英文论文范例：交通专业硕士毕业论文代写 Master Thesis

Master Thesis

交通专业硕士毕业论文代写 You can choose either Python or R as the programming language. Both have non-linear optimizers for non-differentiable objective

Topic: How well do Car-Following Model reproduce "real" driving behavior?  交通专业硕士毕业论文代写

Car-Following Models mathematically map the acceleration and braking behavior of human drivers and automated vehicles. Various models are to be adapted ("calibrated") to the data provided by human drivers and then the predictive power of the models is to be tested ("validated").

Some information on the Topic:

Car-Following models mechanistically map the driving style of drivers who follow other vehicles or who drive freely. How well does that match real driving behavior?

For this purpose, trajectory data from "real" drivers are compared with the results of the previously calibrated models (you can get all data from me).

The deviations provide information about the inadequacies of the various models, e.g., depending on the model:

• The model always "drives" the same, the human does not


• Humans, but not some models, have response times


• Most models do not take into account estimation errors, driver distractions, or random elements


• To do this, human drivers, but not most models, use information from indicators and brake lights and also look at the vehicle next but one in a column

Possible structure:

1. Introduction


2. Presentation of the data I have provided


3. Presentation of selected models, e.g., from chapters 10 to 12 of "Traffic Flow Dynamics"


4. Method of calibrating the models and implementation (see Chapter 16)


5. Results


6. Discussion

Details:  交通专业硕士毕业论文代写

Regarding the models: You can take the IDM and the Full Velocity Difference Model, as well as the Gipps model.

When calibrating the model, select a goodness-of-fit function (GoF), e.g., the sum of squared errors (SSE) of the absolute or relative deviations or the MAPE of the deviations. As a Measure of Performance (MoP), you can use the gaps minus the vehicle lengths or the speeds.

You can choose either Python or R as the programming language. Both have non-linear optimizers for non-differentiable objective functions that you can use "out of the box".

With the results, you can discuss the fitguete to what extent the accuracy of the parameter estimation depends on the data (e.g., if there is no free traffic, the desired speed cannot be estimated) and try a validation (calibration of driver 2 in the first 4 experiments and Validate with the fifth (the order is always the same, the driver is identical).

Data:

find attached extended floating-car data from a platoon driving experiment of a collegue near Napoli, Italy.  Shown is the speed [m/s] of the four cars participating in the experiment and the three distances (including vehicle length of about 4 m) between them in time increments of 0.1 s. Also included are plotting scripts using gnuplot and a pdf visualizing the essential aspects.

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