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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Bi-objective bus scheduling optimization with passenger perception in mind.

Shuai Liu1, Lin Liu2, Dongmei Pei1

  • 1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China.

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This study introduces a new bus scheduling model to reduce passenger wait times and operational costs. The Adaptive Double Probability Genetic Algorithm optimizes bus routes for better passenger satisfaction and efficiency.

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Area of Science:

  • Operations Research
  • Transportation Engineering
  • Computer Science

Background:

  • Traditional bus scheduling relies on empirical methods, often failing to meet dynamic passenger demands.
  • Big traffic data enables a shift towards responsive, data-driven public transit management.
  • Passenger experience, including congestion and waiting times, is crucial for transit system effectiveness.

Purpose of the Study:

  • To develop a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) balancing operational and passenger costs.
  • To improve upon classical Genetic Algorithms (GA) for more efficient bus scheduling.
  • To enhance passenger satisfaction and reduce travel burdens through optimized bus services.

Main Methods:

  • Established a Dual-Cost Bus Scheduling Optimization Model (Dual-CBSOM) considering passenger flow and subjective feelings.
  • Developed an Adaptive Double Probability Genetic Algorithm (A_DPGA) to solve the optimization model.
  • Validated the A_DPGA by comparing it with classical GA and Adaptive Genetic Algorithm (AGA) using Qingdao city data.

Main Results:

  • The A_DPGA achieved a 2.3% reduction in the overall objective function value.
  • Optimized scheduling led to a 4.0% improvement in bus operation cost efficiency.
  • Passenger travel costs were reduced by 6.3%, indicating enhanced passenger experience.

Conclusions:

  • The Dual-CBSOM effectively meets passenger travel demands and improves satisfaction.
  • The A_DPGA demonstrates superior convergence speed and optimization performance compared to traditional algorithms.
  • Optimized bus scheduling significantly reduces both operational and passenger-related travel costs.