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164 I N N O VAT I O N S I N T R AV E L D E M A N D M O D E L I N G , V O L U M E 2 trips are segments of tours broken by model stops, and are REFERENCES the units of demand that are aggregated to zone-to-zone trip tables for use in the highway and transit assignments. 1. Vovsha, P., and M. Bradley. A Hybrid Discrete Choice The hourly base year and forecast distributions of Departure Time and Duration Model for Scheduling Travel modeled tours for 2000 and 2030 traffic are shown in Tours. In Transportation Research Record: Journal of the Table 4. One important finding is that the model Transportation Research Board, No. 1894, Transportation responds to the growth in demand over time and the con- Research Board of the National Academies, Washington, comitant increases in congestion by spreading the peak D.C., 2004. hour demand as expected. The 2030 tour arrival times 2. Hounsell, N.B. Understanding the Effects of Congestion: are later in the day than are modeled for 2000. Also, Peak Spreading and Congestions. Transportation Planning while 4:00 p.m. is the definitive peak hour in 2000, 4:00 Systems, Vol. 1, No. 3, 1991, pp. 3946. and 5:00 p.m. carry almost the same proportion of tours 3. Allen, W.G. An Analysis of Corridor Traffic Peaking. In in 2030, showing that the demand is neither fixed or Transportation Research Record 1305, TRB, National diminished, but is shifted to utilize capacity in other Research Council, Washington, D.C, 1991, pp. 5060. hours of the day with higher level of service. 4. Purvis, C. Peak Spreading Models: Promises and Limita- So while the alignment of the simulated peaking pat- tions. TRB, National Research Council, Washington, D.C., terns in the base year may be somewhat skewed com- 2002. pared with the best available counts, the tour-based 5. Cambridge Systematics, Inc. "Time-of-Day Modeling Pro- nature of the MORPC model supports a TOD model cedures: State-of-the-Art, State-of-the-Practice." DOT-T- that forecasts a reasonable response to growth in 99-01. US Department of Transportation, Washington, congestion--a desirable feature that would be difficult to D.C., 1999. implement within the platform of a conventional model. 6. Bhat, C., and J. Steed. A Continuous-Time Model of Departure Time Choice for Urban Shopping Trips. Trans- portation Research Part B: Methodological, Vol. 36, No. 3, FUTURE RESEARCH AND POTENTIAL 2002, pp. 207224. APPLICATIONS 7. Bhat, C. "Duration Modeling: A Methodological Review with Applications in Activity-Travel Analysis." Paper pre- As noted above, more data need to be developed and sented at 80th Annual Meeting of the Transportation applied before it can be determined if all of the explicit Research Board, Washington, D.C., 2001. TOD information that is produced by the disaggregate 8. Vovsha, P., E. Petersen, and R. Donnelly. Explicit Modeling MORPC travel model can be validated and used in prac- of Joint Travel by Household Members: Statistical Evi- tice for planning and policy analysis. Very few external dence and Applied Approach. In Transportation Research data exist with which to validate the TOD component Record: Journal of the Transportation Research Board, other than traffic counts, and unfortunately traffic counts No. 1831, Transportation Research Board of the National and tours are not directly comparable. If more hourly Academies, Washington, D.C., 2003, pp. 110. traffic counts were collected and the trip tables were gen- 9. Vovsha P., E. Petersen, and R. Donnelly. Experience, Ten- erated and assigned on an hourly basis, the model could dencies, and New Dimensions in the Application of be further calibrated and eventually validated. Activity-Based Demand Modeling Systems for Metropoli- Eventually, the output of the disaggregate tour-based tan Planning Organizations in the United States. Paper pre- MORPC model could be exported to dynamic traffic sented at 10th International Conference on Travel Behavior assignment procedures, used in refining the application Research, Lucerne, Switzerland, 2003. of matrix estimation results to future demand matrices, 10. Time-of-Day Choice Model. Technical Memorandum. and in the development of design hour traffic. As shown MORPC Model Improvement Project. PB Consult. (2003). in this specific exploration of the MORPC TOD model, Available by request from the authors. it can already be used to provide an estimate of peak 11. Anderson, R, A. Al-Akhras, N. Gill, and R. Donnelly. spreading for planning studies. Implementation of a Tour-Based Microsimulation Regional Travel Demand Model. In Ninth TRB Conference on the Application of Transportation Planning Methods (CD- TABLE 4 MORPC Model: Hourly Shares of Half Tours ROM), Transportation Research Board of the National and Trips in the P.M. Peak Period Academies, Washington, D.C. 2004. 2000 2030 Hour % Tours % Trips % Tours % Trips 12. Ohio Department of Transportation. Hourly Percent by 16 35.38 34.01 33.59 32.43 Vehicle Type, 19972004. 17 33.00 33.65 33.69 34.23 18 31.63 32.34 32.72 33.34