Inferring Transportation Modes from Mobile Phone Data Using Machine Learning Methods(1/2)

Project Details


Traffic data – consisting of activity location, origin-destination pair, mode choice and traffic assignment – are essential in transportation planning, or more specifically, travel demand forecasting. Collecting such data via a questionnaire survey, like the home or roadside interview, have long been adopted, but are usually (1) labor intensive, (2) faced with high refusal rates of respondents, and (3) relatively inaccurate due to fade-away memory. Attempts have been made to use GPS data, but GPS data are not readily available and their levels of accuracy are apt to be affected by the shielding effect due to high-rise buildings and obstacles and, hence, are not suitable to be applied in a large transportation network. Mobile phone data, emerging as a vivid data collection method for transportation planning, can automatically and effectively record transportation planning data in time-space dimension without having to add new devices. Thus, the extra cost to retrieve this phone data is small or even negligible. For this study, we will adopt two supervised machine learning methods – support vector machine (SVM) and deep neural network (DNN) – to investigate how modal features (travel times, starting time of traces, traversal speeds between traces, maximum speeds, and average speeds), time of day (peak hours, off-peak hours, whole day), route combinations (bus route, vehicle traversing a bus route, vehicle traversing non-bus routes), and training methods, i.e., support vector machine (SVM) or deep neural network (DNN) affect accuracy in inferring transportation modes (either bus or vehicle).This study takes mode inference as an example to explore the usefulness of mobile phone data in the area of transportation planning. This proposal will be conducted in two consecutive years. In the first year, the major work to be carried out covers: (1) literature review; (2) research framework; (3) field data collection; (4) preprocessing of mobile phone data (sighting data); (5) SVM result analysis; (6) interim project report. In the second year, the major work to be carried out includes: (1) literature review; (2) research framework; (3) classification of vehicle/bus routes for a practical transportation network and collection of smart card data for bus routes; (4) DNN result analysis; (5) final project report. When the two-year project is completed, the results thus obtained are expected to come up with one or two papers to be published in international journals.
Effective start/end date1/08/2031/07/21

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 9 - Industry, Innovation, and Infrastructure


  • mobile phone data
  • oscillation phenomenon
  • mode inference
  • vector support machine
  • deep learning


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