<p>Transportation engineering has taken upon a new role; to empower the alternative modes of travel: walking, biking, and bus transit. In this new era, engineers are rethinking a network designed predominately for the automobile. The ultimate goal of this research is to create a process that can make a vehicle dominant corridor a desirable, livable thoroughfare by livability design and context sensitive performance measures. Balancing travel modes requires an account of vehicular traffic and the impact of reconfiguring existing conditions. The analysis herein is conducted by field data collection, transportation equations and microsimulation. Simulating traffic behavior will be the means to apply livable alternatives comparable to existing Southeast Florida conditions. The results herein have shown that micro-simulation can be utilized in transportation planning to reveal good livability alternatives. </p>

<p> This dissertation presents a methodology for the joint optimization of a variety of pavement construction and management activities for segment and system problems under multiple budget constraints. The objective of pavement management is to minimize the total discounted life time costs for the agency and the highway users by finding optimal policies. The scope of the dissertation is focused on continuous time and continuous state formulations of pavement condition. We use a history-dependent pavement deterioration model to account for the influence of history on the deterioration rate. </p><p> Three topics, representing different aspects of the problem are covered in the dissertation. In the first part, the subject is the joint optimization of pavement design, maintenance and rehabilitation (M&R;) strategies for the segment-level problem. A combination of analytical and numerical tools is proposed to solve the problem. In the second part of the dissertation, we present a methodology for the joint optimization of pavement maintenance, rehabilitation and reconstruction (MR&R;) activities for the segment-level problem. The majority of existing Pavement Management Systems (PMS) do not optimize reconstruction jointly with maintenance and rehabilitation policies. We show that not accounting for reconstruction in maintenance and rehabilitation planning results in suboptimal policies for pavements undergoing cumulative damage in the underlying layers (base, sub-base or subgrade). We propose dynamic programming solutions using an augmented state which includes current surface condition and age. In the third part, we propose a methodology for the joint optimization of rehabilitation and reconstruction activities for heterogeneous pavement systems under multiple budget constraints. Within a bottom-up solution approach, Genetic Algorithm (GA) is adopted. The complexity of the algorithm is polynomial in the size of the system and the policy-related parameters. </p>

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 73-74). Also available via the Internet.

<p> Greenhouse gas (GHG) emissions, known as a major cause of climate change, have been emitted by the combustion of fossil fuels over the past few decades. The transportation sector contributes significantly to global GHG emissions. Inspired by the successful implementation of tradable credit schemes (TCSs) in pollution control programs, this dissertation focuses on multi-period TCSs to minimize vehicular emissions. In this scheme, a central authority (CA) allocates travel credits to travelers (credit allocation scheme) and then, charges them to travel on each link (credit charging scheme). Travelers are able to trade credits amongst themselves in the market. </p><p> To address the long-term planning goals of the CA, the dissertation proposes the concept of a multi-period TCS framework. This framework enables the CA to achieve steady progress toward system-level goals, i.e., reducing traffic congestion and GHG emissions, over the long-term planning horizon. First, a TCS-based multi-period equilibrium modeling framework is developed to address the planning problem of a CA that seeks to achieve system-level goals by varying the credit supply and the link usage credit charging schemes across the various periods of the planning horizon. Further, the CA seeks stable credit prices across these periods to provide them as information to travelers in an operational context. Based on this information, bank interest rate and their travel needs, travelers determine their actions in terms of the consumption or sale of credits in the current period or the transfer of credits to future periods. It is proved that the credit price volatility is dampened by the ability to transfer credits. Since a TCS is subject to market manipulation and the artificial control of credit price, a transfer fee, which is shown to be an effective instrument to control hoarding among travelers, is proposed. </p><p> Using the proposed multi-period TCS framework, the dissertation develops different system optimal (SO) TCS designs, as bi-level models, to derive credit allocation and charging schemes to achieve system-level goals. In the first SO multi- period TCS design, the CA minimizes the vehicular emissions in the upper level over the long-term planning horizon. This enables the CA to plan the trajectory of vehicular emissions during the planning horizon. This trajectory can be used to predetermine the emissions standard for each period to use in the second SO multi-period TCS design, which aims to minimize total system travel time, in the upper level, over the planning horizon. These designs include bounds on increases in travel costs, allowing travelers to better adapt to the TCS implementation. The lower-level models are the equilibrium conditions in which travelers minimize their costs under the obtained multi-period TCS in the upper level. </p><p> To enhance realism in capturing the equilibrium conditions under the multi-period TCS, this dissertation factors different travelers&rsquo; characteristics and bank interest rates. In making route choices, travelers factor value of time (VOT) and tradeoff credit consumption and travel costs. Hence, travelers&rsquo; heterogeneity in terms of VOT is factored. It is shown that if the CA does not factor VOT in SO TCS design, it leads to a socially inequitable policy in practice. Further, the heterogeneity of travelers in terms of perceived future credit prices is factored. Travelers decide to consume or transfer credits in each period based on several factors, including future credit prices. However, due to the uncertainty in traffic network demand/supply forecasts over the long-term horizon, the CA cannot provide an accurate forecast of future credit prices <i>a priori</i>. It is shown that as the difference between travelers&rsquo; perceptions of future CPs and the actual CPs set by the CA for each period increases, the effectiveness of the SO TCS design in minimizing total system travel time decreases; this has implications for traffic congestion management. </p><p> Fourth, the dissertation investigates the robust design of multi-period TCS to account for travel demand uncertainty and achieve system-level goals. To minimize vehicular emissions, the CA leverages the TCS to promote zero-emissions vehicles (ZEVs), which circumvents the need for current subsidy-based incentive policies. The incentive to shift to ZEVs is fostered by allocating more credits and charging fewer credits to ZEV travelers compared to other travelers. To factor the uncertainty in travel demand forecasts, this research proposes a robust multi-period TCS design that minimizes the worst-case vehicular emissions, i.e. maximum vehicular emissions, under different traffic network demand scenarios. It is shown that the robust TCS design increases reliability in achieving system-level goals, compared to the SO TCS design that does not factor travel demand uncertainty. </p><p> Finally, the dissertation analyzes the ability of a TCS to manage morning commute congestion while factoring the market loss aversion of commuters. (Abstract shortened by ProQuest.) </p><p>

<p> In the 21^st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis. </p><p> Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents. </p><p> Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis. </p><p> Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.</p><p>

<p> The recent emergence of social media and online retailing become increasingly important and continue to grow. More and more people use social media to share their real life to the digital world, at the same time, browse the virtual Internet to buy the real products. In the process, a huge amount of data is generated and we investigate the data and crowdsourcing for areas of the public transportation and last-mile delivery for online orders in the perspective of data analytics and operations optimization. </p><p> We first focus on the transit flow prediction by crowdsourced social media data. Subway flow prediction under event occurrences is a very challenging task in transit system management. To tackle this challenge, we leverage the power of social media data to extract features from crowdsourced content to gather the public travel willingness. We propose a parametric and convex optimization-based approach to combine the least squares of linear regression and the prediction results of the seasonal autoregressive integrated moving average model to accurately predict the NYC subway flow under sporting events. </p><p> The second part of the thesis focuses on the last-mile same-day delivery with store fulfillment problem (SDD-SFP) using real-world data from a national retailer. We propose that retailers can take advantage of their physical local stores to ful?ll nearby online orders in a direct-to-consumer fashion during the same day that order placed. Optimization models and solution algorithms are developed to determine store selections, fleet-sizing for transportation, and inventory in terms of supply chain seasonal planning. In order to solve large-scale SDD-SFP with real-world datasets, we create an accelerated Benders decomposition approach that integrates the outer search tree and local branching based on mixed-integer programming and develops optimization-based algorithms for initial lifting constraints. </p><p> In the last part of the dissertation, we drill down SDD-SFP from supply chain planning to supply chain operation level. The aim is to create an optimal exact order ful?llment plan to specify how to deliver each received customer order. We adopt crowdsourced shipping, which utilizes the extra capacity of the vehicles from private drivers to execute delivery jobs on trips, as delivery options, and define the problem as same-day delivery with crowdshipping and store fulfillment (SDD-CSF). we develop a set of exact solution approaches for order fulfillment in form of rolling horizon framework. It repeatedly solves a series of order assignment and delivery plan problem following the timeline in order to construct an optimal fulfillment plan from local stores. Results from numerical experiments derived from real sale data of a retailer along with algorithmic computational results are presented. </p><p>

As states continue to consider taking on more responsibility in transportation, a major issue State Departments of Transportation (DOTs) face relates to financing future transportation investments. At present, many state transportation policymakers and State DOT administrators are considering alternative financing approaches to generate future revenue sources for transportation investments. This dissertation focuses on several user fee based approaches currently being considered by state transportation policymakers and administrators in the U.S. Examples of such approaches include: increasing the current fuel tax and indexing the fuel tax to inflation; implementing an odometer based vehicle miles traveled (VMT) fee approach through vehicle inspection programs in selected states; establishing a global positioning system (GPS) based VMT fee approach for heavy vehicles where privacy and implementation costs are less of a concern; and increasing existing tolls and charging tolls on existing roads that do not have tolls, preferably with open-road tolling (ORT) and all-electronic toll (AET) payment systems. Meanwhile, major questions of interest relate to the potential impacts or consequences of such financing approaches. Central to this dissertation is the development of a conceptual framework and analytical methods to aid state transportation policymakers and administrators in the planning and formulation of alternative financing approaches suitable for consideration in their state. The application of the framework and methods is illustrated in a case study. This case study includes an evaluation of alternative toll scenarios on a section of Interstate 93 in the Boston Metropolitan area where at present tolls are not charged. A major conclusion of the case study is that placing tolls along interstate highways where tolls are not currently collected has the potential to provide a significant source of revenue for State DOTs but that other impacts including route diversion, privacy, and equity need to be considered and addressed in the decision-making process. It is expected that the results of the dissertation will be of interest to state transportation policy makers as well as State DOT administrators currently involved in the development of a comprehensive transportation finance policy.

<p> In this thesis, path planning for an autonomous ground vehicle (AGV) in an urban environment is considered. The following problem is considered. starting from an initial location, the AGV desires to reach a final location by taking the shortest distance, while minimizing the AGVs position estimation error and guaranteeing that the AGVs position estimation uncertainty is below a desired threshold. The AGV is assumed to be equipped with receivers capable of producing pseudodange measurements on Global Navigation Satellite System (GNSS) satellites and cellular long-term evolution (LTE) towers. Using a geographic information system (GIS) three-dimensional (3-D) building map of the urban environment, a signal reliability map is introduced, which provides information about regions where large errors due to cellular signal multipath or poor GNSS line-of-sight (LOS) are expected. The vehicle uses the signal reliability map to calculate the position estimation mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived for a weighted nonlinear least-squares (WNLS) estimator, which is used to find an analytical upper bound on the position bias due to multipath. A path planning approach based on Dijkstra's algorithm is proposed to optimize the AGV's path while minimizing the path length and the position estimation MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath being below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths. Simulation results are presented demonstrating that utilizing ambient cellular LTE signals together with GNSS signals (1) reduces the uncertainty about the AGV's position, (2) increases the number of feasible paths to choose from, which could be useful if other considerations arise, e.g., traffic jams and road blockages due to construction, and (3) yields significantly shorter feasible paths, which would otherwise be infeasible with GNSS signals alone. Experimental results on a ground vehicle navigating in downtown Riverside, California, are presented demonstrating a close match between the simulated and experimental results.</p><p>

Thesis (M.U.R.P.) -- University of Adelaide, Dept. of Architecture, 1980.

This research is designed to study the effects of highly developed information technologies and logistic strategies on freight transportation. A simulation model called TTMNet is formulated as a multi-level product supply chain system that integrates the financial, informational, logistic, and physical aspects of transportation networks, to address freight transportation problems within a much broader decisionmaking and policy sensitive environment. It simulates freight movement in a regional supply chain, given real-time information, to help understand the mechanisms or principles followed by system operation, freight flow patterns, and evolution and interaction of prices and costs across the networks over time. TTMNet is implemented using micro-simulation techniques and GIS tools, and several simulators are involved, including a dynamic freight traffic simulator, a supply chain decision making simulator, and a real-time information simulator. The construction of a prototype dynamic freight traffic simulation model called DyFTS is the focus of this research. DyFTS is designed as a discrete event simulation system, highly adaptable to more comprehensive transportation simulation models. Various decision-making processes are formulated within DyFTS, such as goods-to-vehicle assignment, departure time choice and pre-trip routing, and en-route vehicle redirection. Descriptive real-time traffic information is simulated to study its influence on freight traffic operations. A knowledge-based learning process is established to refine the perceptions of decision-makers to the transportation network based on past experience. The inclusion of the simulation of regional ITS system makes the DyFTS a powerful tool to evaluate the information. A preliminary study is conducted herein to construct an integrated logistic and transportation system, a simplified version of TTMNet. It simulates enterprise-wide freight movements in a comprehensive product supply chain system that integrates the logistic, informational, and physical aspects of transportation networks. The resulting simulation model developed in this dissertation can be applied widely in freight transportation industry, such as, the study of commercial vehicle operations with and without real-time information, freight transportation of different delivery time requirements and different fleet configurations, and freight traffic patterns by product demand. It may also help direct or justify the development of real-time surface transportation information systems.