Vehicles that communicate with one another (connected vehicles) are becoming more ubiquitous each year, and the increase in mobile computing is allowing the proliferation of possible applications for connected vehicles. Many of these applications require vehicles to be connected continuously to the communication infrastructure. This connection could result in congestion of the communication network. This study evaluates a novel dynamic grouping methodology that combines vehicle-to-vehicle and vehicle-to-infrastructure communication schemes for optimal use of the communication infrastructure. The methodology for dynamic grouping of instrumented vehicles was implemented in a realistic and well-calibrated microscopic traffic simulation for application to the collection of sensor data. A 66% to 91% reduction in the load on the communication infrastructure was achieved by dynamic grouping for systematic aggregation of vehicular information. Use of the maximum bandwidth showed that name-address mapping was scalable. The dynamic grouping methodology is thus scalable and achieves a negligible loss of data quality compared with that in a scenario in which each vehicle connects to the communication infrastructure independently. The scalability was shown by the generation of response surfaces for the load on communication channels for different market penetration and communication ranges. The quality of the data was validated by use of the reported speed and estimated travel times over the network. On average, the error in speed was 5.5% to 8%, with far less bandwidth used with the dynamic grouping approach. The travel time along different paths was shown to be within 5% under regular conditions and within 10% under conditions of nonrecurrent congestion.
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering