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MODELING OF PEAK HOUR SPREADING 163 The formulation of the model also affects the TOD beyond the peak hour to the shoulder hours of the peak distribution obtained. The MORPC models are struc- period. Table 1 shows that the peak hour in Ohio's urban tured and applied in an ordered manner determined by a areas is 5:00 to 6:00 p.m. and the peak 3-h period is 5:00 hierarchy of tour purposes. A tour activity lower in the to 8:00 p.m. Despite the lack of direct comparability of the hierarchy is not permitted to start until all tours with measures in this table, it is apparent that the model is not higher priority are scheduled. Therefore, if a person has simulating the same p.m. peak period as is seen in the both a joint eating-out tour and an individual shopping statewide urban area traffic counts, and may also be some- tour, that person is required to complete the joint tour what skewed with respect to diurnal patterns in the Colum- before the shopping tour can be scheduled. Conse- bus region. The p.m. peak 3 h in the 2000 model run are quently, the scheduling of the shopping tour is dependent between 4:00 and 7:00 p.m. This could be a consequence on the available time windows for the other parties in of various and imperfect temporal definitions of travel in the joint tour. As Vovsha and Bradley (1) mention, there both the model and the count data, as mentioned above. is a dearth of information regarding travel prioritization. Table 2 shows the time series count data available for Given that deficit of information, this is probably the Ohio's urban areas and the share of traffic in the peak best we can expect this model to perform at this time. In hour of the peak period by functional class. Also shown addition to travel prioritization, the temporal granular- is the general trend of that share. ity of the TOD model means there is a constraint of only As seen in this table, the peak hour share of peak one half tour per hour. Therefore, if a person arrives period traffic is trending toward a fully flat 3-h peak home from work at 5:15 p.m., that person is not permit- period, approaching a one-third share, with declines on ted to start another tour until 6:00 p.m. However, this the freeways and major arterials to other hours and to definition only affected 1% of the cases from the HIS lower-class facilities. This phenomenon is impossible to and is probably not a major issue (1). simulate with static diurnal factors, and difficult to model in an aggregate travel forecasting model. Because the MORPC disaggregate tour-based TOD PEAK HOUR SPREADING model simulates tour durations and incorporates the feed- back of travel skims, the model accounts for peak spread- Over the last decade or more, as congestion has increased ing as a result of travel time changes due to congestion. in urban transportation networks such as those in Ohio, Table 3 shows the number of half tours and trips by hour peak traffic levels have grown to increasingly extend of the modeled day for both 2000 and 2030. Note that 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