Authors:
Nabil Morri
1
;
Sameh Hadouaj
2
and
Lamjed Ben Said
3
Affiliations:
1
IT Department, Emirates College of Technology, Abu Dhabi, U.A.E., SMART Lab., Institut Supérieur de Gestion de Tunis, Université de Tunis, Tunisia
;
2
Computer Information Systems Department, Higher Colleges of Technology, U.A.E., SMART Lab., Institut Supérieur de Gestion de Tunis, Université de Tunis, Tunisia
;
3
SMART Lab., Institut Supérieur de Gestion de Tunis, Université de Tunis, Tunisia
Keyword(s):
Multi-agent Systems, Public Transportation, Regulation System, Optimization, Key Performance Indicators.
Abstract:
The urban public transport systems deal with dynamic environments and evolve over time. Frequently, we dispose of a lot of correlated information that is not well exploited to improve the public transport quality service, especially in perturbation cases where a regulation system should be used in order to maintain the public transport scheduled time table. The quality service should be measured in terms of public transport key performance indicator (KPI) for the wider urban transport system and issues like regularity, punctuality and correspondence criteria. In fact, in the absence of a set of widely accepted performance measures and transferable methodologies, it is very difficult for public transport to objectively assess the effects of specific regulation system and to make use of lessons learned from other public transport systems. Unfortunately, most of the existing traffic regulation systems do not take into consideration part or most of the performance criteria when they prop
ose a regulation maneuver. Therefore, the applicability of these models is restricted only to specific contexts. This paper sets the context of performance measurement in the field of public traffic management and presents the regulation support system of public transportation (RSSPT). The aim of this regulation support system is (i) to detect the traffic perturbation by distinguishing the non-equability of scheduled and the current time table of vehicle passage at the station (ii) and to find the regulation action by optimizing the performance of the service quality of the public transportation. We adopt a multi-agent approach to model the system. The validation of our model is done by simulating two scenarios on Abu Dhabi transport system and shows the efficiency of our system when we want to use many performance indicators to regulate a disturbance situation.
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