Chicago TRACON

Chicago TRACON

Tracon Tour and Frequencies

Tracon Tour and Frequencies: "Written by Ted Moran"

Review comments for 06-2993

Review comments for TRB2006 papers

06-2293
The paper certainly has merit and good research value. The paper organization and English written is quite good. Several comments:
1) The research topic on dynamic traffic assignment under incidents is quite interesting. But the literature review of the paper is not enough. In the introduction section, the author only cited several papers to mention the "over-reaction" phenomenon and one paper which imposed additional costs to balance the degree of reaction.  We can’t see the state-of-the-art research progress on such topic. The author should better enrich the literature review in a broader range to make sure if the model presented in the paper is an original rollout or just an improvement of previous ones.
2) The model presented in the paper is based on several major assumptions including that all drivers have access to detailed traffic information, and that drivers make their own travel choice based on the travel time information provided, without cooperation, and so on. I am curious that if any of the assumptions was relaxed or can't be tenable, how will the model be revised and how is the output thereby? I'm looking forward to seeing the discussion in the paper's revised version, which will be valuable for both researchers and practitioners.
3) In the section "Road Diversion Function", a diversion function and its inverse function are introduced. It is a little difficult to understand the explanation on why the inverse function (6) is suitable for the multiple alternative routes cases. Suggest the author to give a better explanation. Also please explain the relations between equation (3) and (8).

Other comments,
keywords missing.
In abstract-line 12, "based" should be "basic"; Page 4-line33, "base" should be "basic".
Page 1- line9, "non-current" should be "non-recurrent".
Page 10, right parenthesis mark is missed in equation (7), and there are also several similar cases in the following context.

Other notes

• Assumption 1 : all drivers have access to detailed traffic information


• Assumption 2: drivers make their own travel choice based on the travel time information provided.


• A route-based stochastic variational inequality is used as a basic model for travel time prediction


• Diversion function


• Inverse function of the diversion curve

1. The model presented in the paper base on several major assumptions that all information about the incident has been entirely distributed to the traveler.


2. A little confused in the paragraph explaining why using stochastic model not deterministic model


3. How can the inverse function of diversion be useful in multiply route case?


4. Disutility function?


5. Independent (expected) route choice behavior is rather closer to stochastic route choice than a deterministic one. Furthermore, in a stochastic route choice model, flows are probabilistically assigned based on a choice function that takes the perceived travel disutility as an input.

Corsair Memory works well on my IBM Thinkpad

Finally the memory arrived. It was Friday and I picked it up from the mail room, not sent to my office.
I ordered this strip of Corsair 512Mb notebook memory from newegg.com on Tuesday. The reason to select this type of memory which is not very cheap is that I concern the stability most.
USP 3 days shipping is superb. Actually they delivered it in the morning of Thursday. But the mail room’s service is too bad. Lack of labor, -- funny?
The installment is quite smooth. After I restarted the windows XP, I just can’t believe it. The speed increased at least three times. Now I can say that my old Thinkpad T30 works no worse than a most fashionable computer.

Deadline of TRB2006 review

But I don't think that other reviewers can finish the job on time.

Two papers I seleced to review is as follows

06-0823 Development of Genetic Ant-Based Fuzzy Logic Controller (GAFLC) Algorithm with Learning and Validating from Car-Following Samples

06-2293 A Travel Time Prediction Model under Incidents