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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Multi-Objective Human Resource Allocation Approach for Sustainable Traffic Management.

Soumendra Nath Sanyal1, Izabela Nielsen1, Subrata Saha1

  • 1Department of Materials and Production, Aalborg University, Fibigerstrede 16, DK 9220 Aalborg, Denmark.

International Journal of Environmental Research and Public Health
|April 9, 2020
PubMed
Summary

This study introduces a fuzzy programming model for optimal human resource deployment in traffic management. It aims to reduce costs, improve safety on accident-prone roads, and cut emissions by efficiently allocating personnel.

Keywords:
binary programmingemission controlfuzzy-efficient solutionmanpower allocation

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Area of Science:

  • Operations Research
  • Traffic Engineering
  • Public Policy

Background:

  • Effective human resource deployment is crucial for metropolitan traffic management, impacting public transport, event management, and emergency response.
  • Collaborative efforts of skilled personnel are essential to mitigate traffic congestion, reduce injuries, and lower emissions.

Purpose of the Study:

  • To propose a two-phase fuzzy multi-objective binary programming model for optimal human resource allocation in traffic management.
  • To simultaneously minimize operational costs, maximize allocation to accident-prone areas, minimize volunteer use, and reduce emissions and violations.

Main Methods:

  • Development of a two-phase fuzzy multi-objective binary programming model.
  • Formulation of a binary programming model for individual manpower allocation schedules across different shifts and road segments.
  • Evaluation through a proposed case study to derive managerial insights.

Main Results:

  • The model provides an optimal allocation strategy for five categories of human resources.
  • It balances competing objectives including cost reduction, safety enhancement, and environmental impact mitigation.
  • The case study demonstrates the model's applicability and effectiveness in real-world traffic management scenarios.

Conclusions:

  • The proposed model offers a systematic approach to human resource allocation planning in traffic management.
  • It can significantly contribute to reducing road traffic congestion, injuries, and vehicular emissions.
  • Managerial insights derived from the model can inform strategic decision-making for urban traffic operations.