scholarly journals Long-term urban traffic monitoring based on wireless multi-sensor network

2020 ◽  
Vol 10 (1) ◽  
pp. 197-208
Author(s):  
Wiktoria Loga-Księska ◽  
Justyna Sordyl ◽  
Artur Ryguła
Keyword(s):  
Sensor Network ◽  
Urban Areas ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Transport Systems ◽  
City Centre ◽  
The City

AbstractIncreasing the number of vehicles on the road network and the growing popularity of sustainable development of urban areas have resulted in the need for implementing efficient and cost-effective traffic measurement methods. From the perspective of traffic management, up-to-date information about vehicle density and access to historical data are the key components of traffic variability analyses. Rapid technological development based on Intelligent Transport Systems (ITS) has popularised the wireless sensor networks (WSN) application. The solution enables continuous monitoring of selected area using multiple wireless and low-cost sensors connected within a network. Those systems are dynamically evolving tools for solving an effective traffic management issues in city centres and urban environments. In the study, authors have performed a traffic variability and its dynamics analysis in a selected area using a multi-sensor network for traffic volume monitoring. The article presents the results of research conducted between years 2015 - 2018 throughout the city of Bielsko-Biala with the support of OnDynamic multimodal system. Within the context of the analyses, basic traffic parameters have been determined and variability trends have been identified on selected road sections. Long-term research indicated the minor variation in a number of vehicle detections and relatively stable traffic volume in the city centre during the analysis period.

scholarly journals MUCCnet: Munich Urban Carbon Column network

2021 ◽  
Vol 14 (2) ◽  
pp. 1111-1126
Author(s):  
Florian Dietrich ◽  
Jia Chen ◽  
Benno Voggenreiter ◽  
Patrick Aigner ◽  
Nico Nachtigall ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Sensor Network ◽  
Urban Areas ◽  
Ghg Emissions ◽  
Surface Fluxes ◽  
Mitigation Strategies ◽  
Concentration Gradients ◽  
Modeling Framework ◽  
The City

Abstract. In order to mitigate climate change, it is crucial to understand urban greenhouse gas (GHG) emissions precisely, as more than two-thirds of the anthropogenic GHG emissions worldwide originate from cities. Nowadays, urban emission estimates are mainly based on bottom-up calculation approaches with high uncertainties. A reliable and long-term top-down measurement approach could reduce the uncertainty of these emission inventories significantly. We present the Munich Urban Carbon Column network (MUCCnet), the world's first urban sensor network, which has been permanently measuring GHGs, based on the principle of differential column measurements (DCMs), since summer 2019. These column measurements and column concentration differences are relatively insensitive to vertical redistribution of tracer masses and surface fluxes upwind of the city, making them a favorable input for an inversion framework and, therefore, a well-suited candidate for the quantification of GHG emissions. However, setting up such a stationary sensor network requires an automated measurement principle. We developed our own fully automated enclosure systems for measuring column-averaged CO2, CH4 and CO concentrations with a solar-tracking Fourier transform spectrometer (EM27/SUN) in a fully automated and long-term manner. This also includes software that starts and stops the measurements autonomously and can be used independently from the enclosure system. Furthermore, we demonstrate the novel applications of such a sensor network by presenting the measurement results of our five sensor systems that are deployed in and around Munich. These results include the seasonal cycle of CO2 since 2015, as well as concentration gradients between sites upwind and downwind of the city. Thanks to the automation, we were also able to continue taking measurements during the COVID-19 lockdown in spring 2020. By correlating the CO2 column concentration gradients to the traffic amount, we demonstrate that our network is capable of detecting variations in urban emissions. The measurements from our unique sensor network will be combined with an inverse modeling framework that we are currently developing in order to monitor urban GHG emissions over years, identify unknown emission sources and assess how effective the current mitigation strategies are. In summary, our achievements in automating column measurements of GHGs will allow researchers all over the world to establish this approach for long-term greenhouse gas monitoring in urban areas.

scholarly journals Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 179
Author(s):  
Said Munir ◽  
Martin Mayfield ◽  
Daniel Coca
Keyword(s):  
Data Fusion ◽  
Spatial Variability ◽  
Urban Areas ◽  
Road Traffic ◽  
Dispersion Model ◽  
Low Cost ◽  
Spatial Modelling ◽  
City Centre ◽  
Small Scale ◽  
The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

scholarly journals Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Travel Behavior ◽  
Traffic Management ◽  
Road Traffic ◽  
Traffic Volume ◽  
The Road ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
On The Road ◽  
The Impact ◽  
The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

scholarly journals Green Spaces in Residential Areas of Dar es Salaam City: Types, Coverage and Uses

2021 ◽  
Vol 14 (3) ◽  
pp. 121
Author(s):  
Nicholaus Mwageni ◽  
Robert Kiunsi
Keyword(s):  
Urban Areas ◽  
Green Space ◽  
Dar Es Salaam ◽  
Economic Benefits ◽  
Natural Vegetation ◽  
Green Spaces ◽  
City Centre ◽  
Building Density ◽  
Dar Es Salaam City ◽  
The City

Green spaces in urban areas including in Dar es Salaam City provide multiple ecological, social and economic benefits. Despite their benefits they are inadequately documented in terms types, coverage and uses. This paper attempts to provide information on types, coverage and uses of green space in Dar es Salaam City. A number of methods including literature review, interpretation of remotely sensed image, interviews, focus group discussions and questionnaires were used to document city greenery. The research findings show that residential greenery is made up of greenery found within and external to plots. The dominant green spaces external to residential plots were natural and semi natural vegetation while within plots were woody plants, plots farms vegetable and ornamental gardens. Distribution of greenery varied among the wards due to differences in building density and distance from the city centre. Natural and semi natural vegetation increased with decrease of building density and increase of distance from the city centre, while the number of plots with trees for shade increased with increase of building density. Only Kawe ward that had greenery above Tanzania space planning standards, the other three wards which are informal settlements had green space deficit. Three quarters of the households use green spaces for shade provision and cooling, two thirds as a source of food products and a quarter for recreation and aesthetic purposes. The study reveals that Dar es Salaam City residents invest predominantly on shade trees in their residential plots compared to other green space types.

scholarly journals Spectral Analysis of Traffic Functions in Urban Areas

2015 ◽  
Vol 27 (6) ◽  
pp. 477-484 ◽  
Author(s):  
Florin Nemtanu ◽  
Ilona Madalina Costea ◽  
Catalin Dumitrescu
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Fourier Analysis ◽  
Traffic Flow ◽  
Traffic Control ◽  
Urban Areas ◽  
Urban Traffic ◽  
The Fourier Transform ◽  
The City ◽  
Different Time Scales

The paper is focused on the Fourier transform application in urban traffic analysis and the use of said transform in traffic decomposition. The traffic function is defined as traffic flow generated by different categories of traffic participants. A Fourier analysis was elaborated in terms of identifying the main traffic function components, called traffic sub-functions. This paper presents the results of the method being applied in a real case situation, that is, an intersection in the city of Bucharest where the effect of a bus line was analysed. The analysis was done using different time scales, while three different traffic functions were defined to demonstrate the theoretical effect of the proposed method of analysis. An extension of the method is proposed to be applied in urban areas, especially in the areas covered by predictive traffic control.

Urban DAS recording of a vibroseismic campaign with a 21km-long dark fibre in Potsdam, Germany

2021 ◽  
Author(s):  
CharLotte Krawczyk ◽  
Christopher Wollin ◽  
Stefan Lüth ◽  
Martin Lipus ◽  
Christian Cunow ◽  
...  
Keyword(s):  
Urban Areas ◽  
District Heating ◽  
Telecommunication Networks ◽  
Seismic Exploration ◽  
City Centre ◽  
Data Set ◽  
Geothermal Potential ◽  
First Results ◽  
Local Geology ◽  
The City

<p>The de-carbonization strategy of the city of Potsdam, Germany, incorporates the utilization of its geothermal potential.  As a first step of developing a deep geothermal project for district heating, an urban seismic exploration campaign of the Stadtwerke Potsdam took place in December 2020 in the city centre of Potsdam.  Since urban measurements are often difficult to setup and a low-footprint alternative is sought for, we supplemented the contractor-performed Vibroseis survey along three profiles by distributed acoustic sensing (DAS).  In close cooperation with the municipal utilities, we interrogated a 21 km-long dark telecommunication fibre whose trajectory followed the seismic lines as close as possible.  This was accompanied by a network of 15 three-component geophones for further control and research.</p><p>In this contribution we present the data set, the approach for geo-referencing the fibre, and first results regarding DAS recording capabilities of vibroseismic signals in an urban environment.  Following the paradigm that the high density of telecommunication networks in urban areas may facilitate the exploration of the often insufficiently known local geology, we strive to further shed light on the possibilities of their employment for urban exploration.  In this respect we aim at tackling the question of the accuracy of fibre localization, recording sensitivity and range of active stimulation.</p>

Length-Based Vehicle Classification Schemes and Length Bin Boundaries

2013 ◽  
Vol 2339 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Herbert Weinblatt ◽  
Erik Minge ◽  
Scott Petersen
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
The United States ◽  
Traffic Monitoring ◽  
Vehicle Classification ◽  
Pavement Performance ◽  
Department Of Transportation ◽  
Classification Schemes ◽  
Weigh In Motion

Vehicle classification data are an important component of traffic-monitoring programs. Although most vehicle classification conducted in the United States is axle based, some applications could be supplemented or replaced by length-based data. The typically higher deployment cost and reliability issues associated with collecting axle-based data as compared with length-based data present a challenge. This paper reports on analyses of alternative length-based vehicle classification schemes and appropriate length bin boundaries. The primary analyses use data from a set of 13 Long-Term Pavement Performance weigh-in-motion sites, all in rural areas; additional analyses are conducted with data from 11 Michigan Department of Transportation weigh-in-motion sites located in rural and small urban areas and one site located in an urbanized area. For most states, the recommended length-based vehicle classification scheme is a four-bin scheme (motorcycles, short, medium, and long) with an optional very long bin recommended for use by states in which significant numbers of longer combination vehicles operate.

Sea level rise in the Venetian lagoon inferred from the 150-year-long tidal record

2020 ◽  
Author(s):  
Sara Rubinetti ◽  
Carla Taricco ◽  
Davide Zanchettin ◽  
Enrico Arnone ◽  
Angelo Rubino
Keyword(s):  
Time Series ◽  
Sea Level Rise ◽  
Sea Level ◽  
City Centre ◽  
Reference Level ◽  
Decadal Scale ◽  
Historical Series ◽  
Venetian Lagoon ◽  
The City

<p>The city of Venice (Northern Italy), together with its lagoon, is a historic, cultural and artistic heritage of inestimable value. One of its peculiarities consists in the recurrent storm surge phenomena, referred to as <em>acqua alta</em>. Sea level rise and local subsidence made their frequency to increase dramatically with respect to the past, causing severe damages to the lagoon and in particular to the city centre, as during the exceptional high tide verified on November 12, 2019.<br>Here we show the analysis of the historical time series of tidal maxima and minima recorded in the Venetian lagoon, covering the period 1872-2018. It is the longest and most complete historical series of the Venetian area and one of the longest records of the entire Mediterranean region. During this period, the relative sea level height has increased of about 30 cm with respect to the reference level, while the average number of <em>acqua alta</em> events – evaluated over a 40-year time interval - has passed from about 4 to 70 per year. These events usually occur during the fall season (from October to December), even if a not negligible number has been also recorded during winter. Therefore, we analyse the October-March average annual time series with advanced spectral analysis methods, like Monte Carlo Singular Spectrum Analysis (MC-SSA), to extract and reconstruct the significant variability modes characterizing the record. They are the increasing long-term trend and components with multidecadal, decadal and interannual periods. The trend results from the superposition on the global eustacy of the local subsidence affecting the Venetian lagoon, which is due to both natural causes and human activities. We also discuss the possible linkage of the other significant spectral components to large scale climatic patterns. In particular, the decadal-scale oscillation is one of the most important variability modes affecting Northern Italian hydrology.<br>Finally, we apply simple statistical methods (autoregressive models and feed-forward neural networks) to forecast the long-term evolution of sea level over the next ten years. In this contribution, we illustrate results from this state of the art two-fold statistical prediction system that provides robust predictions of sea level in the Venetian lagoon for the next decade and discuss them in the light of current longer-term projections of future sea level rise. Finally, we will test the predictive skill of the applied methods using tidal measurements recorded during 2019, to verify if our predictions are able to describe tidal variability characterizing the current year.       </p>

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