Kshitij Jerath

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

Self-organizing traffic jams are known to occur in medium-to-high density traffic flows, and it is suspected that adaptive cruise control (ACC) may affect their onset in mixed human-ACC traffic. Unfortunately, closed-form solutions that predict the occurrence of these jams in mixed human-ACC traffic do not exist. In this paper, both human and ACC driving behaviors are modeled using the General Motors fourth car-following model and are distinguished by using different model parameter values. A closed-form solution that explains the impact of ACC on congestion due to the formation of self-organized traffic jams (or “phantom” jams) is presented. The solution approach utilizes the master equation for modeling the self-organizing behavior of traffic flow at a mesoscopic scale and the General Motors fourth car-following model for describing the driver behavior at the microscopic scale. It is found that, although the introduction of ACC-enabled vehicles into the traffic stream may produce higher traffic flows, it also results in disproportionately higher susceptibility of the traffic flow to congestion.

GPS-Free Terrain-Based Vehicle Tracking Performance as a Function of Inertial Sensor Characteristics

Prior experiments have confirmed that specific terrain-based localization algorithms, designed to work in GPS-free or degraded-GPS environments, achieve vehicle tracking with tactical-grade inertial sensors. However, the vehicle tracking performance of these algorithms using low-cost inertial sensors with inferior specifications has not been verified. The included work identifies, through simulations, the effect of inertial sensor characteristics on vehicle tracking accuracy when using a specific terrain-based tracking algorithm based on Unscented Kalman Filters. Results indicate that vehicle tracking is achievable even when low-cost inertial sensors with inferior specifications are used. However, the precision of vehicle tracking decreases approximately linearly as bias instability and angle random walk coefficients increase. The results also indicate that as sensor cost increases, the variance in vehicle tracking error asymptotically tends to zero. Put simply, as desired precision increases, increasingly larger and quantifiable investment is required to attain an improvement in vehicle tracking precision.Copyright

GPS-free terrain-based vehicle tracking on road networks

Prior experiments have confirmed that specific GPS-free terrain-based localization algorithms can perform vehicle tracking in real-time on a single road segment at a time. However, the ability of these algorithms to perform vehicle tracking on large road networks, i.e. across intersections and multiple road segments, has not been verified. In this study, it is shown that it is possible to build upon the existing terrain-based localization algorithms to maintain vehicle tracking in large road networks. A set of estimators based on the Unscented Kalman Filter framework is used to track the vehicle in a section of a road network, i.e. across a few road segments and an intersection. A multiple model estimation scheme, based on comparing incoming attitude measurements with a terrain map, is used to identify the road segment that the vehicle is currently traveling over. Experiments indicate that it is possible to maintain vehicle tracking as a vehicle travels across an intersection in a road network.

Dynamic Prediction of Vehicle Cluster Distribution in Mixed Traffic: A Statistical Mechanics-Inspired Method

The advent of intelligent vehicle technologies holds significant potential to alter the dynamics of traffic flow. Prior work on the effects of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant information about the distribution of vehicle cluster size. Since the absence of large clusters could be offset by the presence of several smaller clusters, the distribution of cluster sizes can be as important as the presence or absence of clusters. To obtain a prediction of vehicle cluster distribution, the included work presents a statistical mechanics-inspired method of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control ( acc)-enabled vehicles exhibit a higher probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of large-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer large localized clusters as opposed to several smaller clusters spread across different locations on a highway.

Statistical mechanics-inspired framework for studying the effects of mixed traffic flows on highway congestion

Intelligent vehicles equipped with adaptive cruise control (ACC) technology have the potential to significantly impact the traffic flow dynamics on highways. Prior work in this area has sought to understand the impact of intelligent vehicle technologies on traffic flow by making use of mesoscopic modeling that yields closed-form solutions. However, this approach does not take into account the self-organization of vehicles into clusters of different sizes. Consequently, the predicted absence of a large traffic jam might be inadvertently offset by the presence of many smaller clusters of jammed vehicles. This study - inspired by research in the domain of statistical mechanics - uses a modification of the Potts model to study cluster formation in mixed traffic flows that include both human-driven and ACC-enabled vehicles. Specifically, the evolution of self-organized traffic jams is modeled as a non-equilibrium process in the presence of an external field and with repulsive interactions between vehicles. Monte Carlo simulations of this model at high vehicle densities suggest that traffic streams with low ACC penetration rates are likely to result in larger clusters. Vehicles spend significantly more time inside each cluster for low ACC penetration rates, as compared to streams with high ACC penetration rates.

Identification of locally influential agents in self-organizing multi-agent systems

Current research methods directed towards measuring the influence of specific agents on the dynamics of a large-scale multi-agent system (MAS) rely largely on the notion of controllability of the full-order system, or on the comparison of agent dynamics via a user-defined macroscopic system property. However, it is known that several large-scale multi-agent systems tend to self-organize, and their dynamics often reside on a low-dimensional manifold. The proposed framework uses this fact to measure an agents influence on the macroscopic dynamics. First, the minimum embedding dimension that can encapsulate the low-dimensional manifold associated with the self-organized dynamics is identified using a modification of the method of false neighbors. Second, the full-order dynamics are projected onto the local low-dimensional manifold using Krylov subspace-inspired model order reduction techniques. Finally, an existing controllability-based metric is applied to the local reduced-order representation to measure an agents influence on the self-organized dynamics. With this technique, one can identify regions of the state space where an agent has significant local influence on the dynamics of the self-organizing MAS. The proposed technique is demonstrated by applying it to the problem of vehicle cluster formation in traffic, a prototypical self-organizing system. As a result, it is now possible to identify regions of the roadway where an individual driver has the ability to influence the dynamics of a self-organized traffic jam.

A failure rate analysis of complex vehicles

When engineered items fail, there are often indicators of decay long before the system collapses. This research explores this concept applied to complex vehicles operated in public transportation, and the results can be extrapolated to any vehicle system. Transit bus reliability data gathered from eight transit agencies distributed across the USA are analysed at a vehicle and subsystem level to identify system failures. The theory of reliability of repairable systems is applied to the in-transit data to determine if major subsystem component failures can be detected by increases in cumulative and subsystem failure rates. Results indicate that major repairs might be detected far in advance of when they are needed.