Correlated parameters in driving behavior models: car-following example and implications for traffic microsimulation

Behavioral parameters in car following and other models of driving behavior are expected to be correlated. An investigation is conducted into the effect of ignoring correlations in three parameters of car-following models on the resulting movement and properties of a simulated heterogeneous vehicle traffic stream. For each model specification, parameters are calibrated for the entire sample of individual drivers with Next Generation Simulation trajectory data. Factor analysis is performed to understand the pattern of relationships between parameters on the basis of calibrated data. Correlation coefficients have been used to show statistically significant correlation between the parameters. Simulation experiments are performed with vehicle parameter sets generated with and without considering such correlation. First, parameter values are sampled from the empirical mass functions, and simulated results show significant difference in output measures when parameter correlation is captured (versus ignored). Next, parameters are sampled under the assumption that they follow the multivariate normal distribution. Results suggest that the use of parametric distribution with known correlation structure may not sufficiently reduce the error due to ignoring correlation if the underlying assumption does not hold for both marginal and joint distributions

Dynamic origin-destination demand estimation using automatic vehicle identification data

Journal ArticleAbstract-This paper proposes a dynamic origin-destination (OD) estimation method to extract valuable point-to-point splitfraction information from automatic vehicle identification (AVI) counts without estimating market-penetration rates and identification rates of AVI tags. A nonlinear ordinary least-squares estimation model is presented to combine AVI counts, link counts, and historical demand information into a multiobjective optimization framework. A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors, and the resulting optimization problems are solved by using an iterative bilevel estimation procedure. Based on a synthetic data set, this study shows the effectiveness of the proposed estimation models under different market-penetration rates and identification rates

Methodological aspects of a decision aid for transportation choices under uncertainty

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1982.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.Bibliography: leaves 253-266.by Hani Sobhi Mahmassani.Ph.D

Likelihood and duration of flow breakdown: modeling the effect of weather

The effect of rain on freeway flow breakdown behavior is investigated. Three aspects of flow breakdown are analyzed for rain versus no rain (clear) weather conditions. First, the probability of breakdown occurrence is examined by analyzing the distribution of prebreakdown flow rates observed immediately before the onset of traffic breakdown by using a survival analysis approach. At all study sections, a reduction with prebreakdown flow rates is observed under rain conditions compared with distributions under no rain and confirms higher breakdown likelihoods at lower flows. Log likelihood ratio tests confirm the statistical significance of differences in the prebreakdown flow rate distribution parameters under rain compared with clear conditions. Second, breakdown duration is examined by estimating a semiparametric Cox proportional hazard model. With a rain event indicator set as an independent variable, the effect of rain on breakdown duration is observed. Rain during a breakdown episode is found to increase its duration, whereas rain before breakdown does not appear to affect duration. Finally, prebreakdown and postbreakdown flow rates are compared. Overall, while a reduction in prebreakdown flow rates is observed because of rain, the flow drop between prebreakdown and postbreakdown is not much different between rain (3.9% to 12.0%) and no rain (7.8% to 12.7%) conditions

Modeling Carrier Behavior in Sequential Auction Transportation Markets

Online markets for transportation services, in the form of Internet sites that dynamically match shipments (shippers? demand) and transportation capacity (carriers? offer) through auction mechanisms are changing the traditional structure of transportation markets. A general framework for the study of carriers? behavior in a sequential auction transportation marketplace is provided. The unique characteristics of these marketplaces and the sources of difficulty in analyzing the behavior of these marketplaces are discussed. Learning and behavior in a sequential Vickrey auction marketplace is analyzed and simulated. Some results and the overall behavioral framework are also discussed

Auction Settings Impacts on the Performance of Truckload Transportation Marketplaces

This paper compares the performance of different sequential auction settings for the procurement of truckload services. In this environment, demands arrive randomly over time and are described by pick up, delivery locations and hard timewindows. Upon demand arrival, carriers compete for the loads. Different auction and information disclosure settings are studied. Learning methodologies are discussed and analyzed. Simulation results are presented

Modeling Lane-Changing Behavior in a Connected Environment: A Game Theory Approach

AbstractVehicle-to-Vehicle communications provide the opportunity to create an internet of cars through the recent advances in communication technologies, processing power, and sensing technologies. Aconnected vehicle receives real-time information from surrounding vehicles; such information can improve drivers’ awareness about their surrounding traffic condition and lead to safer and more efficient driving maneuvers. Lane-changing behavior,as one of the most challenging driving maneuvers to understand and to predict, and a major source of congestion and collisions, can benefit from this additional information.This paper presents a lane-changing model based on a game-theoretical approach that endogenously accounts for the flow of information in a connected vehicular environment.A calibration approach based on the method of simulated moments is presented and a simplified version of the proposed framework is calibrated against NGSIM data. The prediction capability of the simplified model is validated. It is concluded the presented framework is capable of predicting lane-changing behavior with limitations that still need to be addressed.Finally, a simulation framework based on the fictitious play is proposed. The simulation results revealed that the presented lane-changing model provides a greater level of realism than a basic gap-acceptance model

Quantifying Opportunity Costs in Sequential Transportation Auctions for Truckload Acquisition

The principal focus of this research is to quantify opportunity costs in sequential transportation auctions. This paper focuses on the study a transportation marketplace with time-sensitive truckload pickup-and-delivery requests. In this paper, two carriers compete for service requests; each arriving service request triggers an auction where carriers compete with each other to win the right of servicing the load. An expression to evaluate opportunity costs is derived. This paper shows that the impact of evaluating opportunity costs is dependent on the competitive market setting. A simulation framework is used to evaluate different strategies. Some results and the overall simulation framework are also discussed

Urban Network Gridlock: Theory, Characteristics, and Dynamics

AbstractThis study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one- dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and re-distributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc