ALL SEX DATING
clear and disable history
- orlando florida dating
- www hl dating comsd
- Dubai mature open adult chat
- texas dating hayden idaho
- rape dating
- dating website for short people
- Explicit horny chat sites
- mayur parmar singles dating
- adult dating many louisiana
- dating your gibson
- michaels dating timonium
- biggest dating mistakes men make
- dating books torrent
- dating in ukreine
Validating the results of a route choice simulator
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Day-to-day traffic dynamics are generated by individual traveler’s route choice and route adjustment behaviors, which are appropriate to be researched by using agent-based model and learning theory.
Therefore, more appropriate methods appear by using discrete-time systems, in which travelers repeat route choice daily or weekly [2, 8].
The flow convergences of continuous time and discrete time are very different .
Some scholars applied agent-based models to analyze travelers’ behaviors.
Rossetti modeled commuters’ behaviors in the structure of beliefs, desires, and intentions (BDI) which is a classical structure in multiagent simulation.
In their model, travelers could make decisions about route choice and departure time rationally . proposed some models about travelers’ route choice behaviors.
They took into account the limitations of travelers’ cognitive capabilities and used microsimulation to examine the dynamic nature of travelers’ route choice.Gao and Wang explored travelers’ route choice under guidance information by using microsimulation .Travelers’ decision-marking is an important aspect in agent-based models.An agent knows its internal state, the evolution of system, the likely outcomes of its actions, and so forth, and it has some abilities, such as independent decision-making, beliefs, desires, and intentions.These characteristics ensure the scalability and robustness of agent-based models, which are important to portray the complexity of DDTD.It is hard for them to make the best choice every time due to their bounded rationality and the uncertainty of environment. If the travel time of a path was short in the past days, its probability to be chosen is big in the current day. These characteristics are similar with the features of the reinforcement learning (RL) theory.Under RL, if an action yielded a high payoff in the past days, the probability assigned to it increases in the current round, or the behavior associated with the action gets reinforced .Another important branch of DDTD is the research about travelers’ behaviors.The uncertainty inherent of travelers’ behaviors increases the complexity of models about DDTD, which makes traditional models unable to represent complex DDTD well .In the past decades, day-to-day traffic dynamics (DDTD) have been developed substantially in the field of transportation, which are mainly used to study the traffic fluctuations and the evolution process, rather than the final or static equilibrium state .Approaches used to study DDTD are very flexible because they allow a wide range of behavior rules, levels of aggregation, and types of models to be integrated in the same modeling framework .