is implemented in the real system. We also performed a pilot study at the Balderas station to regulate the alighting and boarding of passengers through guide signs on platforms. The analysis of empirical data shows a delay reduction of the waiting time of trains at stations. Finally, we provide recommendations to improve public transportation systems.
- Proceedings of the National Academy of Sciences of the United States of America
- Published over 3 years ago
Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.
Recognizing the mindless nature of many food decisions, it has been suggested that attempts to increase healthy eating should not focus on convincing people what is ‘right’ but rather aim to adjust the environment such that people are automatically directed toward healthy choices. This study investigated a nudge aiming to promote healthy food choices in train station snack shops.
Urban authorities are continuously drawing up policies to promote cycling among commuters. However, these initiatives are counterproductive for the targeted objectives because they increase opportunities for bicycle theft. This paper explores Inner London as a case study to address place-specific risk factors for bicycle theft at the street-segment level while controlling for seasonal variation. The presence of certain public amenities (e.g., bicycle stands, railway stations, pawnshops) was evaluated against locations of bicycle theft between 2013 and 2016 and risk effects were estimated using negative binomial regression models. Results showed that a greater level of risk stemmed from land-use facilities than from area-based socioeconomic status. The presence of facilities such as train stations, vacant houses, pawnbrokers and payday lenders increased bicycle theft, but no evidence was found that linked police stations with crime levels. The findings have significant implications for urban crime prevention with respect to non-residential land use.