The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day.
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Dynamic Integration of Heterogeneous Transportation Modes Under Disruptive Events
An integrated urban transportation system usually
consists of multiple transport modes that have complementary
characteristics of capacities, speeds, and costs, facilitating smooth
passenger transfers according to planned schedules. However,
such an integration is not designed to operate under disruptive
events, e.g., a signal failure at a subway station or a breakdown
of a bus, which have rippling effects on passenger demand
and significantly increase delays. To address these disruptive
events, current solutions mainly rely on a substitute service
to transport passengers from and to affected areas using adhoc
schedules. To fully utilize heterogeneous
transportation systems under disruptive events, we design
a service called eRoute based on a hierarchical receding horizon
control framework to automatically reroute, reschedule, and
reallocate multi-mode transportation systems based on real-time
and predicted demand and supply. Focusing on an integration of
subway and bus, we implement and evaluate eRoute with large
datasets including (i) a bus system with 13,000 buses, (ii) a subway
system with 127 subway stations, (iii) an automatic fare collection
system with a total of 16,840 readers and 8 million card users
from a metropolitan city. The data-driven evaluation results show
that our solution improves the ratio of served passengers (RSP)
by up to 11.5 times and reduces the average traveling time by
up to 82.1% compared with existing solutions.
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- Award ID(s):
- 1521722
- NSF-PAR ID:
- 10059942
- Date Published:
- Journal Name:
- ACM/IEEE International Conference on Cyber-Physical Systems
- ISSN:
- 2375-8317
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
