Private operators and time-of-day tolling on a congested road network

Private-sector involvement in the construction and operation of roads is growing around the world and private toll roads are seen as a useful tool in the battle against congestion. Yet serious concerns remain about exercise of monopoly power if private operators can set tolls freely. A number of theoretical studies have investigated private toll-road pricing strategies, and compared them with first-best and second-best public tolls. But most of the analyses have employed simple road networks and/or used static models that do not capture the temporal dimension of congestion or describe the impacts of tolling schemes that vary by time of day. This paper takes a fresh look at private toll road pricing using METROPOLIS: a dynamic traffic simulator that treats endogenously choices of transport mode, departure time and route at the level of individual travellers. Simulations are performed for the peak-period morning commute on a stylized urban road network with jobs concentrated towards the centre of the city. Tolling scenarios are defined in terms of what is tolled (traffic lanes, whole links, or toll rings) and how tolls are varied over time. Three administration regimes are compared. The first two are the standard polar cases: social surplus maximization by a public-sector operator, and unconstrained profit maximization by a private-sector operator. The third regime entails varying tolls in steps to eliminate queuing on the tolled links. It is a form of third-best tolling that could be implemented either by a public operator or by the private sector under quality-of-service regulation. Amongst the results it is found that the no-queue tolling regime performs favourably compared to public step tolling, and invariably better than private tolling. Another provisional finding is that a private operator has less incentive than does a public operator to implement time-of-day congestion pricing.

Optimizing Transportation Systems with Information Provision, Personalized Incentives and Driver Cooperation

Poor performance of the transportation systems has many detrimental effects such as higher travel times, increased travel costs, higher energy consumption, and greenhouse gas emissions, etc. This thesis optimizes the transportation systems by addressing the traffic congestion problem and climate change impact resulting from the inefficient operation of these systems. I first focus on the key player of the transportation systems e.g., human being/traveler, and model travelers\u27 route choice behavior with real-time information. In this study, I define looking-ahead behavior in route choice as a traveler\u27s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Subjects participated in route-choice experiments in a driving simulator as well a PC-based environment. Three types of maps in increasing levels of complexity and information availability are used. Aggregate data analysis shows that network complexity negatively affects subjects\u27 ratio of choosing the risky route given an experiment environment. Higher cognitive load in the driving simulator results in a higher level of risk aversion than in the PC-based environment for the simplest map. I specify and estimate a mixed logit model with two latent classes, looking-ahead and myopic, taking into account the panel effect. The estimated latent class membership function suggests that some subjects can look ahead while others are myopic in making their route choices, and drivers learn to look ahead over time. The experiment environment plays a role in the risk attitude of myopic subjects. A bias against information is found for subjects who look ahead, however, is not significant among myopic subjects. I then shift my focus to influencing the travel patterns of individual travelers to reduce the energy and environmental impacts of the transportation sector. I present the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation systems. From the user\u27s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives each with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated to the available alternatives, in order to minimize the system-wide energy consumption, under a limited token budget. I present a method to solve this complex optimization problem and describe the system architecture, the multimodal simulation-based optimization model and the heuristic method for the on-line computation of the optimized token allocation. I then present the framework with the simulation results. Finally, I optimize the systems travel time by addressing the equity issue of congestion pricing. I propose an alternative approach to an equitable and Pareto-improving transportation systems based on cooperation among travelers assisted by defector penalty. Theoretical analysis shows the existence condition of the cooperative scheme for heterogeneous value of time (VOT) of travelers. I formulate a mathematical programming problem for the optimal cooperative scheme problem in a general network with Pareto-improving constraints and practical considerations on the length the cooperation cycle. I then conduct computational tests on a simple network and evaluate the solutions in terms of efficiency improvement (total system travel time) and equitability (Gini index)

Pricing, Investment, and Network Equilibrium

Despite rapidly emerging innovative road pricing and investment principles, the development of a long run network dynamics model for necessary policy evaluation is still lagging. This research endeavors to fill this gap and models the impacts of road financing policies throughout the network equilibration process. The manner in which pricing and investment jointly shape network equilibrium is particularly important and explored in this study. The interactions among travel demand, road supply, revenue mechanisms and investment rules are modeled at the link level in a network growth simulator. After assessing several measures of effectiveness, the proposed network growth model is able to evaluate the short- and long-run impacts of a broad spectrum of road pricing and investment policies on large-scale road networks, which can provide valuable information to decision-makers such as the implications of various policy scenarios on social welfare, financial situation of road authorities and potential implementation problems. Some issues hard to address in theoretical analysis can be examined in the agent-based simulation model. As a demonstration, we apply the network growth model to assess marginal and average pricing scenarios on a sample network. Even this relatively simple application provides new insights into issues around road pricing that have not previously been seriously considered. For instance, the results disclose a potential problem of over-investment when the marginal cost pricing scheme is adopted in conjunction with a myopic profit-neutral investment policy.Transportation network equilibrium; Road growth; Pricing; Congestion toll; Investment; Transport policy analysis.

Weighting Waiting: Evaluating the Perception of In-Vehicle Travel Time Under Moving and Stopped Conditions

This paper describes experiments comparing traditional computer administered stated preference with virtual experience stated preference to ascertain how people value stopped delay compared with stop-and- go or freeflow traffic. The virtual experience stated preference experiments were conducted using a wrap around driving simulator. The two methods produced two different results, with the traditional computer assisted stated preference suggesting that ramp delay is 1.6 Ð 1.7 times more onerous than freeway time, while the driving simulator based virtual experience stated preference suggested that freeway delay is more onerous than ramp delay. Several reasons are hypothesized to explain the differences, including recency, simultaneous versus sequential comparison, awareness of public opinion, the intensity of the stop-and-go traffic, and the fact that driving in the real-world is a goal directed activity. However without further research, which, if any, of these will eventually prove to be the reason is unclear. What is clear is that a comparison of the computer administered stated preference with virtual experience stated preference produces different results, even though both procedures strive to find the same answers in nominally identical sets of conditions. Because people experience the world subjectively, and make decisions based on those subjective experiences, future research should be aimed at better understanding the differences between these subjective methodologies.transportation, travel behavior, driving simulator, ramp meters

Traffic Congestion Pricing Methods and Technologies

This paper reviews the methods and technologies for congestion pricing of roads. Congestion tolls can be implemented at scales ranging from individual lanes on single links to national road networks. Tolls can be differentiated by time of day, road type and vehicle characteristics, and even set in real time according to current traffic conditions. Conventional toll booths have largely given way to electronic toll collection technologies. The main technology categories are roadside-only systems employing digital photography, tag and beacon systems that use short-range microwave technology, and in vehicle-only systems based on either satellite or cellular network communications. The best technology choice depends on the application. The rate at which congestion pricing is implemented, and its ultimate scope, will depend on what technology is used and on what other functions and services it can perform. Since congestion pricing calls for the greatest overall degree of toll differentiation, congestion pricing is likely to drive the technology choice.Road pricing; Congestion pricing; Electronic Toll Collection technology

Optimal and Long-Term Dynamic Transport Policy Design: Seeking Maximum Social Welfare through a Pricing Scheme.

This article presents an alternative approach to the decision-making process in transport strategy design. The study explores the possibility of integrating forecasting, assessment and optimization procedures in support of a decision-making process designed to reach the best achievable scenario through mobility policies. Long-term evaluation, as required by a dynamic system such as a city, is provided by a strategic Land-Use and Transport Interaction (LUTI) model. The social welfare achieved by implementing mobility LUTI model policies is measured through a cost-benefit analysis and maximized through an optimization process throughout the evaluation period. The method is tested by optimizing a pricing policy scheme in Madrid on a cordon toll in a context requiring system efficiency, social equity and environmental quality. The optimized scheme yields an appreciable increase in social surplus through a relatively low rate compared to other similar pricing toll schemes. The results highlight the different considerations regarding mobility impacts on the case study area, as well as the major contributors to social welfare surplus. This leads the authors to reconsider the cost-analysis approach, as defined in the study, as the best option for formulating sustainability measures

INVESTIGATION OF SAFETY AT TOLL PLAZAS THROUGH MICROSIMULATION AND DRIVING SIMULATION APPROACHES

Toll plazas are one of the critical components of a roadway system for capital financing, infrastructure maintenance revenue, or traffic maintenance and congestion control strategies. At the same time, they are among the most complex road structures, as drivers are exposed to a large amount of information and have a short amount of time to make a decision. Since the advent of electronic toll collection (ETC) technology, the complexity of toll plazas has greatly increased. The objective of this study is to investigate the effect of toll plaza design and traffic conditions on drivers’ behavior and level of safety. This study contains two approaches: (1) a microsimulation study using VISSIM and the Surrogate Safety Assessment Model (SSAM); and (2) a driving simulation study. The microsimulation model was calibrated and validated using traffic data from recorded video at the West Springfield toll plaza, Massachusetts, connecting Interstate 90 to Interstate 91 and State Route 5. Distribution of traffic volumes, stop delays at cash lanes, and reduced speed distribution at electronic toll collection (ETC) lanes were used as vii calibration variables, and the number of conflicts was used as a validation parameter. Results identified that the safest lane configuration was the one consisting of only ETC lanes, and the second-safest configurations were the ones that grouped ETC lanes and separated them from cash lanes. In the second part of the study, a simulation model of the same toll plaza was created to be used in a fixed-base driving simulator with a 150 degree field of view. The objective of this part of the study was to investigate drivers’ behavior when they are exposed to different lane configurations and traffic conditions at toll plazas. Independent variables of this study were lane configuration (i.e., which lanes were signed as “E-ZPass” and “Cash”), origin-destination of the subject vehicle (i.e., right or left origin ramp, right or left destination ramp), traffic queue (i.e., having a queue or not), traffic composition (i.e., having a leading heavy vehicle or not), and customer type (i.e., cash or E-ZPass). The result of this simulation study was expected to give a better understanding of drivers’ behavior at toll plazas and might lead to safer toll plaza designs