Bayesian Calibration of the Intelligent Driver Model

Accurate calibration of car-following models is essential for investigating microscopic human driving behaviors. This work proposes a memory-augmented Bayesian calibration approach, which leverages the Bayesian inference and stochastic processes (i.e., Gaussian processes) to calibrate an unbiased car-following model while extracting the serial correlations of residual. This calibration approach is applied to the intelligent driver model (IDM) and develops a novel model named MA-IDM. To evaluate the effectiveness of the developed approach, three models with different hierarchies (i.e., pooled, hierarchical, and unpooled) are tested. Experiments demonstrate that the MA-IDM can estimate the noise level of unrelated errors by decoupling the serial correlation of residuals. Furthermore, a stochastic simulation method is also developed based on our Bayesian calibration approach, which can obtain unbiased posterior motion states and generate anthropomorphic driving behaviors. Simulation results indicate that the MA-IDM outperforms Bayesian IDM in simulation accuracy and uncertainty quantification. With this Bayesian approach, we can generate enormous but nonidentical driving behaviors by sampling from the posteriors, which can help develop a realistic traffic simulator

Doctor of Philosophy

dissertationThe Active Traffic and Demand Management (ATDM) initiative aims to integrate various management strategies and control measures so as to achieve the mobility, environment and sustainability goals. To support the active monitoring and management of real-world complex traffic conditions, the first objective of this dissertation is to develop a travel time reliability estimation and prediction methodology that can provide informed decisions for the management and operation agencies and travelers. A systematic modeling framework was developed to consider a corridor with multiple bottlenecks, and a series of close-form formulas was derived to quantify the travel time distribution under both stochastic demand and capacity, with possible on-ramp and off-ramp flow changes. Traffic state estimation techniques are often used to guide operational management decisions, and accurate traffic estimates are critically needed in ATDM applications designed for reducing instability, volatility and emissions in the transportation system. By capturing the essential forward and backward wave propagation characteristics under possible random measurement errors, this dissertation proposes a unified representation with a simple but theoretically sound explanation for traffic observations under free-flow, congested and dynamic transient conditions. This study also presents a linear programming model to quantify the value of traffic measurements, in a heterogeneous data environment with fixed sensors, Bluetooth readers and GPS sensors. It is important to design comprehensive traffic control measures that can systematically address deteriorating congestion and environmental issues. To better evaluate and assess the mobility and environmental benefits of the transportation improvement plans, this dissertation also discusses a cross-resolution modeling framework for integrating a microscopic emission model with the existing mesoscopic traffic simulation model. A simplified car-following model-based vehicle trajectory construction method is used to generate the high-resolution vehicle trajectory profiles and resulting emission output. In addition, this dissertation discusses a number of important issues for a cloud computing-based software system implementation. A prototype of a reliability-based traveler information provision and dissemination system is developed to offer a rich set of travel reliability information for the general public and traffic management and planning organizations

Micro-simulation study of vehicular interactions on upgrades of intercity roads under heterogeneous traffic conditions in India

Purpose: Study of the basic traffic flow characteristics and clear understanding of vehicular interactions are the pre-requisites for highway capacity analysis and to formulate effective traffic management and control measures. The road traffic in India is highly heterogeneous comprising vehicles of wide ranging physical dimensions, weight, power and dynamic characteristics. The problem of measuring volume of such heterogeneous traffic has been addressed by converting the different types of vehicles into equivalent passenger cars and expressing the volume as Passenger Car Unit per hour (PCU/h). Methods: Computer simulation has emerged as an effective technique for modeling traffic flow due to its capability to account for the randomness related to traffic. This paper is concerned with application of a simulation model of heterogeneous traffic flow, named HETEROSIM, to quantify the vehicular interaction, in terms of PCU, for the different categories of vehicles, by considering the traffic flow of representative composition, on upgrades of different magnitudes on intercity roads in India. Results and Conclusions: The PCU estimates, made through microscopic simulation, for the different types of vehicles of heterogeneous traffic, for a wide range of grades and traffic volume, indicate that the PCU value of a vehicle significantly changes with change in traffic volume, magnitude of upgrade and its length. It is found that, the change in PCU value of vehicles is not significant beyond a length of 1600 m on grades. Also, it has been found that the PCU estimates are accurate at 5% level of significance