Welcome to My Webpage
Ming Ni (倪明) is a technology enthusiast, social connector, Google user, coffee lover, and a PhD student at SUNY Buffalo.
He is working as research assistant in Dr. Qing He's Group, and is supposed to be working non-stop.
Research interests include
- Supply Chain Management and Logistics
- Data Analytics and Mining
- Studys of Social Networking.
In sum, the major reseach interest is to explore the power of retailing supply chain and heterogeneous information sources
- Collect social media data by Twitter API and conducting text analysis on tweet contents.
- Build the topic models and classifcation models to detect real-time public event information.
- Propose a convex optimization based approach to predict event transportation ﬂows based on social media data.
Data-Driven Optimization and Planning of Multi-Component Track Responsive Maintenance with Defect Deterioration Modeling
- Build the track failure prediction models based on the historical inspection data.
- Design optimization models for track annual capital planning based on the historical inspection data and failure prediction results.
Cross Sourcing Delivery with Store Fulfillment
- Defne the problem of store fulfllment for local online order with multiple channel delivery, including own ﬂeet, occasional drivers and information sharing drivers.
- Design stochastic and optimization models for both order sourcing decision and delivery method decision with consideration of future demand in a rolling-horizon procedure.
In press and under review
- Ni, M., Q. He, J. Walteros, X. Liu and A. Hampapur, “Same Day Delivery Planning with Store Fulfllment”, submitted to Transportation Science
- Zhang, Z., Y. Cui, Q. He, M. Ni and J. Gao, “An Empirical Study of Trip Purpose Inference with Connected Vehicles Trajectories, Land Use Data, and Social Media Data”, Submitted
- Zhang, Z., Y. Cui, Q. He, M. Ni and J. Gao, “Exploring Human Mobility Pattern with Social Media”, Submitted
- Zhang, Z., Q. He, J. Gao and M. Ni, “A Deep Learning Approach for Detecting Trafc Accidents from Social Media Data”, Submitted to Transportation Research Part C
- Ni, M., Q. He, and J. Gao, “Forecasting the Subway Passenger Flow under Event Occurrences with Social Media”, IEEE Transactions on Intelligent Transportation Systems, Volume: 18, Issue: 6, June 2017, pp 1623-1632
- Zhang, Z., M. Ni, Q. He, J. Gao, J. Gou, and X. Li. “An Exploratory Study on the Correlation between Twitter Concentration and Trafc Surge”, Transportation Research Record, 2016, No. 2553, pp. 90–98
- Lin, L., M. Ni, Q. He, J. Gao, and A. Sadek, “Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: An Exploratory Study Utilizing Social Media Data”, Transportation Research Record: Journal of the Transportation Research Board, Sep 2015, Vol. 2482, pp. 82-8
- Ni, M., Q. He, and J. Gao, “Nonrecurrent Subway Passenger Flow Prediction from Social Media Under Event Occurrences”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016
- Ni, M., Q. He, and J. Gao, “Using Social Media to Predict Trafc Flow under Special Event Conditions”, Proceedings of 93rd Transportation Research Board Annual Meeting Washington DC, January 2014
- Zhang, Z., M. Ni, Q. He, J. Gao, J. Gou, and X. Li. “Extremity and Inﬂuential Factors Analysis on Travel Time of Emergency Vehicles”, Proceedings of 96th Transportation Research Board Annual Meeting Washington DC, January 2017
- Zhang, Z., M. Ni, Q. He, J. Gao, J. Gou, and X. Li. “Identifying On-Site Trafc Accidents Using Both Trafc and Social Media Data”, Proceedings of 95th Transportation Research Board Annual Meeting Washington DC, January 2016