Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear Differential Equations01:27

Linear Differential Equations

246
The integrating factor method provides a systematic way to solve first-order linear differential equations, especially those that cannot be handled by separation of variables. This method is particularly useful in modeling time-dependent physical systems influenced by both constant inputs and resistive forces. A common example is the motion of a car subjected to a constant engine force while experiencing air resistance proportional to its velocity.In such scenarios, Newton’s second law...
246
Distance Problem01:29

Distance Problem

194
When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
194
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

29.8K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
29.8K
Kinematic Equations - II01:17

Kinematic Equations - II

15.8K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
15.8K
Evaluating Limits by Direct Substitution01:29

Evaluating Limits by Direct Substitution

254
In the analysis of functions that represent continuous physical phenomena, it is often necessary to determine the output value as the input approaches a specific point. When a combination of algebraic terms defines the function and exhibits no discontinuities or abrupt changes near the point of interest, the limit of the function can be evaluated directly. This process, known as direct substitution, involves replacing the variable in the expression with the value it approaches.Direct...
254
Trapezoidal Rule01:26

Trapezoidal Rule

210
Estimating the distance traveled by a vehicle using its recorded velocity over time is a common problem in physics and engineering. When velocity data is available at discrete time intervals, rather than as a continuous function, numerical integration methods such as the trapezoidal rule are often employed to approximate the total displacement.The trapezoidal rule works by dividing the total time interval into several equal segments. Within each segment, the recorded velocities at the endpoints...
210

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Riding the waves from epidemic to endemic: Viral mutations, immunological change and policy responses.

Theoretical population biology·2024
Same authorSame journal

The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes?

European journal of operational research·2023
Same author

Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports.

Computers & operations research·2013
Same author

[Accuracy of prenatal diagnoses in terminated pregnancies--a retrospective analysis of results and influences].

Zeitschrift fur Geburtshilfe und Neonatologie·2001
Same author

Commissioning of a micro-multileaf collimator.

Frontiers of radiation therapy and oncology·1999
Same author

TLD array for precise dose measurements in stereotactic radiation techniques.

Physics in medicine and biology·1996

Related Experiment Video

Updated: Apr 15, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.6K

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
PubMed
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.

Keywords:
Dial-a-ride problemDynamic stochasticMultiple plan approachMultiple scenario approachTime-dependentVariable neighborhood search

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

21.1K
Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

5.4K

Related Experiment Videos

Last Updated: Apr 15, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

4.6K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

21.1K
Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
06:38

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

Published on: June 9, 2020

5.4K

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.