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Operations and Supply Chain Management Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
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- Non-recurrent congestion (2)
- Traffic density (2)
- Congestion management (1)
- Markov decision processes (1)
- Markov modulated service (1)
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- Markov-modulated queues (1)
- Mixture distributions (1)
- Queuing theory (1)
- Rail defect prediction (1)
- Random forests (1)
- Random incidents (1)
- Random queues (1)
- Recurrent congestion (1)
- Recurrent neural networks (1)
- Restless bandits (1)
- Stochastic models (1)
- Tandem-queues (1)
- Traffic breakdown (1)
- Traffic incidents (1)
- Travel time reliability (1)
- Whittle index (1)
Articles 1 - 4 of 4
Full-Text Articles in Operations and Supply Chain Management
How Incidents Impact Congestion On Roadways: A Queuing Network Approach, Pedro Cesar Lopes Gerum, Melike Baykal-Gursoy
How Incidents Impact Congestion On Roadways: A Queuing Network Approach, Pedro Cesar Lopes Gerum, Melike Baykal-Gursoy
Supply Chain Management
Motivated by the need for transportation infrastructure and incident management planning, we study traffic density under non-recurrent congestion. This paper provides an analytical solution approximating the stationary distribution of traffic density in roadways where deterioration of service occurs unpredictably. The proposed solution generalizes a queuing model discussed in the literature to long segments that are not space-homogeneous. We compare single and tandem queuing approaches to segments of different lengths and verify whether each model is appropriate. A single-queue approach works sufficiently well in segments with similar traffic behavior across space. In contrast, a tandem-queue approach more appropriately describes the density …
How Random Incidents Affect Travel-Time Distributions, Melike Baykal-Gürsoy, Andrew Reed Benton, Pedro Cesar Lopes Gerum, Marcelo Figueroa Candia
How Random Incidents Affect Travel-Time Distributions, Melike Baykal-Gürsoy, Andrew Reed Benton, Pedro Cesar Lopes Gerum, Marcelo Figueroa Candia
Supply Chain Management
We present a novel analytical model to approximate the travel-time distribution of vehicles traversing a freeway corridor that experiences random quality of service degradations due to non-recurrent incidents. The proposed model derives the generating function of travel times in closed-form using clearance time, incident frequency and severity, and other ordinary traffic characteristics. We validate the model using data from a freeway corridor where weather events and traffic accidents serve as the principal causes of service degradation. The resulting model is equivalent in performance to widely used methodologies while uniquely providing a clear connection on how incidents affect travel time distribution. …
Traffic Density On Corridors Subject To Incidents: Models For Long-Term Congestion Management, Pedro Cesar Lopes Gerum, Andrew Reed Benton, Melike Baykal-Gürsoy
Traffic Density On Corridors Subject To Incidents: Models For Long-Term Congestion Management, Pedro Cesar Lopes Gerum, Andrew Reed Benton, Melike Baykal-Gürsoy
Supply Chain Management
The purpose of this research is to provide a faster and more efficient method to determine traffic density behavior for long-term congestion management using minimal statistical information. Applications include road work, road improvements, and route choice. To this end, this paper adapts and generalizes two analytical models (for non-peak and peak hours) for the probability mass function of traffic density for a major highway. It then validates the model against real data. The studied corridor has a total of 36 sensors, 18 in each direction, and the traffic experiences randomly occurring service deterioration due to accidents and inclement weather such …
Data-Driven Predictive Maintenance Scheduling Policies For Railways, Pedro Cesar Lopes Gerum, Ayca Altay, Melike Baykal-Gürsoy
Data-Driven Predictive Maintenance Scheduling Policies For Railways, Pedro Cesar Lopes Gerum, Ayca Altay, Melike Baykal-Gürsoy
Supply Chain Management
Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the …