Translating research to practice: Implementing real-time control on high-frequency transit routes

Berrebi, Simon; Crudden, Sean Óg; Watkins, Kari

doi:10.1016/j.tra.2018.03.008

Cited by 15 publications

(18 citation statements)

References 15 publications

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“…However, aforementioned studies did not actually determine optimal control strategies in a data-driven manner, but relied on the estimation of arrival times through prediction methodologies and simulation analysis to evaluate proposed models [80,88]. In this context, of specific interest is the work in [95,96], who explored the practical applicability of optimal holding control models proposed in the literature and underlined arising issues on the topic. 3 Main findings and research gaps…”

Section: Real-time Controlmentioning

confidence: 99%

“…Additional data processing required: Many AVL and AFC systems do not archive data in a readily utilized manner, as they are primarily designed for system monitoring [8]. This means that additional data processing and analysis are required in order to render this data useful to transit planners [4,5,96]. Lack of integration among various data sources: Cumbersome procedures are required, so that the inputs required by a planning/design model, specific practitioners' knowledge and the outputs of monitoring systems may be consolidated in a common framework.…”

Section: Practical Challenges Arising In Its Data Exploitationmentioning

confidence: 99%

“…Travel time prediction algorithms could aid flexible routing solutions to estimate how schedule deviations may alter running times [107]. Few such studies were identified [76,77], indicating that this appears to be a promising research path, which could also include performance comparisons among different algorithms [96]. Furthermore, combining optimal control models and prediction methodologies is deemed a promising path [91], as existing studies typically use model-based predictions for arrival times, thus not performing a purely data-driven analysis [88].…”

Section: Real-time Operationsmentioning

confidence: 99%

“…Further, the availability of real-time passenger demand data can significantly improve the performance of control models in cases of overcrowding [87] and in the context of transfer synchronization [80]. Finally, control strategies are almost exclusively verified using simulation, yet the implementation of a real-time holding method involves technical challenges that can be overlooked in a simulation environment [96]. Although it can be hard to convince agencies to allow experimentation [94], such experiments lead to valuable conclusions and advance both research and practice.…”

Section: Real-time Operationsmentioning

confidence: 99%

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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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Section: Real-time Controlmentioning

confidence: 99%

Section: Practical Challenges Arising In Its Data Exploitationmentioning

confidence: 99%

Section: Real-time Operationsmentioning

confidence: 99%

Section: Real-time Operationsmentioning

confidence: 99%

See 2 more Smart Citations

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

Iliopoulou

Κεπαπτσόγλου

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…The model could be used to produce a plan of holding times to account for the current state of the system and also for expected changes in running times and demand. Berrebi, Crudden, and Watkins () applied a real‐time holding method on three high‐frequency transit routes. The performance of the method was evaluated in terms of headway stability, holding time, and mean passenger waiting time.…”

Section: Introductionmentioning

confidence: 99%

Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment

Bie

Xiong

Yan

et al. 2019

Computer aided Civil Eng

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To prevent bus bunching, a dynamic headway control method in the V2I (vehicle to infrastructure) environment for a high‐frequency route with bus lane is developed. Bus operating speed guidance on the mid‐blocks and intersection signal adjustment are two main strategies in the proposed method. A forecasting model of bus travel time under the dynamic control method is developed. The objective function is set up by taking into account differences between actual bus headways and dispatching headways, and the scaling ratios of intersection cycle lengths. The optimization model is solved using genetic algorithm. The proposed method is applied to a real bus route in Meihekou city, China, and compared with the current control plan as well as holding strategy. Results show that the proposed method can reduce bus headway deviations in all investigating periods; negative impacts on cars can be limited by setting reasonable values for the parameters.

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An Open-Source Framework to Implement Kalman Filter Bus Arrival Predictions

Crudden

Berrebi

2021

Netw Spat Econ

Self Cite

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The accuracy of vehicle arrival predictions affects every aspect of transit performance including ridership, reliability, and operating costs. Kalman Filter algorithms have been shown to provide more accurate predictions than simple regression. This paper presents a scalable framework to implement Kalman Filters on an entire bus network running live. A novel architecture to cache the data and weight inputs based on current operating conditions is presented. All the necessary features to support Kalman Filter predictions are described and implemented in TheTransitClock, an open-source prediction tool. TheTransitClock was deployed on the Metro Transit bus network in the Minneapolis-St. Paul region for one month. The Kalman Filter algorithm predicted the arrivals of over 900 buses on 167 routes. The accuracy and sensitivity of the method was compared to a schedule-based prediction method used in practice. The Kalman Filter was found to provide more accurate and stable predictions, especially in times and places that are difficult to predict with conventional methods.

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Translating research to practice: Implementing real-time control on high-frequency transit routes

Cited by 15 publications

References 15 publications

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

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

Dynamic headway control for high‐frequency bus line based on speed guidance and intersection signal adjustment

An Open-Source Framework to Implement Kalman Filter Bus Arrival Predictions

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