Investigating the impact of spatial-temporal grid size on the microscopic forecasting of the inflow and outflow gap in a free-floating bike-sharing system

The COVID-19 pandemic had a significant effect in urban mobility, while essential changes are being observed in travelers’ behavior. Travelers in many cases shifted to other transport modes, especially walking and cycling, for minimizing the risk of infection. This study attempts to investigate the impact that COVID-19 had on travelers’ perceptions towards bike-sharing systems and whether the pandemic could result in a greater or lesser share of trips that are being conducted through shared bikes. For that reason, a questionnaire survey was carried out in the city of Thessaloniki, Greece, and the responses of 223 people were analyzed statistically. The results of the analysis show that COVID-19 will not affect significantly the number of people using bike-sharing for their trips. However, for a proportion of people, bike-sharing is now more attractive. Moreover, the results indicate that bike-sharing is now more likely to become a more preferable mobility option for people who were previously commuting with private cars as passengers (not as drivers) and people who were already registered users in a bike-sharing system. The results also provide evidence about the importance of safety towards COVID-19 for engaging more users in bike-sharing, in order to provide them with a safe mobility option and contribute to the city’s resilience and sustainability.

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Impact of the Grid Resolution and Deterministic Interpolation of Precipitation on Rainfall-Runoff Modeling in a Sparsely Gauged Mountainous Catchment

Water ◽

10.3390/w13020230 ◽

2021 ◽

Vol 13 (2) ◽

pp. 230

Author(s):

Paweł Gilewski

Keyword(s):

Hydrological Cycle ◽

Interpolation Method ◽

Grid Size ◽

Rainfall Runoff ◽

Degree Polynomial ◽

Mountainous Catchment ◽

Runoff Modeling ◽

Simulation Results ◽

The Impact ◽

Inverse Distance

Precipitation is a key variable in the hydrological cycle and essential input data in rainfall-runoff modeling. Rain gauge data are considered as one of the best data sources of precipitation but before further use, the data must be spatially interpolated. The process of interpolation is particularly challenging over mountainous areas due to complex orography and a usually sparse network of rain gauges. This paper investigates two deterministic interpolation methods (inverse distance weighting (IDW), and first-degree polynomial) and their impact on the outputs of semi-distributed rainfall-runoff modeling in a mountainous catchment. The performed analysis considers the aspect of interpolation grid size, which is often neglected in other than fully-distributed modeling. The impact of the inverse distance power (IDP) value in the IDW interpolation was also analyzed. It has been found that the best simulation results were obtained using a grid size smaller or equal to 750 m and the first-degree polynomial as an interpolation method. The results indicate that the IDP value in the IDW method has more impact on the simulation results than the grid size. Evaluation of the results was done using the Kling-Gupta efficiency (KGE), which is considered to be an alternative to the Nash-Sutcliffe efficiency (NSE). It was found that KGE generally tends to provide higher and less varied values than NSE which makes it less useful for the evaluation of the results.

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