Search

Highway emergencies need to be quickly responded. In order to improve the efficiency of response time, emergency vehicles can be dispatched making use of a model-assisted system. This paper deals with the parameters determination of optimization model for emergency service dispatching. Based on extending previous work, calculation methods of four parameters are discussed. The parameters include weight co-efficient, travel time, potential delays and reassigning criterion. On the premise of ignoring sort of indicators, the weight values are determined by Analytic Hierarchy Process (AHP). Through utilizing the data of the integrated platform for Geographic Information System/Global Positioning System/General Packet Radio Service (GIS/GPS/GPRS), the travel time is calculated by the geometric analysis of the call location and the vehicle location. The remaining two parameters are obtained by statistical analysis of historical data. In order to verify the calculation methods of parameters, samples of actual data used are tested. The results show that weight co-efficient is an important parameter, which can impact the dispatching plan of optimization model. Reassigning criterion is also useful to improve system efficiency. Serious emergencies should be responded to with higher priority.

Travel time reliability is considered as an important index on the reliability of a road network. The objective of this article is to study the impact of ice and snowfall on the travel time reliability. For this purpose, the link travel time ISB-BPR (Ice and Snowfall Based- Bureau of Public Roads) model is proposed, which is based on the conventional BPR (Bureau of Public Roads) model and scale functions are added to the corresponding parameters of BPR. To follow the characteristics of route choice, SUE (stochastic user equilibrium) is adopted in this study. We develop an assessment methodology, which integrates sensitivity analysis of SUE and Monte Carlo simulation to evaluate the travel time reliability. The ISB-BPR model and the algorithm are applied to a hypothetical small road network and results are presented. The results show that travel time level of service and intensity of ice and snowfall have the most significant impact on the travel time reliability.