A User Satisfaction Study of the London Congestion Charging e-Service

This research seeks to measure citizen satisfaction with the electronic London Congestion Charging (LCC) payment system offered by Transport For London (TFL) in the United Kingdom (UK). The paper reports on the findings of a survey of 500 users of the TFL LCC online payment system. Satisfaction is measured using the four dimensions from the COBRA framework 0that comprise the cost, opportunity, benefits and risk assessment constructs. The results show that most citizens using the LCC electronic service are satisfied with the service and that the service meets their essential needs. The paper also presents the results of qualitative feedback obtained from the participants that can be used to determine the areas that need further improvement in the current electronic LCC electronic-service (e-service) system and potential influences on user satisfaction

Understanding the role of performance targets in transport policy

The measurement of performance in the public sector has become increasingly important in recent years and it is now commonplace for transport organisations, and local and national governments, to publish performance goals for service supply and quality. Such commitments, when time referenced, are known as targets. This paper explain how changes in management style, consumer rights legislation, contractual obligations and other factors have combined to make management-by targets increasingly common in the public sector. The advantages and disadvantages of management-by-targets are illustrated through discussion of the processes and experience of setting transport targets in UK national transport policy. We conclude that while some of the targets have had a significant impact on policy makers, managers and their agents, the effects have not always been as intended

A User Satisfaction Study of London's Congestion Charge e-Service: A Citizen Perspective

YesThe importance of evaluation and optimization of electronic government (e-government) services is imperative if the government organisations are to have an effective impact on the success and take-up of the services offered. Transport For London's (TFL) London Congestion Charging (LCC) is one of the innovative electronic services (e-services) introduced by the United Kingdom (UK) government to the citizens. While some studies have addressed the impact of the introduction of the congestion charge there has been a dearth of research performed to address user (citizen) satisfaction of the online LCC system. Therefore, this research seeks to measure the citizen satisfaction of using the LCC online payment system offered by TFL. The citizen satisfaction in this context is measured using the four dimensions from the COBRA framework that comprise the cost, opportunity, benefits and risk assessment constructs. This paper presents the findings of a survey of 500 users of the TFL LCC online payment system. It also reports the qualitative feedback obtained from the participants that can be used to determine the areas that need further improvement in the current LCC e-service and potential influences on user satisfaction

The transformation of transport policy in Great Britain? 'New Realism' and New Labour's decade of displacement activity

In a 1999 paper, Goodwin announced ‘the transformation of transport policy in Great Britain’. His central point was that consensus was emerging among policy makers and academics based on earlier work including Transport: The New Realism, which rejected previous orthodoxy that the supply of road space could and should be continually expanded to match demand. Instead a combination of investment in public transport, walking and cycling opportunities and – crucially – demand management should form the basis of transport policy to address rising vehicle use and associated increases in congestion and pollution / carbon emissions. This thinking formed the basis of the 1997 Labour government’s ‘sustainable transport’ policy, but after 13 years in power ministers neither transformed policy nor tackled longstanding transport trends. Our main aim in this paper is to revisit the concept of New Realism and re-examine its potential utility as an agent of change in British transport policy. Notwithstanding the outcome of Labour’s approach to transport policy, we find that the central tenets of the New Realism remain robust and that the main barriers to change are related to broader political and governance issues which suppress radical policy innovation

Overview of Infrastructure Charging, part 4, IMPROVERAIL Project Deliverable 9, “Improved Data Background to Support Current and Future Infrastructure Charging Systems”

Improverail aims are to further support the establishment of railway infrastructure management in accordance with Directive 91/440, as well as the new railway infrastructure directives, by developing the necessary tools for modelling the management of railway infrastructure; by evaluating improved methods for capacity and resources management, which allow the improvement of the Life Cycle Costs (LCC) calculating methods, including elements related to vehicle - infrastructure interaction and external costs; and by improving data background in support of charging for use of railway infrastructure. To achieve these objectives, Improverail is organised along 8 workpackages, with specific objectives, responding to the requirements of the task 2.2.1/10 of the 2nd call made in the 5th RTD Framework Programme in December 1999.This part is the task 7.1 (Review of infrastructure charging systems) to the workpackage 7 (Analysis of the relation between infrastructure cost variation and diversity of infrastructure charging systems).Before explaining the economic characteristics of railway and his basic pricing principles, authors must specify the objectives of railways infrastructure charging.principle of pricing ; rail infrastructure charging ; public service obligation ; rail charging practice ; Europe ; Improverail

Social Equity Impacts of Congestion Management Strategies

This white paper examines the social equity impacts of various congestion management strategies. The paper includes a comprehensive list of 30 congestion management strategies and a discussion of equity implications related to each strategy. The authors analyze existing literature and incorporate findings from 12 expert interviews from academic, non-governmental organization (NGO), public, and private sector respondents to strengthen results and fill gaps in understanding. The literature review applies the Spatial – Temporal – Economic – Physiological – Social (STEPS) Equity Framework (Shaheen et al., 2017) to identify impacts and classify whether social equity barriers are reduced, exacerbated, or both by a particular congestion mitigation measure. The congestion management strategies discussed are grouped into six main categories, including: 1) pricing, 2) parking and curb policies, 3) operational strategies, 4) infrastructure changes, 5) transportation services and strategies, and 6) conventional taxation. The findings show that the social equity impacts of certain congestion management strategies are not well understood, at present, and further empirical research is needed. Congestion mitigation measures have the potential to affect travel costs, commute times, housing, and accessibility in ways that are distinctly positive or negative for different populations. For these reasons, social equity implications of congestion management strategies should be understood and mitigated for in planning and implementation of these strategies

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV and accepted by TCNS. In Version 4, the body of the paper is largely rewritten for clarity and consistency, and new numerical simulations are presented. All source code is available (MIT) at https://dx.doi.org/10.5281/zenodo.324165