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A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting
2024 IEEE International Conference on Smart Computing (SMARTCOMP)Public transit systems are paramount in lowering carbon emissions and reducing urban congestion for environmental sustainability. However, overcrowding has adverse effects on the quality of service, passenger experience, and overall efficiency of public ...
Samir Gupta +6 more
semanticscholar +2 more sources
Synthesis of North American High-Speed Passenger Rail Ridership Forecasting
Transportation Research Record: Journal of the Transportation Research Board, 2017This paper presents a synthesis of two decades of ridership forecasts developed for proposed high-speed intercity passenger rail (HSR) routes in various stages of development across North America. A comprehensive database of ridership forecasts consisted of 210 ridership estimates from 43 unique intercity corridors.
B. Sperry
semanticscholar +2 more sources
Simplifications for single-route transit-ridership forecasting models
Transportation, 1984The growth in popularity of microcomputers has reemphasized the need for simplified transit-planning techniques. This paper describes and evaluates a single-route ridership forecasting model which is designed to fit within a modest-sized microcomputer.
A. Horowitz
semanticscholar +2 more sources
Demand Forecasting in Transportation: A Graph Attention Networks for Predicting Bus Ridership
2025 27th International Conference on Advanced Communications Technology (ICACT)Demand prediction in transportation systems plays a critical role in optimizing resources and improving service efficiency. This study explores demand prediction for Ulaanbaatar's public transportation network using Graph Attention Networks (GATs ...
Tsetsentsengel Munkhbayar +4 more
semanticscholar +2 more sources
Subway Ridership Forecasting Using Seasonal and Holt-Winters Models with Calendar Effects
2025 International Conference on Computer, Information and Telecommunication Systems (CITS)This paper proposes an improved forecasting approach for predicting subway ridership in New York City by integrating calendar effects such as holidays and weekends into Seasonal Exponential Smoothing (SES) and Holt-Winters Exponential Smoothing (HWES ...
Mustafa Akpinar +3 more
semanticscholar +2 more sources
Public Transit Ridership Forecasting Models
International Encyclopedia of Transportation, 2021I. Banerjee, D. L., A. Pinjari
semanticscholar +2 more sources
Market Segmentation Approach to Mode Choice and Ferry Ridership Forecasting
Transportation Research Record: Journal of the Transportation Research Board, 2004The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California legislature. Predicting ferry ridership has historically been difficult because water-transit riders often choose their travel mode based on factors other than travel time and cost.
M. Outwater +3 more
semanticscholar +3 more sources
Stop-level Urban Transit Ridership Forecasting A case Study
, 2011The objective of this study was to develop models for forecasting ridership at an individual transit stop or route section. This study addressed fixed-route transit services provided by the Adelaide Metro in the Western Statistical Sub-division and estimated boardings in one direction (towards the city) during morning peak and inter-peak periods.
Sekhar Somenahalli
semanticscholar +3 more sources
Transportation Research Record: Journal of the Transportation Research Board, 2003
The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process.
M. Outwater +5 more
semanticscholar +3 more sources
The San Francisco Bay Area Water Transit Authority is evaluating expanded ferry service, as required by the California Legislature. As part of this process, Cambridge Systematics developed forecasts using a combination of market research strategies and the addition of nontraditional variables into the mode choice modeling process.
M. Outwater +5 more
semanticscholar +3 more sources
Are Graphs and GCNs necessary for short-term metro ridership forecasting?
Expert Systems with ApplicationsQiong Yang +4 more
semanticscholar +2 more sources