Cloud Computing for Agent-Based Urban Transportation Systems


Intelligent transportation clouds could provide services such as decision support, a standard development environment for traffic management strategies, and so on. With mobile agent technology, an urban-traffic management system based on Agent-Based Distributed and Adaptive Platforms for Transportation Systems (Adapts) is both feasible and effective. However, the large-scale use of mobile agents will lead to the emergence of a complex, powerful organization layer that requires enormous computing and power resources. To deal with this problem, we propose a prototype urban-traffic management system using intelligent traffic clouds.

EXISTING SYSTEMThe function of the agents’ scheduling and agent-oriented task decomposition is based on the MA’s knowledge base, which consists of the performances of different agents in various traffic scenes. If the urban management system cannot deal with a transportation scene with its existing agents, it will send a traffic task to the organization layer for help. The traffic task contains the information about the state of urban transportation, so a traffic task can be decomposed into a combination of several typical traffic scenes.

With knowledge about the most appropriate traffic strategy agent to deal with any typical traffic scene, when the organization layer receives the traffic task, the MA will return a combination of agents and a map about the distribution of agents to solve it.

DisadvantagesComplex systems make it difficult or even impossible to build accurate models and perform experiments.

PROPOSED SYSTEMUrban-traffic management systems using intelligent traffic clouds to overcome the issues we’ve described so far. With the support of cloud computing technologies, it will go far beyond other multi agent traffic management systems, addressing issues such as infinite system scalability, an appropriate agent management scheme, reducing the upfront investment and risk for users, and minimizing the total cost of ownership. The three layers in Adapts are organization, coordination, and execution, respectively.

Mobile agents play a role as the carrier of the control strategies in the system. The organization layer consists of a management agent (MA), three databases (control strategy, typical traffic scenes, and traffic strategy agent), and an artificial transportation system. As one traffic strategy has been proposed, the strategy code is saved in the traffic strategy database. Then, according to the agent’s prototype, the traffic strategy will be encapsulated into a traffic strategy agent that is saved in the traffic strategy agent database.

AdvantagesThe strategy agent to manage a road map.The initial agent-distribution map will be more accurate.


HARDWARE REQUIREMENTCPU type : Intel Pentium 4Clock speed : 3.0 GHzRam size : 512 MBHard disk capacity : 40 GBMonitor type : 15 Inch color monitorKeyboard type : internet keyboard

SOFTWARE REQUIREMENTOperating System: Android

Language : JAVA

Back End : SQLite

Documentation : Ms-Office

MODULES1. Intelligent Traffic Clouds2. Traffic participators3. Traffic-Strategy developers4. Urban-Traffic Management System5. Traffic managers6. Traffic strategy agents and agent-distribution maps7. Connect and share the clouds8. Running and Storing the Traffic strategy agents

MODULE DESCRIPTIONIntelligent Traffic CloudsIntelligent traffic clouds are used to serve urban transportation. The support of cloud computing technologies, it will go far beyond other multiagent traffic management systems, addressing issues.

Traffic participatorsThe Traffic clouds customers such as the urban-traffic management systems and traffic participants exist outside the cloud. All the service providers such as the test bed of typical traffic scenes, ATS, traffic strategy database, and traffic strategy agent database are all veiled in the systems’ core.

Traffic-Strategy developersThe traffic management systems, traffic-strategy performance to the traffic-strategy developer, and the state of urban traffic transportation. The new traffic strategies can be transformed into mobile agents so such systems can continuously improve with the development of transportation science.

Urban-Traffic Management SystemUrban-traffic management System deal with different customers’ requests for services such as storage service for traffic data and strategies, mobile traffic-strategy agents, and so on. Traffic managers

The Urban-traffic management systems must generate, store, manage, test, optimize, and effectively use a large number of mobile agents. Moreover, they need a decision-support system to communicate with traffic managers.

Traffic strategy agents and agent-distribution mapsThe intelligent traffic clouds could provide traffic strategy agents and agent-distribution maps to the traffic management systems, traffic-strategy performance to the traffic-strategy developer, and the state of urban traffic transportation and the effect of traffic decisions to the traffic managers.

Connect and share the cloudsThe development of intelligent traffic clouds, numerous traffic management systems could connect and share the clouds’ infinite capability, thus saving resources.

Running and Storing the Traffic strategy agentsThe intelligent traffic clouds use these distributed resources to cater the peak demand of urban-traffic management systems, support the running of agents efficiently.

Flow Diagram

CONCLUSIONThe Agent-based computing and mobile agents to handle this vexing problem. Only requiring a runtime environment, mobile agents can run computations near data to improve performance by reducing communication time and costs. This computing paradigm soon drew much attention in the transportation field. From multi agent systems and agent structure to ways of negotiating between agents to control agent strategies, all these fields have had varying degrees of success. Cloud computing provides on demand computing capacity to individuals and businesses in the form of heterogeneous and autonomous services. With cloud computing, users do not need to understand the details of the infrastructure in the “clouds;” they need only know what resources they need and how to obtain appropriate services, which shieldsthe computational complexity of providing the required services.