Impact analysis of cordon-based congestion pricing on mode-split for a bimodal transportation network

This paper investigates the impact of cordon-based congestion pricing scheme on the mode-split of a bimodal transportation network with auto and rail travel modes. For any given toll-charge pattern, its impact on the mode-split can be estimated by solving a combined mode-split and traffic-assignment problem. Using a binary logit model for the mode-split, the combined problem is converted into a traffic-assignment problem with elastic demand. Probit-based stochastic user equilibrium (SUE) principle is adopted for this traffic-assignment problem, and a continuously distributed value of time (VOT) is assumed to convert the toll charges and transit fares into time-units. This combined mode-split and traffic-assignment problem is then formulated as a fixed-point model, which can be solved by a convergent Cost Averaging method. The combined mode-split and traffic-assignment problem is then used to analyze a multimodal toll design problem for cordon-based congestion pricing scheme, with the aim of increasing the mode-share of public transport system to a targeted level. Taking the fixed-point model as a constraint, the multimodal toll design problem is thus formulated as a mathematical programming with equilibrium constraints (MPEC) model. A genetic algorithm (GA) is employed to solve this MPEC model, which is then numerical validated by a network example.     

Anticipating new-highway impacts: Opportunities for welfare analysis and credit-based congestion pricing

Pricing of roadways opens doors for infrastructure financing, and congestion pricing seeks to address inefficiencies in roadway operations. This paper emphasizes the revenue-generation opportunities and welfare impacts of flat-tolling schemes, standard congestion pricing, and credit-based congestion pricing policies. While most roadway investment decisions focus on travel time savings for existing trips, this work turns to logsum differences (which quantify changes in consumer surplus) for nested logit specifications across two traveler types, two destinations, three modes and three times of day, in order to arrive at welfare- and revenue-maximizing solutions. This behavioral specification is quite flexible, and facilitates benefit-cost calculations (as well as equity analysis), as demonstrated in this paper.The various cases examined suggest significant opportunities for financing new roadway investment while addressing congestion and equity issues, with net gains for both traveler types. Application results illustrate how, even after roadway construction and maintenance costs are covered, receipts may remain to distribute to eligible travelers so that typical travelers can be made better off than if a new, non-tolled road had been constructed. Moreover, tolling both routes (new and old) results in substantially shorter payback periods (5 versus 20 years) and higher welfare outcomes (in the case of welfare-maximizing tolls with credit distributions to all travelers). The tools and techniques highlighted here illustrate practical methods for identifying welfare-enhancing and cost-recovering investment opportunities, while recognizing multiple user classes and appropriate demand elasticity across times of day, destinations, modes and routes.     

A dynamic traffic assignment model for a continuum transportation system

A predictive continuum dynamic user-optimal (PDUO-C) model is formulated in this study to investigate the dynamic characteristics of traffic flow and the corresponding route-choice behavior of travelers within a region with a dense urban road network. The modeled region is arbitrary in shape with a single central business district (CBD) and travelers continuously distributed over the region. Within this region, the road network is represented as a continuum and travelers patronize a two-dimensional continuum transportation system to travel to the CBD. The PDUO-C model is solved by a promising solution algorithm that includes elements of the finite volume method (FVM), the finite element method (FEM), and the explicit total variation diminishing Runge-Kutta (TVD-RK) time-stepping method. A numerical example is given to demonstrate the utility of the proposed model and the effectiveness of the solution algorithm in solving this PDUO-C problem.     

Development of a microscopic activity-based framework for analyzing the potential impacts of transportation control measures on vehicle emissions

The 1990 Clean Air Act Amendments (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have defined a set of transportation control measures to counter the increase in the vehicle emissions and energy consumption due to increased travel. The value of these TCM strategies is unknown as there is limited data available to measure the travel effects of individual TCM strategies and the models are inadequate in forecasting changes in travel behavior resulting from these strategies. The work described in this paper begins to provide an operational methodology to overcome these difficulties so that the impacts of the policy mandates of both CAAA and ISTEA can be assessed. Although the framework, as currently developed, falls well short of actually forecasting changes in traveler behavior relative to policy options designed to encourage emissions reduction, the approach can be useful in estimating upper bounds of certain policy alternatives in reducing vehicle emissions. Subject to this important limitation, the potential of transportation policy options to alleviate vehicle emissions is examined in a comprehensive activity-based approach. Conclusions are drawn relative to the potential emissions savings that can be expected from efficient trip chaining behavior, ridesharing among household members, as well as from technological advances in vehicle emissions control devices represented by replacing all of the vehicles in the fleet by vehicles conforming to present-day emissions technology.     

Just pricing: the distributional effects of congestion pricing and sales taxes

Those who oppose tolls and other forms of road pricing argue that low-income, urban residents will suffer if they must pay to use congested freeways. This contention, however, fails to consider (1) how much low-income residents already pay for transportation in taxes and fees, or (2) how much residents would pay for highway infrastructure under an alternative revenue-generating scheme, such as a sales tax. This paper compares the cost burden of a value-priced road, State Route 91 (SR91) in Orange County, California with the cost burden under Orange County’s local option transportation sales tax, Measure M. We find that although the sales tax spreads the costs of transportation facilities across a large number of people inside and outside Orange County, it redistributes about $3 million (USD) in revenues from less affluent residents to those with higher incomes. The entire Measure M program redistributes an estimated $26 million from low-income residents to the more affluent. Low-income drivers as individuals save substantially if they do not have to pay tolls, but as a group low-income residents, on average, pay more out-of-pocket with sales taxes.

交通运输服务标准体系构建策略和框架初探

本文在分析服务标准内涵的基础上,提出了交通运输服务标准界定的方法和标准体系框架构建的原则,并按照交通运输服务的重点领域,构建了交通运输服务标准体系的框架结构,提出了标准体系的实施的建议。     

An integrated transportation decision support system for transportation policy decisions: The case of Turkey

From the point of view of the feasibility of providing growth in road capacity parallel to the predicted growth in traffic as well in terms of impact on the environment and health, current trends in transportation are unsustainable. Transport problems are expected to worsen due to the fact that worldwide automobile ownership tripled between 1970 and 2000, and the movement of goods is projected to increase by 50% by 2010. Similar trends can be seen in an even more dramatic way in Turkey. The Turkish transport network has not followed a planned growth strategy, due to political factors. There is no transportation master plan which aims to integrate the transport modes in order to provide a balanced, multimodal system. This study proposes a decision support system that guides transportation policy makers in their future strategic decisions and facilitates analysis of the possible consequences of a specific policy on changing the share of transportation modes for both passenger and freight transportation. For this purpose, based on the wide spectrum of critical issues encountered in the transportation sector, several scenarios have been built and analysed.     

Energy and emissions impacts of a freeway-based dynamic eco-driving system

Surface transportation consumes a vast quantity of fuel and accounts for about a third of the US CO₂ emissions. In addition to the use of more fuel-efficient vehicles and carbon-neutral alternative fuels, fuel consumption and CO₂ emissions can be lowered through a variety of strategies that reduce congestion, smooth traffic flow, and reduce excessive vehicle speeds. Eco-driving is one such strategy. It typically consists of changing a person’s driving behavior by providing general static advice to the driver (e.g. do not accelerate too quickly, reduce speeds, etc.). In this study, we investigate the concept of dynamic eco-driving, where advice is given in real-time to drivers changing traffic conditions in the vehicle’s vicinity. This dynamic strategy takes advantage of real-time traffic sensing and telematics, allowing for a traffic management system to monitor traffic speed, density, and flow, and then communicates advice in real-time back to the vehicles. By providing dynamic advice to drivers, approximately 10–20% in fuel savings and lower CO₂ emissions are possible without a significant increase in travel time. Based on simulations, it was found that in general, higher percentage reductions in fuel consumption and CO₂ emission occur during severe compared to less congested scenarios. Real-world experiments have also been carried out, showing similar reductions but to a slightly smaller degree.     

A framework for urban freight policy analysis

This paper provides an overview of the urban freight process, in the context of the supply and demand aspects of freight. A framework for analysis is developed, and within that framework, particular aspects of the urban freight process are described. Issues of concern from a public policy viewpoint are highlighted.

Seven main instruments related to public policy are introduced and their application described — taxes and subsidies, regulations, investment, operational instruments, planning, public ownership and research.

It is concluded that the importance of urban freight to the community and its relevance to urban transport justifies a higher level of attention in transport planning and policy formulation, and that there are a wide range of policy instruments available to enable this to be done. The objective of such planning and policy making needs to be specified in each specific context.     

Towards integrated land use and transportation: A dynamic disequilibrium based microsimulation framework for built space markets

Investigating the factors and processes that influence the spatiotemporal distribution of built space and population in an urban area, plays an extremely important role in our greater understanding of the urban travel behaviour. Existing location of activity centres, especially home and work, strongly influences the short-term individual-level decisions such as mode of transportation, and long-term household-level decisions such as change in job and residential location. Conditions in the built space market also affect households’ and firms’ location and relocation decisions, and hence influence the general travel patterns in an urban area. In this context, this paper addresses a very important, but at the same time, not very widely investigated dimension that plays a key role in the evolution of built space and population distribution: Market. A disequilibrium based microsimulation modelling framework is developed for the built space markets. This framework is then used to operationalize the Greater Toronto and Hamilton Area’s owner-occupied housing market within Integrated Land Use Transportation and Environment (ILUTE) modelling system. Simulation results captured heterogeneity in the transaction prices, due to type of dwellings and different market conditions, in a very disaggregate fashion. The proposed methodology is validated by running the simulation from 1986 to 2006 and comparing the results with the historic data.     

An assessment of the political acceptability of congestion pricing

There is renewed interest in implementing congestion pricing in metropolitan areas throughout the US. This paper reviews changes in the transportation policy environment that have led to this renewed interest and identifies the major interest groups that support congestion pricing. A case study is used to demonstrate that significant barriers to congestion pricing implementation continue to exist. The paper concludes with some suggestions for developing politically acceptable pricing alternatives.     

Identifying peer states for transportation system evaluation &; policy analyses

The majority of comparisons between state transportation systems do not control for characteristics that may vary greatly between states (e.g., vehicle miles traveled). A shortcoming of such analyses is that a state’s individual characteristics can be highly influential in determining how transportation policy is set and funds are spent. The purpose of this paper is to extend previous efforts to create groups of similar peer states by developing a new methodological framework that incorporates demographic, temporal, and locational variability into the peer group delineations. We collected historical data for 42 variables on transportation infrastructure, population, economy, growth, topography and weather. To examine trends before and after the passage of ISTEA we gathered data over two time periods: 1985 through 1990 and 1995 through 2000. Using principal components analysis (PCA) we reduced variables into seven components, and then statistically clustered states into peer groups for each time period based on the components and the remaining variables. We identified a range of cluster solutions and demonstrate how cluster statistics help to describe the contextual basis behind the peer grouping. The results of this study are to provide government agencies, researchers and the public with a systematic methodological framework for identifying peer states that reflect similar attributes contributing to the development and maintenance of state transportation systems.

Quantifying the benefit of responsive pricing and travel information in the stochastic congestion pricing problem

This paper is concerned with roadway pricing amidst the uncertainty which characterizes long-term transportation planning. Uncertainty is considered both on the supply-side (e.g., the effect of incidents on habitual route choice behavior) and on the demand-side (e.g., due to prediction errors in demand forecasting). The framework developed in this paper also allows the benefits of real-time travel information to be compared directly against the benefits of responsive pricing, allowing planning agencies to identify the value of these policy options or contract terms in publicly-operated toll roads. Specifically, six scenarios reflect different combinations of policy options, and correspond to different solution methods for optimal tolls. Demonstrations are provided on both the Sioux falls and Anaheim networks. Results indicate that providing information to drivers implemented alongside responsive tolling may reduce expected total system travel time by over 9%, though more than 8% of the improvement is due to providing information, with the remaining 1% improvement gained from responsive tolling.     

A network option portfolio management framework for adaptive transportation planning

A real option portfolio management framework is proposed to make use of an adaptive network design problem developed using stochastic dynamic programming methodologies. The framework is extended from Smit’s and Trigeorgis’ option portfolio framework to incorporate network synergies. The adaptive planning framework is defined and tested on a case study with time series origin-destination demand data. Historically, OD time series data is costly to obtain, and there has not been much need for it because most transportation models use a single time-invariant estimate based on deterministic forecasting of demand. Despite the high cost and institutional barriers of obtaining abundant OD time series data, we illustrate how having higher fidelity data along with an adaptive planning framework can result in a number of improved management strategies. An insertion heuristic is adopted to run the lower bound adaptive network design problem for a coarse Iran network with 834 nodes, 1121 links, and 10 years of time series data for 71,795 OD pairs.     

Exploring transportation equity: Development and application of a transportation justice framework

Recent efforts to emphasize social equity in transportation are emerging as local, regional and national governments have set initiatives to identify, existing and potential, disproportionate impacts to low-income and minority populations, also referred to as transportation justice (TJ). Currently, there are suggested methods for identifying transportation justice areas; however, there is no streamlined method instituted across transportation agencies. Each jurisdiction identifies transportation justice (or environmental justice) areas based on their own methodology, typically based on either average regional thresholds, graduated thresholds, or a more unique in-house index methodology. This research explores and evaluates existing methods and develops a rigorous and comprehensive method called the Transportation Justice Threshold Index Framework (TJTIF) using Geographic Information Systems (GIS), as well as factors based on demographics, socio-economics, and transportation/land use. The framework is applied to a case study region in Pennsylvania reflective of the Marcellus Shale impact area, highlighting Sullivan County, PA. The methodology and the case study application serve as an example for how transportation agencies throughout the country can promote social sustainability and enhance transportation equity.     

Minimal-revenue congestion pricing Part II: An efficient algorithm for the general case

The second of a two-part series, this paper derives an efficient solution to the minimal-revenue tolls problem. As introduced in Part I, this problem can be defined as follows: Assuming each trip uses only a path whose generalized cost is smallest, find a set of arc tolls that simultaneously minimizes both average travel time and out-of-pocket cost. As a point of departure, this paper first re-solves the single-origin problem of Part I, modeling it as a linear program. Then with a change of variable, it transforms the LP's dual into a simple longest-path problem on an acyclic network. The multiple-origin problem – where one toll for each arc applies to all origins – solves analogously. In this case, however, the dual becomes an elementary linear multi-commodity max-cost flow problem with an easy bundling constraint and infinite arc capacities. After a minor reformulation that simplifies the model's input to better accommodate output from common traffic assignment software, a solution algorithm is exemplified with a numerical example.     

Determinants of the willingness-to-pay for reducing the environmental impacts of road transportation

This paper examines the willingness-to-pay of people living in a number of villages in Navarre, in the Spanish Pyrenees to reduce noise and air pollution. Several models are used for estimation based on contingent valuation, noting that those living near roads are willing to pay more to reduce air and noise pollution. In addition, younger people, the better educated, and the more environmentally aware individuals are also willing to pay more to reduce those externalities.     

The effects of non-optimal pricing and investment policies for transportation facilities

A simulation model was used to show the impacts of non-optimal pricing and investment timing policies for a major airport and an urban expressway. It was found that: the losses of economic surplus due to non-optimal pricing and investment policies were relatively small (less than 1% of the net present value of economic surplus for optimal pricing and investment), that marginal cost pricing covers the capital cost of all but the first increment of capacity, that higher user fees will permit the facilities to break even with relatively small losses of economic surplus, that marginal cost pricing has some effect relative to existing pricing policies in delaying the dates new capacity is required and achieving better capacity utilization, and that the shape of the cost function has a substantial impact on the amount of capacity required.     

The role of attitude structures,direct experience and reframing for the success of congestion pricing

Congestion pricing was introduced in Stockholm in 2006, first as a trial followed by a referendum, and permanently from 2007. Public attitudes to the charges became more negative during the period from the decision to the start of the system. Once the trial started, public attitudes became dramatically more positive over the following years, going from 2/3 against the charges to more than 2/3 in favor of the charges. Self-reported changes in behavior and attitudes considerably underestimate actual changes: about 3/4 of the decrease in car trips and more than half of the change in attitudes seem to have gone unnoticed by respondents, ex post. Self-interest and belief in the charges’ effectiveness strongly affect attitudes at any given point in time, but can only explain a minor part of the change in attitudes. I suggest that the debate and the shift in attitudes can be understood as a public and political reframing of the congestion charges over time.     

Simulation-based joint optimization framework for congestion mitigation in multimodal urban network: a macroscopic approach

Transportation - Travel demand management (TDM) is an important measure that will aid in the realization of efficient and sustainable transportation systems. However, in cities where the most...