Well done TUPA on TRB 2019. 5 papers accepted this round. The Transportation Research Board (TRB) 98th Annual Meeting will be held January 13–17, 2019, at the Walter E. Washington Convention Center, in Washington, D.C. The information-packed program is expected to attract more than 13,000 transportation professionals from around the world.
Chunliang Wu, Exploring the relationship between built environment and public sharing bike flow in Suzhou, China using geographically weighted regression model
Wenhua Jiang, Imputation of missing transfer passenger flow with self-measuring multi-task gaussian process
Meina Zheng, An analysis of short-term available parking space forecasting method based on LSTM neural network
Duy Nguyen-Phuoc, Turn signal use among car drivers and motorcyclists at intersections: a case study of Danang, Vietnam
Khaled Sabban, Inhi Kim, User Satisfaction of the Road Network: A Structural Equation Model
The 5th China-Korea Joint Seminar on Sustainable Transportation Systems, Sep 14 – Sep 16, 2018, KAIST, Daejeon, South Korea. http://ckjssts.org/program.html
The 5th China-Korea Joint Seminar on Sustainable Transportation Systems (CKJSSTS) is the gathering of leading researchers in the field of transportation from China and South Korea. The purpose of this seminar is to share the innovative ideas and cutting-edge knowledge for transportation research; and to establish a foundation for collaborative research between two countries.
This China-Korea Joint Seminar on Sustainable Transportation Systems was successfully held four times in Korea and China. The 1st and 3rd seminars were held in Seoul and Jeju, Korea, respectively and the 2nd and 4th seminars were held in Beijing and Shanghai, China. Especially, papers presented in the 2nd seminar have been published as a special issue in the International Journal of Transportation.
Professor Seonha Lee and I were invited by China Traffic Force to the 2018 2nd smart parking forum in Yancheng. Thank you for inviting us this wonderful opportunity, specially thanks to ITS China and Shanghai consulate-general Park.
ISTS and IWTDCS 2018 aim to discuss about the analysis and the technologies on transportation field, especially on traffic simulations and data collections. The world’s transportation and traffic researchers and practitioners, as well as people who are interested in contributing to or gaining a deeper understanding of the transportation analysis are expected to join this conference.
Two papers are presented from our team:
The uncapacitated battery swapping facility location problem with localized charging system serving electric buses fleet Wentao Jing, Inhi Kim and Kun An
Short-term prediction for bike-sharing service using machine learning Bo Wang and Inhi Kim
Dr. Susi Susilawati from Malaysia campus and Dr. Marilyn Johnson from Clayton campus gave presentations on their research to the Masters students of the 2017 cohort.
A big congrats for all my students who graduated SEU-Monash Joint Masters Program. The graduation ceremony takes place in the Intercontinental hotel, Suzhou, China. I hope you all have a big success in your near future!!!
It’s not easy to pick up this background image for the post. It shows the Manhattan Peninsula, New York, and the sky reflects the buildings on the ground. This fantasy scene reminds me the complex relationship between space and time, which closely related to the topic of this article: Spatiotemporal forecasting of traffic by using 3d convolutional neural networks.
On December 31, 2014, a deadly stampede occurred in Shanghai, near Chen Yi Square on the Bund, where around 300,000 people had gathered for the new year celebration. 36 people were killed and there were 49 injured, 13 seriously (Wikipedia). From the follow-up reports, it can be known that Tencent’s user online information has roughly detected that the traffic in the area was too dense. So data from social media and cell phone signal can infer the regional crowd density. If the corresponding predictions and analysis can be made, such tragedies will not happen.
Traffic forecasting has been studied for decades. There are many outcomes of models and theories. For the regional prediction, it’s a prevalent way to split the research area into grids and analysis them by computer vision models. Each square in the girds just like the pixel in an image.
Zhang (2016) presented the classic Deep-ST model. It treats the research area as image and the predicted result combined with the convolutional models from different periods. However, the convert not only limited to traffic volume. The vehicle speed can also be transformed into images. The images below shows the traffic speed representation in a small-scale transportation network (Yu, et al 2017).
With the development of computer vision, deeper neural networks like ResNet has been presented recently. Zhang (2017) also upgraded the DeepST to ST-ResNet with ResNet models. However, the convolutional kernel in these models only focuses on spatial relations, not for a spatiotemporal space. It’s been proved that 3D Convolutional Networks can learn the spatiotemporal features (Tran 2015). But these improvements only appears in video related studies like behavior detection, human action detection, etc. So in this post, we will see the application of 3D ConvNets on traffic problems.
Convolutional operations
There are some different operations with the convolutional kernel in hidden layers. The following diagrams show the 2D convolutional operation with padding and dilation (Dumoulin and Visin, 2016 ). It’s different in 3D, but the ideas are same.
With the 3D convolutional operation, the kernel shape is 3 dimensional and it moves in 3 directions. Just like the animation below. For the transportation problems, the directions are latitude, longitude and time. In the model part, we also used padding and dilation with 3D kernels.
Data and Model
We take the bike sharing data in New York (BikeNYC) from the DeepST paper as an example here. Every circle stands for a station and the color means the number of bikes in dock. The research area is split into 16*8 grids, each square has in and out flow at a particular moment. The in and out flow are numbers of return and borrow bikes in the corresponding region.
The input is a time sequence from the time level of closeness, period and trend. They stand for the different extract frequency from raw data. If stack them together, the input shape would be X168. The X is the number of timesteps.
The model has three 3D convolutional layers and the flatten layer combines the external data like weather and holidays.
Experiments
I used Pytorch this time. The windows version just came out last month. It provides a lot of API and very easy to build a custom model structure. The author of ST-ResNet has opened his code, so we can reuse the dataset from Github.
To get the best kernel size, we take different combinations for training test. It turns out that 3*3*3 is also the optimal option for transportation data just like other papers have pointed out in some behavior detection tasks.
Although the model is much simpler than Deep-ST or ST-ResNet, it still achieved the best performance on BikeNYC and TaxiBJ datasets.
Visualization
Firstly, I have tried the Matplotlib in Python. The outcomes are good but they are static and not appropriate for the webpage. So I chose d3.js to draw the diagram from scratch. (The style with CSS and SVG tags is incompatible with this blog responsive CSS sheet. So I just give up displaying it on this page. Here is the gif version, simple and straightforward.)
Zhang, J., Zheng, Y., Qi, D., Li, R., & Yi, X. (2016, October). DNN-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 92). ACM.
Zhang, J., Zheng, Y., & Qi, D. (2017, February). Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In AAAI (pp. 1655-1661).
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015, December). Learning spatiotemporal features with 3d convolutional networks. In Computer Vision (ICCV), 2015 IEEE International Conference on (pp. 4489-4497). IEEE.
Vincent Dumoulin, Francesco Visin – A guide to convolution arithmetic for deep learning
I am pleased to invite Miss Zhe He from Transport Innovation to give a special lecture to my students of 2017 cohort. Transport Innovation focuses on big data analysis and simulation in Suzhou. TUPA and Transport Innovation agreed to collaborate in research and development mutually.
