Rush hour and sustained traffic flows in eight cities are studied using the IBM Mega Traffic Simulator to understand the importance of road structures and vehicle acceleration in the prevention of gridlock. Individual cars among the tens of thousands launched are monitored at every simulation time step using live streaming data transfer from the simulation software to analysis software on another computer. A measure of gridlock is the fraction of cars moving at less than 30\% of their local road speed. Plots of this fraction versus the instantaneous number of cars on the road show hysteresis during rush hour simulations, indicating that it can take twice as long to unravel clogged roads as fill them. The area under the hysteresis loop is used as a measure of gridlock to compare different cities normalized to the same central areas. The differences between cities, combined with differences between idealized models using square or triangular road grids, indicate that gridlock tends to occur most when there are a small number of long roads that channel large fractions of traffic. These long roads help light traffic flow but they make heavy flows worse. Increasing the speed on these long roads makes gridlock even worse in heavy conditions. City throughput rates are also modeled using a smooth ramp up to a constant vehicle launch rate. Models with increasing acceleration for the same road speeds show clear improvements in city traffic flow as a result of faster interactions at intersections and merging points. However, these improvements are relatively small when the gridlock is caused by long roads having many cars waiting to exit at the same intersection. In general, gridlock in our models begins at intersections regardless of the available road space in the network.