Where to put bike counters? Stratifying bicycling patterns in the city using crowdsourced data

Context. For monitoring, analysing and forecasting the impact of weather and climate change on society, we see an increasing need for high-quality, high-resolution gridded weather and climate services. The official networks of the National Meteorological and Hydrological Services (NMHS) sample part of the land-use excluding, for example, urban areas, which motivated us to include third-party data which does sample urban areas.  We have provided the results in real-time as well as historical gridded data services.Approach. By including a statistical, simplified model of the observation error in our gridding approach (i.e. multi-fidelity regression Kriging), we did not include any advanced quality control (QC) of the crowd-sourced data. This might sound surprising in the context of weather and climate data,but is consistent with approaches in other scientific disciplines (e.g. marine engineering, aerospace engineering).Results. In this study, we investigate the quantitative effect of state-of-the-art quality control on the accuracy of gridded services, by analysing hourly temperature observations in The Netherlands for the year 2023. Our results indicate that quality control of crowd-sourced weather data can potentially increase the accuracy of straightforward – and commonly used – nearest-neighbour approximation, but generally deteriorates the accuracy of more advanced gridded services. This finding indicates that using strict QC procedures to turn crowd-sourced data into a dataset with similar fidelity as the NMHS-sourced data is not the way to go. Therefore, we do indeed continue to blend first-party data, crowd-sourced data and land-use data without applying any advanced quality control to the crowd-sourced data.Ecosystem. We emphasize that crowd-sourced weather data only improves services when it is blended with a high-quality network of NMHS data and possibly with land-use data. In other words, the availability of crowd-sourced data does not remove the need for high-quality NMHS observation networks. Therefore, we do not intend to present crowd-sourced data as a stand-alone product; rather, it lives in an integrated ecosystem.

Leveraging the spatial-temporal resolution of crowdsourced cycling data to improve the estimation of hourly bicycle volume

This article summarizes the major results from European projects drawing final conclusions and recommendations from the technoeconomic evaluation of three telecommunications market studies. It presents an analysis addressing a wide range of crucial telecommunications questions debated today. The business cases are: seamless mobile IP service provision, 3G mobile virtual network operators (MVNO), and fixed networks for broadband IP services. Extensive market analysis has been carried out for modeling demand, penetrations, and tariff structures for new mobile services and broadband services as an input to the business cases. Results show that over a 10-year study period, UMTS business is profitable in most cases for established operators with reasonable market share. The 3G MVNO business case is profitable under typical conditions when MVNO yields about 10 percent penetration, depending on the country type. Provision of fixed broadband services with a fiber to the curb solution is viable in dense urban and urban areas, but not in suburban environments, while fiber to the home is viable only in dense urban areas, despite the selection of protocol (ATM vs. Ethernet). Furthermore, broadband wireless access (BWA) systems, in dense urban and urban areas, show quite promising economic results

Mapping ridership using crowdsourced cycling data

Using crowdsourced data to monitor change in spatial patterns of bicycle ridership

This article presents a comprehensive techno-economic evaluation of two upgrade strategy cases for broadband IP services for residential and business customers, and illustrates their respective merits and pitfalls, allowing the definition of a reasonable investment policy. The work should enable establishment of guidelines for broadband infrastructure upgrade strategies from the incumbent operator's view. Following the definition of appropriate service sets, and taking into account demand scenarios established within the project, this work has been focused on developing a techno-economic model, based on the TONIC tool. Tariff structures have been applied to compute the key economic indicators, net present value, internal rate of return, and payback period. This investment analysis was carried using the tool, which was developed by IST-TONIC. The results show that the choice of technology (Ethernet or ATM) has almost no effect on the cost level and profitability of the cases. For the suburban area, a fiber to the cabinet solution is too expensive due to heavy infrastructure investments; for dense urban and urban areas the FTTC solution is worth the investments. The FTTH/office deployment scenario is only profitable in dense urban areas (>5000 potential customers/km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and already highly risky in the urban area.

An evaluation of crowdsourced information for assessing the visitation and perceived importance of protected areas

The Federal Highway Administration (FHWA) requires each state in the United States to report the International Roughness Indices (IRI) of their road network in the annual Highway Performance Monitoring System (HPMS). The reported IRI for road segments are compared to the national standards developed by the FHWA based on national data. Deviations from the national standards are used to identify road segments that need to be included in repair or maintenance programs. The FHWA IRI thresholds for all road classes range between 170 in/mi and 96 in/mi for “acceptable” pavements, and 95 in/mi or less for “good” pavements. The use of the IRI for identifying sections of highways for repair and rehabilitation has been under review in several states. There is the concern that the national values of the IRI often do not reflect the ride smoothness perceived by citizens in the specific jurisdictions. This research obtained the ride quality ratings of residents of a dense urban area (Washington, DC) and correlated those with the IRI values for selected road segments. This research presents the IRI thresholds developed for the urban area based on the regression models obtained and the standard IRI thresholds for newly constructed pavement.

Random parameter models for estimating statewide daily bicycle counts using crowdsourced data

The Town Energy Balance (TEB) scheme computes the surface energy balance for urban areas. It is intended to be coupled with atmospheric models for numerical weather prediction, air quality forecasts or research applications. Up to now, it has been evaluated for dry and hot seasons over light industrial (Vancouver) or dense urban (Mexico City, Marseille) areas. In this study, the evaluation of TEB is extended to two other seasons, fall and winter, using measurements conducted over a dense urban area of Toulouse (France) instrumented from February 2004 to March 2005. Most of the model outputs were measured (individual components of the net radiation, sensible heat flux) as well as state variables of the model (surface temperatures of roofs, roads, walls). Great care has been taken in the design of the surface temperature measurement strategy in order to provide comparable observations to modelled estimates. Focusing on the fall and winter season, this study also proposes an evaluation of the parameterization of anthropogenic heat sources against an inventory of energy consumption.

In summertime and during heat events the urban heat island can negatively impact human health in urban areas. In the context of climate change, climate adaptation receives more attention in urban planning. Microscale urban climate modelling can identify risk areas and evaluate adaptation strategies. Concurrently, evaluating the model results with observational data is essential. So far, model evaluation is mostly limited to short-term field campaigns or a small number of stations. This study uses novel crowdsourcing data from Netatmo citizen weather stations (CWS) to evaluate the urban microscale model PALM for a hot day (Tmax ≥ 30°C) in Bochum in western Germany with anticyclonic atmospheric conditions. Urban-rural air temperature differences are represented by the model. A quality control procedure is applied to the crowdsourced data prior to evaluation. The comparison between the model and the crowdsourced air temperature data reveals a good model performance with a high coefficient of determination (R2) of 0.86 to 0.88 and a root mean squared error (RMSE) around 2 K. Model accuracy shows a temporal pattern and night-time air temperatures during the night are underestimated by the model, likely due to unresolved cloud cover. The crowdsourced air temperature data proved valuable for model evaluation due to the high number of stations within urban areas. Nevertheless, weaknesses related to data quality such as radiation errors must be considered during model evaluation and only the information derived from multiple stations is suitable for model evaluation. The procedure presented here can easily be transferred to planning processes as the model and the crowdsourced air temperature data are freely available. This can contribute to making informed decisions for climate adaptation in urban areas.

Smart card data derived from automatic fare collection (AFC) systems of public transit enable us to study resident movement from a macro perspective. The rhythms of traffic generated by different land uses differ, reflecting differences in human activity patterns. Thus, an understanding of daily ridership and mobility patterns requires an understanding of the relationship between daily ridership patterns and characteristics of stations and their direct environment. Unfortunately, few studies have investigated this relationship. This study aims to propose a framework of identifying urban mobility patterns and urban dynamics from a spatiotemporal perspective and pointing out the linkages between mobility and land cover/land use (LCLU). Relying on 1 month’s transactions data from the AFC system of Nanjing metro, the 110 metro stations are classified into 7 clusters named as employment-oriented stations, residential-oriented stations, spatial mismatched stations, etc., each characterized by a distinct ridership pattern (combining boarding and alighting). A comparison of the peak hourly ridership of the seven clusters is conducted to verify whether the clustering results are reasonable or not. Finally, a multinomial logit model is used to estimate the relationship between characteristics of the local environment and cluster membership. Results show that the classification based on ridership patterns leads to meaningful interpretable clusters and that significant associations exist between local LCLU characteristics, distance to the city center and cluster membership. The analytical framework and findings may be beneficial for improving service efficiency of public transportation and urban planning.

Riding through the pandemic: Using Strava data to monitor the impacts of COVID-19 on spatial patterns of bicycling

Mapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing

A system-of-systems (SoS) approach for wavelength-division multiplexing (WDM) and time-division multiplexing (TDM) fiber-to-the-home (FTTH) telecommunication networks is presented. Cost evolution curves for individual systems as well for whole FTTH WDM and TDM networks are presented. The analysis can be exploited for a fast and accurate analysis of FTTH deployment costs in dense urban, urban, and suburban areas from the technoeconomic point of view, which is of paramount importance for telecom operators, equipment vendors, regulators, and policy makers. The impact of delaying the deployments or adopting different rollout strategies is also investigated and presented. The SoS emergent behavior is further revealed using exploratory modeling. The results reveal that in all cases, the WDM solution is more expensive than TDM. The total cost for suburban areas is almost six times higher than in dense urban areas and four times than urban areas.