Models, Algorithms and Applications of DynasTIM Real-Time Traffic Simulation System
Round 1
Reviewer 1 Report
This paper discusses development and application of a real time traffic simulation system called DynasTIM. The author outlines the architecture of DynasTIM, its underlying algorithms, data and the methods used to calibrate and validate the model.
The article presents important and practical concepts which can be of benefit to transport professionals and researchers. However, there are some points in the paper that if addressed could enhance the message which author is trying to convey. They are as follows,
Page 6/line 224/ 4.1. State estimation and state prediction:
The difference between these two concepts needs to be clarified as the current text does not explain enough how state estimation and prediction differ with each other.
Page 7/line273: “Usually, the control strategy is updated every 5~60 minutes and just once for each cycle of the state estimation, prediction, and optimization” This sentence is unclear. Please expand on this a little bit more and explain what cycle means in this context.
Page 9/line 340: identityà do you mean identify? Same in page 11/line 400
Page 12/line449: generated à do you mean generate?
Page 13/line 493: 6.1. Network representation: The text in this section does not explain Figure 5 properly. Please provide more explanations as to how the network has been modelled and what Figure 9 is illustrating.
Page 14/line 529: what do you mean by “parent segment”
Page 15/line 558: please discuss further as to how variable-length method differs from the fixed length method.
Page 24/line 813: “On the base of” please rephrase this part. Maybe “Based on” will be a better wording.
Author Response
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The paper is well written. However, it appears with only one author, while in the text the word we is used when referring to the author. At the same time, I did not find the personalised contribution of the author. The mathematical model seems taken from another work. The author must clarify these aspects.
Author Response
The words "we" in the paper have been replaced by other expressions.
The main contributions of this paper include the following:
- Architecture of DynasTIM, especially the strategy optimization module.
- Dynamic OD flow estimation with novel formula for assignment matrix computation.
- Parallel SPSA algorithm based real-time optimization of area signal control schemes.
- Mesoscopic traffic model with variable-length speed influence region.
- Online calibration of vehicle speeds in mesoscopic simulator with probe vehicles data.
Thank you for your opinions.
Reviewer 3 Report
A good case study but the Author should improve the background, comparison with other methodologial approach and the potential interest based on the gap in the literature on the topic.
A good rereading should improve the text
Author Response
The relevant contents of the paper have been revised according to your comments. Thank you.
- (lines 798~802) The network and data comes from a cooperative project between Shenzhen Urban Transport Planning Center (SUTPC) and the author. The main purpose of this project is to study whether the existing traffic simulation technology can accurately reproduce the real traffic states for complex urban road networks with rich and accurate flow detection data.
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(lines 863~866)It should be noted that the flow estimation error in this case study is obviously lower than the experimental result of Transmodeler (Yang et al., 2017), which represents the state of the art of traffic simulation, and the estimation error of the latter is at least 30%.
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(lines 895~901)In comparison with the signal optimization methods proposed by Carolina et al. (e.g., 2013, 2017a, 2017b, 2017c), the P-SPSA based optimization method requires fewer simulation evaluations, so it has more potential for real-time application. It does not need a metamodel as a bridge between the traffic simulator and the optimization algorithm, thus reducing the modeling cost. Meanwhile, even for the large-scale signal optimization problem for the congested CBD road network in Shenzhen, it seems that the algorithm can still produce fairly stable and reliable optimization result.
Round 2
Reviewer 2 Report
The author improve the paper. It can be published.
