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The formulation of the model also affects the TOD distribution obtained. The MORPC models are struc- tured and applied in an ordered manner determined by a hierarchy of tour purposes. A tour activity lower in the hierarchy is not permitted to start until all tours with higher priority are scheduled. Therefore, if a person has both a joint eating- out tour and an individual shopping tour, that person is required to complete the joint tour before the shopping tour can be scheduled. Conse- quently, the scheduling of the shopping tour is dependent on the available time windows for the other parties in the joint tour. As Vovsha and Bradley (1) mention, there is a dearth of information regarding travel prioritization. Given that deficit of information, this is probably the best we can expect this model to perform at this time. In addition to travel prioritization, the temporal granular- ity of the TOD model means there is a constraint of only one half tour per hour. Therefore, if a person arrives home from work at 5:15 p.m., that person is not permit- ted to start another tour until 6:00 p.m. However, this definition only affected 1% of the cases from the HIS and is probably not a major issue (1). PEAK HOUR SPREADING Over the last decade or more, as congestion has increased in urban transportation networks such as those in Ohio, peak traffic levels have grown to increasingly extend beyond the peak hour to the shoulder hours of the peak period. Table 1 shows that the peak hour in Ohioâs urban areas is 5:00 to 6:00 p.m. and the peak 3-h period is 5:00 to 8:00 p.m. Despite the lack of direct comparability of the measures in this table, it is apparent that the model is not simulating the same p.m. peak period as is seen in the statewide urban area traffic counts, and may also be some- what skewed with respect to diurnal patterns in the Colum- bus region. The p.m. peak 3 h in the 2000 model run are between 4:00 and 7:00 p.m. This could be a consequence of various and imperfect temporal definitions of travel in both the model and the count data, as mentioned above. Table 2 shows the time series count data available for Ohioâs urban areas and the share of traffic in the peak hour of the peak period by functional class. Also shown is the general trend of that share. As seen in this table, the peak hour share of peak period traffic is trending toward a fully flat 3-h peak period, approaching a one- third share, with declines on the freeways and major arterials to other hours and to lower- class facilities. This phenomenon is impossible to simulate with static diurnal factors, and difficult to model in an aggregate travel forecasting model. Because the MORPC disaggregate tour- based TOD model simulates tour durations and incorporates the feed- back of travel skims, the model accounts for peak spread- ing as a result of travel time changes due to congestion. Table 3 shows the number of half tours and trips by hour of the modeled day for both 2000 and 2030. Note that 163MODELING OF PEAK HOUR SPREADING TABLE 2 Share of Traffic During the P.M. Peak Hour: Ohio Urban Areas Functional 1997 1999 2000 2001 2002 2003 2004 Trend Class (%) (%) (%) (%) (%) (%) (%) (%) 11 34.2 35.7 34.3 36.4 35.1 34.9 34.1 0.015 12 34.9 32.2 34.6 35.5 34.2 34.2 33.8 0.012 14 34.2 33.6 34.0 33.6 33.4 33.3 34.1 0.054 16 32.9 34.6 34.5 34.0 34.1 34.1 34.1 0.097 17 N/A 34.4 34.0 35.0 33.6 34.1 34.8 0.011 TABLE 3 MORPC Model: Tours and Trips by Hour of Day, 2000 and 2030 2000 2030 Hour Half Tours % of Total Trips % of Total Half Tours % of Total Trips % of Total 5 86,111 2.08 110,240 1.95 133,320 2.22 165,170 2.07 6 112,980 2.72 145,468 2.57 179,695 3.00 224,773 2.81 7 336,104 8.10 435,320 7.68 497,961 8.31 636,165 7.95 8 374,728 9.04 480,570 8.48 534,396 8.91 676,048 8.45 9 239,492 5.77 312,174 5.51 331,749 5.53 424,625 5.31 10 181,222 4.37 249,196 4.40 252,912 4.22 336,512 4.21 11 180,142 4.34 246,680 4.35 254,557 4.25 337,227 4.22 12 189,910 4.58 258,258 4.56 272,788 4.55 360,854 4.51 13 221,694 5.35 303,500 5.36 320,789 5.35 426,945 5.34 14 218,487 5.27 295,771 5.22 307,827 5.14 406,733 5.09 15 266,148 6.42 347,570 6.14 359,270 5.99 461,114 5.77 16 309,215 7.46 420,063 7.42 429,524 7.17 574,106 7.18 17 288,419 6.95 415,649 7.34 430,805 7.19 605,927 7.58 18 276,453 6.67 399,523 7.05 418,370 6.98 590,080 7.38 19 227,278 5.48 320,808 5.66 329,388 5.49 451,780 5.65 20 180,435 4.35 256,601 4.53 262,833 4.39 363,319 4.54 21 185,671 4.48 264,169 4.66 268,817 4.48 370,925 4.64 22 133,058 3.21 195,234 3.45 199,684 3.33 283,582 3.55 23 139,771 3.37 208,019 3.67 209,693 3.50 301,945 3.78
