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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

Projects

Project Highlights

Mining Transportation Information from Social Media for Planned and Unplanned Events

  • 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.

publications

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

Journal Publications

  • 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

Conference publications

  • 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

Contact Me

North Campus Map -- University at Buffalo

Email: mingni {at} buffalo dot edu

Adress: 334 Bell Hall, University at Buffalo, Amherst, NY 14260

Department of Industrial & Systems Engineering

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