An approach to designing vehicle routes in evacuation conditions

Abstract

In this paper what to models are considered in order to define optimal emergency vehicle distribution (in terms of vehicle numbers, weak users sequence to visit) and hence paths/routes with the objective to optimize the total time for safe users in evacuation condition. The design problem is tackled with a multilevel approach, which allows, by subsequent steps, to consider the network performances, the vehicle paths (One to One Problem) and the vehicle routes (Vehicle Routing Problem). In this paper the whole model is specified in terms of input and output variables and objective function.

A statistical approach to analyze user behaviour in road evacuation

“…Moreover, from the SICURO research project, models simulating path choice for emergency vehicles are proposed by Vitetta et al [11][12][13], Polimeni et al [14,15]. Models simulating the interaction of demand and supply by Vitetta et al [16][17][18], Marciano et al [19,20]; for this theme we recall also Russo and Vitetta [21].…”

A sequential dynamic choice model to simulate demand in evacuation conditions

“…Their experimentation using data obtained by a real experimentation in an urban area of the South Italy [4,5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]] is a work in progress. Good results have already been obtained from the experimentation of sequential dynamic discrete choice model simulating different user choices, as vehicle ownership [16,[18][19][20].…”

The planning process and logical framework approach in road evacuation: a coherent vision

“…▪ training for drivers as well as teaching and non-teaching staff to support evacuation operations, ▪ training for students concerning rules for behaviour during evacuation, ▪ definition of procedures for information exchange between the control centre and drivers and staff,  outputs, considering constraints, can be measured in terms of maximum feasible number of runs (R) and maximum number of passengers (D max ), R = 4 runs D max = φ * C = 120 users / hour  endogenous activities for the evolving system from outputs to outcomes can be represented by applying a supply-demand interaction which, in this case, is deterministic; from application of the model, run flows can be obtained (f 1 , f 2 , f 3 , f 4 ); to represent in a disaggregate form transport service performance in evacuation conditions, specific models can be applied [18,22]; to consider interaction with private cars and the need to introduce signal settings at the intersections other specific models are required [19,20];  outcomes are measured by the number of users in each run (f 1 = 30, f 2 = 30, f 3 = 30, f 4 = 10); in the considered case the first three runs transfer 90 students (f 1 + f 2 + f 3 ), the last run transfers the remaining 10 students (f 4 );  endogenous activities for the evolving system from outcomes to outputs can be represented by applying a discrete exposure function, that is defined in terms of the number of students to evacuate at the end of each run (E 1 , E 2 , E 3 , E 4 );  goals are measured by the reduction in exposure; in our case, exposure is measured in terms of students to evacuate:…”