Business Process Redesign in Information Technology Incident Management: A teaching case

This paper consists of a detailed case narrative on how a leading Australian Finance organisation has utilised contemporary Business Process Management (BPM) concepts for improving the IT incident management processes within the whole organisation. The target audience includes practitioners who are interested in BPM case studies and Academics who may be seeking case studies for innovative teaching practices.

Decision making effectiveness in wildfire incident management teams

During large scale wildfires, suppression activities are carried out under the direction of an Incident Management Team (IMT). The aim of the research was to increase understanding of decision processes potentially related to IMT effectiveness. An IMT comprises four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to study IMT processes: computer simulation experiments; analyses of wildfire reports; interviews with IMT members; and cognitive ethnographic studies of IMTs. Three processes were important determinants of IMT effectiveness: information management and cognitive overload; matching component function goals to overall goals; and team metacognition to detect and counter task-disruptive developments. These processes appear to be complex multi-person analogues of individual Incident Command processes identified previously. The findings have implications for issues such as: creating IMTs; training IMTs; managing IMTs; and providing decision support to IMTs.

Bushfire incident management team decision making

Large scale bushfire (or wildfire) suppression activities are conducted under the control of an Incident Management Team (IMT) comprising four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to investigate processes determining the effectiveness of IMT decision making activities: (a) laboratory experiments using the Networked Fire Chief computer simulation program; (b) analyses of reports of significant fires; (c) structured interviews with experienced IMT staff; and, (d) cognitive ethnographic studies of IMTs. Three classes of team processes were found to be important determinants of IMT effectiveness: information sharing and management; matching of the four component function goals to overall IMT goals; and monitoring of the overall IMT situation to detect and correct task disruptive processes. Several non-rational processes with the potential for hindering IMT effectiveness were noted. Team metacognition emerged as a key process for understanding effective IMT decision making.

Automated incident management plan using geographic information systems technology for traffic management centers. Final report.

Texas Department of Transportation, Austin

Incident management program for Virginia. Final report.

Virginia Department of Transportation, Richmond

The Pennsylvania Turnpike Commission's Incident Management Team.

Transportation Department, Washington, D.C.

Developing freeway and incident management systems using the national ITS architecture.

Performing organizations: Mitretek Systems and PB Farradyne, Inc.

Massachusetts Incident Management Conference proceedings, June 6, 1991.

"DOT-T-92-05."

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.