Origin-Destination Trip and LRT Ridership Estimation with New Data Source

• Author / Creator

  He, Difei

• Transportation planning are important for improving traveler’s efficiency and saving energies to provide a more sustainable community. Traditional methods of conducting the transportation planning origin-destination estimation and multi-modal analysis are heavily relying on the labour-intensive data collection that are no longer suitable for today’s increasing demand of travel needs. That being said, the rapid development of new technologies in telecommunication networks is producing large amounts of network data regarding how people and their devices move around in the city. In contrast to traditional GPS data that required additional geographical sensors and applications to record the information, network data has the advantage of high market penetration rates, low costs, and daily collected geographical information when considering urban travel behaviour analysis. The geographical information embedded in the network data offers researchers the potential to investigate travel mobility behaviour. However, due to the noise and spatial/temporal sparsity of network data, extracting mobility information, such as transport mode, from these data is challenging. This thesis proposes a complete architecture of transport mode detection based on the network data to monitor the Light Rail Transit ridership during daily use and to estimate the ridership and origin-destination matrices from an “easy-to-detect” transport mode, like the LRT. A hybrid heuristic method that combines a time-window based method and a pattern-based method is proposed to process the raw network data, followed by a binary logit model to estimate the probability of one candidate trip being an LRT trip. The statistical results from the network data analysis are validated by third party data reported by the City of Edmonton that shows passengers boarding and alighting at each station. Although the performance of the proposed methods lacks prior analysis, owing to the absence of ground truth, the results in this study are analyzed based on the prior knowledge and intuitive understanding of the City’s LRT system operation. Finally, this study reviews the current research gaps within the transportation field regarding data cleaning methods, mode detection models, and bias issues.

• Subjects / Keywords

  • Light Rail Transit
  • Trip Trajectories
  • Origin-Destination Matrices
  • Transit Ridership

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-dzat-sz49

• License

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