Innovations in Travel Demand Modeling, Volume 2: Papers (2008)

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trips are segments of tours broken by model stops, and are the units of demand that are aggregated to zone- to- zone trip tables for use in the highway and transit assignments. The hourly base year and forecast distributions of modeled tours for 2000 and 2030 traffic are shown in Table 4. One important finding is that the model responds to the growth in demand over time and the con- comitant increases in congestion by spreading the peak hour demand as expected. The 2030 tour arrival times are later in the day than are modeled for 2000. Also, while 4:00 p.m. is the definitive peak hour in 2000, 4:00 and 5:00 p.m. carry almost the same proportion of tours in 2030, showing that the demand is neither fixed or diminished, but is shifted to utilize capacity in other hours of the day with higher level of service. So while the alignment of the simulated peaking pat- terns in the base year may be somewhat skewed com- pared with the best available counts, the tour- based nature of the MORPC model supports a TOD model that forecasts a reasonable response to growth in congestion— a desirable feature that would be difficult to implement within the platform of a conventional model. FUTURE RESEARCH AND POTENTIAL APPLICATIONS As noted above, more data need to be developed and applied before it can be determined if all of the explicit TOD information that is produced by the disaggregate MORPC travel model can be validated and used in prac- tice for planning and policy analysis. Very few external data exist with which to validate the TOD component other than traffic counts, and unfortunately traffic counts and tours are not directly comparable. If more hourly traffic counts were collected and the trip tables were gen- erated and assigned on an hourly basis, the model could be further calibrated and eventually validated. Eventually, the output of the disaggregate tour- based MORPC model could be exported to dynamic traffic assignment procedures, used in refining the application of matrix estimation results to future demand matrices, and in the development of design hour traffic. As shown in this specific exploration of the MORPC TOD model, it can already be used to provide an estimate of peak spreading for planning studies. REFERENCES 1. Vovsha, P., and M. Bradley. A Hybrid Discrete Choice Departure Time and Duration Model for Scheduling Travel Tours. In Transportation Research Record: Journal of the Transportation Research Board, No. 1894, Transportation Research Board of the National Academies, Washington, D.C., 2004. 2. Hounsell, N.B. Understanding the Effects of Congestion: Peak Spreading and Congestions. Transportation Planning Systems, Vol. 1, No. 3, 1991, pp. 39–46. 3. Allen, W.G. An Analysis of Corridor Traffic Peaking. In Transportation Research Record 1305, TRB, National Research Council, Washington, D.C, 1991, pp. 50–60. 4. Purvis, C. Peak Spreading Models: Promises and Limita- tions. TRB, National Research Council, Washington, D.C., 2002. 5. Cambridge Systematics, Inc. ”Time- of- Day Modeling Pro- cedures: State- of- the- Art, State- of- the- Practice.” DOT- T- 99-01. US Department of Transportation, Washington, D.C., 1999. 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, 2002, pp. 207–224. 7. Bhat, C. “Duration Modeling: A Methodological Review with Applications in Activity- Travel Analysis.” Paper pre- sented at 80th Annual Meeting of the Transportation Research Board, Washington, D.C., 2001. 8. Vovsha, P., E. Petersen, and R. Donnelly. Explicit Modeling of Joint Travel by Household Members: Statistical Evi- dence and Applied Approach. In Transportation Research Record: Journal of the Transportation Research Board, No. 1831, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 1–10. 9. Vovsha P., E. Petersen, and R. Donnelly. Experience, Ten- dencies, and New Dimensions in the Application of Activity- Based Demand Modeling Systems for Metropoli- tan Planning Organizations in the United States. Paper pre- sented at 10th International Conference on Travel Behavior Research, Lucerne, Switzerland, 2003. 10. Time- of- Day Choice Model. Technical Memorandum. MORPC Model Improvement Project. PB Consult. (2003). Available by request from the authors. 11. Anderson, R, A. Al- Akhras, N. Gill, and R. Donnelly. Implementation of a Tour- Based Microsimulation Regional Travel Demand Model. In Ninth TRB Conference on the Application of Transportation Planning Methods (CD- ROM), Transportation Research Board of the National Academies, Washington, D.C. 2004. 12. Ohio Department of Transportation. Hourly Percent by Vehicle Type, 1997–2004. 164 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2 TABLE 4 MORPC Model: Hourly Shares of Half Tours and Trips in the P.M. Peak Period 2000 2030 Hour % Tours % Trips % Tours % Trips 16 35.38 34.01 33.59 32.43 17 33.00 33.65 33.69 34.23 18 31.63 32.34 32.72 33.34