scholarly journals Modelling of intra-urban variability of prevailing ambient noise at different temporal resolution

Noise Mapping ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 20-44 ◽  
Author(s):  
Md Saniul Alam ◽  
Lucy Corcoran ◽  
Eoin A. King ◽  
Aonghus McNabola ◽  
Francesco Pilla
Keyword(s):  
Land Use ◽  
Ambient Noise ◽  
Cross Validation ◽  
Explanatory Power ◽  
Model Development ◽  
Temporal Aspects ◽  
Traffic Volumes ◽  
Resolution Model ◽  
Noise Data ◽  
The Impact

AbstractThe impact of temporal aspects of noise data on model development and intra-urban variability on environmental noise levels are often ignored in the development of models used to predict its spatiotemporal variation within a city. Using a Land Use Regression approach, this study develops a framework which uses routine noise monitors to model the prevailing ambient noise, and to develop a noise variability map showing the variation within a city caused by land-use setting. The impact of data resolution on model development and the impact of meteorological variables on the noise level which are often ignored were also assessed. Six models were developed based on monthly, daily and hourly resolutions of both the noise and predictor data. Cross validation highlighted that only the hourly resolution model having 59%explanatory power of the observed data (adjusted R2) and a potential of explaining at least 0.47% variation of any independent dataset (cross validation R2), was a suitable candidate among all the developed models for explaining intraurban variability of noise.In the hourly model, regions with roads of high traffic volumes, with higher concentrations of heavy goods vehicles, and being close to activity centreswere found to have more impact on the prevailing ambient noise. Road lengthswere found to be the most influential predictors and identified as having an impact on the ambient noise monitors.

Collisions on local roads: model development and policy level scenario analysis

2020 ◽  
Vol 47 (1) ◽  
pp. 77-87
Author(s):  
Ali Farhan ◽  
Lina Kattan ◽  
Richard Tay
Keyword(s):  
Land Use ◽  
Scenario Analysis ◽  
Transportation Planning ◽  
Prediction Models ◽  
Model Development ◽  
Fuel Price ◽  
Development Zone ◽  
Policy Level ◽  
Local Roads ◽  
The Impact

The problem of collisions on local roads has received little specific attention despite the considerable number of such collisions that occur each year. First part of this study identifies the factors that influence local road collision frequency at traffic analysis zone (TAZ) level with a particular focus on the planning and policy related variables. The City of Calgary is used as a case study, where we focus on the impacts of land use, demographic characteristics, and travel characteristics. We also investigate the effects of some key transportation planning parameters for which there have been very limited studies, including the number of personal and commercial trips and the employment numbers in various categories. This study examines the impact of the number of trips made by automobile versus more sustainable transport modes like transit, walking, and biking for personal travel. It also examines the impact of commercial truck movement on the number of collisions on local roads in a TAZ. The impact of transit-oriented development zone initiatives is explored, as is the relationship between the predominant land use type (e.g., residential, commercial, industrial) and the number of collisions on local roads. In the second part, collision prediction models were linked with regional transportation model (RTM), which is calibrated and modeled in EMME. Since the choice of transportation mode is explicitly modeled through utility functions in the RTM, the proposed approach will allow us to do scenario analysis for planning and policy level issues proactively such as impact on local collisions due to change in fuel price, parking cost, transit headway, and transit fare. Results showed that property damage only (PDO) and fatal and injury (FI) collisions decreased by 13% and 6%, respectively, when fuel price was doubled. It was also observed that PDO and FI collisions decreased by 8% and 5%, respectively, when parking cost was doubled. PDO and FI collisions decreased by 7% and 4%, respectively, when transit headway was reduced to half. When transit fare was reduced to half, PDO and FI collisions decreased by 5% and 2%, respectively. PDO and FI collisions decreased by 10% and 5%, respectively, when transit fare was set to zero. These scenario analyses demonstrate how the impact of transportation planning or policy level issues on the collision count on local roads can be incorporated in our proposed model.

scholarly journals Development and Validation of a Sub-National, Satellite-Based Land-Use Regression Model for Annual Nitrogen Dioxide Concentrations in North-Western China

2021 ◽  
Vol 18 (24) ◽  
pp. 12887
Author(s):  
Igor Popovic ◽  
Ricardo J. Soares Magalhães ◽  
Shukun Yang ◽  
Yurong Yang ◽  
Erjia Ge ◽  
...  
Keyword(s):  
Land Use ◽  
Nitrogen Dioxide ◽  
Cross Validation ◽  
Model Development ◽  
Western China ◽  
Land Use Regression ◽  
Final Model ◽  
Continental Scale ◽  
Surrounding Areas ◽  
North Western

Existing national- or continental-scale models of nitrogen dioxide (NO2) exposure have a limited capacity to capture subnational spatial variability in sparsely-populated parts of the world where NO2 sources may vary. To test and validate our approach, we developed a land-use regression (LUR) model for NO2 for Ningxia Hui Autonomous Region (NHAR) and surrounding areas, a small rural province in north-western China. Using hourly NO2 measurements from 105 continuous monitoring sites in 2019, a supervised, forward addition, linear regression approach was adopted to develop the model, assessing 270 potential predictor variables, including tropospheric NO2, optically measured by the Aura satellite. The final model was cross-validated (5-fold cross validation), and its historical performance (back to 2014) assessed using 41 independent monitoring sites not used for model development. The final model captured 63% of annual NO2 in NHAR (RMSE: 6 ppb (21% of the mean of all monitoring sites)) and contiguous parts of Inner Mongolia, Gansu, and Shaanxi Provinces. Cross-validation and independent evaluation against historical data yielded adjusted R2 values that were 1% and 10% lower than the model development values, respectively, with comparable RMSE. The findings suggest that a parsimonious, satellite-based LUR model is robust and can be used to capture spatial contrasts in annual NO2 in the relatively sparsely-populated areas in NHAR and neighbouring provinces.

scholarly journals Kriging-Based Land-Use Regression Models That Use Machine Learning Algorithms to Estimate the Monthly BTEX Concentration

2020 ◽  
Vol 17 (19) ◽  
pp. 6956 ◽  
Author(s):  
Chin-Yu Hsu ◽  
Yu-Ting Zeng ◽  
Yu-Cheng Chen ◽  
Mu-Jean Chen ◽  
Shih-Chun Candice Lung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Environmental Protection Agency ◽  
Learning Algorithms ◽  
Explanatory Power ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Land Use Regression ◽  
Extreme Gradient Boosting

This paper uses machine learning to refine a Land-use Regression (LUR) model and to estimate the spatial–temporal variation in BTEX concentrations in Kaohsiung, Taiwan. Using the Taiwanese Environmental Protection Agency (EPA) data of BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations from 2015 to 2018, which includes local emission sources as a result of Asian cultural characteristics, a new LUR model is developed. The 2019 data was then used as external data to verify the reliability of the model. We used hybrid Kriging-land-use regression (Hybrid Kriging-LUR) models, geographically weighted regression (GWR), and two machine learning algorithms—random forest (RF) and extreme gradient boosting (XGBoost)—for model development. Initially, the proposed Hybrid Kriging-LUR models explained each variation in BTEX from 37% to 52%. Using machine learning algorithms (XGBoost) increased the explanatory power of the models for each BTEX, between 61% and 79%. This study compared each combination of the Hybrid Kriging-LUR model and (i) GWR, (ii) RF, and (iii) XGBoost algorithm to estimate the spatiotemporal variation in BTEX concentration. It is shown that a combination of Hybrid Kriging-LUR and the XGBoost algorithm gives better performance than other integrated methods.

Impact of COVID-19 on Traffic Signal Systems: Survey of Agency Interventions and Observed Changes in Pedestrian Activity

2021 ◽  
pp. 036119812110263
Author(s):  
Patrick A. Singleton ◽  
Mark Taylor ◽  
Christopher Day ◽  
Subhadipto Poddar ◽  
Sirisha Kothuri ◽  
...  
Keyword(s):  
Land Use ◽  
Urban Areas ◽  
Adaptive Systems ◽  
Transportation Systems ◽  
Early Spring ◽  
Signalized Intersections ◽  
Traffic Signal ◽  
Health Crisis ◽  
Traffic Volumes ◽  
The Impact

The COVID-19 pandemic, the most significant public health crisis since the 1918–1919 influenza epidemic, is the first such event to occur since the development of modern transportation systems in the twentieth century. Many states across the U.S. imposed lockdowns in early spring 2020, which reduced demand for trips of various types and affected transportation systems. In urban areas, the shift resulted in a reduction in traffic volumes and an increase in bicycling and walking in certain land use contexts. This paper seeks to understand the changes occurring at signalized intersections as a result of the lockdown and the ongoing pandemic, as well as the actions taken in response to these impacts. The results of a survey of agency reactions to COVID-19 with respect to traffic signal operations and changes in pedestrian activity during the spring 2020 lockdown using two case study examples in Utah are presented. First, the effects of placing intersections on pedestrian recall (with signage) to stop pedestrians from pushing the pedestrian button are examined. Next, the changes in pedestrian activity at Utah signalized intersections between the first 6 months of both 2019 and 2020 are analyzed and the impact of land use characteristics is explored. Survey results reveal the importance of using technologies such as adaptive systems and automated traffic signal performance measures to drive decisions. While pedestrian pushbutton actuations decreased in response to the implementation of pedestrian recalls, many pedestrians continued to use the pushbutton. Pedestrian activity changes were also largely driven by surrounding land uses.

THE IMPACT OF DIFFERENT URBAN LAND USE TYPES ON AIR POLLUTION IN THE MEGACITY OF SÃO PAULO

2020 ◽  
Vol 7 (1) ◽  
pp. 91
Author(s):  
Júlio Barboza Chiquetto ◽  
Maria Elisa Siqueira Silva ◽  
Rita Yuri Ynoue ◽  
Flávia Noronha Dutra Ribieiro ◽  
Débora Souza Alvim ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Urban Land ◽  
Sao Paulo ◽  
Urban Land Use ◽  
São Paulo ◽  
Land Use Types ◽  
The Impact

A poluição do ar é influenciada por fatores naturais e antropogênicos. Quatro pontos de monitoramento (veicular, comercial, residencial e background urbano (BGU))da poluição do ar em São Paulo foram avaliados durante 16 anos, revelando diferenças significativas devidoao uso do solo em todas as escalas temporais. Na escala diurna, as concentrações de poluentes primários são duas vezes mais altas nos pontos veicular e residencial do que no ponto BGU, onde a concentração de ozonio (O3) é 50% mais alta. Na escala sazonal, as concentrações de monóxido de carbono(CO) variaram em 80% devido ao uso do solo, e 55% pela sazonalidade.As variações sazonais ede uso do solo exercem impactos similares nas concentrações de O3 e monóxido de nitrogênio (NO). Para o material particulado grosso (MP10) e o dióxido de nitrogênio(NO2), as variações sazonais são mais intensas do que as por uso do solo. Na série temporal de 16 anos, o ponto BGU apresentou correlações mais fortes e significativas entre a média mensal de ondas longas (ROL) e o O3 (0,48) e o MP10 (0,37), comparadas ao ponto veicular (0,33 e 0,22, respectivamente). Estes resultados confirmam que o uso do solo urbano tem um papel significativo na concentração de poluentes em todas as escalas de análise, embora a sua influência se torne menos pronunciada em escalas maiores, conforme a qualidade do ar transita de um sistema antropogênico para um sistema natural. Isto poderá auxiliar decisões sobre políticas públicas em megacidades envolvendo a modificação do uso do solo.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

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