other
presentation
research report
An investigation into the relative safety of freeway-to-freeway connectors with respect to heavy trucks is undertaken. Using a three-dimensional, time-domain, force-based simulation model of heavy vehicle dynamics, boundaries are established relating various vehicle configurations, connector geometries, and driver behaviors to dynamic responses describing potential vehicle loss-of-control. A series of case studies is examined, attempting to position current driver/vehic/roadway interactions within the proposed envelopes of safety. Results include a method for predicting vehicle performance in response to roadway design enhancements that improves existing margins of safety at freeway-to-freeway connectors.
Phd Dissertation |
Jun 1991
published journal article
Phd Dissertation
A spectrum of traffic engineering and modern transportation planning problems requires the knowledge of the underlying trip pattern, commonly represented by dynamic Origin- Destination (OD) trip tables. In view of the fact that direct survey of trip pattern is technically problematic and economically infeasible, there have been a great number of methods proposed in the literature for updating the existing OD tables from traffic counts and/or other data sources. Unfortunately, there remain several common theoretical and practical aspects which impact the estimation accuracy and limit the use of these methods from most real-world applications. This dissertation itemizes and examines these critical issues. Then, the dissertation presents the developments, evaluations, and applications of two new frameworks intended to be used with the current and near-future data, respectively.The first framework offers a systematic and practical procedure for preparing dynamic demand inputs for microscopic traffic simulation under planning applications with an estimation module based solely on traffic counts. Under this framework, the traditional planning model is augmented with a filter traffic simulation step, which captures important spatial-temporal characteristics of route and traffic patterns within a large surrounding network, to improve the flow estimates entering and leaving the final microscopic simulation network. A new bounded dynamic OD estimation model and a solution algorithm for solving a large problem are also proposed.The second framework utilizes additional information from small probe samples collected over multiple days. There are two steps under this framework. The first step includes a suite of empirical and hierarchical Bayesian models used in estimating time dependent travel time distributions, destination fractions, and route fractions from probe data. These models provide multi-level posterior parameters and tend to moderate extreme estimates toward the overall mean with the magnitude depending on their precision, thus overcoming several problems due to non-uniform (over time and space) small sampling rates. The second step involves a construction of initial OD tables, an estimation of route-link fractions via a Monte Carlo simulation, and an updating procedure using a new dynamic OD estimation formulation which can also take into account the stochastic properties of the assignment matrix.
Phd Dissertation
Online shopping has increased steadily over the past decade that has led to a dramatic increase in the demand for urban package deliveries. Crowdsourced delivery, or crowd shipping, has been proposed and implemented by logistics companies in response to the growth in package delivery business. Crowdsourced delivery is a delivery service in which logistics service providers contract delivery services from the public (i.e., non-employees), instead of providing delivery services exclusively with an in-house logistics workforce. This dissertation studies different types of urban last-mile crowdsourced delivery services and provides a taxonomy for crowdsourced package delivery. Urban package crowdsourced delivery can be categorized in terms of the way packages are delivered and the role/tasks of crowdsourced drivers. Given these two dimensions, this study identifies three types of urban package crowdsourced delivery, namely, crowdsourced time-based delivery, crowdsourced trip-based delivery, and crowdsourced shared-trip delivery. Crowdsourced time-based delivery drivers are paid for their idle time and work as sub-contractors. Crowdsourced trip-based delivery matches drivers with individual tasks and utilizes the drivers for specific delivery trips. The last type, crowdsourced shared-trip delivery utilizes the common segments of a crowdsourced personal vehicle trip to deliver packages. In this type, the package shares part of the driver trip. The literature formulates the crowdsourced delivery problem as a Vehicle Routing Problem (VRP) and proposes a variety of solution approaches. However, all the solution algorithms are limited to relatively small-scale problems. In addition, the factors that impact the efficiency and effectiveness of crowdsourced delivery have not been thoroughly analyzed. To bridge the gap in crowdsourced delivery and urban freight logistics, this dissertation provides an alternative formulation for the static crowdsourced shared-trip delivery problem and proposes a novel decomposition heuristic to solve the problem. The alternative formulation is based on the set partitioning problem. The novel decomposition heuristic handles packages that are served by shared personal vehicles (SPVs) and dedicated vehicles (DVs), separately. After that, the algorithm deploys a package switch procedure, which rearranges packages between SPVs and DVs. The dissertation discusses various algorithms employed to solve different sub-problems, such as the budgeted k-shortest path, large scale bi-partite matching, decision of package switching and vehicle routing. To validate the models and algorithms, this dissertation presents a numerical case study that uses the network of the City of Irvine, CA, USA. The results of the numerical study unveil interesting results that are valuable to both researchers and industrial practitioners. The results indicate that crowdsourced shared-trip delivery service can reduce total cost by between 20% to 50%, compared to a delivery service that exclusively uses its own dedicated vehicles and drivers. However, the results show that dedicated vehicles are still required since the shared vehicles are not able to serve all packages even with a considerably large set of candidate shared vehicles. Vehicle Miles Traveled (VMT) savings depend on the crowdsourced driver selection and their trip origins. The dissertation also analyzes and discusses important factors that impact the effectiveness of crowdsourced delivery. In particular, the dissertation includes sensitivity analysis results with respect to changes in the depot location and the willingness of shared vehicles to detour.
book/book chapter
Phd Dissertation
As sensors that measure daily human activity become increasingly affordable and ubiquitous, there is a corresponding need for algorithms that unearth useful information from the resulting sensor observations. Many of these sensors record a time series of counts reflecting two behaviors: (1) the underlying hourly, daily, and weekly rhythms of natural human activity, and (2) bursty periods of unusual behavior. This dissertation explores a probabilistic framework for human-generated count data that (a) models the underlying recurrent patterns and (b) simultaneously separates and characterizes unusual activity via a Poisson-Markov model. The problems of event detection and characterization using real world, noisy sensor data with significant portions of data missing and corrupted measurements due to sensor failure are investigated. The framework is extended in order to perform higher level inferences, such as linking event models in a multi-sensor building occupancy model, and incorporating the occupancy measurement from loop detectors (in addition to the count measurement) to apply the model to problems in transportation research.
Phd Dissertation
Global Navigation Satellite Systems (GNSSs) have long been the cornerstone for positioning, navigation, and timing. Despite their widespread use, GNSS signals face vulnerabilities such as jamming, spoofing, and unreliable coverage in various environments like urban canyons, indoors, tunnels, and parking structures. These limitations make exclusive reliance on GNSS inadequate for the rigorous demands of future applications, including autonomous vehicles (AVs), intelligent transportation systems, and location-based services. To enhance GNSS performance in challenging settings, traditional methods have typically incorporated dead-reckoning sensors like inertial measurement units, lidars, or cameras. These sensors, however, accumulate errors over time and only offer navigation solutions within a local frame, relative to the user equipment’s (UE) initial position. In contrast, alternative signal-based approaches, known as signals of opportunity (SOPs) – encompassing AM/FM radio, satellite communication signals, digital television signals, Wi-Fi, and cellular – hold considerable promise as global navigation sources in GNSS-challenged environments. Among SOPs, cellular signals, particularly from third-generation (3G, code-division multiple access (CDMA)), fourth-generation (4G, long-term evolution (LTE)), and fifth-generation (5G, new radio (NR)) networks, stand out as potential navigation aids. Their navigation-friendly characteristics include ubiquity, geometric diversity, high carrier frequencies, spectral diversity, spatial diversity, broad bandwidth, strong signal strength, and free accessibility. Nevertheless, as SOPs are primarily designed for communication rather than navigation, utilizing cellular signals for navigational purposes presents several challenges. These include (1) the lack of specific low-level signal and error models for optimal state and parameter extraction for positioning and timing, (2) the absence of published robust, efficient, and reliable receiver architectures to generate navigation observables, (3) continual updates and changes in cellular protocols, and (4) the scarcity of frameworks for high-accuracy navigation using such signals. This dissertation addresses these challenges, focusing on cellular signals from 4G and 5G networks, with potential extensions to future cellular systems. The foundational contributions of this work are empirically validated on various platforms including ground vehicles (GVs), unmanned aerial vehicles (UAVs), and high-altitude aircraft, demonstrating GNSS-level navigation accuracy.
research report |
Nov 2023
Phd Dissertation |
Jan 2024