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Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem
M Schilde1, K F Doerner2, R F Hartl3
1Johannes Kepler University Linz, Institute of Production and Logistics Management, Altenberger Strasse 69, 4040 Linz, Austria ; University of Vienna, Department of Business Administration, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria.
European Journal of Operational Research
|April 7, 2015
Summary
This study improves urban logistics by using historical accident data to predict travel times. Exploiting this stochastic information for the dynamic dial-a-ride problem (dynamic DARP) enhances service reliability and reduces passenger ride times.
Area of Science:
- Operations Research
- Transportation Science
- Logistics Management
Background:
- Urban logistics face challenges due to variable travel speeds impacting service reliability.
- Time-dependent and stochastic travel speeds lead to missed time windows and extended passenger journeys.
- Accurate modeling of travel speed variations is crucial for efficient transportation solutions.
Purpose of the Study:
- To investigate the impact of utilizing historical accident data within stochastic solution approaches for the dynamic dial-a-ride problem (dynamic DARP).
- To compare the effectiveness of deterministic versus stochastic planning methods in dynamic DARP.
- To enhance the feasibility and reliability of urban logistic and passenger transportation services.
Main Methods:
- Development of two pairs of metaheuristic solution approaches: deterministic (average speeds) and stochastic (exploiting historical accident data).
- Application of these methods to dynamic dial-a-ride problem instances.
- Testing on a real-world road network with up to 762 requests.
Main Results:
- Stochastic approaches leveraging historical accident data demonstrated significant improvements over deterministic methods under specific conditions.
- The study validates the benefit of incorporating real-time traffic variability into transportation planning.
- Effectiveness shown in reducing missed time windows and optimizing passenger ride times.
Conclusions:
- Exploiting stochastic information on travel speeds, particularly from historical accident data, offers a superior approach for the dynamic dial-a-ride problem.
- This enhances the reliability and efficiency of urban transportation systems.
- The findings support the integration of predictive analytics for dynamic transportation management.
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