Activity-based model development to support transport planning in the Stockholm region

The environment in which transportation analysis and infrastructure planning take place has changed dramatically during the last years. The focus is now, to a considerable extent, on how to transform the transportation system in a direction that could be sustainable in the long run, rather than on planning for infrastructure investment to meet new demand. At the same time information technology penetrates all sectors of the society. This will change how the transportation system will be used by travellers and conveyers, both directly, through new products and services, and, indirectly, through a spatial reorganisation of many activities that govern the transport demand. In such a situation it must be questioned whether the analytical tools that may have functioned reasonably well in the past, also are appropriate, or possible to adapt, to be useful for the issues we will face in the future. A survey is made of ideas for model development for travel analysis with an emphasis on activity based models based on an international literature review. The study treats tools for the whole chain from location decisions to network effects. The main focus is on such development that is of interest for a medium-sized city like Stockholm. It stresses demands that might be raised on modelling tools with a background in the planning issues that can expected to be central within the next ten-year period. Different ideas for model development, and existing models that could be considered for implementation, are evaluated with respect to their usefulness for planning, need for resources, demand for competence and data, and obstacles of implementation. Finally, we are suggesting some specific model development that should be tested in Stockholm, including a pilot study concerning the implementation of an activity-based model.

Integrated Machine Learning and Optimization Frameworks with Applications in Operations Management

Incorporation of contextual inference in the optimality analysis of operational problems is a canonical characteristic of data-informed decision making that requires interdisciplinary research. In an attempt to achieve individualization in operations management, we design rigorous and yet practical mechanisms that boost efficiency, restrain uncertainty and elevate real-time decision making through integration of ideas from machine learning and operations research literature. In our first study, we investigate the decision of whether to admit a patient to a critical care unit which is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient’s individual health metrics can be incorporated while considering the hospital’s operational constraints. We model the problem as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A data-driven optimization methodology then approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are also examined. In the second study, we analyze the efficiency of surgical unit operations in the era of big data. The accuracy of surgical case duration predictions is a crucial element in hospital operational performance. We propose a comprehensive methodology that incorporates both structured and unstructured data to generate individualized predictions regarding the overall distribution of surgery durations. Consequently, we investigate methods to incorporate such individualized predictions into operational decision-making. We introduce novel prescriptive models to address optimization under uncertainty in the fundamental surgery appointment scheduling problem by utilizing the multi-dimensional data features available prior to the surgery. Electronic medical records systems provide detailed patient features that enable the prediction of individualized case time distributions; however, existing approaches in this context usually employ only limited, aggregate information, and do not take advantages of these detailed features. We show how the quantile regression forest, can be integrated into three common optimization formulations that capture the stochasticity in addressing this problem, including stochastic optimization, robust optimization and distributionally robust optimization. In the last part of this dissertation, we provide the first study on online learning problems under stochastic constraints that are "soft", i.e., need to be satisfied with high likelihood. Under a Bayesian framework, we propose and analyze a scheme that provides statistical feasibility guarantees throughout the learning horizon, by using posterior Monte Carlo samples to form sampled constraints that generalize the scenario generation approach commonly used in chance-constrained programming. We demonstrate how our scheme can be integrated into Thompson sampling and illustrate it with an application in online advertisement.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145936/1/meisami_1.pd

Development and Application of Advanced Econometric Models for Exploring Activity-Travel Behavior

Historically, transportation planning relied on aggregate, trip-based procedures, namely, four-step modeling, for modeling travel demand. The aggregate approaches served well when the capacity oriented policies were of primary interest. However, in the last few decades, with the growing demand for travel and the increasing externalities (e.g. congestion, energy implications, pollution), there is a widespread acknowledgement that capacity oriented approach to transportation planning is unsustainable. Instead, the focus of the transportation planners has shifted towards sustainable demand management strategies wherein the idea is to alter existing behaviors and promote new behaviors such that demand for travel can be met while also reducing the externalities of travel choices. This swing in policy necessitated a shift to disaggregate, activity-based approaches for analyzing travel behavior. One of the fundamental differences between the trip- and activity-based travel behavior analyses lies in the treatment of time. In the trip-based approach, time is merely treated as a cost of accessing activity opportunities separated in space. On the other hand, activity-based approach, dwells on the understanding of time expenditure behavior of individual including how, where, and with whom individuals spend their time. Subsequently, trips are organically derived from activity engagement behavior. As can be seen, a robust understanding of time engagement decision of individuals forms the backbone of current day transportation planning process. Individuals’ allocation of time has intrigued researchers not only from the field of transportation, but also from various other disciplines such as economics, philosophy, psychology, and sociology. The overarching objective of this dissertation is to advance the time engagement research with the goal of enriching the state-of-the-art activity-based travel analysis techniques. To this end, the contributions of the research are twofold. First, on the substantive side, the dissertation utilizes a multidisciplinary approach by incorporating theories from various disciplines such as economics, and psychology to further our understanding of the time engagement decisions of individuals. Second, on the methodological side, the dissertation develops, and applies advanced econometric methodologies to characterize the time engagement behavior of the individuals. The substantive and methodological findings allowed for an enriched formulation of time engagement in activity-based travel behavior models

Measuring the Values for Time

Most economic models for time allocation ignore constraints on what people can actually do with their time. Economists recently have emphasized the importance of considering prior consumption commitments that constrain behavior. This research develops a new model for time valuation that uses time commitments to distinguish consumers' choice margins and the different values of time these imply. The model is estimated using a new survey that elicits revealed and stated preference data on household time allocation. The empirical results support the framework and find an increasing marginal opportunity cost of time as longer time blocks are used.

Mechanism Design for Demand Response Programs

Demand Response (DR) programs serve to reduce the consumption of electricity at times when the supply is scarce and expensive. The utility informs the aggregator of an anticipated DR event. The aggregator calls on a subset of its pool of recruited agents to reduce their electricity use. Agents are paid for reducing their energy consumption from contractually established baselines. Baselines are counter-factual consumption estimates of the energy an agent would have consumed if they were not participating in the DR program. Baselines are used to determine payments to agents. This creates an incentive for agents to inflate their baselines. We propose a novel self-reported baseline mechanism (SRBM) where each agent reports its baseline and marginal utility. These reports are strategic and need not be truthful. Based on the reported information, the aggregator selects or calls on agents to meet the load reduction target. Called agents are paid for observed reductions from their self-reported baselines. Agents who are not called face penalties for consumption shortfalls below their baselines. The mechanism is specified by the probability with which agents are called, reward prices for called agents, and penalty prices for agents who are not called. Under SRBM, we show that truthful reporting of baseline consumption and marginal utility is a dominant strategy. Thus, SRBM eliminates the incentive for agents to inflate baselines. SRBM is assured to meet the load reduction target. SRBM is also nearly efficient since it selects agents with the smallest marginal utilities, and each called agent contributes maximally to the load reduction target. Finally, we show that SRBM is almost optimal in the metric of average cost of DR provision faced by the aggregator