Conditional priority for buses at signalized intersections means that late buses are given priority and early buses are not. This scheme is a method of operational control that improves service quality by keeping buses on schedule. A conditional bus priority implementation in Eindhoven, the Netherlands, is described. Results show the strong improvement in schedule adherence compared with a no-priority situation. Traffic impacts at an intersection were studied for three scenarios—no priority, absolute priority, and conditional priority. Compared with no priority, absolute priority increased delays significantly while conditional priority had almost no impact.

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This research note investigates the possibilities and limitations of coordinated transfers in public transit. The object is to minimize passenger's transfer time. It will be shown that optimum transfer times can be defined only if fluctuations in passenger arrival times at the boarding point can be contained within certain time limits.

To improve reliability, transit routes have time points at which early vehicles are held. Holding reduces waiting time and the amount of time passengers have to budget for a trip. However, it also reduces operating speed and thus increases passenger riding time and, potentially, operating cost. A new approach is presented for quantifying the user costs associated with unreliability. User cost has three components: excess waiting time, potential travel time or buffer time (related to budgeted travel time), and mean riding time, of which the first two are reliability impacts. For long headway service, these costs can be determined from 2-percentile departure times, 95-percentile arrival times, and mean arrival and departure times at stops. With a simple route operations model on a hypothetical route, impacts of scheduling with different numbers of time points and with different levels of running time and cycle time supplements are explored, and optimal running time schedules are determined. For a typical case, the optimal time point schedule offers net benefits equivalent to 4.5 min of riding time per passenger compared with operation without time point control. Optimal route running times are roughly mean plus one standard deviation of uncontrolled running time, and optimal cycle time is roughly mean plus two to three standard deviations of uncontrolled route running time. Surprisingly, it was found that in an optimal schedule, inserting slack at time points does not increase cycle time, because slack time inserted en route simply substitutes for slack time needed in layover.

Traditional transit service quality measures separate waiting time from service reliability and thereby underestimate the real cost of waiting and fail to evaluate the effect of unreliability on passengers. This study's analysis of passenger behavior shows that, for short headway service, the cost of waiting involves not only the mean time spent waiting on the platform but also potential waiting time, that is, the additional time that passengers have to budget for waiting. Budgeted waiting time is based on an extreme of the waiting time distribution such as its 95th percentile value, which is extremely sensitive to service reliability. Methods for determining the distribution of passenger waiting time from automatic vehicle location (AVL) data are derived. For long headway service, actual and budgeted waiting times are shown to be related to high and low extremes of the schedule deviation distribution, which can likewise be determined from AVL data. Two other components of long headway waiting, schedule inconvenience and synchronization cost, are also analyzed. Waiting cost functions and waiting time measures that account for both headway and service reliability are developed and are harmonized in a framework that provides a smooth transition from short to long headway waiting. Examples show how service reliability can be measured as a waiting cost and how service reliability improvements can reduce waiting cost as much as a large reduction in headway can.