Check out also our GitHub for further code, usage instructions and examples.
SimMobility is an integrated mobility simulation platform that comprehensively simulates Future Mobility scenarios by integrating long, medium, and short-term travel behavior. Various mobility-sensitive behavioral models are integrated within the state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services and vehicular emissions. The platform simulates the effects of a portfolio of technology, policy and investment options under alternative future scenarios. SimMobility encompasses the modeling of millions of agents, including pedestrians, drivers, phones, traffic lights, GPS, cars, buses, and trains, from second-by-second to year-by-year simulations and across countries. Its development is ongoing by the Intelligent Transportation System Lab at the Massachusetts Institute of Technology (MIT) and the Singapore-MIT Alliance for Research and Technology (SMART).
For more information and for citation purposes, please refer to the following paper:
- Adnan, M., Pereira, F.C., Azevedo, C.M.L., Basak, K., Lovric, M., Raveau, S., Zhu, Y., Ferreira, J., Zegras, C. and Ben-Akiva, M., 2016. Simmobility: A multi-scale integrated agent-based simulation platform. In 95th Annual Meeting of the Transportation Research Board Forthcoming in Transportation Research Record.
A neural network layer that enables training of deep neural networks directly from crowdsourced labels (e.g. from Amazon Mechanical Turk) or, more generally, labels from multiple annotators with different biases and levels of expertise, as proposed in the paper:
- Rodrigues, F. and Pereira, F. Deep Learning from Crowds. In Proc. of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18).
Deep joint mean and quantile regression for spatio-temporal problems, as proposed in the paper:
- Rodrigues, F. and Pereira, F.C., 2018. Beyond expectation: Deep joint mean and quantile regression for spatio-temporal problems. arXiv:1808.08798
A deep learning approach for combining time-series and textual data for taxi demand prediction in event areas.
This code reproduces the experiments in the paper:
- Rodrigues, F. and Markou, I. and Pereira, F. C. Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach. In Information Fusion, Elsevier, 2018.