A data-driven agent-based model of congestion and scaling dynamics of rapid transit systems

Othman, Nasri Bin; Legara, Erika Fille; Selvam, Vicknesh; Monterola, Christopher

doi:10.1016/j.jocs.2015.03.006

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…Examples of these approaches are often developed under the banner of 'Data-Driven Agent-Based Modelling' (DDABM), which itself emerged from a broader work in data-driven application systems [2]. A number of recent attempts have been made to allow agent-based models to react to new data [12,18,20,11,7,10,19,13,8,6]. However, whilst promising these applications all exhibit a number of limitations that this work will begin to address.…”

Section: Relevant Researchmentioning

confidence: 99%

Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter

Clay

Kieu

Ward

et al. 2020

Lecture Notes in Computer Science

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Section: Relevant Researchmentioning

confidence: 99%

Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter

Clay

Kieu

Ward

et al. 2020

Lecture Notes in Computer Science

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“…The disaggregate nature of AFC transactions and the presence of trajectory data calls for new data mining methods and algorithms, as well as advanced statistical inference techniques [19,20,38,122]. Responding to the overarching need for better decision support tools for ITS data, there exists some work on the development of data-driven platforms for public transportation planning [5,124]. The latter integrate data mining methods, regression models and visualization techniques to assist in performance monitoring, predict and evaluate potential impact of different transit strategies and provide a more comprehensive understanding of network dynamics overall.…”

Section: Integrated Transit Modelingmentioning

confidence: 99%

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Iliopoulou

Κεπαπτσόγλου

2019

Eur. Transp. Res. Rev.

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Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.

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“…Predicting the effects of service changes on objective ride experiences is becoming more feasible via the use of agent-based models (ABMs). These computational models simulate each vehicle and/or passenger as an independent agent, and so capture aggregate trends by modeling individual-level behaviours and interactions (McDonnell & Zellner, 2011;Othman, Legara, Selvam, & Monterola, 2015;Wahba & Shalaby, 2011). This micro-level simulation means ABMs are able to capture higher-level phenomena which emerge from lower-level interactions and feedback loops.…”

Section: System Changes Ride Experiences and Passenger Satisfactionmentioning

confidence: 99%

A Predictive Model of Ride Satisfaction in Mass Transit Users

Jjp¹,

Farber²

2018

Preprint

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We describe a model for predicting subjective user satisfaction based on objective ride conditions. The model takes ride features (e.g., duration, crowding, waiting time) as inputs and estimates users' single-ride satisfaction. This model enables transit authorities to predict the effect of service changes on user satisfaction. This in turn makes it possible to compare multiple candidate system configurations to determine which would yield the highest level of satisfaction. Using a sample of mass rapid transit users (n=641), the model is first trained then validated against unseen data. Prediction uncertainty is accounted for using a residual term. We also provide an example of how this model can be used to derive predictions, using artificial data. Highlights We derive a predictive model of single-ride satisfaction from a diverse sample of transit users (n=641). Transit authorities can use this model to predict the impact of changes to service on rider satisfaction. Validation analysis is provided to confirm the model's applicability to new cases. PREDICTIVE MODEL OF TRANSIT SATISFACTION 3

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A data-driven agent-based model of congestion and scaling dynamics of rapid transit systems

Cited by 18 publications

References 13 publications

Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter

Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter

Combining ITS and optimization in public transportation planning: state of the art and future research paths

A Predictive Model of Ride Satisfaction in Mass Transit Users

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