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Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Typical Model Studies01:30

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Co-designing transport models as a heuristic planning tool.

Tanvi Maheshwari1, Pieter Fourie2,3

  • 1Department of Architecture, Monash University, Melbourne, Victoria 3145, Australia.

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|November 13, 2024
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Summary

New urban planning methods integrate design and transport modeling for complex city challenges. This approach uses heuristic models to guide decisions, improving stakeholder engagement and understanding of urban systems.

Keywords:
agent-based simulationsautomated vehiclesco-designurban design

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Area of Science:

  • Urban Planning and Transportation Science
  • Computational Social Science

Background:

  • Technological disruptions in transportation increase urban planning complexity.
  • Traditional decision-making processes are insufficient for current urban challenges.

Purpose of the Study:

  • To propose an integrated approach combining urban design and transport modeling.
  • To develop goal-driven, agile heuristic models for early-stage planning.

Main Methods:

  • Combining qualitative urban design with quantitative transport modeling.
  • Utilizing heuristic modeling in an iterative loop with design optioning.
  • Employing design workshops for stakeholder engagement and co-creation of simulation models.

Main Results:

  • Demonstrated operationalization of the approach through a case study on automated vehicles.
  • Enhanced understanding of emergent effects in complex urban systems.
  • Improved communication and collaboration across disciplines.

Conclusions:

  • The integrated approach enhances stakeholder engagement in urban planning.
  • Heuristic modeling, informed by design, provides a valuable tool for navigating planning complexity.
  • This method offers a more effective way to understand and plan for future urban mobility.