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

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Suggested Citation:"T57054 txt_106.pdf." National Academies of Sciences, Engineering, and Medicine. 2008. Innovations in Travel Demand Modeling, Volume 2: Papers. Washington, DC: The National Academies Press. doi: 10.17226/13678.
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time difference for vehicle f, and therefore its travel time on Link 1, is 30 – 12 = 18 s. All the vehicles before f, a–e, have travel times of 12 s on Link 1, which is the free- flow travel time for Link 1. Therefore, vehicle f has 6 s of delay on Link 1. Following similar trajectories for the other vehicles indicates that the most delay occurs for vehicle n on Link 1 (30 s). EFFECT OF CONGESTION ON ATLANTA DTA RESULTS The above exercise illustrates clearly that with the simul- taneous arrival of too many vehicles, only some could continuously move along, while others had to wait. The number allowed to move at each time step was deter- mined by the saturation flow rate defined for the link, which determines the number of vehicles that can occupy a cell during any time step. The number of time steps that elapsed before the vehicles could completely tra- verse the link determined the total delay experienced on the link. For the entire Atlanta network, once cells became sat- urated while vehicles continued to arrive, the cell satura- tion effect moved upstream. The original overcongested link caused many more links upstream of it to become oversaturated. When propagation of congestion affected a freeway link, routes between origins and destinations spanning nearly the entire region became affected, and the simulation broke down. It was not possible, due to the excessive times on important links, for all the vehi- cles to complete their trips during the simulation period. With many vehicles having route attributes associated with incomplete trips, the dynamic user- equilibrium results and subsequent route- generation steps were suspect. The dynamics of a traffic jam produced by the simu- lation results showed that the software was responding the way it was intended but did not provide any expla- nation for why so many arrivals occurred at these links. The next course of action was to look more carefully at the set of vehicle arrivals at this congested link, ordering them by their arrival time. Doing so revealed that some of the vehicles exiting a link in question were exiting at essentially free- flow speed while others exiting the same link at the same time but toward a different downstream link were exiting with times in the 2,000+ s range. Even- tually, all vehicles regardless of their downstream link would experience huge travel times, but initially as con- gestion built up on this one link, there was a large vari- ability in exiting travel times, or in other words, a large variability in movement times passing through a given link at a given time. Routes are determined on the basis of the link time averaged over the link time aggregation period as defined earlier. Where there is a large variability in vehicle travel times over movements through a link, the result might be that the congested link appeared to the route generator to be suitable long after it was congested due to the aver- aging. In such an instance, one should use movement- based times in TDSP, at least in problematic areas, to address the problem described here. Movement- based times would cause the route through the congested link pair to be considered much differently from the link pair that shares a common link but has essentially free- flow times. It is more resource intensive to do movement- based TDSP, which is why the default is to use link- based TDSP, but it is possible to identify a select number of locations for which movement- based times should be used. In general, in dynamic network models, one must be aware of problems that could be caused by highly variable movement travel times exiting links. It was believed that high variability in exiting link times can be used to identify the locations requiring movement- based times. A query of vehicles arriving at links during the same time period but destined for differ- ent downstream links, where the vehicles have much dif- ferent downstream arrival times, identified those links. By using the GIS, a spatial correlation between links with high variability in movement times and links with exces- sive travel times was confirmed. Only a couple dozen such locations were identified, and the changes necessary for Vista to consider movement- based times in TDSP were easily made. SOME PRELIMINARY VISTA SUMMARY RESULTS Calibration of the Vista DTA model of the Atlanta region is still in progress, but some preliminary results can be described. Table 1 shows the link summary statistics for the Vista model for the 6:00 to 7:00 a.m. demand period. The number of links, total observed count, and total esti- mated flow are listed for volume ranges, along with rel- ative error and percent root mean square error statistics. Figure 2 shows the scatter plot of the same DTA results. The results were determined from the DTA model run resulting from the first traffic signal setting retime fol- lowing the initial Vista DTA model results. Table 2 and Figure 3 show the DTA results after four iterations of retiming traffic control settings and running the DTA model to solve for dynamic user- equilibrium. The results after the first iteration show a good fit with observed data. The results after the fourth iteration show an even better fit. Results from the second and third iteration, not shown here, had successively better fit, leading up to the fourth iteration results. The iterations involved figuring traffic control set- tings, solving for an equilibrium- based distribution of vehicles to routes, updating the traffic control settings, 106 INNOVATIONS IN TRAVEL DEMAND MODELING, VOLUME 2

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TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 2: Papers includes the papers that were presented at a May 21-23, 2006, conference that examined advances in travel demand modeling, explored the opportunities and the challenges associated with the implementation of advanced travel models, and reviewed the skills and training necessary to apply new modeling techniques. TRB Conference Proceedings 42, Innovations in Travel Demand Modeling, Volume 1: Session Summaries is available online.

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