APPLICATION OF PARAMETER ESTIMATION AND CALIBRATION METHOD FOR CAR-FOLLOWING MODELS

Both safety and the capacity of the roadway system are highly dependent on the car-following characteristics of drivers. Car-following theory describes the driver behavior of vehicles following other vehicles in a traffic stream. In the last few decades, many car-following models have been developed; however, studies are still needed to improve their accuracy and reliability. Car-following models are a vital component of traffic simulation tools that attempt to mimic driver behavior in the real world. Microscopic traffic simulators, particularly car-following models, have been extensively used in current traffic engineering studies and safety research. These models are a vital component of traffic simulation tools that attempt to mimic real-world driver behaviors. The accuracy and reliability of microscopic traffic simulation models are greatly dependent on the calibration of car-following models, which requires a large amount of real world vehicle trajectory data. In this study, the author developed a process to apply a stochastic calibration method with appropriate regularization to estimate the distribution of parameters for car-following models. The calibration method is based on the Markov Chain Monte Carlo (MCMC) simulation using the Bayesian estimation theory that has been recently investigated for use in inverse problems. This dissertation research includes a case study, which is based on the Linear (Helly) model with a different number of vehicle trajectories in a highway network. The stochastic approach facilitated the calibration of car-following models more realistically than the deterministic method, as the deterministic algorithm can easily get stuck at a local minimum. This study also demonstrates that the calibrated model yields smaller errors with large sample sizes. Furthermore, the results from the Linear model validation effort suggest that the performance of the calibration method is dependent upon size of the vehicle trajectory

A stochastic mesoscopic cell-transmission model for operational analysis of large-scale transportation networks

The cell transmission model (CTM), developed by Daganzo in 1994 was not fully exploited as an operations model for analysis of large-scale traffic networks. Because of its macroscopic / mesoscopic features, CTM offers calibration and computational advantages over microscopic models. This study presents a series of enhancements to the original form of CTM. These enhancements show potential to increase the model’s accuracy and realism of traffic flow representation. For example, topological enhancements and modifications to the flow advancing equation are introduced to allow variable cell lengths and non-discrete movements of vehicles between cells. In addition, implementation of lane-changing behavioral logics and algorithmic enhancements to model vehicle flows at network junctions demonstrate potential in modeling realistic non-homogeneous traffic streams in CTM. A calibration exercise was conducted to account for randomness in driving behavior using vehicle trajectory data. This proves the models potential in modeling stochastic variations of real-life networks. A sample freeway network of I-10 corridor in Baton Rouge was used to evaluate and compare the performance of the improved version of CTM versus CORSIM. The simulation results showed comparable performance of both platforms in terms of link occupancy (density) and total network travel time and demonstrate the potential of employing CTM in traffic operations applications

Real-time estimation of lane-based queue lengths at isolated signalized junctions

In this study, we develop a real-time estimation approach for lane-based queue lengths. Our aim is to determine the numbers of queued vehicles in each lane, based on detector information at isolated signalized junctions. The challenges involved in this task are to identify whether there is a residual queue at the start time of each cycle and to determine the proportions of lane-to-lane traffic volumes in each lane. Discriminant models are developed based on time occupancy rates and impulse memories, as calculated by the detector and signal information from a set of upstream and downstream detectors. To determine the proportions of total traffic volume in each lane, the downstream arrivals for each cycle are estimated by using the Kalman filter, which is based on upstream arrivals and downstream discharges collected during the previous cycle. Both the computer simulations and the case study of real-world traffic show that the proposed method is robust and accurate for the estimation of lane-based queue lengths in real time under a wide range of traffic conditions. Calibrated discriminant models play a significant role in determining whether there are residual queued vehicles in each lane at the start time of each cycle. In addition, downstream arrivals estimated by the Kalman filter enhance the accuracy of the estimates by minimizing any error terms caused by lane-changing behavior.postprin

Modelling of Driver and Pedestrian Behaviour – A Historical Review

Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.</p

TOWARDS MODELING DRIVER BEHAVIOR UNDER EXTREME CONDITIONS

The purpose of this study is to investigate the representation of driver behavior under extreme conditions, towards development of a micro-simulation modeling framework of traffic flow to support evaluation of management strategies and measures in emergency situations. To accomplish this objective, particular attention is given to understanding and representing "panic behavior" of individuals and how this behavior may be translated into driver actions. Related background from psychology and sociology is examined. Following a systematic review of previous traffic models, a model is selected as a starting point for modification towards the micro-simulation of traffic flow under extreme conditions. The model is based on Gipps' (1981) Car-Following Model. To evaluate the proposed modification, a prototype implementation is proposed for the micro-simulation of traffic flow on a stretch of highway with simplified geometric features. The vehicle trajectories and aggregate traffic properties are evaluated with respect to different scenarios through a sensitivity analysis

Modeling influencing factors in a microscopic traffic simulator

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 93-95).Microscopic traffic simulation is an important tool for traffic analysis and dynamic traffic management as it enables planners to evaluate traffic flow patterns, predict and evaluate the outcome of various response plans and assists in decision making. It is a vital tool for traffic management centers and can be helpful in developing contingency plans to enhance the safety and security of the transportation system. This thesis investigates the current state-of-the-practice in traffic microsimulation tools. A survey was developed and administered to developers. Results of the survey indicate critical gaps in including influencing external factors beyond the interaction of vehicles, such as incidents, work zones, or inclement weather, in traffic simulators. This thesis introduces a framework for incorporating such factors in existing models. The nature of the influencing factors limits disaggregate trajectory data collection generally needed to estimate driving behavior models. Therefore, an approach using aggregate calibration to refine and enhance existing driving behavior models is formulated. The aggregate calibration methodology is illustrated with a case study incorporating the effects of weather in driving behavior models using a freeway corridor in the Hampton Roads region of Virginia.(cont.) MITSIMLab, a microscopic traffic simulation laboratory that was developed for evaluating the impacts of alternative traffic management system designs at the operational level, is used for evaluation. The presence of precipitation was found to be significant in reducing speeds in the case study and was incorporated into the driving behavior models with aggregate calibration. This methodology was found to improve the simulation results, by reducing bias and variability. Assessment of the approach is discussed and recommendations for improvement and further study are offered.by Emily D. Sterzin.S.M

Do microscopic merging models reproduce the observed priority sharing ratio in congestion?

A classical way to represent vehicle interactions at merges at the microscopic scale is to combine a gap-acceptance model with a car-following algorithm. However, in congested conditions (when a queue spills back on the major road), outputs of such a combination may be irrelevant if anticipatory aspects of vehicle behaviours are disregarded (like in single-level gap-acceptance models). Indeed, the insertion decision outcomes are so closely bound to the car-following algorithm that irrelevant results are produced. On the one hand, the insertion decision choice is sensitive to numerical errors due to the car-following algorithm. On the other hand, the priority sharing process observed in congestion cannot be correctly reproduced because of the constraints imposed by the car-following on the gap-acceptance model. To get over these issues, more sophisticated gap-acceptance algorithms accounting for cooperation and aggressiveness amongst drivers have been recently developed (multi-level gap-acceptance models). Another simpler solution, with fewer parameters, is investigated in this paper. It consists in introducing a relaxation procedure within the car-following rules and proposing a new insertion decision algorithm in order to loosen the links between both model components. This approach will be shown to accurately model the observed flow allocation pattern in congested conditions at an aggregate scale. (C) 2009 Elsevier Ltd. All rights reserved

Modeling gap acceptance at freeway merges

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.Includes bibliographical references (p. 103-105).This thesis develops a merging model that captures the gap acceptance behavior of drivers that merge from a ramp into a congested freeway. Merging can be classified into three types: normal, forced and cooperative lane changing. The developed merging model uses a single critical gap function, which incorporates explanatory variables that capture all three types of merging behavior. Thus, the model combines all three types in a single model. The merging gap acceptance model is estimated using the maximum likelihood method with detailed trajectory data that was collected on two freeway sections in California. Estimation results show that the merging gap acceptance model is affected by traffic conditions such as average speed in the mainline, interactions with lead and lag vehicles, and urgency of the merge. Transferability tests for the stability of the model parameters between the two datasets are conducted. The single level gap acceptance model is implemented and compared with an existing gap acceptance model in the microscopic traffic simulation model, MITSIMLab. The results show that the proposed model is better than the existing gap acceptance model.by Gunwoo Lee.S.M