Framework for Within Day Rescheduling due to UnexpectedIncidents in Transportation Networks

Abstract: In activity based modelling the concept of rescheduling is very important in order to gain dynamic scheduling of activities and to adjust the effect of unexpected incidents in individual agendas to keep them realistic and valid. This report describes a new framework to investigate algorithms for rescheduling on a large scale. This framework models the information of traffic demand and results of micro simulation of traffic on a loaded network; it enables agents to adapt their schedules by providing them with information about the traffic flow. A perception filter for each agent is included in this framework. It models the concept that some agents can notice the broadcast traffic information about the incident and get their own prediction of the expected delay, while other agents who do not notice the information can become aware only by experiencing traffic jam. Initial agendas are created by means of the FEATHERS activity based schedule generator for mutually independent agents. FEATHERS has no knowledge about the actual transportation network but makes use of an impedance matrix that specifies the minimal travel time between traffic analysis zones. The matrix specifies a free-flow value for the uncongested case and correction values for the loaded network. In this new framework the network state can be changed by agent behaviour and external incidents; the effect of this change in network state is perceived differently by each agent through a perception filter, and according to the perceived value individual adaption is calculated by a ReScheduler. The modified behaviour again creates new traffic demand hence creating a new traffic state; this phenomenon continues for the complete day. Each activity in the agenda is assumed to generate some utility. Each individual is assumed to maximize the total utility over the day. The ReScheduler is implemented using a marginal utility function that monotonically decreases with activity duration. This results in a monotonically converging relaxation algorithm to efficiently determine the new activity timing when less time is available for activities due to increased travel time caused by the incident.