The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

The demand for gasoline in intra-urban travel: A simulation and some policy implications

The daily gasoline consumption of an individual household for intra-urban travel is simulated, under the assumption of constrained maximization of daily travel distance, using Zahavi's recently developed UMOT model for urban travel demand. A sensitivity analysis based on variations in various conditions pertaining to the cost and speed attributes of private car travel and public transportation, the characteristics of the urban area (including its transportation network) and the socio-economic features of the population provides quantitative estimates of the effects of potential strategies for controlling and/or reducing gasoline consumption. Suggestions for urban transportation system development policy are made in conclusion.     

A review of the impact of parking policy measures on travel demand

This paper reviews the empirical evidence relating to the impact of parking policy measures on the demand for parking and for travel. Disaggregate modal choice models, disaggregate parking location models and site‐specific studies of parking behaviour are examined. With regard to modal choice models, it is concluded that few studies deal adequately with parking factors, but that there is some support for the view that parking policy measures are a relatively important influence on modal choice. When parking location models are examined parking policy variables are shown to have a substantial impact on choice of parking location. With regard to site‐specific studies, the paper concludes that there is a great variation in the parking price elasticities quoted, which reflects partly the methodological problems associated with such studies. Suggestions to improve model specification are made.     

Retrospect and prospect for subarea travel demand modeling: an empirical study

Abstract

At present, customized subarea models have been widely used in local transportation planning throughout the USA. A subarea model's biggest strengths lie in its more detailed and accurate modeling outputs which better meet local planning requirements. In addition, a subarea model can substantially reduce database size and model running time. In spite of these advantages, subarea models remain quite weak in modeling transit projects, smart growth measures, air quality conformity, and other areas. In addition to evaluating subarea models, this paper uses the Irvine Transportation Analysis Model (ITAM) as an empirical case of subarea model to illustrate the remedial procedures in maintaining its consistency with the regional model of the Orange County Transportation Analysis Model (OCTAM). Looking into the future, subarea models face both opportunities and challenges. More GIS applications, travel surveys, micro-simulation software utilization, and modeling improvements are expected to be incorporated into the subarea modeling process.     

A suppressed demand analysis method of the transportation disadvantaged in policy making

Abstract

This paper proposes a method for estimating transportation supply requirements when the suppressed demand of the transportation disadvantaged (TD) can be calculated and added to existing demand for travel. The underlying assumption is that the travel conditions of these TD groups must be equal to the ‘conventional’ demand, known as ‘full release’. Utilising the modelling approach for TD, suppressed demand analysis, diagnosis of difficulties and equity between conventional and disadvantaged groups were realised, while elaborating special cases for the most vulnerable TD groups (such as elderly and disabled persons) and simultaneously identifying areas of difficulty. From the early virtual results, it is concluded that, for the full release of suppressed trips (only a 5% increase), policy makers must be ready to face some financial burdens, requiring coordination of effort to both standardise these TD groups and reduce the costs incurred by operators.     

Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities

Abstract

Malaysia is one of the few countries in the world that provides a fuel subsidy to consumers. Due to the recent economic crisis, the Malaysian Government decided to revise its fuel subsidization policy from a fixed price subsidy to a floating price subsidy dependent on global oil demand. Recognizing that the change in fuel subsidization policy can have an impact on travel behavior, this article investigates the short-term impact of the policy change on private and public transportation in the Klang Valley region of Malaysia. Spectral analyses are performed to investigate if the policy change has an impact on private vehicle travel demand, measured in terms of road traffic, and short-term travel demand elasticity with respect to fuel price is estimated. To measure the impact on the public transportation system, the demand cross-elasticity values of rail transit and buses are also estimated. It was found that traffic flow reduces with an increase in fuel price, although elasticity and cross-elasticity values obtained are low. The article finds that there is a potential mode shift from private vehicles to rail transit with increasing fuel price. It is demonstrated that reducing fuel price subsidy can be an effective travel demand management strategy to alleviate congestion.     

Deciding whether and how to improve statewide travel demand models based on transportation planning application needs

Many states in the USA have developed statewide travel demand models for transportation planning at the state level and along intercity corridors. Travel demand models at mega-region and provincial levels are also widely used in Europe and Asia. With modern transportation planning applications requiring enhanced model capabilities, many states are considering improving their four-step statewide demand models. This paper synthesizes representative statewide models developed with traditional four-step, advanced four-step, and integrated micro-simulation methods. The focus of this synthesis study is as much on model applications and data requirements as on modeling methods. An incremental model improvement approach toward advanced statewide models is recommended. Review findings also suggest model improvement activities should be justified by planning application needs. For statewide model improvement plans to be successful and financially sustainable, the return on model improvement investment needs to be demonstrated by timely applications that rely on improved model capabilities.     

Demand management as an element of transportation policy: using carrots and sticks to influence travel behavior

This paper describes the characteristics of transportation demand management. The origin of transportation demand management (TDM) as it has evolved in the US is related to federal policy initiatives that first focused on improving the efficiency of the urban transportation system through operational improvements, and then incorporated concerns such as air quality and energy conservation into the transportation planning process. The paper then examines the effectiveness of TDM actions, and concludes that those actions most likely to increase the “price” of travel for single occupant vehicle use will be most effective. The paper identifies several strategies for improving the effectiveness of TDM actions in the context of regional transportation planning, including: incorporating TDM as part of the solutions for regional transportation planning, linking TDM to land use decisions, making the costs of travel more apparent to the user, and making TDM implementation more palatable to the general public.     

Latent temporal preferences: An application to airline travel

An essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.     

Structural equation modeling for national travel demand: an examination of the relationships between communication modes

This paper examines the relationships among different transportation modes, and between transportation and telecommunications, by applying the structural equation modeling (SEM) technique. For this purpose, we collected and compiled time series data on national travel demand, and socioeconomic and telecommunications conditions in Taiwan, and built national travel demand models using SEM. The estimation results show that the relationship between telecommunications and transportation demand (either car ownership or public transportation) is more complementary than substitutional. Moreover, car ownership is a type of inelastic necessity good, and its relationship with public transportation is more substitutional than complementary. Finally, among the three public transportation modes – rail, bus and domestic air – it is found that air is weakest in terms of competitive power. From the viewpoint of long-term forecasting trends, the bus holds its competitive power in comparison with other public transportation modes and would not be replaced in the future.     

Correction to: The role of travel demand and network centrality on the connectivity and resilience of an urban street system

Transportation - In the original publication of the article, the first, second and fourth authors’ affiliations are published incorrectly.     

Evaluation of travel demand strategies: a microscopic traffic simulation approach

This research investigates the design and impact of a new travel demand management approach, namely the Downtown Space Reservation System (DSRS), using a microscopic traffic simulation approach executed in VISSIM. The DSRS is part of a Travel Demand Management strategy that is designed to mitigate traffic congestion in a downtown urban setting. The simulation is conducted according to an experimental design procedure for a revised road network representing downtown Boise, Idaho. The issues that are tested in the simulation include: (1) whether the DSRS improves traffic performance when compared with the case without the DSRS; (2) how the DSRS performs compared with a reservation system that uses a First Come First Serve principle; and (3) how the specific DSRS parameters (such as, the relative importance of throughput versus revenue generation) influence network system performance. Conclusions and future research recommendations are provided based on the insights from the simulation modeling work.     

The accuracy of the multinomial logit model as an approximation to the multinomial probit model of travel demand

The multinomial probit model of travel demand is considerably more general but much less tractable than the better-known multinomial logit model. In an effort to determine the effects of using the relatively simple logit model in situations where the assumptions of probit modeling are satisfied but those of logit modeling are not, the accuracy of the multinomial logit model as an approximation to a variety of three-alternative probit models has been evaluated. Multinomial logit can give highly erroneous estimates of the choice probabilities of multinomial probit models. However, logit models appear to give asymptotically accurate estimates of the ratios of the coefficients of the systematic components of probit utility functions, even when the logit choice probabilities differ greatly from the probit ones. Large estimation data sets are not necessarily needed to enable likelihood ratio tests to distinguish three-alternative probit models from logit models that give seriously erroneous estimates of the probit choice probabilities. Inclusion of alternative-specific dummy variables in logit utility functions cannot be relied upon to reduce significantly the errors of logit approximations to the choice probabilities of probit models whose utility functions do not contain the dummies.     

Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art

ABSTRACT

The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.     

Public financing of private freight rail infrastructure to reduce highway congestion: A case study of public policy and decision making in the United States

As goods movement continues to increase it is expected to outpace infrastructure capacity in the United States. Moving a larger share of goods by rail rather than truck is a potentially cost effective part of a solution. Freight rail not only offers a substitute for truck trips but is a cleaner, more energy efficient, and safer alternative. Recently a number of private freight rail projects have received public funding. The public funds are aimed at increasing freight rail capacity with the goal of diverting some goods currently moved by truck to rail. While the benefits of moving goods by rail are relatively clear, it is unclear if public decision makers can effectively identify strategic rail investments that will achieve their policy goals. This study critically examines the analytical methods, models, and data that are commonly used to support decisions to provide public funds for private freight rail projects. This is accomplished through a case study of California’s Trade Corridors Improvement Fund program which provided $680 million for 11 freight rail projects. The study’s contributions include identifying critical analytical flaws and challenges affecting the benefit estimates that public funding decisions rely on. Improvements to current evaluation methods are also identified as are regulatory reforms and policy interventions that may offer more effective and reliable outcomes.     

Urban travel demand: The impact of Box-Cox transformations with nonspherical residual errors

We develop an algorithm for solving regression models with Box-Cox transformations on both the dependent and independent variables, while simultaneously taking into account corrections for serial correlation of several orders and for heteroscedasticity. The latter correction is of a general form which contains as special cases most specifications of heteroscedasticity found in practice. We apply the procedure to three urban travel demand functions, two of which are currently used in their linear form by the Montreal Transit Authority, and analyze more than 100 specifications. Our results show that taking into account nonsphericalness of the residuals has a major impact on model parameter estimates, notably on those which determine the functional form of the model, and that, conversely, modifications of the functional form have strong implications for both the structure of autocorrelation and the importance of heteroscedasticity; moreover, we find interactions between autocorrelation and heteroscedasticity structures. We introduce a special measure of elasticity for variables which contain zero observations, particularly dummy variables. Moreover, we find that elasticities of demand and implicit values of time depend to a large extent on the stochastic specification of the model.     

A method to assess demand growth vulnerability of travel times on road network links

Many national governments around the world have turned their recent focus to monitoring the actual reliability of their road networks. In parallel there have been major research efforts aimed at developing modelling approaches for predicting the potential vulnerability of such networks, and in forecasting the future impact of any mitigating actions. In practice—whether monitoring the past or planning for the future—a confounding factor may arise, namely the potential for systematic growth in demand over a period of years. As this growth occurs the networks will operate in a regime closer to capacity, in which they are more sensitive to any variation in flow or capacity. Such growth will be partially an explanation for trends observed in historic data, and it will have an impact in forecasting too, where we can interpret this as implying that the networks are vulnerable to demand growth. This fact is not reflected in current vulnerability methods which focus almost exclusively on vulnerability to loss in capacity. In the paper, a simple, moment-based method is developed to separate out this effect of demand growth on the distribution of travel times on a network link, the aim being to develop a simple, tractable, analytic method for medium-term planning applications. Thus the impact of demand growth on the mean, variance and skewness in travel times may be isolated. For given critical changes in these summary measures, we are thus able to identify what (location-specific) level of demand growth would cause these critical values to be exceeded, and this level is referred to as Demand Growth Reliability Vulnerability (DGRV). Computing the DGRV index for each link of a network also allows the planner to identify the most vulnerable locations, in terms of their ability to accommodate growth in demand. Numerical examples are used to illustrate the principles and computation of the DGRV measure.     

Extension of Newell's dispatching policy to the case of sensitive demand

This paper has extended Newell's dispatching policy to the case of sensitive demand which is characterized by a linear mode split model with two major factors, wait time and transit fare. Three objective functions both with and without vehicle capacity constraint are analyzed, including profit maximization, maximization of a combination of net user benefit and operator profit, and maximization of net user benefit subject to a deficit constraint. Closed-form solutions associated with various system parameters are obtained. It is shown that under sensitive demand conditions the optimal dispatching rate is approximately proportional to the square root of the total demand rate, if vehicle size is not binding and it is strictly proportional to the total demand rate, if vehicle size is binding.     

Asymmetric responses of highway travel demand to changes in fuel price: An explanation via fuel price uncertainty

Previous research has examined asymmetric effects of fuel price uncertainty on energy demand. If we consider that energy demand is related to travel demand, the changes in fuel prices may have asymmetric effects on highway travel demand via fuel price uncertainty. In other words, when in general fuel price is steadily rising, the highway traffic volume decreases by a small percentage. On the other hand, the highway traffic volume increases by a large percentage when fuel prices are falling. We hypothesize that the uncertainty in fuel prices generates this kind of asymmetric effect on highway traffic volume in Korea. We use the Korean monthly fuel price and highway traffic volume data from 2001 to 2009, and define the intra-month (weekly) fuel price changes as monthly fuel price volatility which is a proxy for monthly fuel price uncertainty. We found that the direction of the change in fuel prices had asymmetric effects on highway travel demand and that the fuel price uncertainty led drivers to respond asymmetrically to the changes in fuel prices.     

Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data

Ambient concentrations of pollutants are correlated with emissions, but the contribution to ambient air quality of on-road mobile sources is not necessarily equal to their contribution to regional emissions. This is true for several reasons such as the distribution of other pollution sources and regional topology, as well as meteorology. In this paper, using a dataset from a travel demand model for the Sacramento metropolitan area for 2005, regional vehicle emissions are disaggregated into hourly, gridded emission inventories, and transportation-related concentrations are estimated using an atmospheric dispersion model. Contributions of on-road motor vehicles to urban air pollution are then identified at a regional scale. The contributions to ambient concentrations are slightly higher than emission fractions that transportation accounts for in the region, reflecting that relative to other major pollution sources, mobile sources tend to have a close proximity to air quality monitors in urban areas. The contribution results indicate that the impact of mobile sources on PM₁₀ is not negligible, and mobile sources have a significant influence on both NO_x and VOC pollution that subsequently results in secondary particulate matter and ozone formation.