Traffic congestion is a natural phenomenon that seems to be a routine for the people who lived in urban areas. The traffic congestion has become a significant factor in disrupting traffic flows. The rate of growth of vehicles in several major cities is increasingly difficult to be balanced by the development of road infrastructure. Various methods have been tested to solve this problem, but there are no ideal results. The presence of Vehicular Ad-hoc Networks (VANETs) can be a promising vehicular communication technology, and so can be implemented for transportation systems management in the future. However, VANETs have several limitations such as mobility pattern, freedom of movement, and limited bandwidth. The implementation of VANETs will have different handling levels in an urban area compared to others. Each urban area has a distinct uniqueness so that an appropriate mobility model is needed to accommodate various attributes and parameters in different urban areas. Based on those limitations, this research proposes the Vehicle-to-Vehicle (V2V) mobility modeling and simulation using Periodic Broadcast (PBC) messages for mitigating traffic congestion on the transportation system. The goal of this research is to provide a better mobility model and simulation that can provide a realistic model to fulfill various attributes and parameters in the different urban area. Another goal is to analyze the using of PBC messages for V2V implementation in a transportation system to mitigate the traffic congestion. The simulation results show that the V2V mobility modeling using PBC messages can parse the congestion at high traffic density points. This model presents a better travel time estimation and average vehicle speed compared to the conventional model. According to network performance test, the packet delivery ratio (PDR) of V2V-PBC is better than that of non-PBC.

V2V Mobility Modeling and Simulation Using PBC Messages for Traffic Congestion Mitigation

Vehicular ad hoc networks aim to provide communication among vehicles, providing safety and entertainment to passengers. The performance of these networks is entirely dependent on the existence of routes and the time they remain established (the route lifetime). Several factors can influence route lifetime and network connectivity, for instance: speed, density, direction of movement, and radio transmission range. The investigation of route lifetime and what are the critical physical factors, can assist in building more appropriate protocols and applications, developing efficient mobile communication infrastructures. Therefore, this article proposes strategies to increase the performance of V2V vehicular networks, through the analysis of which factors most influence performance and the development of a new routing protocol, which based on these factors, is able to select the best routes. The simulations prove that the route lifetime increases in denser networks and when vehicle movement occurs in the same direction. Meanwhile, the route lifetime does not undergo significant changes depending on the speed of vehicles. Based on these results, the Improved Connectivity Ad Hoc On‐Demand Multipath Distance Vector (IC_AOMDV) protocol was developed which, without the use of any fixed infrastructure (eg, GPS), surpassed the classic AOMDV and AODV protocols in all metrics tested.

Strategies to increase connectivity and performance of VANETs: Analysis of route lifetime and routing protocol proposal

Vehicular Ad Hoc Network (VANET) is a practical solution of intelligent transportation system which combines the interaction between technology and network traffic. To develop VANET communication services, a realistic mobility environment must be primarily generated because it has a significant effect on routing protocol performance. In this paper, a realistic mobility model from Makassar, Indonesia is proposed to test the performance of AODV and OLSR routing protocols in Packet Delivery Ratio (PDR), End To End Delay (E2ED) and Overhead Ratios. The simulation results show that random mobility model produce better PDR with 83% for AODV and 87% for OLSR. Then E2ED showed similar result for both of mobility model. But AODV has the longest delay with 0.54 second on proposed mobility model. It's different with PDR and E2ED, overhead value showed better performance on proposed mobility model ie 0.83 for AODV and 0.67 for OLSR.

Intan Sari Areni

Papers

A dense and high traffic scenario for vehicular ad hoc network implementation in metro city