Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998. / Includes bibliographical references (p. 181-184) and index. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / This study develops a model of a person's day activity schedule that can be used to forecast urban travel demand. It is motivated by the notion that travel outcomes are part of an activity scheduling decision, and uses discrete choice models to address the basic modeling problem-capturing decision interactions among the many choice dimensions of the immense activity schedule choice set. An integrated system of choice models represents a person's day activity schedule as an activity pattern and a set of tours. A pattern model identifies purposes, priorities and structure of the day's activities and travel. Conditional tour models describe timing, location and access mode of on-tour activities. The system captures trade-offs people consider, when faced with space and time constraints, among patterns that can include at-home and on-tour activities, multiple tours and trip chaining. It captures sensitivity of pattern choice to activity and travel conditions through a measure of expected tour utility arising from the tour models. When travel and activity conditions change, the relative attractiveness of patterns changes because expected tour utility changes differently for different patterns. An empirical implementation of the model system for Portland, Oregon, establishes the feasibility of specifying, estimating and using it for forecasting. Estimation results match a priori expectations of lifestyle effects on activity selection, including those of (a) household structure and role, such as for females with children, (b) capabilities, such as income, and (c) activity commitments, such as usual work levels. / (cont.) They also confirm the significance of activity and travel accessibility in pattern choice. Application of the model with road pricing and other policies demonstrates its lifestyle effects and how it captures pattern shifting-with accompanying travel changes-that goes undetected by more narrowly focused trip-based and tour-based systems. Although the model has not yet been validated in before-and-after prediction studies, this study gives strong evidence of its behavioral soundness, current practicality, potential to generate cost-effective predictions superior to those of the best existing systems, and potential for enhanced implementations as computing technology advances. / by John L. Bowman. / Ph.D.

The object of this research is to develop a travel demand model. The model predicts outdoor recreational travel of urban recreationists for camping. The development of this model is structured by a set of methodological criteria. These criteria relate to destination choice behavior in the context of recreation travel, and analytical structures of travel demand, in addition to the criterion of model performance. The thrust of this research is to define and evaluate a destination choice function with respect to recreational travel.

Channel access fairness and efficiency in capacity utilization are the two main objectives for Quality of Service (QoS) specific to Medium Access Control (MAC) protocols in computer networks. For bursty and unpredictable traffic in networks, fairness and efficiency involve a mutual tradeoff with the currently popular QoS mechanisms. We propose a QoS MAC framework for carrier sensing multiple access (CSMA) networks, that achieves fairness with improved efficiency through extensive state-keeping based on the MAC evolution. This CSMA with Implicit Scheduling through State-keeping (CSMA/ISS) framework involves the tracking of traffic arrival at active nodes, the nodes that need channel access frequently. It also involves implicit channel access grants to different active nodes according to their estimated queue backlogs and the fair scheduling requirements. These methods save channel capacity that may otherwise be required for disseminating the access requirements of various nodes, and their access rights according to fairness rules. A static, hierarchical, and weighted fair access scheme is designed in CSMA/ISS by allowing repeated rounds of access that are weighted fairly according to requirements. Weighted fairness across classes is achieved by invoking channel access for each traffic class in a round as many times as its weight. Within each class, all active nodes are allowed equal access through in-order channel access based on a looped list of active nodes. Although CSMA/ISS is proposed as a distributed control framework for efficiency, it may also be employed in central control protocols. It may also be adapted to different types of CSMA networks, both wireless and wired, by an appropriate choice of the underlying classical access mechanism. The CSMA/ISS framework was modeled and simulated as a QoS capable MAC protocol for a wired fully connected local network environment. We present the CSMA/ISS framework, the example implementation, and the results of performance evaluation of the example implementation. Significant performance improvements were observed, and the memory and processing trade-off was found to be low to moderate.

Researchers in multiple areas have shown that characterizing and accounting for the uncertainty inherent in decision support models is critical for developing more efficient planning and operational strategies. This is particularly applicable for the transportation engineering domain as most strategic decisions involve a significant investment of money and resources across multiple stakeholders and has a considerable impact on the society. Moreover, most inputs to transportation models such as travel demand depend on a number of social, economic and political factors and cannot be predicted with certainty. Therefore, in recent times there has been an increasing emphasis being placed on identifying and quantifying this uncertainty and developing models which account for the same. This dissertation contributes to the growing body of literature in tackling uncertainty in transportation models by developing methodologies which address the uncertainty in input parameters in traffic assignment models. One of the primary sources of uncertainty in traffic assignment models is uncertainty in origin destination demand. This uncertainty can be classified into long term and short term demand uncertainty. Accounting for long term demand uncertainty is vital when traffic assignment models are used to make planning decisions like where to add capacity. This dissertation quantifies the impact of long term demand uncertainty by assigning multi-variate probability distributions to the demand. In order to arrive at accurate estimates of the expected future system performance, several statistical sampling techniques are then compared through extensive numerical testing to determine the most "efficient" sampling techniques for network assignment models. Two applications of assignment models, network design and network pricing are studied to illustrate the importance of considering long term demand uncertainty in transportation networks. Short term demand uncertainty such as the day-to-day variation in demand affect traffic assignment models when used to make operational decisions like tolling. This dissertation presents a novel new definition of equilibrium when the short term demand is assumed to follow a probability distribution. Various properties of the equilibrium such as existence, uniqueness and presence of a mathematical programming formulation are investigated. Apart from demand uncertainty, operating capacity in real world networks can also vary from day to day depending on various factors like weather conditions and incidents. With increasing deployment of Intelligent Transportation Systems, users get information about the impact of capacity or the state of the roads through various dissemination devices like dynamic message signs. This dissertation presents a new equilibrium formulation termed user equilibrium with recourse to model information provision and capacity uncertainty, where users learn the state or capacity of the link when they arrive at the upstream node of that link. Depending on the information received about the state of the upstream links, users make different route choice decisions. In this work, the capacity of the links in the network is assumed to follow a discrete probability distribution. A mathematical programming formulation of the user equilibrium with recourse model is presented along with solution algorithm. This model can be extended to analytically model network flows under information provision where the arcs have different cost functional form depending on the state of the arc. The corresponding system optimal with recourse model is also presented where the objective is minimize the total system cost. The network design problem where users are routed according to the user equilibrium with recourse principle is studied. The focus of this study is to show that planning decisions for networks users have access to information is significantly different from the no-information scenario. / text

This creative project has analyzed the feasibility of project #35 of the Delaware County Long Range Plan. This analysis was accomplished by determining if present capacity is adequate to accommodate traffic volumes projected using current data. Once this relationship was determined, alternatives were developed that would aid in maximizing the efficiency of traffic movement in the corridor. Eventually it was concluded that the project should be undertaken with some minor additions. / Department of Urban Planning

Thesis (doctoral)--Rijksuniversiteit Groningen), 1996. / In English, with summary in Dutch.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1995. / Vita. Abstract. Includes bibliographical references (leaves 137-140). Also available via the Internet.

Thesis (Ph. D.)--University of Maryland, College Park, 2005. / "Bayesian scoring is used to evaluate and compare results from actual data collected for the Baltimore Metropolitan Area with the set of predominant conceptual frameworks linking travel behavior and land use obtained from the literature"--Abstract. Includes bibliographical references (p. 167-176) and abstract.

Since nearly the beginning of the wide spread adoption of the automobile, motorized traffic data collection has occurred so that decision makers have information to plan the transportation system. Widespread motorized traffic data collection has allowed for estimating traffic volumes using developed extrapolation methods whereby short-term counts in sample locations can be expanded to longer periods. As states and local planning agencies make investments in bicycle infrastructure and count programs develop, similar extrapolation methods will be needed. The only available guidance on extrapolating bicycle counts comes from the National Bicycle and Pedestrian Documentation Project (NBPDP), yet no validation of these factors have been done to assess their usability in specific area. Using bicycle traffic count data from the Central Lane Metropolitan Planning Organization Count Program in Oregon, this research demonstrates that using study area data to generate time-of-day factors produces results with less error compared to application of the NBPDP time-of-day factors. Factors are generated in two separate way in order to reduce error from estimating daily bicycle volumes. Factors groups are developed using bicycle facility type where counts are collected. This research also seeks to add to the literature concerning bicycle travel patterns by using study area data to establish a university travel pattern exemplified by a flat hourly distribution from morning to evening.

Despite the significant investments over the last few decades to enhance and improve road infrastructure worldwide, the capacity of road networks has not kept pace with the ever increasing growth in demand. As a result, congestion has become endemic to many highways and city streets. As an alternative to costly and sometimes infeasible construction of new roads, transportation departments are increasingly looking at ways to improve traffic flow over the existing infrastructure. The biggest challenge in accomplishing this goal is the ability to sample traffic data, estimate traffic current state, and forecast its future behavior. In this thesis, we first address the problem of traffic sampling where we propose strategies for frugal sensing where we collect a fraction of the observed traffic information to reduce costs while achieving high accuracy. Next we demonstrate how traffic estimation using deterministic traffic models can be improved using proposed data reconstruction techniques. Finally, we propose how mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function can improve short-term and long-term traffic forecasting. As mobile devices become more pervasive, participatory sensing is becoming an attractive way of collecting large quantities of valuable location-based data. An important participatory sensing application is traffic monitoring, where GPS-enabled smartphones can provide invaluable information about traffic conditions. We propose a strategy for frugal sensing in which the participants send only a fraction of the observed traffic information to reduce costs while achieving high accuracy. The strategy is based on autonomous sensing, in which participants make decisions to send traffic information without guidance from the central server, thus reducing the communication overhead and improving privacy. To provide accurate and computationally efficient estimation of the current traffic, we propose to use a budgeted version of the Gaussian Process model on the server side. The experiments on real-life traffic data sets indicate that the proposed approach can use up to two orders of magnitude less samples than a baseline approach with only a negligible loss in accuracy. The estimation of the state of traffic provides a detailed picture of the conditions of a traffic network based on limited traffic measurements and, as such, plays a key role in intelligent transportation systems. Most often, traffic measurements are aggregated over multiple time steps, and this procedure raises the question of how to best use this information for state estimation. Reconstructing the high-resolution measurements from the aggregated ones and using them to correct the state estimates at every time step are proposed. Several reconstruction techniques from signal processing, including kernel regression and a reconstruction approach based on convex optimization, were considered. Experimental results show that signal reconstruction leads to more accurate traffic state estimation as compared with the standard approach for dealing with aggregated measurements. Accurate traffic speed forecasting can help in trip planning by allowing travelers to avoid congested routes, either by choosing alternative routes or by changing the departure time. An important feature of traffic is that it consists of free flow and congested regimes, which have significantly different properties. Training a single traffic speed predictor for both regimes typically results in suboptimal accuracy. To address this problem, a mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function was developed. Experimental results showed that mixture of experts approach outperforms several popular benchmark approaches. / Computer and Information Science