TY - GEN

T1 - Shortest Path of Emergency Vehicles Under Uncertain Urban Traffic Conditions

AU - Hadas, Y.

AU - Ceder, A.

N1 - Place of conference:USA

PY - 1996

Y1 - 1996

N2 - Emergency vehicle characteristics amplify the stochastic nature of transportation networks. The emergency vehicle operator who aims at reaching his destination in the fastest time possible cannot rely on "average" data alone. Each emergency event has its own implications (accident, fire, injury, security event, etc.) and must be dealt with as an individual incident. The need to deal with each event separately led, first, to the development of a stochastic shortest-path algorithm that refers to the dynamic traffic flow and then to a presentation method of the results so as to incorporate the operator's accumulated knowledge. The whole algorithm is based on a K shortest-path model incorporated with a simulation element in order to consider stochastic characteristics. The stochastic model uses a new definition, namely, the probability that a given path is the shortest. In contrast to a deterministic model, which yields a single shortest path, the stochastic model yields a set of paths, each having a different probability. This set of paths, along with relevant information for the emergency vehicle, is presented in a particular way to the operator. In addition, it was found that the arrangement of information is vital to the selection of the most promising path.

AB - Emergency vehicle characteristics amplify the stochastic nature of transportation networks. The emergency vehicle operator who aims at reaching his destination in the fastest time possible cannot rely on "average" data alone. Each emergency event has its own implications (accident, fire, injury, security event, etc.) and must be dealt with as an individual incident. The need to deal with each event separately led, first, to the development of a stochastic shortest-path algorithm that refers to the dynamic traffic flow and then to a presentation method of the results so as to incorporate the operator's accumulated knowledge. The whole algorithm is based on a K shortest-path model incorporated with a simulation element in order to consider stochastic characteristics. The stochastic model uses a new definition, namely, the probability that a given path is the shortest. In contrast to a deterministic model, which yields a single shortest path, the stochastic model yields a set of paths, each having a different probability. This set of paths, along with relevant information for the emergency vehicle, is presented in a particular way to the operator. In addition, it was found that the arrangement of information is vital to the selection of the most promising path.

UR - https://scholar.google.co.il/scholar?q=+Shortest+Path+of+Emergency+Vehicles+Under+Uncertain+Urban+Traffic+Conditions&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - The 75th Annual Meeting of the Transportation Research Board

ER -