EVALUATE PROBE SPEED DATA QUALITY TO IMPROVE TRANSPORTATION MODELING

Probe speed data are widely used to calculate performance measures for quantifying state-wide traffic conditions. Estimation of the accurate performance measures requires adequate speed data observations. However, probe vehicles reporting the speed data may not be available all the time on each road segment. Agencies need to develop a good understanding of the adequacy of these reported data before using them in different transportation applications. This study attempts to systematically assess the quality of the probe data by proposing a method, which determines the minimum sample rate for checking data adequacy. The minimum sample rate is defined as the minimum required speed data for a segment ensuring the speed estimates within a defined error range. The proposed method adopts a bootstrapping approach to determine the minimum sample rate within a pre-defined acceptance level. After applying the method to the speed data, the results from the analysis show a minimum sample rate of 10% for Kentucky’s roads. This cut-off value for Kentucky’s roads helps to identify the segments where the availability is greater than the minimum sample rate. This study also shows two applications of the minimum sample rates resulted from the bootstrapping. Firstly, the results are utilized to identify the geometric and operational factors that contribute to the minimum sample rate of a facility. Using random forests regression model as a tool, functional class, section length, and speed limit are found to be the significant variables for uninterrupted facility. Contrarily, for interrupted facility, signal density, section length, speed limit, and intersection density are the significant variables. Lastly, the speed data associated with the segments are applied to improve Free Flow Speed estimation by the traditional model

Characterizing Queue Dynamics at Signalized Intersections From Probe Vehicle Data

Probe vehicles instrumented with location-tracking technologies have become increasingly popular for collecting traffic flow data. While probe vehicle data have been used for estimating speeds and travel times, there has been limited research on predicting queuing dynamics from such data. In this research, a methodology is developed for identifying the travel lanes of the GPS-instrumented vehicles when they are standing in a queue at signalized intersections with multilane approaches. In particular, the proposed methodology exploits the unequal queue lengths across the lanes to infer the specific lanes the probe vehicles occupy. Various supervised and unsupervised clustering methods were developed and tested on data generated from a microsimulation model. The generated data included probe vehicle positions and shockwave speeds predicated on their trajectories. Among the tested methods, a Bayesian approach that employs probability density functions estimated by bivariate statistical mixture models was found to be effective in identifying the lanes. The results from lane identification were then used to predict queue lengths for each travel lane. Subsequently, the trajectories for non-probe vehicles within the queue were predicted. As a potential application, fuel consumption for all vehicles in the queue is estimated and evaluated for accuracy. The accuracies of the models for lane identification. queue length prediction, and fuel consumption estimation were evaluated at varying levels of demand and probe-vehicle market penetrations. In general, as the market penetration increases, the accuracy improves. For example. when the market penetration rate is about 40%, the queue length estimation accuracy reaches 90%. The dissertation includes various numerical experiments and the performance of the models under numerous scenarios

Development and evaluation of advanced traveler information system (ATIS) using vehicle-to-vehicle (V2V) communication system

This research develops and evaluates an Advanced Traveler Information System (ATIS) model using a Vehicle-to-Vehicle (V2V) communication system (referred to as the GATIS-V2V model) with the off-the-shelf microscopic simulation model, VISSIM. The GATIS-V2V model is tested on notional small traffic networks (non-signalized and signalized) and a 6X6 typical urban grid network (signalized traffic network). The GATIS-V2V model consists of three key modules: vehicle communication, on-board travel time database management, and a Dynamic Route Guidance System (DRGS). In addition, the system performance has been enhanced by applying three complementary functions: Autonomous Automatic Incident Detection (AAID), a minimum sample size algorithm, and a simple driver behavior model. To select appropriate parameter ranges for the complementary functions a sensitivity analysis has been conducted. The GATIS-V2V performance has been investigated relative to three underlying system parameters: traffic flow, communication radio range, and penetration ratio of participating vehicles. Lastly, the enhanced GATIS-V2V model is compared with the centralized traffic information system. This research found that the enhanced GATIS-V2V model outperforms the basic model in terms of travel time savings and produces more consistent and robust system output under non-recurrent traffic states (i.e., traffic incident) in the simple traffic network. This research also identified that the traffic incident detection time and driver's route choice rule are the most crucial factors influencing the system performance. As expected, as traffic flow and penetration ratio increase, the system becomes more efficient, with non-participating vehicles also benefiting from the re-routing of participating vehicles. The communication radio ranges considered were found not to significantly influence system operations in the studied traffic network. Finally, it is found that the decentralized GATIS-V2V model has similar performance to the centralized model even under low flow, short radio range, and low penetration ratio cases. This implies that a dynamic infrastructure-based traffic information system could replace a fixed infrastructure-based traffic information system, allowing for considerable savings in fixed costs and ready expansion of the system off of the main network corridors.Ph.D.Committee Chair: Hunter, Michael; Committee Member: Fujimoto, Richard; Committee Member: Guensler, Randall; Committee Member: Leonard, John; Committee Member: Meyer, Michae

Connected Vehicle Technology: User and System Performance Characteristics

The emerging connected vehicle (CV) technology plays a promising role in providing more operable and safer transportation environments. Yet, many questions remain unanswered as to how various user and system characteristics of CV-enabled networks can shape the successful implementation of the technology to maximize the return on investment. This research attempts to capture the effect of multiple factors such as traffic density, market penetration, and transmission range on the communication stability and overall network performance by developing a new CONnectivity ROBustness (CONROB) model. The model was tested with data collected from microscopic simulation of a 195 sq-mile traffic network and showed a potential to capture the effect of such factors on the communication stability in CV environments. The information exchanged among CVs can also be used to estimate traffic conditions in real time by invoking the probe vehicle feature of CV technology. Since factors affecting the connectivity robustness also have an impact on the performance of traffic condition estimation models, a direct relationship between connectivity robustness and traffic condition estimation performance was established. Simulation results show that the CONROB model can be used as a tool to predict the accuracy of the estimated traffic conditions (e.g. travel times), as well as the reliability of such estimates, given specific system characteristics. The optimal deployment of road-side units (RSUs) is another important factor that affects the communication stability and the traffic conditions estimates and reliability. Thus, an optimization approach was developed to identify the optimal RSUs locations with the objective function of maximizing the connectivity robustness. Simulation results for the developed approach show that CONROB model can help identify the optimal RSUs locations. This shows the importance of CONROB model as a planning tool for CV environments. For the individual user performance characteristics, a preliminary driving simulator test bed for CV technology was developed and tested on thirty licensed drivers. Forward collision warning messages were delivered to drivers when predefined time-to-collision values take place. The findings show improved reaction times of drivers when receiving the warning messages which lend credence to the safety benefits of the CV technology

A case study on cooperative car data for traffic state estimation in an urban network

The use of Floating Car Data (FCD) as a particular case of Probe Vehicle Data (PVD) has been the object of extensive research for estimating traffic conditions, travel times and Origin to Destination trip matrices. It is based on data collected from a GPS-equipped vehicle fleet or available cell phones. Cooperative Cars with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication capabilities represent a step forward, as they also allow tracking vehicles surrounding the equipped car. This paper presents the results of a limited experiment with a small fleet of cooperative cars in Barcelona’s Central Business District (CBD) known as L’Eixample. Data collected from the experiment were used to build and calibrate the emulation of cooperative functions in a microscopic simulation model that captured the behavior of vehicle sensors in Barcelona’s CBD. Such a calibrated model allows emulating fleet data on a large scale that goes far beyond what a small fleet of cooperative vehicles could capture. To determine the traffic state, several approaches are developed for estimating traffic variables based on extensions of Edie’s definition of the fundamental traffic variables with the emulated data, whose accuracy depends on the penetration level of the technology.Peer ReviewedPostprint (author's final draft

Estimation/updating of origin-destination flows: recent trends and opportunities from trajectory data

Understanding the spatial and temporal dynamics of mobility demand is essential for many applications over the entire transport domain, from planning and policy assessment to operation, control, and management. Typically, mobility demand is represented by origin-destination (o-d) flows, each representing the number of trips from one traffic zone to another, for a certain trip purpose and mode of transport, in a given time interval (Cascetta, 2009, Ortuzar and Willumsen, 2011). O-d flows have been generally unobservable for decades, thus the problem of o-d matrix estimation is still one of the most challenging in transportation studies. In recent times, unprecedented tracing and tracking capabilities have become available. The pervasive penetration of sensing devices (smartphones, black boxes, smart cards, ...) adopting a variety of tracing technologies/methods (GPS, Bluetooth, ...) could make in many cases o-d flows now observable. The increasing availability of trajectory data sources has provided new opportunities to enhance observability of human mobility and travel patterns between origins and destinations, recently explored by researchers and practitioners, bringing innovation and new research directions on origin-destination (o-d) matrix estimation. The purpose of this thesis is to develop a deep understanding of the opportunities and the limitations of trajectory data to assess its potential for ameliorating the o-d flows estimation/updating problem and for conducting o-d related analysis. The proposed work involves both real trajectory data analysis and laboratory experiments based on synthetic data to investigate the implications of the trajectory data sample distinctive features (e.g. sample representativeness and bias) on demand flows accuracy. Final considerations and results might provide useful guidelines for researchers and practitioners dealing with various types of trajectory data sample and conducting o-d related applications

Doctor of Philosophy

dissertationThis dissertation aims to develop an innovative and improved paradigm for real-time large-scale traffic system estimation and mobility optimization. To fully utilize heterogeneous data sources in a complex spatial environment, this dissertation proposes an integrated and unified estimation-optimization framework capable of interpreting different types of traffic measurements into various decision-making processes. With a particular emphasis on the end-to-end travel time prediction problem, this dissertation proposes an information-theoretic sensor location model that aims to maximize information gains from a set of point, point-to-point and probe sensors in a traffic network. After thoroughly examining a number of possible measures of information gain, this dissertation selects a path travel time prediction uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework. To better measure the quality of service for ransportation systems, this dissertation investigates the path travel time reliability from two perspectives: variability and robustness. Based on calibrated travel disutility functions, the path travel time variability in this research is represented by its standard deviation in addition to the mean travel time. To handle the nonlinear and nonadditive cost functions introduced by the quadratic forms of the standard deviation term, a novel Lagrangian substitution approach is introduced to estimate the lower bound of the most reliable path solution through solving a sequence of standard shortest path problems. To recognize the asymmetrical and heavy-tailed travel time distributions, this dissertation proposes Lagrangian relaxation based iterative search algorithms for finding the absolute and percentile robust shortest paths. Moreover, this research develops a sampling-based method to dynamically construct a proxy objective function in terms of travel time observations from multiple days. Comprehensive numerical experiment results with real-world travel time measurements show that 10-20 iterations of standard shortest path algorithms for the reformulated models can offer a very small relative duality gap of about 2-6%, for both reliability measure models. This broadly-defined research has successfully addressed a number of theoretically challenging and practically important issues for building the next-generation Advanced Traveler Information Systems, and is expected to offer a rich foundation beneficial to the model and algorithmic development of sensor network design, traffic forecasting and personalized navigation