Theoretical Maximum Capacity as Benchmark for Empty Vehicle Redistribution in Personal Rapid Transit

A personal rapid transit system uses compact, computer-guided vehicles running on dedicated guideways to carry individuals or small groups directly between pairs of stations. Vehicles move on demand when a passenger requests service at his or her origin station. Because the number of trips requested from a station need not equal the number of trips ending there, some vehicles must run empty to balance the flows. The empty vehicle redistribution (EVR) problem is to decide which empty vehicles to move and when and where to move them; an EVR algorithm makes these decisions in real time, as passengers arrive and request service. A method was developed for finding the theoretical maximum demand (with a given spatial distribution) that a given system could serve with any EVR algorithm, which provides a benchmark against which particular EVR algorithms can be compared. The maximum passenger demand that a particular EVR algorithm can serve can be determined by simulation and then compared with the benchmark. The method is applied to two simple EVR heuristics on two example systems. The results suggest that this is a useful method for determining the strengths and weaknesses of a variety of EVR heuristics across a range of networks, passenger demands, and fleet sizes.