Should Urban Transit Subsidies Be Reduced?

This paper derives intuitive and empirically useful formulas for the optimal pricing of passenger transit and for the welfare effects of adjusting current fare subsidies, for peak and off-peak urban rail and bus systems. The formulas are implemented based on a detailed estimation of parameter values for the metropolitan areas of Washington (D.C.), Los Angeles, and London. Our analysis accounts for congestion, pollution, and accident externalities from automobiles and from transit vehicles; scale economies in transit supply; costs of accessing and waiting for transit service as well as service crowding costs; and agency adjustment of transit frequency, vehicle size, and route network to induced changes in demand for passenger miles. The results support the efficiency case for the large fare subsidies currently applying across mode, period, and city. In almost all cases, fare subsidies of 50% or more of operating costs are welfare improving at the margin, and this finding is robust to alternative assumptions and parameters.Transit subsidies; Scale economies; Traffic congestion; Welfare effects

Methodology for an integrated modelling of macro and microscopic processes in urban transport demand

The paper presents the theoretical formulation and the underlying assumptions for an activity-based approach of transport demand modelling. Starting with the definition of a time hierarchy of decision-making in the urban environment, rules are formulated that dictate the general hierarchic structure of individuals’ choices in the urban system. The temporal scale defines decisions for activities and their daily sequence, the geographical scale decisions associated to destination choice processes. We build activity plans (number and daily sequence of activities) from an empirical data set and calculate trip paths (time-spatial trajectories including transport modes and travel destinations) assuming consumers to maximize their utility in the decision-making process. First results of the translation of the theoretical model into a real-world application are shown for the city of Santiago, Chile

Electricity transmission: an overview of the current debate

Electricity transmission has emerged as critical for successfully liberalising powermarkets. This paper surveys the issues currently under discussion and provides a framework for the remaining papers in this issue. We conclude that signalling the efficient location of generation investment might require even a competitive LMP system to be complemented with deep connection charges. Although a Europe-wide LMP system is desirable, it appears politically problematic, so an integrated system of market coupling, possibly evolving by voluntary participation, should have high priority. Merchant investors may be able to increase interconnector capacity, although this is not unproblematic and raises new regulatory issues. A key issue that needs further research is how to better incentivize TSOs, especially with respect to cross-border issues

Models and Solution Algorithms for Asymmetric Traffic and Transit Assignment Problems

Modeling the transportation system is important because it provides a “common ground” for discussing policy and examining the future transportation plan required in practices. Generally, modeling is a simplified representation of the real world; however, this research added value to the modeling practice by investigating the asymmetric interactions observed in the real world in order to explore potential improvements of the transportation modeling. The Asymmetric Transportation Equilibrium Problem (ATEP) is designed to precisely model actual transportation systems by considering asymmetric interactions of flows. The enhanced representation of the transportation system by the ATEP is promising because there are various asymmetric interactions in real transportation such as intersections, highway ramps, and toll roads and in the structure of the transit fares. This dissertation characterizes the ATEP with an appropriate solution algorithm and its applications. First, the research investigates the factors affecting the convergence of the ATEP. The double projection method is applied to various asymmetric types and complexities in the different sizes of networks in order to identify the influential factors including demand intensities, network configuration, route composition between modes, and sensitivity of the cost function. Secondly, the research develops an enhancement strategy for improvement in computational speed for the double projection method. The structural characteristics of the ATEP are used to develop the convergence enhancement strategy that significantly reduces the computational burdens. For the application side, instances of asymmetric interactions observed in in-vehicle crowding and the transit fare structure are modeled to provide a suggestion on policy approach for a transit agency. The direct application of the crowding model into the real network indicates that crowd modeling with multi user classes could influence the public transportation system planning and the revenue achievement of transit agencies. Moreover, addition of the disutility factor, crowding, not always causes the increase of disutility from the transit uses. The application of the non-additive fare structure in the Utah Transit Authority (UTA) network addresses the potential of the distance-based fare structure should the UTA make a transition to this fare structure from their current fare model. The analysis finds that the zero base fare has the highest potential for increasing the transit demand. However, collecting less than $0.50 with a certain buffer distance for the first boarding has potential for attracting the users to UTA\u27s transit market upon the fare structure change

Policy framework and systems management of global climate change

Climate change is representative of a general class of environmental issues where decisions have to be taken under controversies. The policy framework for these kinds of decisions is defined by three important traits: scientific ignorance, mediatization and the need for innovation. Scientific ignorance is an issue here because decisions must be taken before the end of scientific controversies about the predictability of future climate. Mediatization is key because agents can't have a sensible experience of the global climate change, and some interest-holders (future generations, distant countries) cannot participate directly in the decision. Third, the need for innovation is crucial because today's technology offers the only alternative between fossil fuels and nuclear power as a main primary energy source.In the case of climate change, the institutional context is the United Nations Framework Convention on Climate Change. The making of global environmental policy is framed not upon a hypothetical code of international law (there is no such a thing), but upon a body of doctrine arising from consistent reference to a given set of principles. The key principles are sustainability (satisfying the need of present generations without preventing future generations to satisfy theirs), precaution (ignorance is not an excuse for inaction), the common but differentiated responsibility (developed countries take the lead in action against climate change), and economic efficiency (which lead to prefer flexible instruments over blind regulation).Given the scientific controversies and the fuzziness of guiding principles, no clear-cut demonstration could justify the choice of a theoretically optimum course of action, even in the short term. Historically, climate negotiations can be seen as an oscillation between two regulation modes. On one side is coordinated policies and measures, where countries adopt an uniform international rate of carbon tax. On the other side is emission trading, where a defined emission reduction target is allocated to each country.changement climatique; Protocole de Kyoto

A multi-objective particle swarm optimized fuzzy logic congestion detection and dual explicit notification mechanism for IP networks.

Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2006.The Internet has experienced a tremendous growth over the past two decades and with that growth have come severe congestion problems. Research efforts to alleviate the congestion problem can broadly be classified into three groups: Cl) Router based congestion detection; (2) Generation and transmission of congestion notification signal to the traffic sources; (3) End-to-end algorithms which control the flow of traffic between the end hosts. This dissertation has largely addressed the first two groups which are basically router initiated. Router based congestion detection mechanisms, commonly known as Active Queue Management (AQM), can be classified into two groups: conventional mathematical analytical techniques and fuzzy logic based techniques. Research has shown that fuzzy logic techniques are more effective and robust compared to the conventional techniques because they do not rely on the availability of a precise mathematical model of Internet. They use linguistic knowledge and are, therefore, better placed to handle the complexities associated with the non-linearity and dynamics of the Internet. In spite of all these developments, there still exists ample room for improvement because, practically, there has been a slow deployment of AQM mechanisms. In the first part of this dissertation, we study the major AQM schemes in both the conventional and the fuzzy logic domain in order to uncover the problems that have hampered their deployment in practical implementations. Based on the findings from this study, we model the Internet congestion problem as a multi-objective problem. We propose a Fuzzy Logic Congestion Detection (FLCD) which synergistically combines the good characteristics of the fuzzy approaches with those of the conventional approaches. We design the membership functions (MFs) of the FLCD algorithm automatically by using Multi-objective Particle Swarm Optimization (MOPSO), a population based stochastic optimization algorithm. This enables the FLCD algorithm to achieve optimal performance on all the major objectives of Internet congestion control. The FLCD algorithm is compared with the basic Fuzzy Logic AQM and the Random Explicit Marking (REM) algorithms on a best effort network. Simulation results show that the FLCD algorithm provides high link utilization whilst maintaining lower jitter and packet loss. It also exhibits higher fairness and stability compared to its basic variant and REM. We extend this concept to Proportional Differentiated Services network environment where the FLCD algorithm outperforms the traditional Weighted RED algorithm. We also propose self learning and organization structures which enable the FLCD algorithm to achieve a more stable queue, lower packet losses and UDP traffic delay in dynamic traffic environments on both wired and wireless networks. In the second part of this dissertation, we present the congestion notification mechanisms which have been proposed for wired and satellite networks. We propose an FLCD based dual explicit congestion notification algorithm which combines the merits of the Explicit Congestion Notification (ECN) and the Backward Explicit Congestion Notification (BECN) mechanisms. In this proposal, the ECN mechanism is invoked based on the packet marking probability while the BECN mechanism is invoked based on the BECN parameter which helps to ensure that BECN is invoked only when congestion is severe. Motivated by the fact that TCP reacts to tbe congestion notification signal only once during a round trip time (RTT), we propose an RTT based BECN decay function. This reduces the invocation of the BECN mechanism and resultantly the generation of reverse traffic during an RTT. Compared to the traditional explicit notification mechanisms, simulation results show that the new approach exhibits lower packet loss rates and higher queue stability on wired networks. It also exhibits lower packet loss rates, higher good-put and link utilization on satellite networks. We also observe that the BECN decay function reduces reverse traffic significantly on both wired and satellite networks while ensuring that performance remains virtually the same as in the algorithm without BECN traffic reduction.Print copy complete; page numbering of 105-108 incorrect

Electricity transmission: an overview of the current debate

Electricity transmission has emerged as critical for successfully liberalising power markets. This paper surveys the issues currently under discussion and provides a framework for the remaining papers in this issue. We conclude that signalling the efficient location of generation investment might require even a competitive LMP system to be complemented with deep connection charges. Although a Europe-wide LMP system is desirable, it appears politically problematic, so an integrated system of market coupling, possibly evolving by voluntary participation, should have high priority. Merchant investors may be able to increase interconnector capacity, although this is not unproblematic and raises new regulatory issues. A key issue that needs further research is how to better incentivize TSOs, especially with respect to cross-border issues.Electricity, Transmission, Regulation, Prices, Merchant Investment

A framework for personalized dynamic cross-selling in e-commerce retailing

Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized dynamic cross-selling generates targeted bundle offers and can help maximize revenues and profits. In resolving the two basic problems of dynamic cross-selling, which involves selecting the right complementary products and optimizing the discount, the issue of computational complexity becomes central as the customer base and length of the product list grows. Traditional recommender systems are built upon simple collaborative filtering techniques, which exploit the informational cues gained from users in the form of product ratings and rating differences across users. The retail setting differs in that there are only records of transactions (in period X, customer Y purchased product Z). Instead of a range of explicit rating scores, transactions form binary datasets; 1-purchased and 0-not-purchased. This makes it a one-class collaborative filtering (OCCF) problem. Notwithstanding the existence of wider application domains of such an OCCF problem, very little work has been done in the retail setting. This research addresses this gap by developing an effective framework for dynamic cross-selling for online retailing. In the first part of the research, we propose an effective yet intuitive approach to integrate temporal information regarding a product\u27s lifecycle (i.e., the non-stationary nature of the sales history) in the form of a weight component into latent-factor-based OCCF models, improving the quality of personalized product recommendations. To improve the scalability of large product catalogs with transaction sparsity typical in online retailing, the approach relies on product catalog hierarchy and segments (rather than individual SKUs) for collaborative filtering. In the second part of the work, we propose effective bundle discount policies, which estimate a specific customer\u27s interest in potential cross-selling products (identified using the proposed OCCF methods) and calibrate the discount to strike an effective balance between the probability of the offer acceptance and the size of the discount. We also developed a highly effective simulation platform for generation of e-retailer transactions under various settings and test and validate the proposed methods. To the best of our knowledge, this is the first study to address the topic of real-time personalized dynamic cross-selling with discounting. The proposed techniques are applicable to cross-selling, up-selling, and personalized and targeted selling within the e-retail business domain. Through extensive analysis of various market scenario setups, we also provide a number of managerial insights on the performance of cross-selling strategies

Multi-Dimensional Resource Orchestration in Vehicular Edge Networks

In the era of autonomous vehicles, the advanced technologies of connected vehicle lead to the development of driving-related applications to meet the stringent safety requirements and the infotainment applications to improve passenger experience. Newly developed vehicular applications require high-volume data transmission, accurate sensing data collection, and reliable interaction, imposing substantial constrains on vehicular networks that solely rely on cellular networks to fetch data from the Internet and on-board processors to make driving decisions. To enhance multifarious vehicular applications, Heterogeneous Vehicular Networks (HVNets) have been proposed, in which edge nodes, including base stations and roadside units, can provide network connections, resulting in significantly reduced vehicular communication cost. In addition, caching servers are equipped at the edge nodes, to further alleviate the communication load for backhaul links and reduce data downloading delay. Hence, we aim to orchestrate the multi-dimensional resources, including communication, caching, and sensing resources, in the complex and dynamic vehicular environment to enhance vehicular edge network performance. The main technical issues are: 1) to accommodate the delivery services for both location-based and popular contents, the scheme of caching contents at edge servers should be devised, considering the cooperation of caching servers at different edge nodes, the mobility of vehicles, and the differential requirements of content downloading services; 2) to support the safety message exchange and collective perception services for vehicles, communication and sensing resources are jointly allocated, the decisions of which are coupled due to the resource sharing among different services and neighboring vehicles; and 3) for interaction-intensive service provisioning, e.g., trajectory design, the forwarding resources in core networks are allocated to achieve delay-sensitive packet transmissions between vehicles and management controllers, ensuring the high-quality interactivity. In this thesis, we design the multi-dimensional resource orchestration schemes in the edge assisted HVNets to address the three technical issues. Firstly, we design a cooperative edge caching scheme to support various vehicular content downloading services, which allows vehicles to fetch one content from multiple caching servers cooperatively. In particular, we consider two types of vehicular content requests, i.e., location-based and popular contents, with different delay requirements. Both types of contents are encoded according to fountain code and cooperatively cached at multiple servers. The proposed scheme can be optimized by finding an optimal cooperative content placement that determines the placing locations and proportions for all contents. To this end, we analyze the upper bound proportion of content caching at a single server and provide the respective theoretical analysis of transmission delay and service cost (including content caching and transmission cost) for both types of contents. We then formulate an optimization problem of cooperative content placement to minimize the overall transmission delay and service cost. As the problem is a multi-objective multi-dimensional multi-choice knapsack one, which is proved to be NP-hard, we devise an ant colony optimization-based scheme to solve the problem and achieve a near-optimal solution. Simulation results are provided to validate the performance of the proposed scheme, including its convergence and optimality of caching, while guaranteeing low transmission delay and service cost. Secondly, to support the vehicular safety message transmissions, we propose a two-level adaptive resource allocation (TARA) framework. In particular, three types of safety message are considered in urban vehicular networks, i.e., the event-triggered message for urgent condition warning, the periodic message for vehicular status notification, and the message for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, the proposed TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmission for each vehicle group at the first level, and the group is formed by a set of neighboring vehicles. To learn the implicit relation between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, the SRA scheme offline optimizes the allocation of sensing resources (i.e., choosing vehicles to provide perception data) and communication resources. With the resource reservation result for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real time. Extensive simulation results are provided to demonstrate the effectiveness of the proposed TARA framework in terms of the high successful reception ratio and low latency for message transmissions, and the high quality of collective environmental perception. Thirdly, we investigate forwarding resource sharing scheme to support interaction intensive services in HVNets, especially for the delay-sensitive packet transmission between vehicles and management controllers. A learning-based proactive resource sharing scheme is proposed for core communication networks, where the available forwarding resources at a switch are proactively allocated to the traffic flows in order to maximize the efficiency of resource utilization with delay satisfaction. The resource sharing scheme consists of two joint modules: estimation of resource demands and allocation of available resources. For service provisioning, resource demand of each traffic flow is estimated based on the predicted packet arrival rate. Considering the distinct features of each traffic flow, a linear regression scheme is developed for resource demand estimation, utilizing the mapping relation between traffic flow status and required resources, upon which a network switch makes decision on allocating available resources for delay satisfaction and efficient resource utilization. To learn the implicit relation between the allocated resources and delay, a multi-armed bandit learning-based resource sharing scheme is proposed, which enables fast resource sharing adjustment to traffic arrival dynamics. The proposed scheme is proved to be asymptotically approaching the optimal strategy, with polynomial time complexity. Extensive simulation results are presented to demonstrate the effectiveness of the proposed resource sharing scheme in terms of delay satisfaction, traffic adaptiveness, and resource sharing gain. In summary, we have investigated the cooperative caching placement for content downloading services, joint communication and sensing resource allocation for safety message transmissions, and forwarding resource sharing scheme in core networks for interaction intensive services. The schemes developed in the thesis should provide practical and efficient solutions to manage the multi-dimensional resources in vehicular networks