A Method of Designing an Intelligent Public Transportation System in Metropolitan Area Using Emergent Intelligence

Abstract

Metropolitan area consists of huge population density, one or more urban areas, satellite cities, rural areas and towns. It highly concentrates economic activities to attract people from rural areas and is a complex spatial structures and reached a milestone in the development of road infrastructure to support transportation systems. Due to increase in the number of vehicles, many traffic related problems have arised, such as increase in delays, wastage of resources, high traffic accidents, economic losses, environmental pollution, security, privacy, etc. These problems are solved by using Intelligent Public Transportation System (IPTS), which consists of advanced technologies for transportation engineering including information technology (IT), computational intelligence, system control technology and system engineering. These advanced technologies play significant role in reducing traffic congestion, traffic accidents, traffic pollution, energy consumption and improving road traffic safety. In this thesis, we have designed IPTS for public transport in a metropolitan area. It consists of 5 major functional units, such as commuters’ arrival prediction, transport resource allocation, privacy preservation, context-based service management and transport management. We have used Emergent Intelligence (EI) technique with agents, as it is efficiently used, in several applications for collecting, analyzing, monitoring, and sharing information. Also, it takes dynamic decisions to give independence to each agent to take a decision. Some of these features are essential for public transport system. First, we have designed Commuters’ Arrival Prediction (CAP) scheme, which analyzes and predicts commuters’ patterns in the bus station using commuters’ arrival density, resource availability, history, and dynamic arrivals of commuters and vehicles. The proposed scheme provides an accurate analysis and prediction of commuters’ arrival density pattern at each bus station in the vicinity of transport depot. The predicted commuters’ density will be used to choose an optimal route to reach the desired destination in a metropolitan area. We have designed a Transport Resource Allocation (TRA) scheme using EI technique. The proposed scheme efficiently manages and provides resource over space and time based on predicted commuters’ arrival pattern, availability of resources, history, deficit resources and surplus resources of neighborhood transport depots. The EI technique is used to collect, analyze and share resources, reliability of resources, reliability of neighborhood depots, resource gathering time delay reliability and travel time reliability. We have built an analytical model for TRA scheme using discrete time Markov chain to analyse the system at steady state. The proposed scheme optimally allocates reliable resources and provides reliable public transport services to the commuters. In the metropolitan area, there will be privacy breach during communication among transport depot’s staff and agents, which results in anomalies like impersonation, malicious activities and greedy behavior and they lead to issues, such as traffic accidents, wastage of resources, delays, etc. For this, we have designed Public Transport Depot Privacy Preservation Scheme (PTDPPS) using EI technique in a metropolitan area. The proposed scheme consists of two phases: First, policy based privacy preservation for depot staff and Second, pseudonymous authentication based privacy preservation for communicating agents. The proposed scheme preserves privacy of resources, vehicle dispatch, allocation, private and public information of transport depot staff during communication inside and outside the transport depot. In a metropolitan area, managing and providing suitable services are difficult or impossible due to the non-linear and complex nature of transportation system. Thus, we have designed Context based Public Transportation Service Management (CPTSM). The EI technique is used for collecting, analyzing and sharing context information of vehicles, staff, commuters, routes, bus station units and environmental. The collected and analyzed context information are used to develop the policies to provide relevant services to commuters. The proposed system makes public transportation system most reliable and efficient for the commuters. We have designed an efficient public transportation management system in a metropolitan area by combining CAP, TRA, PTDPPS, and CPTSM, and made as a single entity at a transport depot in a metropolitan area. We have presented management of public transportation during normal conditions and disaster situations in one of the zone in the vicinity of transport depot using EI technique in a metropolitan area. During normal conditions, we have estimated and predicted the commuters’ arrival pattern and their density, and also estimated and allocated the resources at every bus stations. Using these information public transportation management is done at every bus stations in the vicinity of transport depot. During the disaster situations, proposed evacuation management system estimates boundary of disaster, evacuation exit points, victims density, severity of victims and resources required. The evacuation management system assigns fraction of victims to the desired destination through multiple evacuation exit points, and avoids the occurrence of secondary disaster in the same disaster zone in the metropolitan area. In summary, we have used EI technique with agents to design: 1) commuters’ arrival prediction, which efficiently analyzes and predicts the commuters arrival density; 2) transport resource allocation scheme, which dynamically allocates optimal resources; 3) privacy preservation schemes, which preserves the privacy of transport depot staff and agents; 4) context based service management, which efficiently makes use of context information of various transportation entities to provide accurate services; and 5) transport management for evacuation of victims, which effectively manages the disaster victims. We have simulated above mentioned schemes in different scenarios with various performance parameters. The results obtained in simulation and analysis show the importance of the approaches and effectiveness of the proposed system