Driver Behavior in Traffic

DTFH61-09-H-00007Existing traffic analysis and management tools do not model the ability of drivers to recognize their environment and respond to it with behaviors that vary according to the encountered driving situation. The small body of literature on characterizing drivers behavior is typically limited to specific locations (i.e., by collecting data on specific intersections or freeway sections) and is very narrow in scope. This report documented the research performed to model driver behavior in traffic under naturalistic driving data. The research resulted in the development of hybrid car-following model. In addition, a neuro-fuzzy reinforcement learning, an agent-based artificial intelligence machine-learning technique, was used to model driving behavior. The naturalistic driving database was used to train and validate driver agents. The proposed methodology simulated events from different drivers and proved behavior heterogeneities. Robust agent activation techniques were also developed using discriminant analysis. The developed agents were implemented in VISSIM simulation platform and were evaluated by comparing the behavior of vehicles with and without agent activation. The results showed very close resemblance of the behavior of agents and driver data. Prototype agents prototype (spreadsheets and codes) were developed. Future research recommendations include training agents using more data to cover a wider region in the Wiedemann regime space, and sensitivity analysis of agent training parameters

Computationally efficient simulation-based optimization algorithms for large-scale urban transportation problems

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (pages 145-151).In this thesis, we propose novel computationally efficient optimization algorithms that derive effective traffic management strategies to reduce congestion and improve the efficiency of urban transportation systems. The proposed algorithms enable the use of high-resolution yet computationally inefficient urban traffic simulators to address large-scale urban transportation optimization problems in a computationally efficient manner. The first and the second part of this thesis focus on large-scale offline transportation optimization problems with stochastic simulation-based objective functions, analytical differentiable constraints and high-dimensional decision variables. We propose two optimization algorithms to solve these problems. In the first part, we propose a simulation-based metamodel algorithm that combines the use of an analytical stationary traffic network model and a dynamic microscopic traffic simulator. In the second part, we propose a metamodel algorithm that combines the use of an analytical transient traffic network model and the microscopic simulator. In the first part, we use the first metamodel algorithm to solve a large-scale fixed-time traffic signal control problem of the Swiss city of Lausanne with limited simulation runs, showing that the proposed algorithm can derive signal plans that outperform traditional simulation-based optimization algorithms and a commercial traffic signal optimization software. In the second part, we use both algorithms to solve a time-dependent traffic signal control problem of Lausanne, showing that the metamodel with the transient analytical traffic model outperforms that with the stationary traffic model. The third part of this thesis focuses on large-scale online transportation problems, which need to be solved with limited computational time. We propose a new optimization framework that combines the use of a problem-specific model-driven method, i.e., the method proposed in the first part, with a generic data-driven supervised machine learning method. We use this framework to address a traffic responsive control problem of Lausanne. We compare the performance of the proposed framework with the performance of an optimization framework with only the model-driven method and an optimization framework with only the data-driven method, showing that the proposed framework is able to derive signal plans that outperform the signal plans derived by the other two frameworks in most cases.by Linsen Chong.Ph. D. in Transportatio

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

This paper addresses large-scale urban transportation optimization problems with time-dependent continuous decision variables, a stochastic simulation-based objective function, and general analytical differentiable constraints. We propose a metamodel approach to address, in a computationally efficient way, these large-scale dynamic simulation-based optimization problems. We formulate an analytical dynamic network model that is used as part of the metamodel. The network model formulation combines ideas from transient queueing theory and traffic flow theory. The model is formulated as a system of equations. The model complexity is linear in the number of road links and is independent of the link space capacities. This makes it a scalable model suitable for the analysis of large-scale problems. The proposed dynamic metamodel approach is used to address a time-dependent large-scale traffic signal control problem for the city of Lausanne. Its performance is compared to that of a stationary metamodel approach. The proposed approach outperforms the stationary approach. This comparison illustrates the added value of providing the algorithm with analytical dynamic problem-specific structural information. The performance of a signal plan derived by the proposed approach is also compared to that of an existing signal plan for the city of Lausanne, and to that of a signal plan derived by a mainstream commercial signal control software. The proposed method can systematically identify signal plans with good performance. Keywords: simulation-based optimization; transient queuing theory; metamode

Mechanisms for Overpressure Development in Marine Sediments

Overpressure is widely developed in marine sediments; it is not only a critical factor related to hydrocarbon accumulation, but also a serious safety issue for oil/gas exploration and exploitation. Although the mechanisms for overpressure development in sedimentary basins have been intensively studied, some new mechanisms are proposed for overpressure development with the advancements in marine geological investigation, e.g., natural gas hydrate formation and microbial activity. In this study, the mechanisms for overpressure development are reviewed and further classified as being related to associated physical, chemical, and biological processes. The physical overpressure mechanisms include disequilibrium compaction, hydrate formation sealing, degasification, buoyancy, hydrothermal pressuring, tectonic movement, overpressure transfer, etc. The chemical overpressure mechanisms are ascribed to hydrate decomposition, diagenesis, hydrocarbon generation, etc. The biological overpressure mechanisms are mainly induced by microbial gas production and microbial plugging. In gas hydrate-bearing sediments, overpressure is a critical factor affecting the formation and distribution of gas hydrate. The mechanisms for overpressure development in marine gas hydrate systems are associated with permeability deterioration due to hydrate formation and free gas accumulation below bottom-simulating reflectors (BSR). In marine sediments, overpressure developments are generally related to a sediment layer of low permeability above and natural gas accumulation below, and overpressure is mainly developed below a sulphate–methane interface (SMI), because methane will be consumed by anaerobic oxidation above SMI

Simulation of driver behavior with agent-based back-propagation neural network

but does require sufficient real traffic and action data to capture the underlying relationship between states and actions. Therefore, ANN models estimate actions on the basis of the real state-action mapping of natural behavior. Data from the naturalistic driving database of the Naturalistic Truck Driving Study (NTDS) (1), collected by the Virginia Tech Transportation Institute and Blanco et al. (2), are used to find the real causalities and responses of truck drivers in car-following situations. In this paper, first the process of calibrating the well-known Gazis-Herman-Rothery (GHR) car-following model for an individual driver is described. Then, a back-propagation (BP) neural network is constructed to train agents that represent individual drivers. Both methods use the same naturalistic data set. CAR-FOLLOWING MODELS Brief Review Many car-following models have been developed in the past 50 years to represent longitudinal driver behavior, including safety distance models, collision avoidance models, psycho-physical action point models, and models based on fuzzy logic (3). Most well-known car-following models have been embedded in microsimulation software, such as the Pipes model in CORSIM (4), the Gipps model in AIMSUN (5), the Fritzsche model in Paramics (6), and the Wiedemann model in VISSIM (7). Car-following models assume that the following vehicle reacts according to observed stimulus from its leader according to predefined rules. The models mentioned above require specific defined functions to relate stimuli that the following vehicle observes to the reaction it takes. For example, the GHR model uses speed difference and space headway as stimuli to determine acceleration of the following vehicle. The Wiedemann model divides headway and speed difference space into several driving regimes with predefined thresholds, where the following vehicle reacts differently each regime. The Wiedemann model uses the differences between actual and desired following distances as a stimulus in the closing-in regime, a calibrated acceleration in the following regime, and a desired speed as the driving objective in the free-driving regime (7). The Gipps model uses vehicle dynamics as constraints and derives acceleration of the following vehicle from estimated deceleration of the leading vehicle (5). Driver Behavior Simulation The calibration of a car-following model is an important process to represent driver behavior and simulate vehicle trajectory. Calibration parameters are considered to be driver dependent and to remain The simulation of driver actions in traffic is an important part of modeling microscopic driver behavior. A driver action indicates driver behavior in terms of causalities and responses to traffic flow. Microscopic car-following models provide many powerful simulation tools to study individual driver behavior, interactions between leading and following vehicles, and cumulative macroscopic traffic phenomena. The performance of car-following models relies on the parameters of individual drivers that can represent unique driving behavior. Parameter calibration becomes a necessary process before car-following models can be applied to a simulation environment. Driver actions in car-following models are defined by predefined rules. These rules are mostly interpreted by relating the traffic state that a driver observes to the response or action that the driver takes. Different car-following models consider different criteria as causalities that stimulate a driver's reactions. However, in reality, these rules specified by car-following models might not capture natural driving behavior because of the complexity and instability of the human decision-making process. In the proposed approach, instead of using predefined driving rules from car-following models, a reactive-structure artificial neural network (ANN) is used to relate traffic states to driver actions. An ANN does not require a function to connect traffic states to actions L. Chong, 301-D Patton Hall, and M. M. Abbas