Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.
Cooperative Driving and Lane Changing Modeling for Connected Vehicles in the Vicinity of Traffic Signals: A Cyber-Physical Perspective
