City District Scale Research Articles

Revealing the impact of storm surge on taxi operations: Evidence from taxi and typhoon trajectory data

Storm surges are one of the most destructive natural ocean disasters in the world, which pose a great threat to economic development, public safety, and transport in coastal cities. Investigating the response of urban traffic conditions during storm surges and discovering sensitive urban areas are crucial to urban planning, disaster prevention, and emergency rescue. Therefore, this study investigates the impact of typhoons on taxi operations to target sensitive areas of the city. In this study, the change in taxi service operation in Shenzhen during typhoon storm surges from 2012 to 2014 was explored at the city district scale using a diverse set of data including taxi trajectory data, typhoon track data, and tide level data. Several aspects were included to access this impact, for example the number of daily orders per taxi, the trip length, the degree of closeness to the typhoon-affected area, the coverage area of the taxi service, and the spatial dynamics of the origin and destination matrix. The regular trend and correlation of storm surge elements and taxi parameters were first analyzed. The distribution of the taxi trajectory data was then used to detect variations in the service area, and the reasons for these variations were discussed. The result shows that storm surges with diverse characteristics do affect taxi operations to differing degrees, and that there was a potential correlation between tide level and taxi service area. The sensitive areas for taxis during typhoon storm surges were mainly located at the boundary between the central urban area and other districts of Shenzhen, they were constrained by the dynamic changes in the origins and destinations, and they were likely related to important transportation hubs in the city. This finding provides a reference to support disaster prevention preparedness and decision-making by local authorities.

Applications of graph theory in district heat network analysis at national scale

District heat networks can make a significant contribution to decarbonising domestic heat supply and have been successfully applied in a number of countries. Traditionally, district heat networks have been analysed at the local or city district scale. Performing this analysis at national scale presents new methodological challenges. Efficient techniques are required to work with large datasets and enable rapid iteration of results. As district heating fundamentally involves networks, graph theory is highly relevant for providing well established analysis methods that can scale to very large datasets. Two applications of graph theory are presented for this work. The first presents a method for estimating minimum pipe lengths needed to connect buildings, from which demand densities may be derived. The second presents a graph-based clustering method for connecting distributed supplies and demand regions. These methods have been applied as Swiss national scale.

Incorporating machine learning with building network analysis to predict multi-building energy use

Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

Comparison of building modelling assumptions and methods for urban scale heat demand forecasting

Building energy evaluation tools available today are only able to effectively analyse individual buildings and usually either they require a high amount of input data or they are too imprecise in energy predictions at a city (district) scale because of too many assumptions made. In this paper, two tools based on 3D models are compared to see whether there is an approach that would probably be able to fit both – the amount of data available and the number of assumptions made.A case study in the German town of Essen was chosen in the framework of the research project WeBest, where six building types representing the most important building periods were analysed. The urban simulation tool SimStadt, an in-house development of HFT Stuttgart, based on 3D urban geometry, is used to calculate the heat demand for both single building scale and city district scale. The individual building typology results are compared with the commercial dynamic building simulation software TRNSYS.The influence of the availability and quality of data input regarding the geometrical building parameters on the accuracy of simulation models are analysed. Different Levels of Details (LoDs) of the 3D building models are tested to prove the scalability of SimStadt from single buildings to city districts without loss of quality and accuracy in larger areas with a short computational time.

A multi-scale modeling approach for simulating urbanization in a metropolitan region

Metropolitan regions worldwide are experiencing rapid urban growth and the planners often employ prediction models to forecast the future expansion for improving the land management policies and practices. These regions are a mix of urban, peri-urban and rural areas where each sector has its unique expansion properties. This study examines the differences in urban and peri-urban growth characteristics, and their impact at different stages of prediction modeling, in city district Lahore, Pakistan. The analysis of multi-temporal land use/land cover maps revealed that the associations between major land transitions and the factors governing land changes were unique at city district, urban and peri-urban scales. A multilayer perceptron neural network was employed for modeling urbanization, and it was found that the sub-models developed for urban and peri-urban subsets returned better accuracies than those produced at the city district scale. The prediction maps of 2021 and 2035 were also produced through this approach.

MESCOS—A Multienergy System Cosimulator for City District Energy Systems

This work introduces a multidomain simulation platform that enables a holistic analysis of city district scale energy systems. The objective for the development of the simulation platform is to provide a tool that supports the design of control and energy management algorithms for those systems. The platform allows long-term simulations of a large number of buildings, including internal energy supply or energy conversion systems, in combination with external energy supply systems like the electrical grid. The simulation of those physical systems represents the environment for sophisticated control and energy management algorithms that can be tested on the platform. The concept of this work is to combine commercial-off-the-shelf software packages, here simulators and runtime infrastructure (RTI), to a high performance multidomain cosimulation platform. The high performance of the platform regarding computation time has been achieved by exploiting the parallel computing capabilities of modern simulation servers. Especially the computation time of large numbers of instances of Modelica-based models has been reduced significantly by the development of the parallel execution framework (PEF). The implementation of the PEF, including the interface to the individual models and to the RTI, is described in detail. The partitioning of the simulated system among different simulators does not influence the simulation results, as shown on the basis of a small-scale simulation scenario. The performance regarding the computation time is demonstrated on several example simulation scenarios showing the scalability of the platform.

Low order thermal network models for dynamic simulations of buildings on city district scale

Dynamic building models can be a valuable part of integral energy system models at city district or urban scale, if parameterization and computation times do not exceed a practical limit. Low order thermal network models are able to represent a thermal zone by thermal resistances and capacitances with low computational costs. Yet, the widely used first-order model of ISO 13790 is only validated for a monthly time step. Following a similar approach, the Guideline VDI 6007 proposes a second-order model explicitly for transient simulations. Thus, the aim of this paper is to show how a building model based on this guideline performs in dynamic building performance simulations, compare its results to those of the ISO 13790 model, and evaluate its suitability for city district simulation.To this end, we implemented both building models from Guideline VDI 6007 and ISO 13790 in Modelica. We verified the VDI-model by simulating 12 test cases given in the guideline. To compare both models we defined a test case which evaluates the influence of outdoor air temperature on indoor air conditions. The VDI-model was subsequently used to simulate the buildings of a research campus as a performance test on city district scale. We parameterized the campus using building archetypes depending on four basic values.In all tests, the VDI-model proved to be a suitable solution for city district applications. Even so, defining suitable boundary conditions is of high importance and uncertain parameters like the periodic depth of penetration are subject to further investigations.

EMERGING CITIES ON THE ARABIAN PENINSULA: URBAN SPACE IN THE KNOWLEDGE ECONOMY CONTEXT

Networks of the growing knowledge economy significantly influence spatial development on different scales. This paper proposes a framework for analyzing the impact of global knowledge economy networks on the rapidly developing urban space of emerging cities on the Arabian Peninsula, and vice versa. Two aspects of the described research are innovative: First, a global relational geography-perspective builds the basis for approaching the analysis of urban space development in emerging cities on the Arabian Peninsula. Second, the empirical methodology of the research project is a newly defined method triangulation, setting an example for systematic analysis of local urban development in a global context. The method triangulation combines three different research angles: A knowledge economy firm perspective, an on-site observation perspective and a planner perspective. The method triangulation defines the procedure for the research application in selected case study cities on the Arabian Peninsula. Initial results from applying the research methodology in the city of Dubai give a first indication, that emerging cities on the Arabian Peninsula play a significant role in the global and regional knowledge economy networks. Locally developed urban spaces reflect and influence the significance of cities in the global knowledge economy context. Especially the global visibility of urban spaces on a city district scale, which specifically address the needs of knowledge economy players, contributes significantly to the attractiveness of emerging cities on the Arabian Peninsula.