Abstract
Emergency-vehicle drivers who aim to reach their destinations through the fastest possible routes cannot rely solely on expected average travel times. Instead, the drivers should combine this travel-time information with the characteristics of data variation and then select the best or optimal route. The problem can be formulated on a graph in which the origin point and destination point are given. To each arc in the graph a random variable is assigned, characterized by the expected time to traverse the arc and the variance of that time. The problem is then to minimize the total origin-destination expected time, subject to the constraint that the variance of the travel time does not exceed a given threshold. This paper proposes an exact pseudo-polynomial algorithm and an e-approximation algorithm (so-called FPTAS) for this problem. The model and algorithms were tested using real-life data of travel times under uncertain urban traffic conditions and demonstrated favorable computational results.
Highlights
The public’s concern for safety and improving quality of life has generated a need for improved service of numerous public-safety and transportation agencies
Efficient routing under uncertain traffic conditions can improve the performance of intelligent transportation systems, wherein real-time traffic information is available at emergency dispatch centers
This research aims to develop and enhance a real-time emergency response system that uses real-time travel time information to assist the dispatchers of emergency vehicles in assigning the vehicles to optimal routes
Summary
The public’s concern for safety and improving quality of life has generated a need for improved service of numerous public-safety and transportation agencies. Efficient routing under uncertain traffic conditions can improve the performance of intelligent transportation systems, wherein real-time traffic information is available at emergency dispatch centers. This research aims to develop and enhance a real-time emergency response system that uses real-time travel time information to assist the dispatchers of emergency vehicles in assigning the vehicles to optimal routes. To this end, this paper proposes a mathematical model for a decision-making process in real-time route dispatching. The model and algorithms were tested using real-life data of travel time under uncertain urban traffic conditions and demonstrated favorable computational results
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