Since their appearance at the end of the 19th century, traffic lights have been the primary mode of granting access to road intersections. Today, this centuries-old technology is challenged by advances in intelligent transportation, which are opening the way to new solutions built upon slot-based systems similar to those commonly used in aerial traffic: what we call Slot-based Intersections (SIs). Despite simulation-based evidence of the potential benefits of SIs, a comprehensive, analytical framework to compare their relative performance with traffic lights is still lacking. Here, we develop such a framework. We approach the problem in a novel way, by generalizing classical queuing theory. Having defined safety conditions, we characterize capacity and delay of SIs. In the 2-road crossing configuration, we provide a capacity-optimal SI management system. For arbitrary intersection configurations, near-optimal solutions are developed. Results theoretically show that transitioning from a traffic light system to SI has the potential of doubling capacity and significantly reducing delays. This suggests a reduction of non-linear dynamics induced by intersection bottlenecks, with positive impact on the road network. Such findings can provide transportation engineers and planners with crucial insights as they prepare to manage the transition towards a more intelligent transportation infrastructure in cities.

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Revisiting Street Intersections Using Slot-Based Systems.

BIM-based integrated management workflow design for schedule and cost planning of building fabric maintenance

Analysis of a finite-buffer bulk-service queue under Markovian arrival process with batch-size-dependent service

https://biblio.ugent.be/publication/3079134/file/3205774.pdf

Analysis of a versatile batch-service queueing model with correlation in the arrival process

https://biblio.ugent.be/publication/3033378/file/3205773.pdf

Tail probabilities of the delay in a batch-service queueing model with batch-size dependent service times and a timer mechanism

Reducing congestion in bulk-service finite-buffer queueing system using batch-size-dependent service

Analysis of finite-buffer discrete-time batch-service queue with batch-size-dependent service

Over the past few decades, bulk-arrival bulk-service queues have found wide application in several areas including computer-communication and telecommunication systems. In this paper, we consider a single server finite-buffer queue where customers arrive in batches according to the compound Poisson process and are served in batches of variable service capacity. The service times of the batches are arbitrarily distributed and depend on the size of the batch taken into for service. We obtain the joint distribution of the number of customers in the queue and number with the server, and other distributions such as number of customers in the queue, in the system, and number with the server. Various performance measures such as average number of customers in the system (queue), with the server, blocking probabilities, etc. are obtained. Several numerical results are presented and comparative studies of batch-size-dependent service with the one when service time of the batches are independent of the size of the batch have been carried out.

Analysis of finite-buffer bulk-arrival bulk-service queue with variable service capacity and batch-size-dependent service:

In a recent paper, Chang et al. (2004) analysed finite-buffer bulk-arrival bulk-service queue with variable server capacity:

A. Banerjee

Papers

Analysis of a finite-buffer bulk-service queue under Markovian arrival process with batch-size-dependent service

Reducing congestion in bulk-service finite-buffer queueing system using batch-size-dependent service

Analysis of finite-buffer discrete-time batch-service queue with batch-size-dependent service

Analysis of finite-buffer bulk-arrival bulk-service queue with variable service capacity and batch-size-dependent service:

Computing system length distribution of a finite-buffer bulk-arrival bulk-service queue with variable server capacity