Bus ridership prediction using machine learning integrated with geographic information system
Date
2020
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Publisher
University of Delaware
Abstract
Public transit is vital for people who do not have personal vehicles to travel and commute. Unlike large cities where subways and trolley cars are available, bus is the only form of public transit besides a commuter rail line in the state of Delaware. Thus, it is important to provide extensive service and always seek bus route and service changes to ensure the most transit service coverage is achieved for the changing population. ☐ This research finds that the annual bus ridership in Delaware has been decreasing for several years. Since the population of the state is growing gradually each year, the decrease of ridership suggests that the bus agency faces challenges to provide service that is sufficiently attractive to maintain the number of riders. Appropriate bus route changes might be able to attract more bus riders, thereby helping the ridership to reach a historical high, or even higher. ☐ Aiming to solve this problem, this research proposes a framework that predicts ridership at any location (hypothetically proposed bus stop locations) using different machine learning techniques. The data include historical on, off and total ridership data for DART’s 1,864 bus stops for 2018, demographic data from the American Community Survey (ACS), employment and land use data, and the road network. The framework integrates the spatial data to the point data for bus stops to predict ridership. The best performing machine learning model is determined for this study, which demonstrated the power of integrating geographic information systems with machine learning and being able to present the results visually. The prediction results indicate that there might be new locations that are more suitable bus stops. ☐ While the results are promising, analysis suggests that the aggregation level that American Community Survey (ACS) data provides is not ideal for bus ridership prediction. Meanwhile, more variables that influence ridership can be added to this framework if bus agencies can obtain more detailed data. These limitations are listed at the end to give directions for future work.
Description
Keywords
Bus ridership forecasting, Geographic information system, Machine learning, Transit planning