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Urban public bicycle dispatching optimization method.

ABSTRACT: Unreasonable public bicycle dispatching area division seriously affects the operational efficiency of the public bicycle system. To solve this problem, this paper innovatively proposes an improved community discovery algorithm based on multi-objective optimization ( CDoMO ). The data set is preprocessed into a lease/return relationship, thereby it calculated a similarity matrix, and the community discovery algorithm Fast Unfolding is executed on the matrix to obtain a scheduling scheme. For the results obtained by the algorithm, the workload indicators (scheduled distance, number of sites, and number of scheduling bicycles) should be adjusted to maximize the overall benefits, and the entire process is continuously optimized by a multi-objective optimization algorithm NSGA2 . The experimental results show that compared with the clustering algorithm and the community discovery algorithm, the method can shorten the estimated scheduling distance by 20%-50%, and can effectively balance the scheduling workload of each area. The method can provide theoretical support for the public bicycle dispatching department, and improve the efficiency of public bicycle dispatching system.

SUBMITTER: Lin F

PROVIDER: S-EPMC7924461 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPublic bicycle share programs in many cities are used by a small segment of the population. To better understand the market for public bicycle share, this study examined the socio-demographic and transportation characteristics of current, potential, and unlikely users of a public bicycle share program and identified specific motivators and deterrents to public bicycle share use.MethodsWe used cross-sectional data from a 2017 Vancouver public bicycle share (Mobi by Shaw Go) member survey (n = 1272) and a 2017 population-based survey of Vancouver residents (n = 792). We categorized non-users from the population survey as either potential or unlikely users based on their stated interest in using public bicycle share within the next year. We used descriptive statistics to compare the demographic and transportation characteristics of current users to non-users, and multiple logistic regression to compare the profiles of potential and unlikely users.ResultsPublic bicycle share users in Vancouver tended to be male, employed, and have higher educations and incomes as compared to non-users, and were more likely to use active modes of transportation. The vast majority of non-users (74%) thought the public bicycle share program was a good idea for Vancouver. Of the non-users, 23% were identified as potential users. Potential users tended to be younger, have lower incomes, and were more likely to use public transit for their main mode of transportation, as compared to current and unlikely users. The most common motivators among potential users related to health benefits, not owning a bicycle, and stations near their home or destination. The deterrents among unlikely users were a preference for riding their own bicycle, perceived inconvenience compared to other modes, bad weather, and traffic. Cost was a deterrent to one-fifth of unlikely users, notable given they tended to have lower incomes than current users.ConclusionFindings can help inform targeted marketing and outreach to increase public bicycle share uptake in the population.

Project description:Research on the emerging COVID-19 pandemic is demonstrating that wastewater infrastructures can be used as public health observatories of virus circulation in human communities. Important efforts are being organized worldwide to implement sewage-based surveillance of SARS-CoV-2 that can be used for preventive or early warning purposes, informing preparedness and response measures. However, its successful implementation requires important and iterative methodological improvements, as well as the establishment of standardized methods. The aim of this study was to develop a continuous monitoring protocol for SARS-CoV-2 in wastewater, that could be used to model virus circulation within the communities, complementing the current clinical surveillance. Specific objectives included (1) optimization and validation of a method for virus quantification; (2) monitoring the time-evolution of SARS-CoV-2 in wastewater from two wastewater treatment plants (WWTPs) in the city of Porto, Portugal. Untreated wastewater samples were collected weekly from the two WWTPs between May 2020 and March 2021, encompassing two COVID-19 incidence peaks in the region (mid-November 2020 and mid-January 2021). In the first stage of this study, we compared, optimized and selected a sampling and analysis protocol that included virus concentration through centrifugation, RNA extraction from both liquid and solid fractions and quantification by reverse transcription quantitative PCR (RT-qPCR). In the second stage, we used the selected methodology to track SARS-CoV-2 in the collected wastewater over time. SARS-CoV-2 RNA was detected in 39 and 37 out of 48 liquid and solid fraction samples of untreated wastewater, respectively. The copy numbers varied throughout the study between 0 and 0.15 copies/ng RNA and a good fit was observed between the SARS-CoV-2 RNA concentration in the untreated wastewater and the COVID-19 temporal trends in the study region. We also analyzed eight samples from the treated effluent and found no SARS-CoV-2 RNA detection after tertiary treatment and UV disinfection. In agreement with the recent literature, the results from this study support the use of wastewater-based surveillance to complement clinical testing and evaluate temporal and spatial trends of the current pandemic.

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Urban public bicycle dispatching optimization method.

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