Distribution Of Traffic Information Research Articles

Irresponsible Forwarding Under Real Intervehicle Spacing Distributions

One approach to the distribution of traffic information in a self-organizing traffic information system is multihop broadcasting. In this approach, each vehicle may simply rebroadcast an information packet that it receives from the others. However, rebroadcasting results in redundant retransmissions of the same information, leading to a useless occupation of the radio channel. Minimizing redundancy, while still guaranteeing reachability in the network, is a challenging task in multihop broadcasting. In our previous work, we introduced a new probabilistic-based broadcasting scheme, i.e., Irresponsible Forwarding (IF) , which could effectively reduce the redundancy. However, in the previous work, we only considered the scenario where the intervehicle spacing was exponentially distributed. In this paper, we generalize the concept of IF such that it can be applied to any intervehicle spacing distribution. In addition, we evaluate the performance of the IF protocol when it is applied in a realistic scenario (i.e., using real traffic traces). This paper shows that the IF protocol is able to limit packet redundancy all hours of the day effectively.

Architecturing Future-proof Distribution of Traffic and Travel Information Using TPEG

The article summarizes key recommendations for creating a traffic and travel information distribution architecture, which will function properly and without changes for longer time. Recommendations are based on a work proposing an architecture over a distributing traffic and travel information over digital radio broadcasting and Internet using TPEG formats and protocols. The whole process of architecturing is briefly described starting with the planning scope, expected results and steps to achieve them; and the process of requirement engineering. For the requirements a list of user needs, created for intelligent transportation systems (ITS) by project KAREN, was found to be very useful. Lessons learned from existing traffic information distribution systems are also discussed as they were another source of requirements. Before describing the architecture, unique design concepts of TPEG itself are mentioned, namely the possibility to seamlessly convert a TPEG message from binary format to XML and back; promising location referencing methods incl. AGORA C; and the ability to be backward compatible even after enhancing the TPEG message to the server for new or updated TPEG applications. All those TPEG design concepts allowed the development of very modular and flexible architecture for distribution. The architecture itself is then briefly outlined by means of selected set of use cases, describing core functionality of reception and distribution; and by a sample deployment model, depicting the possible physical structure of a future working system. Finally, based on the created architecture, recommendations are stated. The system must be designed as a pure distribution channel, changes expected in the future are identified, suitable requirement engineering methods are proposed and the need for monitoring and auditing tooling is highlighted. Open issue is practical availability of the dynamic location referencing method which shall be evaluated in the near future.

Optimal Universal Schedules for Discrete Broadcast

We study broadcast systems that distribute a series of data updates to a large number of passive clients. The updates are sent over a broadcast channel in the form of discrete packets. We assume that clients periodically access the channel to obtain the most recent update. Such scenarios arise in many practical applications, such as distribution of traffic information and market updates to mobile wireless devices. Our goal is to design broadcast schedules that minimize the waiting time, i.e., the amount of time the client needs to wait in order to obtain the most recent update. We assume that each client has a different access pattern depending on the channel conditions, computing power, and storage capabilities. We introduce and analyze optimal universal schedules that guarantee low waiting time for any client, regardless of its behavior.