Dataset Information

Expanding Bicycle-Sharing Systems: Lessons Learnt from an Analysis of Usage.

ABSTRACT: Bike-sharing programs, with initiatives to increase bike use and improve accessibility of urban transit, have received increasing attention in growing number of cities across the world. The latest generation of bike-sharing systems has employed smart card technology that produces station-based data or trip-level data. This facilitates the studies of the practical use of these systems. However, few studies have paid attention to the changes in users and system usage over the years, as well as the impact of system expansion on its usage. Monitoring the changes of system usage over years enables the identification of system performance and can serve as an input for improving the location-allocation of stations. The objective of this study is to explore the impact of the expansion of a bicycle-sharing system on the usage of the system. This was conducted for a bicycle-sharing system in Zhongshan (China), using operational usage data of different years following system expansion. To this end, we performed statistical and spatial analyses to examine the changes in both users and system usage between before and after the system expansion. The findings show that there is a big variation in users and aggregate usage following the system expansion. However, the trend in spatial distribution of demand shows no substantial difference over the years, i.e. the same high-demand and low-demand areas appear. There are decreases in demand for some old stations over the years, which can be attributed to either the negative performance of the system or the competition of nearby new stations. Expanding the system not only extends the original users' ability to reach new areas but also attracts new users to use bike-sharing systems. In the conclusions, we present and discuss the findings, and offer recommendations for the further expansion of system.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC5158202 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Expanding Bicycle-Sharing Systems: Lessons Learnt from an Analysis of Usage.

Zhang Ying Y Thomas Tom T Brussel M J G MJ van Maarseveen M F A M MF

PloS one 20161215 12

Bike-sharing programs, with initiatives to increase bike use and improve accessibility of urban transit, have received increasing attention in growing number of cities across the world. The latest generation of bike-sharing systems has employed smart card technology that produces station-based data or trip-level data. This facilitates the studies of the practical use of these systems. However, few studies have paid attention to the changes in users and system usage over the years, as well as the ...[more]

PMID: 27977794

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Project description:ObjectiveTo model the impacts of the bicycle sharing system in London on the health of its users.DesignHealth impact modelling and evaluation, using a stochastic simulation model.SettingCentral and inner London, England.Data sourcesTotal population operational registration and usage data for the London cycle hire scheme (collected April 2011-March 2012), surveys of cycle hire users (collected 2011), and London data on travel, physical activity, road traffic collisions, and particulate air pollution (PM2.5, (collected 2005-12).Participants578,607 users of the London cycle hire scheme, aged 14 years and over, with an estimated 78% of travel time accounted for by users younger than 45 years.Main outcome measuresChange in lifelong disability adjusted life years (DALYs) based on one year impacts on incidence of disease and injury, modelled through medium term changes in physical activity, road traffic injuries, and exposure to air pollution.ResultsOver the year examined the users made 7.4 million cycle hire trips (estimated 71% of cycling time by men). These trips would mostly otherwise have been made on foot (31%) or by public transport (47%). To date there has been a trend towards fewer fatalities and injuries than expected on cycle hire bicycles. Using these observed injury rates, the population benefits from the cycle hire scheme substantially outweighed harms (net change -72 DALYs (95% credible interval -110 to -43) among men using cycle hire per accounting year; -15 (-42 to -6) among women; note that negative DALYs represent a health benefit). When we modelled cycle hire injury rates as being equal to background rates for all cycling in central London, these benefits were smaller and there was no evidence of a benefit among women (change -49 DALYs (-88 to -17) among men; -1 DALY (-27 to 12) among women). This sex difference largely reflected higher road collision fatality rates for female cyclists. At older ages the modelled benefits of cycling were much larger than the harms. Using background injury rates in the youngest age group (15 to 29 years), the medium term benefits and harms were both comparatively small and potentially negative.ConclusionLondon's bicycle sharing system has positive health impacts overall, but these benefits are clearer for men than for women and for older users than for younger users. The potential benefits of cycling may not currently apply to all groups in all settings.

Project description:Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens.

Dataset Information

Expanding Bicycle-Sharing Systems: Lessons Learnt from an Analysis of Usage.

Publications

Expanding Bicycle-Sharing Systems: Lessons Learnt from an Analysis of Usage.

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