Financial and Technical Feasibility of Dynamic Congestion Pricing As A
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Chapter 5 Conclusions and General Recommendations
Conclusions and general recommendations Chapter 5 Chapter 5 Conclusions and general recommendations Economic Instruments have a long history – both in developed and developing countries. Transport has always been used to generate state revenues. Many instruments that have been discussed in this book, in fact, can be found in various forms of horse ownership charges, and road and bridge tolls in many countries’ economic histories. Many of these roots, however, have been neglected and forgotten in the recent debate about sustainable transport policy. By 1776, Adam Smith, in “The Wealth of Nations,” had already outlined the basic principles of a sound transport policy. These included most of the aforementioned principles and recommendations for taxation and financing schemes.1 Economic Instruments thus are not new transport policy “tools.” But these tools have too long been idle. It is time to relearn their use, and to use them wisely in order to meet the economic, social and ecological challenges that occur today. 1 An excellent modern outline of the principles formulated by Adam Smith can be found in Metschies 2001. 117 Chapter 5 Conclusions and general recommendations Eight basic insights In the face of diminishing public budgets but increasing internal and toward a wise use of external costs of infrastructure and environmental damage a rethinking Economic of transport policy is needed. In this rethinking, Economic Instruments Instruments. should play an important role. The existing experience with the use of Economic Instruments, as presented in this book, lead to a number of conclusions. These can be summarised as follows: 1. There is a diverse toolbox of Economic Instruments that can be used to address economic, ecological and social goals with tailor-made measures based on economic incentives. -
The Magazine of Aecon Group Winter 2015, Volume 2, Issue 4
THE MAGAZINE OF AECON GROUP WINTER 2015, VOLUME 2, ISSUE 4 IN THIS ISSUE: ALL ABOARD THE UP EXPRESS CLOSE QUARTERS A DEFINING RELATIONSHIP CONNECTING TO OPPORTUNITY Union Pearson Express Enbridge South Edmonton Highway 407 Interchange Neskantaga Training Centre Terminal ı (Tı) Station Terminal Tank Farm and Brock Road Realignment Expansion PUBLICATION MAIL AGREEMENT #41329046 THE MAGAZINE OF AECON GROUP WINTER 2015, VOLUME 2, ISSUE 4 4 24 34 50 ALL ABOARD CLOSE QUARTERS A DEFINING RELATIONSHIP CONNECTING TO THE UP EXPRESS Enbridge South Highway 407 OPPORTUNITY Union Pearson Express Edmonton Terminal Interchange and Brock Neskantaga Terminal ı (Tı) Station Tank Farm Expansion Road Realignment Training Centre IN THIS ISSUE: 2 Living Our Core Values of Trust, Excellence and a Learning Culture By Teri McKibbon 4 All Aboard the UP Express Union Pearson Express Terminal 1 (T1) Station 18 Did You Know? Distant Early Warning (DEW) Line 24 Close Quarters Enbridge South Edmonton Terminal Tank Farm Expansion 30 Meet Carlos Travassos 34 A Defining Relationship Highway 407 Interchange and Brock Road Realignment 46 Meet Brandon Alpine 50 Connecting to Opportunity Neskantaga Training Centre 58 The Aecon Safety Opportunity Program By Mike Archambault COVER: UP EXPRESS TERMINAL 1 STATION, TORONTO, ONTARIO LEFT: AECON’S VISION: 407 EXTENSION, PICKERING, ONTARIO To be the first company people go to for building things that matter. ABOVE: ENBRIDGE SOUTH EDMONTON TANK FARM, EDMONTON, ALBERTA ONE is a magazine published by Aecon Michele Walter, Editor-in-Chief BACK COVER: Group Inc. for its employees and clients. Matthew Le Blanc, Features Editor GIRDER WORK AT 407 ETR, PICKERING, ONTARIO For more information about Aecon, visit Rick Radell, Photographer This magazine includes certain forward-looking statements our website at aecon.com. -
Annual Information Form
407 INTERNATIONAL INC. Annual Information Form For the Year Ended December 31, 2020 As at February 11, 2021 ANNUAL INFORMATION FORM TABLE OF CONTENTS FORWARD-LOOKING STATEMENTS ............................................................................................... 5 CORPORATE STRUCTURE ............................................................................................................... 5 1. Name, Address, Incorporation and Ownership ................................................................. 5 2. Intercorporate Relationships ................................................................................................. 5 GENERAL DEVELOPMENT OF BUSINESS .................................................................................... 6 1. Traffic ......................................................................................................................................... 6 2. Construction ............................................................................................................................. 6 3. Information Technology ......................................................................................................... 7 4. Customer Service .................................................................................................................... 7 5. Other Developments ............................................................................................................... 8 5.1. International Organization for Standardization (“ISO”) .......................................................... -
Dynamic Pricing Efficiency with Strategic Retailers and Consumers: an Analytic Analysis of Short-Term Markets Interactions
CHAIRE EUROPEAN ELECTRICITY MARKETS/WORKING PAPER #46 RETAILERS’STRATEGIES FACING DEMAND RESPONSE AND MARKETS INTERACTIONS Cédric CLASTRES and Haikel KHALFALLAH 1 RETAILERS’STRATEGIES FACING DEMAND RESPONSE AND MARKETS INTERACTIONS Cédric CLASTRES1,2 and Haikel KHALFALLAH1 Avril 2020 Abstract Demand response programmes reduce peak-load consumption and could increase off- peak demand as a load-shifting effect often exists. In this research we use a three-stage game to assess the effectiveness of dynamic pricing regarding load-shifting and its economic consequences. We consider a retailer’s strategic supplies on forward or real time markets, when demand is uncertain and with consumer disutility incurred from load-shedding or load-shifting. Our main results show that a retailer could internalize part of demand uncertainty by using both markets. A retailer raises the quantities committed to the forward market if energy prices or balancing costs are high. If the consumer suffers disutility, then the retailer contracts larger volumes on the forward market for peak periods and less off peak, due to a lower load-shifting effect and lower off-peak energy prices. Keywords: Dynamic and stochastic model, electricity markets, load-shifting, disutility. JEL codes : C61, D12, L11, L22, L94, Q41. Acknowledgments: This paper has benefited from the support of the Chaire European Electricity Markets (CEEM) of the Université Paris-Dauphine – PSL, under the aegis of the Foundation Paris-Dauphine, supported by RTE, EDF, EPEX Spot and Total Direct Energie. Disclaimer: The views and opinions expressed in this paper are those of the authors and do not necessarily reflect those of the partners of the CEEM. -
Dynamic Pricing: Building an Advantage in B2B Sales
Dynamic Pricing: Building an Advantage in B2B Sales Pricing leaders use volatility to their advantage, capturing opportunities in market fluctuations. By Ron Kermisch, David Burns and Chuck Davenport Ron Kermisch and David Burns are partners with Bain & Company’s Customer Strategy & Marketing practice. Ron is a leader of Bain’s pricing work, and David is an expert in building pricing capabilities. Chuck Davenport is an expert vice principal specializing in pricing. They are based, respectively, in Boston, Chicago and Atlanta. The authors would like to thank Nate Hamilton, a principal in Boston; Monica Oliver, a manager in Boston; and Paulina Celedon, a consultant in Atlanta, for their contributions to this work. Copyright © 2019 Bain & Company, Inc. All rights reserved. Dynamic Pricing: Building an Advantage in B2B Sales At a Glance Nimble pricing behavior from Amazon and other online sellers has raised the imperative for everyone else to develop dynamic pricing capabilities. But dynamic pricing is more than just a defensive action. Pricing leaders use volatility to their advantage, capturing opportunities in market fluctuations and forcing competitors to chase their pricing moves. Building better pricing capabilities is about more than improving processes, technology and communication. Pricing leadership requires improving your understanding of customer needs, competitors’ behavior and market economics. Dynamic pricing is not a new strategy. For decades, companies in travel and transportation have system- atically set and modified prices based on shifting market and customer factors. Anyone who buys plane tickets should be familiar with this type of dynamic pricing, but what about other industries? Does dynamic pricing have a role? Increasingly, the answer is yes. -
The Marginal Cost of Traffic Congestion and Road Pricing: Evidence from a Natural Experiment in Beijing
The Marginal Cost of Traffic Congestion and Road Pricing: Evidence from a Natural Experiment in Beijing Shanjun Li Avralt-Od Purevjav Jun Yang1 Preliminary and Comments Welcome December 2016 ABSTRACT Leveraging a natural experiment and big data, this study examines road pricing, the first-best policy to address traffic congestion in Beijing. Based on fine-scale traffic data from over 1500 monitoring stations throughout the city, this paper provides the first empirical estimate of the marginal external cost of traffic congestion (MECC) and optimal congestion charges based on the causal effect of traffic density on speed, a key input for measuring the MECC. The identification of the causal effect relies on the plausibly exogenous variation in traffic density induced by the driving restriction policy. Our analysis shows that the MECC during rush hours is about 92 cents (or $0.15) per km on average, nearly three times as much as what OLS regressions would imply and larger than estimates from transportation engineering models. The optimal congestion charges range from 5 to 38 cents per km depending on time and location. Road pricing would increase traffic speed by 10 percent within the city center and lead to a welfare gain of 1.4 billion and revenue of 40 billion Yuan per year. Keywords: Traffic Congestion, Road Pricing, Natural Experiment JEL Classification: H23, R41, R48 1 Shanjun Li is an Associate Professor in the Dyson School of Applied Economics and Management, Cornell University, [email protected]; Avralt-Od Purevjav is a doctoral student in the Dyson School of Applied Economics and Management, Cornell University, [email protected]; Jun Yang is a research fellow in Beijing Transportation Research Center, [email protected]. -
Congestion Pricing
Congestion Pricing A PRIMER December 2006 FEDERAL HIGHWAY ADMINISTRATION Office of Transportation Management, HOTM 400 Seventh St. SW, Room 3404 Washington, DC 20590 Publication Number: FHWA-HOP-07-074 Table of Contents I. THE CONGESTION PROBLEM .....................................................................................................................1 Costs of Congestion ........................................................................................................................................1 Alarming Trends ...............................................................................................................................................1 Causes of Congestion .....................................................................................................................................1 II. WHAT IS CONGESTION PRICING? ..............................................................................................................1 Technology for Congestion Pricing .................................................................................................................2 Variably Priced Lanes ......................................................................................................................................2 Variable Tolls on Roadways .............................................................................................................................3 Cordon Pricing .................................................................................................................................................4 -
Dynamic Pricing When Consumers Are Strategic: Analysis of a Posted Pricing Scheme
OPERATIONS RESEARCH INFORMS Vol. 00, No. 0, Xxxxx 0000, pp. 000{000 doi 10.1287/xxxx.0000.0000 issn 0030-364X j eissn 1526-5463 j 00 j 0000 j 0001 °c 0000 INFORMS Dynamic pricing when consumers are strategic: Analysis of a posted pricing scheme Sriram Dasu Marshall School of Business, University of Southern California, [email protected], Chunyang Tong Marshall School of Business, University of Southern California, [email protected], We study dynamic pricing policies for a monopolist selling perishable products over a ¯nite time horizon to buyers who are strategic. Buyers are strategic in the sense that they anticipate the ¯rm's pricing policies. We are interested in situations in which auctions are not feasible and in which it is costly to change prices. We begin by showing that unless strategic buyers expect shortages dynamic pricing will not increase revenues. We investigate two pricing schemes that we call posted and contingent pricing. In the posted pricing scheme at the beginning of the horizon the ¯rm announces a set of prices. In the contingent pricing scheme price evolution depends upon demand realization. Our focus is on the posted pricing scheme because of its ease of implementation. In equilibrium, buyers will employ a threshold policy in both pricing regimes i.e., they will buy only if their private valuations are above a particular threshold. We show that a multi-unit auction with a reservation price provides an upper bound for the expected revenues for both pricing schemes. Numerical examples suggest that a posted pricing scheme with two or three price changes is enough to achieve revenues that are close to the upper bound. -
Welfare Implications of Congestion Pricing: Evidence from Sfpark
Welfare Implications of Congestion Pricing: Evidence from SFpark Pnina Feldman Haas School of Business, University of California, Berkeley, [email protected] Jun Li Stephen M. Ross School of Business, University of Michigan, [email protected] Hsin-Tien Tsai Department of Economics, University of California, Berkeley, [email protected] Congestion pricing offers an appealing solution to urban parking problems. Charging varying rates across time and space as a function of congestion levels may shift demand and improve allocation of limited resources. It aims to increase the accessibility of highly desired public goods to consumers who value them and to reduce traffic caused by drivers searching for available parking spaces. Using data from the City of San Francisco, both before and after the implementation of a congestion pricing parking program, we estimate the welfare implications of the policy. We use a two-stage dynamic search model to estimate consumers' search costs, distance disutilities, price sensitivities and trip valuations. We find that congestion pricing increases consumer and social welfare in congested regions but may hurt welfare in uncongested regions. Interestingly, despite the improved availability, congestion pricing may not necessarily reduce search traffic, because highly dispersed prices also induce consumers to search for more affordable spaces. In such cases, a simpler pricing policy may actually achieve higher welfare than a complex one. Lastly, compared to capacity rationing that imposes limits on parking durations, congestion pricing increases social welfare and has an ambiguous effect on consumer welfare. The insights from SFpark offer important implications for local governments considering alternatives for managing parking and congestion, and for public sector managers to evaluate the tradeoffs between regulation vs. -
Skimming Or Penetration? Strategic Dynamic Pricing for New Products
This article was downloaded by: [113.197.9.114] On: 26 March 2015, At: 16:51 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Marketing Science Publication details, including instructions for authors and subscription information: http://pubsonline.informs.org Skimming or Penetration? Strategic Dynamic Pricing for New Products Martin Spann, Marc Fischer, Gerard J. Tellis To cite this article: Martin Spann, Marc Fischer, Gerard J. Tellis (2015) Skimming or Penetration? Strategic Dynamic Pricing for New Products. Marketing Science 34(2):235-249. http://dx.doi.org/10.1287/mksc.2014.0891 Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact [email protected]. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. Copyright © 2015, INFORMS Please scroll down for article—it is on subsequent pages INFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org Vol. -
Annual Information Form
407 INTERNATIONAL INC. Annual Information Form For the Year Ended December 31, 2017 As at February 15, 2018 ANNUAL INFORMATION FORM TABLE OF CONTENTS FORWARD-LOOKING STATEMENTS ............................................................................................... 5 CORPORATE STRUCTURE ............................................................................................................... 5 1. Name, Address, Incorporation and Ownership ................................................................. 5 2. Intercorporate Relationships ................................................................................................ 5 GENERAL DEVELOPMENT OF BUSINESS .................................................................................... 6 1. Traffic ......................................................................................................................................... 6 2. Construction ............................................................................................................................. 6 3. Information Technology ......................................................................................................... 7 4. Customer Service .................................................................................................................... 7 5. Other Developments ............................................................................................................... 8 5.1. International Organization for Standardization (“ISO”) .......................................................... -
Pricing in Ride-Share Platforms: a Queueing-Theoretic Approach
Pricing in Ride-share Platforms: A Queueing-Theoretic Approach Carlos Riquelme, Siddhartha Banerjee, Ramesh Johari Department of Management Science and Engineering, Stanford University, Stanford, CA 94025 [email protected], [email protected], [email protected] We study optimal pricing strategies for ride-sharing platforms, such as Lyft, Sidecar, and Uber. Analysis of pricing in such settings is complex: On one hand these platforms are two-sided – this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support high temporal-resolution for data collection and pricing – this requires stochastic models that capture the dynamics of drivers and passengers in the system. In this paper we build a queueing-theoretic economic model to study optimal platform pricing. In particular, we focus our attention on the value of dynamic pricing: where prices can react to instantaneous imbalances between available supply and incoming demand. We find two main results: We first show that performance (throughput and revenue) under any dynamic pricing strategy cannot exceed that under the optimal static pricing policy (i.e., one which is agnostic of stochastic fluctuations in the system load). This result belies the prevalence of dynamic pricing in practice. Our sec- ond result explains the apparent paradox: we show that dynamic pricing is much more robust to fluctuations in system parameters compared to static pricing. Thus dynamic pricing does not necessarily yield higher performance than static pricing – however, it lets platforms realize the benefits of optimal static pricing, even with imperfect knowledge of system parameters. Key words: Ride-Sharing, Dynamic Pricing, Matching Markets, Queueing Networks 1.