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diff --git a/README.md b/README.md
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-# PythonDTALite
+# DTALite
 
-## Step 1: White Paper and Application: 
-Zhou, Xuesong, and Jeffrey Taylor. "[DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration.](https://www.tandfonline.com/doi/full/10.1080/23311916.2014.961345)" Cogent Engineering 1.1 (2014): 961345.
+This site mains the source code and Windows-based release for DTALite+NeXTA
+package.
 
-Marshall, Norman L. "[Forecasting the impossible: The status quo of estimating traffic flows with static traffic assignment and the future of dynamic traffic assignment.](https://www.sciencedirect.com/science/article/pii/S2210539517301232)" Research in Transportation Business & Management 29 (2018): 85-92.
+For the Python version of DTALite and Path4GMNS package portable on Windows,
+Linux and MacOS, please go to <https://github.com/jdlph/Path4GMNS>.
+
+## Step 1: White Paper and Application:
+
+Zhou, Xuesong, and Jeffrey Taylor. "[DTALite: A queue-based mesoscopic traffic
+simulator for fast model evaluation and
+calibration.](https://www.tandfonline.com/doi/full/10.1080/23311916.2014.961345)"
+Cogent Engineering 1.1 (2014): 961345.
+
+Marshall, Norman L. "[Forecasting the impossible: The status quo of estimating
+traffic flows with static traffic assignment and the future of dynamic traffic
+assignment.](https://www.sciencedirect.com/science/article/pii/S2210539517301232)"
+Research in Transportation Business & Management 29 (2018): 85-92.
 
 ## Step 2: Youtube Teaching Videos on Use of DTALite/NEXTA Packages
-[NeXTA/DTALite Workshop Webinar](https://www.youtube.com/channel/UCUHlqojCQ4f7VvqroUhbaFA) by Jeff Taylor
+
+[NeXTA/DTALite Workshop
+Webinar](https://www.youtube.com/channel/UCUHlqojCQ4f7VvqroUhbaFA) by Jeff
+Taylor
 
 ## Step 3: Mini-Lesson on the Internal Algorithmic Details
-[Mini-lessson](https://youtu.be/rorZAhNNOf0) : What is the best way to learn dynamic traffic simulation and network assignment for a beginner? Do you want to integrate a powerful traffic simulator in your deep learning framework? We would like to offer a collaborative learning experience through 500 lines of python codes and real-life data sets. This is part of our mini-lessons through teaching dialog.
 
-[Python source codes](https://github.com/asu-trans-ai-lab/DTALite/blob/main/src/python/dtalite_s.py)
+[Mini-lessson](https://youtu.be/rorZAhNNOf0) : What is the best way to learn
+dynamic traffic simulation and network assignment for a beginner? Do you want to
+integrate a powerful traffic simulator in your deep learning framework? We would
+like to offer a collaborative learning experience through 500 lines of python
+codes and real-life data sets. This is part of our mini-lessons through teaching
+dialog.
+
+C++ source codes
+
+Python source code
+
+## References:
+
+**1. Parallel computing algorithms**: Qu, Y., & Zhou, X. (2017). Large-scale
+dynamic transportation network simulation: A space-time-event parallel computing
+approach. Transportation research part c: Emerging technologies, 75, 1-16.
+
+**2. OD demand estimation**: Lu, C. C., Zhou, X., & Zhang, K. (2013). Dynamic
+origin–destination demand flow estimation under congested traffic conditions.
+Transportation Research Part C: Emerging Technologies, 34, 16-37.
+
+**3. Simplified emission estimation model**: Zhou, X., Tanvir, S., Lei, H.,
+Taylor, J., Liu, B., Rouphail, N. M., & Frey, H. C. (2015). Integrating a
+simplified emission estimation model and mesoscopic dynamic traffic simulator to
+efficiently evaluate emission impacts of traffic management strategies.
+Transportation Research Part D: Transport and Environment, 37, 123-136.
+
+**4. Eco-system optimal time-dependent flow assignment**: Lu, C. C., Liu, J.,
+Qu, Y., Peeta, S., Rouphail, N. M., & Zhou, X. (2016). Eco-system optimal
+time-dependent flow assignment in a congested network. Transportation Research
+Part B: Methodological, 94, 217-239.
+
+**5. Transportation-induced population exposure assessment**: Vallamsundar, S.,
+Lin, J., Konduri, K., Zhou, X., & Pendyala, R. M. (2016). A comprehensive
+modeling framework for transportation-induced population exposure assessment.
+Transportation Research Part D: Transport and Environment, 46, 94-113.
 
-[C++ source codes](https://github.com/asu-trans-ai-lab/DTALite/blob/main/src/v2/Exe_src/AgentLite/main_api.cpp)
+**6. Integrated ABM and DTA**: Xiong, C., Shahabi, M., Zhao, J., Yin, Y., Zhou,
+X., & Zhang, L. (2020). An integrated and personalized traveler information and
+incentive scheme for energy efficient mobility systems. Transportation Research
+Part C: Emerging Technologies, 113, 57-73.
 
-[Python DLL testing environment](https://github.com/asu-trans-ai-lab/DTALite/tree/main/DTALite_DLL_test)
+**7. State-wide transportation modeling**: Zhang. L. (2017) Maryland SHRP2 C10
+Implementation Assistance – MITAMS: Maryland Integrated Analysis Modeling
+System, Maryland State Highway Administration
 
-## Other References: 
-**1. Parallel computing algorithms**: Qu, Y., & Zhou, X. (2017). Large-scale dynamic transportation network simulation: A space-time-event parallel computing approach. Transportation research part c: Emerging technologies, 75, 1-16.
+**8. Workzone applications**: Schroeder, B, et al. Work zone traffic analysis &
+impact assessment. (2014) FHWA/NC/2012-36. North Carolina. Dept. of
+Transportation. Research and Analysis Group.
 
-**2. OD demand estimation**: Lu, C. C., Zhou, X., & Zhang, K. (2013). Dynamic origin–destination demand flow estimation under congested traffic conditions. Transportation Research Part C: Emerging Technologies, 34, 16-37.
+![](media/69b2706fcca1b04fc52d1cbf45fade38.png)
 
-**3. Simplified emission estimation model**:  Zhou, X., Tanvir, S., Lei, H., Taylor, J., Liu, B., Rouphail, N. M., & Frey, H. C. (2015). Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies. Transportation Research Part D: Transport and Environment, 37, 123-136.
+>   nexta
 
-**4. Eco-system optimal time-dependent flow assignment**: Lu, C. C., Liu, J., Qu, Y., Peeta, S., Rouphail, N. M., & Zhou, X. (2016). Eco-system optimal time-dependent flow assignment in a congested network. Transportation Research Part B: Methodological, 94, 217-239.
+![](media/3d51a8c44607ef5d2ce200dc9ff8cee6.png)
 
-**5. Transportation-induced population exposure assessment**: Vallamsundar, S., Lin, J., Konduri, K., Zhou, X., & Pendyala, R. M. (2016). A comprehensive modeling framework for transportation-induced population exposure assessment. Transportation Research Part D: Transport and Environment, 46, 94-113.
+>   nexta
 
-**6. Integrated ABM and DTA**: Xiong, C., Shahabi, M., Zhao, J., Yin, Y., Zhou, X., & Zhang, L. (2020). An integrated and personalized traveler information and incentive scheme for energy efficient mobility systems. Transportation Research Part C: Emerging Technologies, 113, 57-73.
+>   DTALite/NeXTA applications in the United States
 
-**7. State-wide transportation modeling**: Zhang. L.  (2017) Maryland SHRP2 C10 Implementation Assistance – MITAMS: Maryland Integrated Analysis Modeling System, Maryland State Highway Administration
+![](media/d2a334644e5a5655c61ea2a2991011e7.png)
 
-**8. Workzone applications**: Schroeder, B, et al. Work zone traffic analysis & impact assessment. (2014) FHWA/NC/2012-36. North Carolina. Dept. of Transportation. Research and Analysis Group.
+>   maps
diff --git a/bin/DTALite b/bin/DTALite
diff --git a/bin/DTALite.exe b/bin/DTALite.exe
diff --git a/bin/libstalite.dll b/bin/libstalite.dll
diff --git a/bin/libstalite_base.dll b/bin/libstalite_base.dll
diff --git a/bin/libstalite_base.so b/bin/libstalite_base.so
diff --git a/DTALite/v1/AgentLite/Debug.txt → data/3_corridor/NeXTA.log b/DTALite/v1/AgentLite/Debug.txt → data/3_corridor/NeXTA.log