RTMAS: An Expert System for Real Time Monitoring and Analysis of Traffic During Evacuations

  • Frank Southworth
  • Shih-Miao Chin
  • Paul Der-Ming Cheng

Abstract

An imminent hurricane, a problem with a nuclear reactor, the escape of a dangerous gas, a large chemical spill, or even a serious threat from a foreign country are all examples of some very recent reasons for large urban populations to leave their homes until the threat to their lives and property has passed. Whether largely spontaneous, or carefully orchestrated by local authorities, such evacuations may take place over many hours or even days. To date, there has been no significant monitoring of large scale evacuations, since they occur as either rapidly developing localized threats, such as hurricanes, or for more protracted threat build-ups.

Keywords

Transportation Cane Dial Cali 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahmed, M.S. and A.R. Cook, 1979. “Analysis of freeway traffic time series data using Box and Jenkins techniques”, Transportation Research Record, 722, 1–9.Google Scholar
  2. Ahmed, M.S. and A.R. Cook, 1982. “Application of time-series analysis techniques to freeway incident detection”, Transportation Research Record, 841, 19–21.Google Scholar
  3. Bell, W., 1983. “A computer program (TEST) for detecting outliers in time series”, in Proceedings of the American Statistical Association, Business and Economic Statistics Section, pp 624–639.Google Scholar
  4. Benjamin, J., 1986. “A time-series forecast of average daily traffic volume”. Transportation Research, 30A, 51–60.Google Scholar
  5. Box, G.E.P. and G.M. Jenkins, 1970, 1976. Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco.MATHGoogle Scholar
  6. Box, G.E.P. and G.C. Tiao, 1975. “Intervention analysis with applications to environmental problems”. Journal of the America Statistical Association, 70, 70–79.MathSciNetMATHCrossRefGoogle Scholar
  7. Chang, I. and G.C. Tiao, 1983. “Estimation of time series parameters in the presence of outliers”, Technical Report No. 8, University of Chicago, Statistical Research Center.Google Scholar
  8. Davis, G.A. and N.L. Nihan, 1984. “Using time-series designs to estimate changes in freeway level of service, despite missing data”, Transportation Research 18A, 431–438.Google Scholar
  9. Eldor, M., 1977. “Demand predictors for computerized freeway control systems”, In Proceedings of 7th International Symposium on Transportation and Traffic Theory, (edited by T. Sasaki and T. Yanoka), 341–270.Google Scholar
  10. Federal Emergency Management Agency, 1984. “Behavior and attitudes under crisis conditions”, Selected issues and findings, Washington D.C.Google Scholar
  11. Heathington, K.W., R.D. Worrall, and G.C. Hoff, 1970. “An evaluation of the priorities associated with the provision of traffic information in real time”, Highway Research Record 336, 15–17.Google Scholar
  12. Helliwell, J., 1986. “Guru: brave new expert system?” PC Magazine, 5, No. 10, 151–163.Google Scholar
  13. Kalman, R.E., 1960. “A new approach to linear filtering and prediction problems”. Journal of Basic Engineering, ASME Transactions 82, Part D, 35–45.Google Scholar
  14. Nihan, N.L. and K.O. Holmesland, 1980. “Use of Box and Jenkins time series technique in traffic forecasting”, Transportation 9, 125–143.CrossRefGoogle Scholar
  15. Reilly, D.P., 1984. “Automatic intervention detection system”, Automatic Forecasting Systems, Inc. P. O. Box 536, Hatboro, Pennsylvania 19040.Google Scholar
  16. Tampa Bay Regional Planning Council, 1984. “Tampa Bay Region Hurricane Evacuation Plan”, Technical Data Report Update, St. Petersburg, Florida.Google Scholar
  17. Southworth, F. and S-M. Chin, 1986. “Quantifying spontaneous evacuation in time of threat: a feasibility study”, Prepared for the Federal Emergency Management Agency, Washington D.C.Google Scholar
  18. Southworth, F. and S-M. Chin, 1987. “Network evacuation modeling for flooding as a result of dam failure”, Environment and Planning A, 1543–1556.Google Scholar
  19. Southworth, F., S-M. Chin, and P.D. Cheng, 1987. “Quantifying spontaneous population evacuations in time of threat: a real time traffic monitoring system for the Tampa Bay area”, Prepared for the Federal Emergency Management Agency, Washington D.C.Google Scholar
  20. Tiao, G., 1986. “ARIMA models, intervention problems”, Technical Report No. 27, University of Chicago, Statistical Research Center.Google Scholar
  21. Tsay, R.S., 1986. “Time series model specification in the presence of outliers”, Journal of the American Statistical Association, 81, No. 393, pp 132–140.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1990

Authors and Affiliations

  • Frank Southworth
  • Shih-Miao Chin
  • Paul Der-Ming Cheng

There are no affiliations available

Personalised recommendations