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This paper explores short duration disturbances in the traffic stream that are large enough to impact the traffic dynamics and disrupt stationarity when establishing the fundamental diagram, FD, but small enough that they are below the resolution of conventional vehicle detector data and cannot be seen using conventional methods. This empirical research develops the Exclusionary Vehicle Aggregation method (EVA) to extract high fidelity time series data from conventional loop detectors and then extends the method to measure the standard deviation of headways in a given fixed time sample, stdevh. Using loop detector data spanning 18 years and five sites, all of the sites show that samples with low stdevh tend towards a triangular FD while samples with high stdevh tend towards a concave FD that falls inside the triangular FD. The stdevh is also shown to be strongly correlated with the duration of the longest headway within the sample. The presence of a long headway means the state is perceptively different over the sample and thus, the measurement is non-stationary. A review of the earliest FD literature by Greenshields finds strong supporting evidence for these trends. Collectively, the loop detector and historical FD results span over 75 years of empirical traffic data. Based on the EVA analysis, this work offers the following insights: the shape of equilibrium FD appears to be triangular and that conventional detector data mask critical features needed by hydrodynamic traffic flow models, HdTFM. Because the driver behind a long headway can act independent of their leader, the long headways can correspond to unobserved boundary conditions that generate kinematic waves. If these boundaries were detected many HdTFM could accommodate them, especially multi-class models. But the stochastic nature of the long headways also challenges the predictive abilities of deterministic HdTFM. Perhaps the largest of these challenges is driver agency- the driver behind a long headway can maintain it, resulting in signals propagating downstream or they can close the gap, resulting in signals propagating upstream. Meanwhile, this work provides a test for stationary conditions to help ensure an empirical FD supports the assumptions placed upon it. 

This paper develops the partial trajectory method to align the views from successive fixed cameras that are used for video-based vehicle tracking across multiple camera views. The method is envisioned to serve as a validation tool of whatever alignment has already been performed between the cameras to ensure high fidelity with the actual vehicle movements as they cross the boundaries between cameras. The strength of the method is that it operates on the output of vehicle tracking in each camera rather than secondary features visible in the camera view that are unrelated to the traffic dynamics (e.g., fixed fiducial points). Thereby providing a direct feedback path from the tracking to ensure the quality of the alignment in the context of the traffic dynamics. The method uses vehicle trajectories within successive camera views along a freeway to deduce the presence of an overlap or a gap between those cameras and quantify how large the overlap or gap is. The partial trajectory method can also detect scale factor errors between successive cameras. If any error is detected, ideally one would redo the original camera alignment, if that is not possible, one could use the calculations from the algorithm to post hoc address the existing alignment. This research manually re-extracted the individual vehicle trajectories within each of the seven camera views from the NGSIM I-80 dataset. These trajectories are simply an input to the algorithm. The resulting method transcends the dataset and should be applicable to most methods that seek to extract vehicle trajectories across successive cameras. That said, the results reveal fundamental errors in the NGSIM dataset, including unaccounted for overlap at the boundaries between successive cameras, which leads to systematic speed and acceleration errors at the six camera interfaces. This method also found scale factor errors in the original NGSIM homographies. In response to these findings, we identified a new aerial photo of the NGSIM site and generated new homographies. To evaluate the impact of the partial trajectory method on the actual trajectory data, the manually re-extracted data were projected into the new coordinate system and smoothed. The re-extracted data shows much greater fidelity to the actual vehicle motion. The re-extracted data also tracks the vehicles over a 14% longer distance and adds 23% more vehicles compared to the original NGSIM dataset. As of publication, the re-extracted data from this paper will be released to the research community. 

This paper undertakes a detailed empirical study of traffic dynamics on a freeway. The results show the traffic dynamics that systematically determine the shape of the fundamental diagram, FD, can also violate the stationarity assumptions of both shockwave analysis and Lighthill, Whitham and Richard's models, thereby inhibiting the applicability of these classical macroscopic traffic flow theories. The outcome is challenging because there is no way to identify the problem using only the macroscopic detector data. The research examines conditions local to vehicle detector stations to establish the FD while the single vehicle passage method is used to analyze the composition of vehicles underlying the aggregate samples. Then, traffic states are correlated between successive stations to measure the actual signal velocities and show they are inconsistent with the classical theories. This analysis also revealed that conditions in one lane can induce signals in another lane. Rather than exhibiting a single signal passing a given point in time and space, the induced and intrinsic signals are superimposed on one another in the given lane. We suspect the subtle dynamics revealed in this research have gone unnoticed because they are far below the resolution of conventional traffic monitoring. The findings could have implications to other traffic flow models that rely on the FD, so care should be taken to assess if a given model is potentially sensitive to the non-stationary dynamics presented herein. The results have a direct impact on practice. Traffic flow theory is a critical input to many aspects of surface transportation, e.g., traffic management, traffic control, network design, vehicle routing, traveler information, and transportation planning all depend on models or simulation software that are based upon traffic flow theory. If the underlying traffic flow theory is flawed it puts the higher level applications at risk. So, the findings in this paper should lead to caution in accepting the predictions from traffic flow models and simulation software when the traffic exhibits a concave FD. 

This paper develops a non-model based vehicle tracking methodology for extracting road user trajectories as they pass through the field of view of a 3D LiDAR sensor mounted on the side of the road. To minimize the errors, our work breaks from conventional practice and postpones target segmentation until after collecting LiDAR returns over many scans. Specifically, our method excludes all non-vehicle returns in each scan and retains the ungrouped vehicle returns. These vehicle returns are stored over time in a spatiotemporal stack (ST stack) and we develop a vehicle motion estimation framework to cluster the returns from the ST stack into distinct vehicles and extract their trajectories. This processing includes removing the impacts of the target's changing orientation relative to the LiDAR sensor while separately taking care to preserve the crisp transition to/from a stop that would normally be washed out by conventional data smoothing or filtering. This proof of concept study develops the methodology using a single LiDAR sensor, thus, limiting the surveillance region to the effective range of the given sensor. It should be clear from the presentation that, provided sufficient georeferencing, the surveillance region can be extended indefinitely by deploying multiple LiDAR sensors with overlapping fields of view.