Designing an intelligent emergency response system to minimize the impacts of traffic incidents: a new approximation queuing model

Hamid R. Sayarshad

International Journal of Urban Sciences, 2022, vol. 26, issue 4, 691-709

Abstract: A road traffic accident is an unexpected, irregular activity on the road network that sources a high excess demand relative to the reduced road capacity, resulting in traffic congestion and delays for travellers. The emergency response agencies need to shortly discover, respond to, and clear road traffic accidents in order to decrease the impacts of incidents on traffic congestion. To create an intelligent incident management response system for road networks, real-time data on traffic volumes and accident rates can be used in a queuing model for the allocation/relocation of available resources in response to incidents. In this study, a new queuing-based formulation is proposed for determining the positioning of emergency response units. The greatest benefit of the proposed dynamic model is a reduction in the time it takes response teams to clear accidents, remove debris on the roadway, and restore the normal traffic network. The analysis of actual accident data from New York City demonstrated that the proposed dynamic allocation strategy can contribute to reducing the total waiting time caused by accidents on roads instead of simply minimizing the average response times. The obtained results from testing the presented model revealed that the average costs in terms of the response time and the average delay reduced by 45% and 38%, in comparison to the static deployment model, respectively.HIGHLIGHTSA queuing model by characterizing the traffic congestion information is proposed.A dynamic policy of allocating response units using a queue system is studied.We study the advantages of our non-myopic model over the alternative myopic case.We show the effectiveness of the model by testing it on New York city incident data.The proposed dispatching strategy reduces the response time and the average delay.

Date: 2022
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DOI: 10.1080/12265934.2022.2044890

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