A real-time decision support system for roadway network incident response logistics
Introduction
Traffic incidents are unplanned events that occur randomly in time and space, e.g., accidents, spilled cargoes, disabled vehicles, and debris on the road, which reduce substantially the capacity of the roadway section in which they occur, and result in traffic congestion. The high economic and social impact associated with incident-related congestion explains the increased effort allocated by the various Traffic Management Agencies world-wide for developing incident management systems (IMS) aiming to alleviate incident consequences (Zografos and Vasilakis, 1997a). Incident management is defined as the co-ordinated, pre-planned and/or real-time use of human resources and equipment for reducing the duration and consequently incident related impacts. Incident management involves a systematic approach for reducing the time it takes to detect and verify an incident occurrence, mount the appropriate response, clear the incident and manage the incoming traffic until capacity is restored. An IMS consists of the following three subsystems: (i) incident detection, (ii) incident response logistics (IRL), and (iii) motorist information and traffic management. The focus of this paper is on the IRL sub-system.
IRL decisions posses the following characteristics: (1) involve multiple agencies, i.e., roadway assistance, traffic police, fire department, health emergency services, etc., (2) are usually taken under time pressure in a very dynamic environment, i.e., traffic conditions evolve rapidly over time, (3) rely mostly on real-time information coming from a variety of spatially distributed sources, i.e., response units (RUs) dispatched to service an incident etc., and (4) require the co-operation and co-ordination of a number of actors involved in the on-scene management of the incident. The characteristics of the incident management decision making environment suggest that decision support systems (DSSs) can contribute to improve the efficiency and effectiveness of IRL.
DSS for emergency response operations have been proposed by a number of researchers (Belardo et al., 1984, Andersen and Rasmussen, 1987, Belardo and Wallace, 1987, Zografos et al., 1998, 1999) as tools that can provide vital assistance to decision makers. In addition, DSS for traffic congestion management (Logi and Ritchie, 1997) and freeway incident detection (Ritchie and Prosser, 1990) have been also proposed. However, specialized DSS for roadway IRL have not been developed to-date. Most of the work in IRL relates to the development of isolated models that can be used to formulate parts of the decision making process mostly for off-line system evaluation purposes (Zografos et. al., 1993). Real-time DSS that take advantage of the capabilities of modern technologies related to data acquisition, data processing, and data visualisation have been identified as promising means for improving the decision making capabilities in IRL (Zografos et al., 1991, 1995b).
The objective of this paper is to present the development of a real-time DSS for guiding decisions related to IRL. The remainder of this paper is organised as follows: Section 2 presents the user-requirements analysis, Section 3 describes the DSS functions, Section 4 presents the DSS design while Section 5 summarises the conclusions of this paper.
Section snippets
User-needs analysis
The proposed DSS was developed following the typical DSS life-cycle which involves the following stages: (1) identification of user needs, (2) development of functional specifications, (3) system design and implementation, (4) system evaluation. It is important to stress here the crucial role of the user requirements phase on the successful development and implementation of the proposed DSS. Lack of understanding of (i) the actual user needs and (ii) the requirements of the decision making
Decision support system functionalitities
The primary objective of any IMS is the minimisation of incident duration. Incident duration is defined as the time elapsed between the occurrence of an incident and the clearance of the incident and the restoration of the roadway capacity to its normal level. The total incident duration consists of the following time components:
- 1.
Detection time (T1): This is the time interval from the occurrence of an incident until the time that the incident is detected and verified (i.e., the exact location,
System design
The proposed DSS consists of the following components:
- 1.
model base, that contains all models, algorithms, rules and knowledge needed to provide decision support for all incident response functions;
- 2.
data base, that stores all information needed for the DSS to operate;
- 3.
a human–machine interface (HMI) that allows the effective interaction of the user with the system.
Concluding remarks
DSS for supporting real-time decisions related to roadway IRL has been presented. The DSS was developed based on the typical life-cycle of a DSS and fulfils the user requirements as were identified through extensive assessment survey across six European countries. The system aims to reduce incident duration by integrating mathematical models, rules and algorithms with innovative telematics technologies in a user friendly (GIS based) environment. The proposed DSS has been demonstrated
Acknowledgements
The authors would like to express their acknowledgements to the European Commission DG XIII and the Greek General Secretariat for Research and Technology for supporting part of this research within the framework of INRESPONSE Project and EPET II Programme respectively. In addition the contribution of Dr. Evangelos Kotsakis in developing the software for the proposed Decision Support System is acknowledged.
References (25)
- et al.
The maximal covering location planning problem
Papers on Regional Science Association
(1974) Application of the transportation model to a large scale districting problem
Computers and Operations Research
(1981)- Andersen, V., Rasmussen, J., 1987. A decision support system for emergency management. In: Gow, H.B.F., Kang, R.W....
- et al.
Managing the response to disasters using microcomputers
Interfaces
(1984) - Belardo, S., Wallace, W.A., 1987. Expert system technology to support emergency response operations: Prospects and...
- Berlin, G.N., 1976. Method for delineating districts of varying shape. Transportation Engineering Journal, ASCE 192,...
- Bertsimas, D.J., Van Ryzin, G., 1991. A stochastic and dynamic vehicle routing problem in the Euclidean plane....
Simple heuristic for the school assignment problem
Journal of the Operational Research Society
(1882)- et al.
Extending and applying the hypercube queueing model to deploy ambulances in Boston
TIMS Studies in the Management Sciences
(1986) A note of two problems in connection with graphs
Numeriche Mathematik
(1959)
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