A Closer Look at Scoring

Abstract

As outlined in Section 1.4 and by Figures 1.1 and 1.4, MATSim is based on a co-evolutionary algorithm: Each individual agent learns by maintaining multiple plans, which are scored by executing them in the mobsim, selected according to the score and sometimes modi ed. In somewhat more detail, the iterative process contains the following elements: mobsim The mobility simulation takes one "selected" plan per agent and executes it in a synthetic reality. This may also be called network loading. scoring The actual performance of the plan in the synthetic reality is taken to compute each executed plan's score. replanning consists of several steps: 1. If an agent has more plans than the maximum number of plans (a con guration parameter), then plans are removed according to a (con gurable) plan selector (choice set reduction, plans removal). 2. For some agents, a plan is copied, modi ed and then selected for the next iteration (choice set extension, innovation). 3. All other agents choose between their plans (choice). An agent's plans in a given iteration may be considered the agent's choice set in that iteration. As a result, steps 1 and 2 of replanning modify the choice set, while step 3 implements the actual choice between options. Choice is typically based on the score; higher score plans are more likely to be selected. This is discussed in more detail in Chapters 47 and 49. For the time being, note that the three steps of replanning must cooperate for the approach to work: the plans removal step should remove "bad" plans, the innovation step should generate "good" plans, and the choice should, ingeneral, How to cite this book chapter:

Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

“…The demand for SAV has been modelled based on the general population dynamics implemented in MATSim (see Nagel et al 2016). In MATSim, travellers follow a daily plan, which consists of a set of activities that they want to perform.…”

“…The learning behaviour simulated in MATSim is based on the concept of utility. The utility of performing an activity is described by the activity duration, the waiting time in case of arriving too early, a potential delay, a potential early departure and the potential reduction of the desired time spend on the activity (Nagel et al 2016). The coefficient for the utility of performing an activity, duration is based on the value of the average hourly wage in Amsterdam in the year 2017, which is 16.25 Euro per hour (Gemeente Amsterdam 2018).…”

Evaluating the Impact of Autonomous Vehicles on Accessibility Using Agent-Based Simulation —A Case Study of Gunma Prefecture

“…3 All settings are in accordance with the MATSim default setting method 37) only differing in that factors such as early departure penalty is not included. By default, the marginal utility of performing an activity is set as the same absolute value as , (drop the minus sign, which is 2.824/h in this study).…”

Alternative Activity Pattern Generation for Stated Preference Surveys

“…The primary drawbacks to the AURORA model are the difficulty of estimating the large number of parameters of the S-shaped utility function ( 18 , 19 ) and its lack of multi-day schedule integration ( 20 ). Another agent-based modeling framework, MATSim, uses a simpler two-parameter utility formulation, in which utility is a logarithmic function of activity duration ( 21 ). In other words, utility is monotonically increasing with activity duration with an ever-diminishing marginal effect.…”