Thesis (MTech (Civil Engineering))--Cape Peninsula University of Technology, 2008. / Includes bibliographical references (leaves 91-94). Also available online.

The High Occupancy Vehicle (HOV) lanes on Atlanta, Georgia’s radial I-85 had long been providing sub-optimal throughput in the peak traffic hours, as the two-person occupancy requirement allowed the lanes to become heavily congested. The Georgia Department of Transportation converted 15.5 miles of HOV 2+ lanes to High Occupancy Toll (HOT) lanes, one in each direction on I-85. The lanes use dynamic value pricing to set toll levels based on the volume and average speed of traffic in the lanes. The goal of this research was to investigate the responses to toll lane pricing and the factors that appear to inform lane choice decisions, as well as examining values of travel time savings and toll price elasticity for users of the Express Lanes. This study of the metropolitan Atlanta I-85 Express Lanes operates at the microscopic level to examine the impact of demographic characteristics, congestion levels, and pricing on users’ decisions to use or not use the I-85 Express Lanes. The dissertation examined the value of travel time savings distributions across income segments. The differences in these distributions among lower, medium, and higher income households were marginal at best. The results did not indicate that higher income households had the highest value of travel time savings results, as may have been expected. The modeling work performed here provided a number of insights into toll lane use. The determinants of lane choice decision-making in the morning peak had notable differences from the determinants of the afternoon peak. The initial analysis involved models which were estimated across three different income segments to examine differences in decision making between low, medium, and higher income households. The results indicated that the parameters were largely consistent across the three segments. Further segmenting the households showed that lane choice determinants varied more within the ‘Higher’ income segment than across the original three-segment structure. In particular, the five-segment models illustrated lower elasticities with regard to corridor segment counts and toll levels for the highest-income households in the sample, as well as higher household income level elasticities for afternoon trips by that same cohort. The research was among the first in the available literature to use revealed preference lane use data for both the toll lane users and the unpriced general purpose lane users. The use of household level marketing data, rather than census or survey data, was another unique characteristic of this research. The analysis of value of travel time savings with a demographic component that looks at household income has not yet been seen in the literature; similarly, the findings regarding differing behavior among very high income households appear to be unseen in the existing literature. The results from this analysis, such as willingness-to-pay values for different population segments, will be useful inputs to the decisions surrounding future HOT implementations in the Atlanta region. The use of new data sources, the evaluation of those types of data sources, and the application of methods that have previously been unused in this field make up the primary contributions of this dissertation.

<p>Congestion charging is a now accepted means of influencing traffic to behave in a more socio-economic optimal way, like e.g. in the Stockholm project. Already early work, in the 1920’s, showed that road use can be inefficient due externalities, i.e. that users don’t experience their own (negative) effect on other users: an extra car on a traffic link causes delays for other cars, but the driver himself does not experience this cost.In the 1950’s it was further shown - for a congested road network with homogeneous users – that if each user is charged a toll equal to the total value of time loss incurred on other users of the network, then -if we have fixed travel demand - this will induce an equilibrium that is system optimal in the sense that the total cost of network usage is minimal (assuming that all users have fixed and identical time values).  But toll charges need to be levied in monetary units, and different travelers have different values of time. Therefore, to account for the effects of tolls, and to be able to compute equilibria, one needs to introduce different user classes, differing in their time values.</p><p>In this thesis, consisting of four papers, we study congestion pricing of road networks with users differing only in their time values. In particular, we analyze marginal social cost (MSC) pricing, a tolling scheme that charges each user a penalty corresponding to the value of the delays inflicted on other users, as well as its implementation through fixed tolls.</p><p>Paper III contains the main theoretical work of the thesis. In that paper we show that the variational inequalities characterizing the equilibria in question can be stated in symmetric or non-symmetric forms. The symmetric forms correspond to optimization problems, convex in the fixed-toll case and non-convex in the MSC case, which hence may have multiple equilibria. The objective of the latter problem is the total value of travel time, which thus is minimized at the global optima of that problem. Implementing close-to-optimal MSC tolls as fixed tolls leads to equilibria with possibly non-unique class specific flows, but with identical close-to-optimal values of the total value of travel time. Finally we give an adaptation, to the MSC setting, of the Frank-Wolfe algorithm, which is further applied to some test cases, including Stockholm.</p><p>Paper I is an early application using Frank-Wolfe, after having realized the possibility to symmetrize the problem.</p><p>Paper II gives a convexification of non-convex equilibrium problem for MSC tolls. We have used these convexifications to compute lower bounds when computing equilibria.</p><p>Paper IV is a short note commenting some flaws in two papers by Dial on MSC tolls.</p>

In the area of transportation policy, congestion pricing has been used to alleviate traffic congestion in metropolitan areas. The focus of Chapter 1 is to examine drivers’ perceived risk of traffic delay as one determinant of reactions to congestion pricing. The experiment reported in this essay recruits commuters from the Atlanta and Orlando metropolitan areas to participate in a naturalistic experiment where they are asked to make repeated route decisions in a driving simulator. Chapter 1 examines belief formation and adjustments under an endogenous information environment where information about a route can be obtained only conditional on taking the route. If the subjects arrive to the destination late, i.e. beyond an assigned time threshold, they are faced with a discrete (flat) penalty. In contrast, Chapter 2 examines subjective beliefs in a setting where the penalty for a late arrival is continuous, such that a longer delay incurs additional penalty on the driver. The primary research question is: does belief formation differ when the late penalty is induced as a continuous amount compared to when it is induced as a discrete amount? In particular, will we observe a difference in learning across the range of congestion probabilities under different penalty settings? In the continuous penalty setting, we do not observe a difference in learning across the range of congestion probabilities. In contrast, in the discrete penalty setting we observe significant belief adjustments in the lowest congestion risk scenario. In Chapter 3 the “source method” is used to examine how uncertainty aversion differs across events that have the same underlying objective probabilities but are presented under varying degrees of uncertainty. Subjects are presented with three lottery tasks that rank in order of increasing uncertainty. Given the choices observed in each task a source function is estimated jointly with risk attitudes under different probability weighting specifications of the source function. Results from the Prelec probability weighting suggest that, as the degree of uncertainty increases, subjects display increased pessimism; in contrast, the Tversky-Kahneman (1992) and the Power probability weightings detect no such difference. Thus, the conclusion regarding uncertainty aversion are contingent on which probability weighting specification is assumed for the source function.

In this thesis, I analyze the impacts of the design and implementation of different environmental policy tools from a theoretical and empirical perspective: certificates providing information on the energy performance of buildings (chapter 1); urban road pricing schemes such as congestion charges (chapter 2); quantity-based policy tools to support production with non-polluting technologies (chapter 3).In chapter 1, co-authored with Luisa Dressler, we study how energy performance certificates (EPCs) impact the residential rental market. These certificates can help solve information asymmetries between landlords and tenants about the thermal quality of dwellings for rent, which, in turn, is expected to facilitate investment aimed at improving dwellings' energy performance. However, disclosure of EPCs is often incomplete, which hampers their effectiveness in relieving such information asymmetries. Moreover, even when a certificate is available, landlords do not always disclose it. This contradicts the so-called information unraveling result, according to which all landlords should disclose quality information unless it is costly to do so: in such a setting, information eventually unravels. Using a cross-sectional dataset of residential rental advertisements from the Belgian region of Brussels, we empirically evaluate incentives to disclose energy performance ratings. We find that two fundamental assumptions underlying the unraveling result are not confirmed in our setting: firstly, tenants value energy performance of rental property only when dwellings are of very high quality; secondly, tenants do not appear to rationally adjust their expectations when faced with dwellings that withhold their energy performance rating. Finally, we formulate specific policy advice for reforming EPC mechanisms to increase disclosure rates.In chapter 2, I study how urban congestion pricing impacts the use of sustainable mobility options such as bike sharing, presenting evidence from the city of Milan, Italy.As concern for air pollution grows in cities across the world, policies such as urban road pricing are rolled out to induce urban residents to opt for greener transport options. While several papers have analyzed the impact of urban road pricing on air pollution and on car use, this is the first analysis of its impact on sustainable travel behaviors, such as the use of bike sharing.I extend a stylized theoretical model of travel behavior to formalize the drivers of bike-sharing demand. Then, I exploit a panel dataset covering all bike-sharing trips carried out over an 8-year period in the city of Milan to estimate the impact of congestion pricing on bike-sharing use. The empirical strategy I employ in this study is based on the sudden suspension and reintroduction of congestion pricing, which generate a quasi-experimental setting. Adopting an event study approach, I find that suspending the congestion charge reduces daily bike-sharing traffic by about 5% in the short run. I show that, in Milan, congestion pricing mainly impacts bike-sharing use through the reduction of road traffic congestion, which makes cycling safer and more pleasant. The direct effect of the increased relative cost of car use is secondary in individual decisions to use bike-sharing. The role of these effects is likely to be context-specific, as they may be affected by the baseline level of urban congestion, the broader policy mix affecting the cost of driving and the specific design of the congestion pricing scheme.In chapter 3, co-authored with Renaud Foucart, we study the impact of different quantity-based tools that governments can use to support the production of homogeneous goods through clean rather than polluting inputs in a setting where production costs are uncertain.In recent years, many sectors have been disrupted by clean innovation, as clean inputs have emerged as close substitutes of polluting ones: for example, in the power sector renewable energy sources are increasingly used for electricity generation instead of fossil fuels. Whenever the negative externalities caused by polluting incumbent technologies are not internalized in production costs, emerging clean technologies are left at a disadvantage. For this reason, governments may want to design policy support schemes for emerging clean technologies.We develop a theoretical framework in which well-established polluting technologies entail known production and pollution costs, while using emerging green technologies requires higher, steeper and uncertain production costs. In this context, a government chooses between a range of quantity-based instruments to support the deployment of clean technologies based on cost estimates, as costs of production with green inputs are uncertain.We show that a cap on production with polluting inputs is the least distortionary among quantity instruments; next is a mandatory share of production with green inputs out of total production. Setting a policy objective in terms of a precise level of green inputs for production is the least efficient policy approach. This ranking results from the so-called “technology effect”, which determines the extent to which the market corrects cost estimation errors after real costs are observed. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

This thesis is concerned with addressing operational issues in two types of dynamic markets where queueing plays an important role: limit order books (financial industry), and dynamic matching markets (residential real estate). We first study the smart order routing decisions of investors in fragmented limit order book markets and the implications on the market dynamics. In modern equity markets, participants have a choice of many exchanges at which to trade. Exchanges typically operate as electronic limit order books operating under a “price-time” priority rule and, in turn, can be modeled as multi-class FIFO queueing systems. A market with multiple exchanges can be thought as a decentralized, parallel queueing system. Heterogeneous traders that submit limit orders select the exchange to place their orders by trading off delays until their order may fill against financial considerations. Simultaneously, traders that submit market orders select the exchange to direct their orders. These market orders trigger instantaneous service completions of queued limit orders. Taking into account the effect of investors’ order routing decisions, we find that the equilibrium of this decentralized market exhibits a state space collapse property. The predicted dimension reduction is the result of high-frequency order routing decisions that essentially couple the dynamics across exchanges. Analyzing a TAQ dataset for a sample of stocks over a one month period, we find empirical support for the predicted state space collapse. In the second part of this thesis, we model an electronic limit order book as a multi-class queueing system under fluid dynamics, and formulate and solve a problem of limit and market order placement to optimally buy a block of shares over a short, predetermined time horizon. Using the structure of the optimal execution policy, we identify microstructure variables that affect trading costs over short time horizons and propose a resulting microstructure-based model of market impact costs. We use a proprietary data set to estimate this cost model, and highlight its insightful structure and increased accuracy over conventional (macroscopic) market impact models that estimate the cost of a trade based on its normalized size but disregarding measurements of limit order book variables. In the third part of this thesis, we study the residential real estate markets as dynamic matching systems with an emphasis on their microstructure. We propose a stylized microstructure model and analyze the market dynamics and its equilibrium under the simplifying approximation where buyers and sellers use linear bidding strategies. We motivate and characterize this near closed-form approximation of the market equilibrium, and show that it is asymptotically accurate. We also provide numerical evidence in support of this approximation. Then with the gained tractability, we characterize steady-state properties such as market depth, price dispersion, and anticipated delays in selling or buying a unit. We characterize congestion and matching patterns for sellers and buyers, taking into account market dynamics, heterogeneity, and supply and demand imbalance manifested in the competition among buyers and sellers. Furthermore, we show the effects of market primitives with comparative statics results.

The current Internet architecture presents a barrier to accommodate the vigorous arising demand for deploying new network services and applications. The next-generation architecture views the network virtualization as the gateway to overcome this limitation. Network virtualization promises to run efficiently and securely multiple dedicated virtual networks (VNs) over a shared physical infrastructure. Each VN is tailored to host a unique application based on the user’s preferences. This thesis addresses the problem of the efficient embedding of multiple VNs onto a shared substrate network (SN). The contribution of this thesis are twofold: First, a novel hierarchical SN management framework is proposed that efficiently selects the optimum VN mapping scheme for the requested VN from more than one proposed VN mapping candidates obtained in parallel. In order to accommodate the arbitrary architecture of the VNs, the proposed scheme divides the VN request into smaller subgraphs, and individually maps them on the SN using a variation of the exact subgraph matching techniques. Second, the physical resources pricing policy is introduced that is based on time-ofuse, that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption and time-of-use. A novel model of time-varying VNs is presented, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the VNs embedding cost. In contrast to existing solutions, the proposed work does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Extensive simulations are then conducted to demonstrate the improvement achieved through the proposed work in terms of network utilization, the ratio of accepted VN requests and the SP profits.

The current Internet architecture presents a barrier to accommodate the vigorous arising demand for deploying new network services and applications. The next-generation architecture views the network virtualization as the gateway to overcome this limitation. Network virtualization promises to run efficiently and securely multiple dedicated virtual networks (VNs) over a shared physical infrastructure. Each VN is tailored to host a unique application based on the user’s preferences. This thesis addresses the problem of the efficient embedding of multiple VNs onto a shared substrate network (SN). The contribution of this thesis are twofold: First, a novel hierarchical SN management framework is proposed that efficiently selects the optimum VN mapping scheme for the requested VN from more than one proposed VN mapping candidates obtained in parallel. In order to accommodate the arbitrary architecture of the VNs, the proposed scheme divides the VN request into smaller subgraphs, and individually maps them on the SN using a variation of the exact subgraph matching techniques. Second, the physical resources pricing policy is introduced that is based on time-ofuse, that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption and time-of-use. A novel model of time-varying VNs is presented, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the VNs embedding cost. In contrast to existing solutions, the proposed work does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Extensive simulations are then conducted to demonstrate the improvement achieved through the proposed work in terms of network utilization, the ratio of accepted VN requests and the SP profits.

The City of Toronto has done much to reduce congestion through transportation system management and travel demand measures. Yet, while measures to eliminate the traffic congestion problem have been necessary, they simply have not been sufficient to accommodate over 2.5 million residents and the many more who find their way into the area from points beyond particularly from other regions in the Greater Toronto Area (GTA). In addition, the transportation improvements certainly do not provide capacity adequate to address the needs of the future predicted residents and added economic activity. Congestion pricing is an untapped transportation strategy that can reduce traffic congestion, improve air quality, and raise the revenue essential to implement needed transportation measures that are effective in improving transportation services and facilities. While experience with congestion pricing is limited, there are sufficient examples and experiences around the world to demonstrate that, when implemented properly, it virtually never fails to be an effective tool to curb congestion. Yet, when initially proposed, it never fails to be controversial. This is due in part to the lack of research on the equity impacts on different socio-economic groups. This is the dichotomy and the dilemma of congestion pricing that every city must face in seeking this new approach to congestion management. The main goal of the research is to provide empirical research that enhances our understanding of the equity implications of cordon pricing for the urban region of Toronto, Canada. Three research objectives are identified to address the research goal. The first objective is to examine the ways that the GTA is moving toward or away the principles of sustainable transportation, and thus to make a case that Downtown Toronto is a candidate for cordon pricing. The second objective is to investigate if particular socio-economic groups would be disproportionately affected by the implementation of cordon pricing in Downtown Toronto, as one way of approaching the equity dimensions of such a policy. The third objective is to explore some of the policy aspects associated with implementing cordon pricing in Toronto, including public perceptions of such a policy as well as probable responses to the policy. The major findings of this analysis are that the GTA is not moving in the direction of sustainable transportation, which provides a concrete justification for demand-management interventions and that Downtown Toronto is a candidate for cordon pricing. A Downtown Toronto cordon pricing scheme would be progressive in its effects on the various socio-economic groups, and that the progressivity holds up even when travel is disaggregated by demographic factors such as age, gender, household size and occupational category. Full-time workers account for a larger proportion of the affected trips and the percentage of trips that would be affected is highest for those in the full-time high-income neighborhoods. The analyses show that toll charge is an important factor that would trigger some income groups to change their travel behaviour. People from high-income neighborhoods are more willing to pay the charges and drive as usual than people from other income neighborhoods. Revenue redistribution is critical to assess and achieve equity of congestion pricing.

The City of Toronto has done much to reduce congestion through transportation system management and travel demand measures. Yet, while measures to eliminate the traffic congestion problem have been necessary, they simply have not been sufficient to accommodate over 2.5 million residents and the many more who find their way into the area from points beyond particularly from other regions in the Greater Toronto Area (GTA). In addition, the transportation improvements certainly do not provide capacity adequate to address the needs of the future predicted residents and added economic activity. Congestion pricing is an untapped transportation strategy that can reduce traffic congestion, improve air quality, and raise the revenue essential to implement needed transportation measures that are effective in improving transportation services and facilities. While experience with congestion pricing is limited, there are sufficient examples and experiences around the world to demonstrate that, when implemented properly, it virtually never fails to be an effective tool to curb congestion. Yet, when initially proposed, it never fails to be controversial. This is due in part to the lack of research on the equity impacts on different socio-economic groups. This is the dichotomy and the dilemma of congestion pricing that every city must face in seeking this new approach to congestion management. The main goal of the research is to provide empirical research that enhances our understanding of the equity implications of cordon pricing for the urban region of Toronto, Canada. Three research objectives are identified to address the research goal. The first objective is to examine the ways that the GTA is moving toward or away the principles of sustainable transportation, and thus to make a case that Downtown Toronto is a candidate for cordon pricing. The second objective is to investigate if particular socio-economic groups would be disproportionately affected by the implementation of cordon pricing in Downtown Toronto, as one way of approaching the equity dimensions of such a policy. The third objective is to explore some of the policy aspects associated with implementing cordon pricing in Toronto, including public perceptions of such a policy as well as probable responses to the policy. The major findings of this analysis are that the GTA is not moving in the direction of sustainable transportation, which provides a concrete justification for demand-management interventions and that Downtown Toronto is a candidate for cordon pricing. A Downtown Toronto cordon pricing scheme would be progressive in its effects on the various socio-economic groups, and that the progressivity holds up even when travel is disaggregated by demographic factors such as age, gender, household size and occupational category. Full-time workers account for a larger proportion of the affected trips and the percentage of trips that would be affected is highest for those in the full-time high-income neighborhoods. The analyses show that toll charge is an important factor that would trigger some income groups to change their travel behaviour. People from high-income neighborhoods are more willing to pay the charges and drive as usual than people from other income neighborhoods. Revenue redistribution is critical to assess and achieve equity of congestion pricing.