CIVLS702-Traffic Engineering & Discretionary Lane Changing Model - Engineering Assignment Help

Assignment Task

Task 

Traffic Engineering

• This individual assignment is worth 10% of the total mark.
• You must submit the solutions electronically through Canvas.
• Late penalties apply (5% for each day). No technological excuses will be accepted. Submission information is listed in the unit of study outline.

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Students are always encouraged to help each other with studying, however copying solutions from anyone, where you have little or no academic input is not acceptable. Any form of copying is not acceptable.

Problem 1 
Show that the Pipes car-following model in steady-state results in the simplified triangular fundamental diagram. Hint: A challenge is to characterise steady-state conditions. See slide 15 of Week 1- TFT slide deck. 

Problem 2
Use Microsoft Excel to simulate the motion of two vehicles for 10 seconds (using increments of 0.1 s). The first vehicle (L for Leader), travels at a speed of 30 m/sec for one second, decelerates at 2.5 m/s for three seconds, accelerates at 2 m/s2 for three seconds, and travels at this speed for the final three seconds. The initial position of the second vehicle (F for Follower) is 60m behind the first car (distance measured between the front bumpers) and the initial speedis 30m/sec. The acceleration of the second vehicle, ar, is proportional to the speed difference between the two vehicles, vi(t)-ve{t). The second driver responds with 1.0 second delay. The driver sensitivity C is 0.4/sec1. Hence: ar(t+1.0)=0.4(vlt)-ve{t).

a. Draw a graph showing the relative speed over the simulated 10 seconds.

b. Draw a graph showing the distance between the vehicles over the simulated 10 seconds.

Discuss the graphs and if needed include more diagrams to support your arguments. 

Problem 3 
Propose a discretionary lane-changing model, which makes conceptual sense, as a function of the dynamics of surrounding vehicles (e.g. position, speed, acceleration, etc.). Describe your model in detail by defining relevant parameters and variables. Describe in full detail the calibration and validation of your proposed model.

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