Dealing with the Empty Vehicle Movements in Personal Rapid Transit System with Batteries Constraints in a Dynamic Context

Abstract

The Personal Rapid Transit is a new emergent transportation tool. It relies on using a set of small driverless electric vehicles to transport people on demand. Because of the specific on-demand characteristic of the Personal Rapid Transit system, many Personal Rapid Transit vehicles would move empty which results in a high level of wasted transportation capacity. This is enhanced while using Personal Rapid Transit vehicles with limited electric battery capacity. This paper deals with this problem in a real time context while minimizing the set of empty vehicle movements. First, a mathematical formulation to benchmark waiting time of passengers in Personal Rapid Transit systems is proposed. Then, a simulation model that captures the main features of the Personal Rapid Transit system is developed. A decision support system which integrates several real time solution strategies as well as a simulation module is proposed. Our dispatching strategies are evaluated and compared based on our simulation model. The efficiency of our method is tested through extensive test studies.

Empty Vehicle Redistribution with Time Windows in Autonomous Taxi Systems

“…Fatnassi et al [16], Fagnant and Kockelman [15], and Lees-Miller [24]. In this article, we use our implementation of the SNN algorithms as described by these authors.…”

Empty vehicle redistribution and fleet size in autonomous taxi systems

“…Fatnassi et al [16,17] introduce the 'battery-restricted' PRT problem with empty run minimising as the objective function. The strategy assigns vehicles to passenger requests in real time without knowing the expectancy of next request.…”

“…Simple nearest neighbours (SNNs): The nearest empty vehicle is called to the longest waiting passenger in the system. This type of algorithm is used in [3,16,19,23]. iii.…”

Untitled

“…Most reactive methods are based on nearest neighbour heuristics. Such methods of calling the nearest available vehicle to a passenger are used by Fagnant 4 , Kockelman 5 , Bell and Wong 8 , Andreasson 1,13,14,15 , Lees-Miller 3, 9, 10 , Fatnassi et al 12,11 and Kek 16 . These methods may be modified to send the vehicle to the longest waiting passenger in the system (it can be both current waiting time and predicted waiting time at the moment the vehicle will arrive).…”

“…The proactive methods use the statistics of supply and demand in different stations or regions of the city. These are used to predict the necessary number of the vehicles in each station (Fatnassi et al 12,11 , Anderson 7 ). Such methods may sort the stations by some predicted criteria such as available vehicles, waiting times etc.…”