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Register a new userPublic Transit for Special Events: Ridership Prediction and Train Optimization
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Added by
Anthony Trasatti
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Many special events, including sport games and concerts, often cause surges in demand and congestion for transit systems. Therefore, it is important for transit providers to understand their impact on disruptions, delays, and fare revenues. This paper proposes a suite of data-driven techniques that exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems during recurring congestion peaks due to special events. This includes an extensive analysis of ridership of the two major stadiums in downtown Atlanta using rail data from the Metropolitan Atlanta Rapid Transit Authority (MARTA). The paper first highlights the ridership predictability at the aggregate level for each station on both event and non-event days. It then presents an unsupervised machine-learning model to cluster passengers and identify which train they are boarding. The model makes it possible to evaluate system performance in terms of fundamental metrics such as the passenger load per train and the wait times of riders. The paper also presents linear regression and random forest models for predicting ridership that are used in combination with historical throughput analysis to forecast demand. Finally, simulations are performed that showcase the potential improvements to wait times and demand matching by leveraging proposed techniques to optimize train frequencies based on forecasted demand.
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We present a flexible public transit network design model which optimizes a social access objective while guaranteeing that the systems costs and transit times remain within a preset margin of their current levels. The purpose of the model is to find a set of minor, immediate modifications to an existing bus network that can give more communities access to the chosen services while having a minimal impact on the current networks operator costs and user costs. Design decisions consist of reallocation of existing resources in order to adjust line frequencies and capacities. We present a hybrid tabu search/simulated annealing algorithm for the solution of this optimization-based model. As a case study we apply the model to the problem of improving equity of access to primary health care facilities in the Chicago metropolitan area. The results of the model suggest that it is possible to achieve better primary care access equity through reassignment of existing buses and implementation of express runs, while leaving overall service levels relatively unaffected.
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Transit ridership flow and origin-destination (O-D) information is essential for enhancing transit network design, optimizing transit route and improving service. The effectiveness and preciseness of the traditional survey-based and smart card data-driven method for O-D information inference have multiple disadvantages due to the insufficient sample, the high time and energy cost, and the lack of inferring results validation. By considering the ubiquity of smart mobile devices in the world, several methods were developed for estimating the transit ridership flow from Wi-Fi and Bluetooth sensing data by filtering out the non-passenger MAC addresses based on the predefined thresholds. However, the accuracy of the filtering methods is still questionable for the indeterminate threshold values and the lack of quantitative results validation. By combining the consideration of the assumed overlapped feature space of passenger and non-passenger with the above concerns, a three steps data-driven method for estimating transit ridership flow and O-D information from Wi-Fi and Bluetooth sensing data is proposed in this paper. The observed ridership flow is used as ground truth for calculating the performance measurements. According to the results, the proposed approach outperformed all selected baseline models and existing filtering methods. The findings of this study can help to provide real-time and precise transit ridership flow and O-D information for supporting transit vehicle management and the quality of service enhancement.
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