Trajectories drawn in a common reference system by all the vehicles on a road are the ultimate empirical data to investigate traffic dynamics. The vast amount of such data made freely available by the Next Generation SIMulation (NGSIM) program is therefore opening up new horizons in studying traffic flow theory. Yet the quality of trajectory data and its impact on the reliability of related studies was a vastly underestimated problem in the traffic literature even before the availability of NGSIM data. The absence of established methods to assess data accuracy and even of a common understanding of the problem makes it hard to speak of reproducibility of experiments and objective comparison of results, in particular in a research field where the complexity of human behaviour is an intrinsic challenge to the scientific method. Therefore this paper intends to design quantitative methods to inspect trajectory data. To this aim first the structure of the error on point measurements and its propagation on the space travelled are investigated. Analytical evidence of the bias propagated in the vehicle trajectory functions and a related consistency requirement are given. Literature on estimation/filtering techniques is then reviewed in light of this requirement and a number of error statistics suitable to inspect trajectory data are proposed. The designed methodology, involving jerk analysis, consistency analysis and spectral analysis, is then applied to the complete set of NGSIM databases.

On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data

This paper shows that the behavior of driver models, either individually or entangled in stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories. To this aim, a “traffic-informed” methodology is proposed to restore physical and platoon integrity of trajectories in a finite time–space domain, and it is applied to one NGSIM I80 dataset. However, as the actual trajectories are unknown, it is not possible to verify directly whether the reconstructed trajectories are really “nearer” to the actual unknowns than the original measurements. Therefore, a simulation-based validation framework is proposed, that is also able to verify indirectly the efficacy of the reconstruction methodology. The framework exploits the main feature of NGSIM-like data that is the concurrent view of individual driving behaviors and emerging macroscopic traffic patterns. It allows showing that, at the scale of individual models, the accuracy of trajectories affects the distribution and the correlation structure of lane-changing model parameters (i.e. drivers heterogeneity), while it has very little impact on car-following calibration. At the scale of traffic simulation, when models interact in trace-driven simulation of the I80 scenario (multi-lane heterogeneous traffic), their ability to reproduce the observed macroscopic congested patterns is sensibly higher when model parameters from reconstructed trajectories are applied. These results are mainly due to lane changing, and are also the sought indirect validation of the proposed data reconstruction methodology.

Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns

Drawing upon fundamental research in human behavior prediction, recently there has been a research focus on how to predict driver behaviors. In this paper we review the field of driver behavior and intent prediction, with a specific focus on tactical maneuvers, as opposed to operational or strategic maneuvers. The aim of a driver behavior prediction system is to forecast the trajectory of the vehicle prior in real-time, which could allow a Driver Assistance System to compensate for dangerous or uncomfortable circumstances. This review provides insights into the scope of the problem, as well as the inputs, algorithms, performance metrics, and shortcomings in the state-of-the-art systems.

http://cvrr.ucsd.edu/publications/2011/Doshi_ITSC2011.pdf

Tactical driver behavior prediction and intent inference: A review

Lane changing maneuvers have a significant impact on macroscopic and microscopic characteristics of traffic flows due to the interference effect they have on surrounding traffic. Understanding the factors which affect drivers' lane changing behavior is important due to the implication of lane changing models in variety of traffic and transportation studies. This paper reviews the existing lane changing models and assesses the strengths and weaknesses of each model type. In addition, the lane changing models are classified according to their characteristics. Then, limitations of the existing lane changing models are identified. Finally, the findings and conclusions of the paper are summarized and more promising research directions are suggested.

Lane changing models: a critical review

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people-centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual’s day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model—SimMobility—for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility-sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: (a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; (b) a midterm level that handles agents’ activities and travel patterns; and (c) a short-term level that si...

/pdf/microsimulation-of-demand-and-supply-of-autonomous-mobility-1k59o4pr7j.pdf

Microsimulation of Demand and Supply of Autonomous Mobility On Demand

This paper presents the methodology and results of estimation of an integrated driving behavior model that attempts to integrate various driving decisions. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these behaviors and represents drivers' planning capabilities. It introduces new models that capture drivers' choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers' behavior to facilitate the completion of a desired lane change using the target gap. The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, Virginia. The estimation results are presented and discussed in detail.

https://toledo.net.technion.ac.il/files/2016/02/TR-C_IntegratedModel2_09.pdf

Estimation of an integrated driving behavior model

Advanced Public Transport Systems : Simulation-Based Evaluation

This report presents a review of existing travel simulators and ongoing data collection and modeling efforts using these simulators. It begins with general background information on simulators highlights the advantages and disadvantages of using simulators to investigate travelers' behavior, and establishes the basis of evaluation of existing travel simulators. The next section reviews the most recent literature on travel simulators and discusses major findings on data collected by the simulators and associated model development. This is followed by a discussion of the advantages and disadvantages of the developed simulators and ans assessment of travel simulators.

Haris Koutsopoulos

Papers

Estimation of an integrated driving behavior model

Advanced Public Transport Systems : Simulation-Based Evaluation

Public acceptance and user response to atis products and services: the use of travel simulators to investigate the response to traffic information