UTRAN Transmission Infrastructure Planning and Optimisation

This paper presents an electrical infrastructure planning method for transit systems that operate with partially grid-connected vehicles incorporating on-board batteries. First, the state-of-the-art of electric transit systems that combine grid-connected and battery-based operation is briefly described. Second, the benefits of combining a grid connection and battery supply in Bus Rapid Transit (BRT) systems are introduced. Finally, the planning method is explained and tested in a BRT route in Medellin, Colombia, using computational simulations in combination with real operational data from electric buses that are currently operating in this transit line. Unlike other methods and approaches for Battery Electric Bus (BEB) infrastructure planning, the proposed technique is system-focused, rather than solely limited to the vehicles. The objective of the technique, from the vehicle’s side, is to assist the planner in the correct sizing of batteries and power train capacity, whereas from the system side the goal is to locate and size the route sections to be electrified. These decision variables are calculated with the objective of minimizing the installed battery and achieve minimum Medium Voltage (MV) network requirements, while meeting all technical and reliability conditions. The method proved to be useful to find a minimum feasible cost solution for partially electrifying a BRT line with In-motion Charging (IMC) technology.

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Delivering on the Sustainable Development Goals through long-term infrastructure planning

Global Environmental Change ◽

10.1016/j.gloenvcha.2019.101975 ◽

2019 ◽

Vol 59 ◽

pp. 101975 ◽

Cited By ~ 11

Author(s):

Daniel Adshead ◽

Scott Thacker ◽

Lena I. Fuldauer ◽

Jim W. Hall

Keyword(s):

Sustainable Development ◽

Sustainable Development Goals ◽

Infrastructure Planning ◽

The Sustainable Development ◽

Development Goals

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Prediction of Socio-Economic Indicators for Urban Planning Using VHR Satellite Imagery and Spatial Analysis

Remote Sensing ◽

10.3390/rs12111730 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1730 ◽

Cited By ~ 2

Author(s):

Gebhard Warth ◽

Andreas Braun ◽

Oliver Assmann ◽

Kevin Fleckenstein ◽

Volker Hochschild

Keyword(s):

Spatial Analysis ◽

Urban Planning ◽

Urban Areas ◽

Residential Buildings ◽

Dynamic Change ◽

Training Data ◽

Infrastructure Planning ◽

Economic Point ◽

Steady Growth ◽

Building Typology

Ongoing urbanization leads to steady growth of urban areas. In the case of highly dynamic change of municipalities, due to the rates of change, responsible administrations often are challenged or struggle with capturing present states of urban sites or accurately planning future urban development. An interest for urban planning lies on socio-economic conditions, as consumption and production of disposable goods are related to economic possibilities. Therefore, we developed an approach to generate relevant parameters for infrastructure planning by means of remote sensing and spatial analysis. In this study, the single building defines the spatial unit for the parameters. In the case city Belmopan (Belize), based on WorldView-1 data we manually define a city covering building dataset. Residential buildings are classified to eight building types which are locally adapted to Belmopan. A random forest (RF) classifier is trained with locally collected training data. Through household interviews focusing on household assets, income and educational level, a socio-economic point (SEP) scaling is defined, which correlates very well with the defined building typology. In order to assign socio-economic parameters to the single building, five socio-economic classes (SEC) are established based on SEP statistics for the building types. The RF building type classification resulted in high accuracies. Focusing on the three categories to describe residential socio-economic states allowed high correlations between the defined building and socio-economic points. Based on the SEP we projected a citywide residential socio-economic building classification to support supply and disposal infrastructure planning.

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