This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership.
Chintan Amrit +2 more
exaly +3 more sources
A comparison of time series methods for post-COVID transit ridership forecasting
Transit agencies conduct system-level ridership forecasting for planning, budgeting, and other administrative purposes. However, the COVID-19 pandemic introduced substantial changes in transit ridership levels and seasonal patterns, which has impacted the performance of ridership forecasting.
Ashley Hightower +2 more
exaly +3 more sources
Forecasting bus ridership using a “Blended Approach” [PDF]
AbstractAs sources of “Big Data” continue to grow, transportation planners and researchers seek to utilize these new resources. Given the current dependency on traditional transportation data sources and conventional tools (e.g., spreadsheets and propriety models), how can these new resources be used?
Catherine T. Lawson +2 more
openaire +2 more sources
Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China
Many large cities rely on Mass Rapid Transit (MRT) to increase passenger mobility. For efficiency, MRT stations should be arranged to attract maximal number of travelers. It is therefore important to develop methods for estimating MRT ridership forecasting models, which are important for policies on land use development or new MRT lines.
Ruili Guo, Zhengdong Huang
openaire +2 more sources
Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership
Since its establishment in 1999, the Metro Rail Transit Line 3 (MRT3) has served as a transportation option for numerous passengers in Metro Manila, Philippines. The Philippine government's transportation department records more than a thousand people using the MRT3 daily and forecasting the daily passenger count may be rather challenging.
Jayrald Empino +5 more
openaire +3 more sources
Mixed Data and Classification of Transit Stops
An analysis of the characteristics and behavior of individual bus stops can reveal clusters of similar stops, which can be of use in making routing and scheduling decisions, as well as determining what facilities to provide at each stop.
Handley, John C. +2 more
core +1 more source
Micromobility evolution and expansion: Understanding how docked and dockless bikesharing models complement and compete – A case study of San Francisco [PDF]
Shared micromobility – the shared use of bicycles, scooters, or other low-speed modes – is an innovative transportation strategy growing across the United States that includes various service models such as docked, dockless, and e-bike service models ...
Feng, Frank +4 more
core
Integrating geographic information systems into transit ridership forecast models
AbstractResearchers have produced sophisticated modal split and transit demand models, including forecasts that are sensitive to the level of service. However, little effort has been made to integrate these models into corridor studies and route alignment analyses since (a) re‐routing is itself an extremely complex modeling task, and (b) the results of
Kamal T. Azar, Joseph Ferreira
openaire +1 more source
Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43 [PDF]
Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger ...
Loukaitou-Sideris, Anastasia +2 more
core +1 more source
The Impact of Demographic Change on the Accessibility to Public Services for the Elderly on Children of Hannover, Germany [PDF]
Late in 2009, the German government conducted an exercise to determine population trends for the next 50 years. This study indicated that the German population, which is approximately 82 million, is expected to decrease by 12 to 17 million people as well
Frazier, Tyler J. +1 more
core +1 more source

