Analytical Prediction of Self-Organized Traffic Jams as a Function of Increasing ACC Penetration

Modeling and simulations on automated vehicles to alleviate traffic congestion

“…Intersections are usually the most congested places [14], and two intersections are chosen for the analysis. The third section is chosen because it is connected with SR167, and the traffic is more congested.…”

On an efficient and effective Intelligent Transportation System (ITS) using field and simulation data

“…In the U.S., about $78.2 billion is annually wasted as a result of road traffic congestion [1][2][3][4]. Lost productivity, resources, time, gas, etc., are examples of undesirable consequences of road traffic congestion.…”

“…[14,15]. Adaptive cruise control (ACC), advanced driver-assistance systems (ADASs), variable speed limits (VSLs)/variable speed signs (VSSs), ramp metering, and dynamic cruise control (DCC), etc., are the existing mechanisms used to ensure safety, efficiency, and effective utilization of vehicle gas, as the main goals of ITS [3,6,10,11,[14][15][16][17][18]. Static traffic controls (foreseen and predictable) and dynamic traffic controls (unforeseen and unpredictable) are congestion control mechanisms currently in use [17].…”

Counteracting traffic congestion using intelligent driver feedback

“…Congestion which cannot be traced to a bottleneck is often referred to as a selforganized or "phantom" jam and occurs when the critical threshold of vehicle density is exceeded (Sugiyamal et al, 2008). Jerath and Brennan (2012) show both that the traffic density (vehicles per unit distance) at which congestion criticality is reached is higher in a fleet of adaptive cruise control (ACC) equipped vehicles and that adding non-ACC vehicles to the fleet, where humans are responsible for longitudinal control (acceleration and deceleration), increases the likelihood of a phantom jam. One may therefore conclude that driver reaction time and subsequent overreaction to a braking stimulus is a key contributing factor to triggering a phantom jam (Zielke et al, 2008).…”

“…At the leading edge of automobile technology are autonomous vehicle projects by Audi, BMW, Daimler, Ford, GM, Google, Nissan, Toyota, Volvo, and others that aim to reduce human driver fallibility (Knight, 2013), but general availability and widespread adoption is many years away not only due to a patchwork of regulatory frameworks (Kalra et al, 2009;Lassa, 2013) but also because the current cost of technology for a fully autonomous vehicle significantly exceeds the cost of an average vehicle (Rebsamen et al, 2012;Litman, 2013). Additionally, autonomous vehicles in a heterogeneous fleet will only be able to drive as efficiently as (though more safely than) the human-controlled vehicles around them (Jerath and Brennan, 2012 (Ma et al, 2012). However, that particular computer model required a very high percentage of adoption in order to realize overall improved flow and environmental benefit (Ma et al, 2012).…”