J
Jinhua Zhao
Researcher at Massachusetts Institute of Technology
Publications - 184
Citations - 4113
Jinhua Zhao is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Travel behavior & Public transport. The author has an hindex of 25, co-authored 170 publications receiving 2617 citations. Previous affiliations of Jinhua Zhao include University of British Columbia.
Papers
More filters
Journal ArticleDOI
Understanding the usage of dockless bike sharing in Singapore
Yu Shen,Xiaohu Zhang,Jinhua Zhao +2 more
TL;DR: In this paper, a new generation of bike-sharing services without docking stations is introduced, which is currently revolutionizing the traditional bike sharing market as it dramatically expands around the world.
Journal ArticleDOI
Estimating a Rail Passenger Trip Origin-Destination Matrix Using Automatic Data Collection Systems
TL;DR: This research presents a case study of the automatic fare collection system of the Chicago Transit Authority (CTA) rail system and develops a method for inferring rail passenger trip origin‐destination matrices from an origin‐only AFC system to replace expensive passenger OD surveys.
Journal ArticleDOI
Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore
TL;DR: The results show that the integrated system has the potential of enhancing service quality, occupying fewer road resources, being financially sustainable, and utilizing bus services more efficiently.
Understanding the usage of dockless bike sharing in Singapore
Yu Shen,Xiaohu Zhang,Jinhua Zhao +2 more
TL;DR: This study aims at understanding the usage of new dockless bike-sharing services through the lens of Singapore's prevalent service, and adopted spatial autoregressive models to analyze the spatiotemporal patterns of bike usage during the study period.
Journal ArticleDOI
Inferring patterns in the multi-week activity sequences of public transport users
TL;DR: Passenger heterogeneity is investigated based on a longitudinal representation of each user’s multi-week activity sequence derived from smart card data and reveals that significant connections exist between the demographic attributes of users and activity patterns identified exclusively from fare transactions.