Urban Network Model Research Articles

A conjugate method for simulating the dynamics of stochastic urban spatial network models

Urban networks are integral components of urban systems, contributing to their functioning and shaping the overall dynamics of urban areas. They are characterized by their complexity, interdependence, and dynamic nature. The construction, analysis and understanding of urban network models is therefore essential to address complex urban challenges, fostering sustainable development, and improving the overall quality of life in systems like cities and regions. In this work, we present and analyze the properties of a stochastic spatial-interaction model of urban structures. In addition, we devise a suitable time-stepping integrator that allows analyzing the evolution of this stochastic system at large times intervals, providing information of the dynamical behavior of the system in its equilibrium state. Numerical simulation studies are carried out to illustrate the practical effectiveness of the proposed approach.

Evaluating the impact of social housing policies: Measuring accessibility changes when individuals move to social housing projects

Addressing housing deficits and inequalities remains a key challenge for cities in promoting more sustainable urban development. In response to these challenges, governments around the world, particularly in the Global South, have made substantial investments in housing policies for middle- and low-income individuals. Nevertheless, while these initiatives increase housing provision, they often face criticism for not adequately considering the location of new residences. This oversight has far-reaching effects on the accessibility to essential facilities, which play a pivotal role in determining spatial advantages and disadvantages, and consequently, in the degree of inclusion of individuals in both the city and society. Addressing this critical role of accessibility, this paper introduces a methodology for assessing the potential impact of housing policies on the lives of their beneficiaries, by quantifying changes in cumulative accessibility levels between individuals' former house locations and the location of the housing projects into which they moved. Accessibility is calculated for three distinct transport modes: walking, cycling, and public transport, using unimodal and multimodal urban network models. A case study was conducted in Natal, northeastern Brazil, on the implementation of the Minha Casa, Minha Vida (My House, My Life, MCMV) housing policy, initiated in 2009 and still active today. The results of the study revealed a significant decrease in accessibility across all transportation modes when individuals moved to the new housing estates. The decline was particularly pronounced among individuals with lower incomes, potentially raising their regular expenses after relocation and, ultimately, leading to spatial isolation and social exclusion. These findings demonstrate the contribution of the methodology to capturing the impacts of housing policies on the everyday accessibility of their beneficiaries, while emphasizing the importance of re-evaluating these policies with a particular focus on fostering the social and urban inclusion of beneficiaries.

Impacts of COVID-19 on urban networks: Evidence from a novel approach of flow measurement based on nighttime light data

The coronavirus disease 2019 (COVID-19) has caused significant changes in urban networks due to epidemic prevention policies (e.g., social distancing strategies) and personal concerns. Previous measurements of urban networks were mainly based on flow data or were simulated from statistical data using models (e.g., Gravity model). However, these measurements are not directly applicable to the mapping of directional urban networks during unexpected events, such as COVID-19. Since nighttime light (NTL) data offer a unique opportunity to track near real-time human activities, the radiation model, traditionally used for routine situations only, was modified to measure directional urban networks using NTL data under three scenarios: the routine scenario (before the Shanghai lockdown), the COVID-19 scenario (during the Shanghai lockdown), and the extreme scenario (without Shanghai's participation). When compared with the Baidu migration index, the modified radiation model achieved an acceptable accuracy of 0.74 under the routine scenario and 0.44 under the COVID-19 scenario. Our mapping of each scenario's urban networks in the Yangtze River Delta Region (YRDR) shows that the Shanghai lockdown reduced the urban interaction index between Shanghai and its surrounding cities. However, it led to an increase in the urban interaction index centered on the periphery cities of YRDR. Our findings suggest that urban interactions within YRDR are resilient, even under extreme scenarios. Considering the long time series and global coverage of NTL data, the proposed NTL-based urban network model can be readily updated and applied to other regions.

Modelling concentration heterogeneities in streets using the street-network model MUNICH

Abstract. Populations in urban areas are exposed to high local concentrations of pollutants, such as nitrogen dioxide and particulate matter, because of unfavourable dispersion conditions and the proximity to traffic. To simulate these concentrations over cities, models like the street-network model MUNICH (Model of Urban Network of Intersecting Canyons and Highways) rely on parameterizations to represent the air flow and the concentrations of pollutants in streets. In the current version, MUNICH v2.0, concentrations are assumed to be homogeneous in each street segment. A new version of MUNICH, where the street volume is discretized, is developed to represent the street gradients and to better estimate peoples' exposure. Three vertical levels are defined in each street segment. A horizontal discretization is also introduced under specific conditions by considering two zones with a parameterization taken from the Operational Street Pollution Model (OSPM). Simulations are performed over two districts of Copenhagen, Denmark, and one district of greater Paris, France. Results show an improvement in the comparison to observations, with higher concentrations at the bottom of the street, closer to traffic, of pollutants emitted by traffic (NOx, black carbon, organic matter). These increases reach up to 60 % for NO2 and 30 % for PM10 in comparison to MUNICH v2.0. The aspect ratio (ratio between building height and street width) influences the extent of the increase of the first-level concentrations compared to the average of the street. The increase is higher for wide streets (low aspect ratio and often higher traffic) by up to 53 % for NOx and 18 % for PM10. Finally, a sensitivity analysis with regard to the influence of the street network highlights the importance of using the model MUNICH with a network rather than with a single street.

Simplified geodata models for integrated urban and public transport planning

Abstract. The current division between urban and transport planning is a significant obstacle to achieving sustainable urban development. To transform cities towards sustainability, both fields must adopt shared or at least compatible models of the urban systems, namely transport, street and public space networks for all users and urban activities. Although several models and tools have emerged in recent years to facilitate this integration, there are still usability gaps that hinder their wider adoption. One of the gaps is a lack of flexibility to operate at different stages of integrated planning. To address this gap, the study aims to develop a set of aligned and flexible multimodal urban network models and tools to support different stages of planning. This paper focuses on the public transport geodata models, which were built by aggregating a General Transit Feed Specification (GTFS) dataset at various spatial and temporal levels. The aggregation levels range from a baseline data model that is useful for detailed planning stages, up to a topological data model that is suitable for macro scale and strategic planning. By using this unified set of models, the dialogue between the two fields at different integrated planning phases can be facilitated, and decision-making can be enhanced.

Combined Effects of Photochemical Processes, Pollutant Sources and Urban Configuration on Photochemical Pollutant Concentrations

Rapid urbanization, dense urban configuration and increasing traffic emissions have caused severe air pollution, resulting in severe threats to public health. Particularly, photochemical pollution is associated with chemical transformation introducing more complexity. The understanding of the combined effects of pollutant sources, urban configuration and chemical transformation is still insufficient because most previous studies focused on non-reactive pollutant dispersions. In this study, we adopt a simplified street network model including complex photochemical reactions, i.e., the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), with the real traffic and street data of a region in Guangzhou to investigate the combined effects of the three factors above on photochemical pollution. Our simulations show that the overall reduction in traffic emissions decreases NOx pollution while increasing O3 concentration. Controlling VOC emission can effectively mitigate O3 pollution. Moreover, irregular building heights and arrangements can lead to certain hot spots of air pollution. High-rise buildings will obstruct ventilation and exacerbate pollution. If higher buildings have lower vehicle use, the deep canyon can offset the effect of lower emissions. In conclusion, urban planners and policy makers should avoid deep canyons and irregular street networks to achieve better pollutant dispersion and pay attention to controlling VOC emissions.

MUNICH v2.0: a street-network model coupled with SSH-aerosol (v1.2) for multi-pollutant modelling

Abstract. A new version of a street-network model, the Model of Urban Network of Intersecting Canyons and Highways version 2.0 (MUNICH v2.0), is presented. The comprehensive aerosol model SSH-aerosol is implemented in MUNICH v2.0 to simulate the street concentrations of multiple pollutants, including secondary aerosols. The implementation uses the application programming interface (API) technology so that the SSH-aerosol version may be easily updated. New parameterisations are also introduced in MUNICH v2.0, including a non-stationary approach to model reactive pollutants, particle deposition and resuspension, and a parameterisation of the wind at roof level. A test case over a Paris suburb is presented for model evaluation and to illustrate the impact of the new functionalities. The implementation of SSH-aerosol leads to an increase of 11 % in PM10 concentration because of secondary aerosol formation. Using the non-stationary approach rather than the stationary one leads to a decrease in NO2 concentration of 16 %. The impact of particle deposition on built surfaces and road resuspension on pollutant concentrations in the street canyons is low.

Parameterizing the aerodynamic effect of trees in street canyons for the street network model MUNICH using the CFD model Code_Saturne

Abstract. Trees provide many ecosystem services in cities such as urban heat island reduction, water runoff limitation, and carbon storage. However, the presence of trees in street canyons reduces the wind velocity in the street and limits pollutant dispersion. Thus, to obtain accurate simulations of pollutant concentrations, the aerodynamic effect of trees should be taken into account in air quality models at the street level. The Model of Urban Network of Intersecting Canyons and Highways (MUNICH) simulates the pollutant concentrations in a street network, considering dispersion and physico-chemical processes. It can be coupled to a regional-scale chemical transport model to simulate air quality over districts or cities. The aerodynamic effect of the tree crown is parameterized here through its impact on the average wind velocity in the street direction and the vertical transfer coefficient associated with the dispersion of a tracer. The parameterization is built using local-scale simulations performed with the computational fluid dynamics (CFDs) code Code_Saturne. The two-dimensional CFD simulations in an infinite street canyon are used to quantify the effect of trees, depending on the tree characteristics (leaf area index, crown volume fraction, and tree height to street height ratio) using a drag porosity approach. The tree crown slows down the flow and produces turbulent kinetic energy in the street, thus impacting the tracer dispersion. This effect increases with the leaf area index and the crown volume fraction of the trees, and the average horizontal velocity in the street is reduced by up to 68 %, while the vertical transfer coefficient by up to 23 % in the simulations performed here. A parameterization of these effects on horizontal and vertical transfers for the street model MUNICH is proposed. Existing parameterizations in MUNICH are modified based on Code_Saturne simulations to account for both building and tree effects on vertical and horizontal transfers. The parameterization is built to obtain similar tree effects (quantified by a relative deviation between the cases without and with trees) between Code_Saturne and MUNICH. The vertical wind profile and mixing length depend on leaf area index, crown radius, and tree height to street height ratio. The interaction between the trees and the street aspect ratio is also considered.

Parametrization of Horizontal and Vertical Transfers for the Street-Network Model MUNICH Using the CFD Model Code_Saturne

Cities are heterogeneous environments, and pollutant concentrations are often higher in streets compared with in the upper roughness sublayer (urban background) and cannot be represented using chemical-transport models that have a spatial resolution on the order of kilometers. Computational Fluid Dynamics (CFD) models coupled to chemistry/aerosol models may be used to compute the pollutant concentrations at high resolution over limited areas of cities; however, they are too expensive to use over a whole city. Hence, simplified street-network models, such as the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), have been developed. These include the main physico-chemical processes that influence pollutant concentrations: emissions, transport, deposition, chemistry and aerosol dynamics. However, the streets are not discretized precisely, and concentrations are assumed to be homogeneous in each street segment. The complex street micro-meteorology is simplified by considering only the vertical transfer between the street and the upper roughness sublayer as well as the horizontal transfer between the streets. This study presents a new parametrization of a horizontal wind profile and vertical/horizontal transfer coefficients. This was developed based on a flow parametrization in a sparse vegetated canopy and adapted to street canyons using local-scale simulations performed with the CFD model Code_Saturne. CFD simulations were performed in a 2D infinite street canyon, and three streets of various aspect ratios ranging from 0.3 to 1.0 were studied with different incoming wind directions. The quantities of interest (wind speed in the street direction and passive tracer concentration) were spatially averaged in the street to compare with MUNICH. The developed parametrization depends on the street characteristics and wind direction. This effectively represents the average wind profile in a street canyon and the vertical transfer between the street and the urban roughness sublayer for a wide range of street aspect ratios while maintaining a simple formulation.

An integrated air quality modeling system coupling regional-urban and street models in Beijing

Recently, ozone (O3) pollution has become a significant problem for Beijing owing to its high traffic volume. To improve the simulation of street-scale nitrogen oxides (NOx) and O3, an integrated air quality modeling system coupling regional urban/street (IAQMS-street) was developed for Beijing. A weather research and forecasting atmospheric model, regional nested air quality prediction system, model of urban network of intersection canyons and highways, and a real-time on-road emission model were coupled by a downscaling method. A two-week simulation with high O3 concentrations (September 25, 2020 to October 8, 2020) was conducted to evaluate the impact of high-resolution traffic emissions and the street-scale simulation by three sensitivity scenarios. The results showed that IAQMS-street improved the model performance for NO, NO2, and O3 compared to regional models. The correlation coefficients (R) and normal mean bias (NMB) increased from ~0.3 to ~0.6 and from −83.3% to 14.8% for NO, respectively. For O3, the R increased by ~10%, and the NMB decreased from 48.7% to −4.4%. In particular, the model performance for O3, NO, and NO2 in high-concentration periods was significantly improved by IAQMS-street. Both high-resolution vehicle emissions and street-scale simulations contributed to this improvement. Our results showed that IAQMS-street provides a more realistic tool for simulating and resolving finer urban photochemical pollution heterogeneity in Beijing.

Simulation of O3 and NOx in São Paulo street urban canyons with VEIN (v0.2.2) and MUNICH (v1.0).

We evaluate the performance of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) in simulating ozone (O3) and nitrogen oxides (NOx) concentrations within the urban street canyons in the São Paulo metropolitan area (SPMA). The MUNICH simulations are performed inside the Pinheiros neighborhood (a residential area) and Paulista Avenue (an economic hub), which are representative urban canyons in the SPMA. Both zones have air quality stations maintained by the São Paulo Environmental Agency (CETESB), providing data (both pollutant concentrations and meteorological) for model evaluation. Meteorological inputs for MUNICH are produced by a simulation with the Weather Research and Forecasting model (WRF) over triple-nested domains with the innermost domain centered over the SPMA at a spatial grid resolution of 1 km. Street coordinates and emission flux rates are retrieved from the Vehicular Emission Inventory (VEIN) emission model, representing the real fleet of the region. The VEIN model has an advantage to spatially represent emissions and present compatibility with MUNICH. Building height is estimated from the World Urban Database and Access Portal Tools (WUDAPT) local climate zone map for SPMA. Background concentrations are obtained from the Ibirapuera air quality station located in an urban park. Finally, volatile organic compound (VOC) speciation is approximated using information from the São Paulo air quality forecast emission file and non-methane hydrocarbon concentration measurements. Results show an overprediction of O3 concentrations in both study cases. NOx concentrations are underpredicted in Pinheiros but are better simulated in Paulista Avenue. Compared to O3, NO2 is better simulated in both urban zones. The O3 prediction is highly dependent on the background concentration, which is the main cause for the model O3 overprediction. The MUNICH simulations satisfy the performance criteria when emissions are calibrated. The results show the great potential of MUNICH to represent the concentrations of pollutants emitted by the fleet close to the streets. The street-scale air pollutant predictions make it possible in the future to evaluate the impacts on public health due to human exposure to primary exhaust gas pollutants emitted by the vehicles.

Simulation of primary and secondary particles in the streets of Paris using MUNICH.

High particle concentrations are observed in the streets. Regional-scale chemistry-transport models are not able to reproduce these high concentrations, because their spatial resolution is not fine enough. Local-scale models are usually employed to simulate the high concentrations in street networks, but they often adopt substantial simplifications to determine background concentrations and use simplified chemistry. This study presents the new version of the local-scale Model of Urban Network of Intersecting Canyons and Highways (MUNICH) that integrates background concentrations simulated by the regional-scale chemistry-transport model Polair3D, and uses the same complex chemistry module as Polair3D, SSH-aerosol, to represent secondary aerosol formation. Gas and aerosol concentrations in Paris streets are simulated with MUNICH, considering a street-network with more than 3800 street segments, between 3 May and 30 June. Comparisons with PM10 and PM2.5 measurements at several locations of Paris show that the high PM10 and PM2.5 concentrations are well represented. Furthermore, the simulated chemical composition of fine particles corresponds well to a yearly measured composition. To understand the influence of the secondary pollutant formation, several sensitivity simulations are conducted. Simulations with and without gas-phase chemistry show that the influence of gas-phase chemistry on the formation of NO2 is large (37% on average over May and across all modelled streets), but the influence on condensables is lower (less than 2% to 3% on average at noon for inorganics and organics), but may reach more than 20% depending on the street. The assumption used to compute gas/particle mass transfer by condensation/evaporation is important for inorganic and organic compounds of particles, as using the thermodynamic equilibrium assumption leads to an overestimation of the organic concentrations by 4.7% on average (up to 31% at noon depending on the streets). Ammonia emissions from traffic lead to an increase in inorganic concentrations by 3% on average, reaching 26% depending on the street segments. Not taking into account gas-phase chemistry and aerosol dynamics in the modelling leads to an underestimation of organic concentrations by about 11% on average over the streets and time, but this underestimation may reach 51% depending on the streets and the time of the day.

Nonstationary modeling of NO<sub>2</sub>, NO and NO<sub><i>x</i></sub> in Paris using the Street-in-Grid model: coupling local and regional scales with a two-way dynamic approach

Abstract. Regional-scale chemistry-transport models have coarse spatial resolution (coarser than 1 km ×1 km) and can thus only simulate background concentrations. They fail to simulate the high concentrations observed close to roads and in streets, where a large part of the urban population lives. Local-scale models may be used to simulate concentrations in streets. They often assume that background concentrations are constant and/or use simplified chemistry. Recently developed, the multi-scale model Street-in-Grid (SinG) estimates gaseous pollutant concentrations simultaneously at local and regional scales by coupling them dynamically. This coupling combines the regional-scale chemistry-transport model Polair3D and a street-network model, the Model of Urban Network of Intersecting Canyons and Highway (MUNICH), with a two-way feedback. MUNICH explicitly models street canyons and intersections, and it is coupled to the first vertical level of the chemical-transport model, enabling the transfer of pollutant mass between the street-canyon roof and the atmosphere. The original versions of SinG and MUNICH adopt a stationary hypothesis to estimate pollutant concentrations in streets. Although the computation of the NOx concentration is numerically stable with the stationary approach, the partitioning between NO and NO2 is highly dependent on the time step of coupling between transport and chemistry processes. In this study, a new nonstationary approach is presented with a fine coupling between transport and chemistry, leading to numerically stable partitioning between NO and NO2. Simulations of NO, NO2 and NOx concentrations over Paris with SinG, MUNICH and Polair3D are compared to observations at traffic and urban stations to estimate the added value of multi-scale modeling with a two-way dynamical coupling between the regional and local scales. As expected, the regional chemical-transport model underestimates NO and NO2 concentrations in the streets. However, there is good agreement between the measurements and the concentrations simulated with MUNICH and SinG. The two-way dynamic coupling between the local and regional scales tends to be important for streets with an intermediate aspect ratio and with high traffic emissions.

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatiotemporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban areas by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1)

Abstract. A new multi-scale model of urban air pollution is presented. This model combines a chemistry–transport model (CTM) that includes a comprehensive treatment of atmospheric chemistry and transport on spatial scales down to 1 km and a street-network model that describes the atmospheric concentrations of pollutants in an urban street network. The street-network model is the Model of Urban Network of Intersecting Canyons and Highways (MUNICH), which consists of two main components: a street-canyon component and a street-intersection component. MUNICH is coupled to the Polair3D CTM of the Polyphemus air quality modeling platform to constitute the Street-in-Grid (SinG) model. MUNICH is used to simulate the concentrations of the chemical species in the urban canopy, which is located in the lowest layer of Polair3D, and the simulation of pollutant concentrations above rooftops is performed with Polair3D. Interactions between MUNICH and Polair3D occur at roof level and depend on a vertical mass transfer coefficient that is a function of atmospheric turbulence. SinG is used to simulate the concentrations of nitrogen oxides (NOx) and ozone (O3) in a Paris suburb. Simulated concentrations are compared to NOx concentrations measured at two monitoring stations within a street canyon. SinG shows better performance than MUNICH for nitrogen dioxide (NO2) concentrations. However, both SinG and MUNICH underestimate NOx. For the case study considered, the model performance for NOx concentrations is not sensitive to using a complex chemistry model in MUNICH and the Leighton NO–NO2–O3 set of reactions is sufficient.

Optimal design of electric vehicle public charging system in an urban network for Greenhouse Gas Emission and cost minimization

In this paper, we address the optimization problem of allocation of Electric Vehicle (EV) public fast charging stations over an urban grid network. The objective is to minimize Greenhouse Gas Emissions (GHG) under multiple constraints including a limited agency budget, accessibility of charging stations in every possible charging request and charging demands during peak hours. Additionally, we address bi-criteria problems to consider user costs as the second objective. A convex parsimonious model that depends on relatively few assumptions and input parameters is proposed and it is shown to be useful for obtaining conceptual insights for high-level planning. In a parametric study using a hypothetical urban network model generated based on realistic parameters, we show that GHG emissions decrease with agency budget, and that the reductions vary depending on multiple factors related to EV market and EV technologies. The optimal solutions found from the bi-criteria problems are shown to be close to the solution minimizing GHG emissions only, meaning that the emission minimizing policy can also minimize user costs.

Simulating infrastructure networks in the Yangtze River Delta (China) using generative urban network models

This paper explores the urban-geographical potential of simulation approaches combining spatial and topological processes. Drawing on Vertes et al.'s (2012) economical clustering model, we propose a generative network model integrating factors captured in traditional spatial models (e.g., gravity models) and more recently developed topological models (e.g., actor-oriented stochastic models) into a single framework. In our urban network-implementation of the generative network model, it is assumed that the emergence of inter-city linkages can be approximated through probabilistic processes that speak to a series of contradictory forces. Our exploratory study focuses on the outline of the infrastructure networks connecting prefecture-level cities in the highly urbanized Yangtze River Delta (China). Possible hampering factors in the emergence of these networks include distance and administrative boundaries, while stimulating factors include a measure of city size (population, gross domestic product) and a topological rule stating that the formation of connections between cities sharing nearest neighbors is more likely (i.e., a transitive effect). Based on our results, two wider implications of our research are discussed: (1) it confirms the potential of the proposed method in urban network simulation in that the inclusion of a topological factor alongside geographical factors generates an urban network that better approximates the observed network; (2) it allows exploring the differential extent to which driving forces influence the structure of different urban networks. For instance, in the Yangtze River Delta, transitivity plays a less important role in the Internet-network formation; GDP and boundaries more strongly affect the rail network; and distance decay effects play a more prominent role in the road network.

Modelling Altitude Information in Two-Dimensional Traffic Networks for Electric Mobility Simulation

Elevation data is important for electric vehicle simulation. However, traffic simulators are often two-dimensional and do not offer the capability of modelling urban networks taking elevation into account. Specifically, SUMO - Simulation of Urban Mobility, a popular microscopic traffic simulator, relies on networks previously modelled with elevation data as to provide this information during simulations. This work tackles the problem of adding elevation data to urban network models - particularly for the case of the Porto urban network, in Portugal. With this goal in mind, a comparison between different altitude information retrieval approaches is made and a simple tool to annotate network models with altitude data is proposed. The work starts by describing the methodological approach followed during research and development, then describing and analysing its main findings. This description includes an in-depth explanation of the proposed tool. Lastly, this work reviews some related work to the subject.

Analyzing the Configuration of Multimodal Urban Networks

This article proposes urban network models as instruments to measure urban form, structure, and function indicators for the assessment of the sustainable mobility of urban areas, thanks to their capacity to describe the detail of a local environment in the context of a wider city‐region. Drawing from the features of existing street network models that offer disaggregate, scalable, and relational analysis of the spatial configuration of urban areas, it presents a multimodal urban network (MMUN) model that describes an urban environment using three systems—private transport (i.e., car, bicycle, and pedestrian), public transport (i.e., rail, tram, metro, and bus), and land use. This model offers a unifying framework that allows the use of a range of analysis metrics and conceptions of distance (i.e., physical, topological, and cognitive), and aims to be simple and applicable in practice. An implementation of theMMUNis created for the Randstad city‐region in theNetherlands. This is analyzed with network centrality measures in a series of experiments, testing its performance against empirical data. The experiments yield conclusions regarding the use of different distance parameters, the choice of network centrality metrics, and the relevant combinations of multimodal layers to describe the structure and configuration of a city‐region.

The Influence of the Network Impedance on the Nonsinusoidal (Harmonic) Network Current and Flicker Measurements

The influence that small variations of the network impedance may have on the nonsinusoidal (harmonic) network current at the point of connection of a harmonic distortion measurement system, as well as on flicker measurements, has been studied. In this respect, an equivalent circuit was devised as a possible low-voltage urban network model. Harmonic disturbances, using three different disturbance sources, and voltage variations that induce the flicker sensation, using square modulation waveforms, were simulated on a computer. The variation with ±10% of the magnitude of the network impedance at 50 Hz was also simulated, and the influence it may have on the spectral distribution of current and power, as well as on the modulation depth generating the flicker sensation, was analyzed.