Abstract

In the urban planning literature, it is frequently explicitly asserted or strongly implied that ongoing urban sprawl and decentralization can lead to development patterns that are unsustainable in the long run. One manifestation of such an outcome is that if extensive road investments occur, urban sprawl and decentralization are advanced and locked-in, making subsequent investments in public transit less effective in reducing vehicle kilometers traveled by car, gasoline use and carbon dioxide emissions. Using a simple core-periphery model of Beijing, the authors numerically assess this effect. The analysis confirms that improving the transit travel time in Beijing?s core would reduce the city?s overall carbon dioxide emissions, whereas the opposite would be the case if peripheral road capacity were expanded. This effect is robust to perturbations in the model?s calibrated parameters. In particular, the effect persists for a wide range of assumptions about how location choice depends on travel time and a wide range of assumptions about other aspects of consumer preferences. Document type: Book

Abstract

Abstract\r Purpose — In this paper we describe a total design data collection method (expanding the definition of the usual “total design” terminology used in typical household travel surveys) to emphasize the need to describe individual and group behaviors embedded within their spatial, temporal, and social contexts.\r \r Methodology/approach — We first offer an overview of recently developed modeling and simulation applications predominantly in North America followed by a summary of the data needs in typical modeling and simulation modules for statewide and regional travel demand forecasting. We then proceed to describe an ideal data collection scheme with core and satellite survey components that can inform current and future model building. Mention is also made to the currently implemented California Household Travel Survey that brings together multiple agencies, modeling goals, and data collection component surveys.\r \r Findings — The preparation of this paper involved reviewing emerging transportation modeling approaches and paradigms, policy questions, and behavioral issues and considerations that are important in the multimodal transportation planning context. It was found that many of the questions being asked of policy makers in the transportation domain require a deep understanding of the interactions and constraints under which individuals make activity-travel choices, the learning processes at play, and the attitudes and perceptions that shape ways in which people adjust their travel behavior in response to policy interventions. Based on the work, it was found that many of the traditional travel survey designs are not able to provide the comprehensive data needed to estimate activity-based model systems that truly capture the full range of behavioral considerations and phenomena of importance.\r \r Originality/value of paper — This paper offers a review of the emerging transportation modeling approaches and behavioral paradigms of importance in activity-based travel demand forecasting. The paper discusses how traditional travel survey designs are inadequate to meet the data needs of emerging modeling approaches. Based on a review of all of the data needs and new data collection methods that are making it possible to observe a full range of human behaviors, the paper offers a total survey data collection design that brings together many different surveys and data collection protocols. The core household travel survey is augmented by a full slate of special purpose surveys that together yield a rich behavioral database for activity-based microsimulation modeling. The paper is a valuable reference for transportation planners and modelers interested in developing data collection enterprises that will feed the next generation of transportation models. Document type: Part of book or chapter of book

Abstract

An agent-based simulation model for supporting the decision making in urban transport planning is presented. The model can be used to investigate how different transport infrastructure investments and policy instruments will affect the travel choices of passengers. We identified four main categories of factors influencing the choice of travel: cost, time, convenience, and social norm. However, travelers value these factors differently depending on their individual characteristics, such as age, income, work flexibility and environmental engagement, as well as on external factors, such as the weather. Moreover, instead of modeling the transport system explicitly, online web services are used to generate travel options. The model can support transport planners by providing estimations of modal share, as well as economical and environmental consequences. As a first step towards validation of the model, we have conducted a simple case study of three scenarios where we analyze the effects of changes to the public transport fares on commuters' travel choices in the Malmo-Lund region in Sweden. Document type: Part of book or chapter of book

Abstract

In this paper we describe how we included travel time variability in the national Dutch transport forecasting model and what the policy impacts of this new forecasting tool are. Until now, travel time reliability improvements for road projects were included in Dutch cost-benefit analysis (CBA) by multiplying the travel time benefits from reduced congestion by a factor 1.25. This proportionality is based on the linkage between congestion reduction and reliability improvements. However, this treatment of reliability is not useful to evaluate policies that especially affect travel time variability. From the start, this method was provisional and meant to be replaced by a better method capturing travel time variability. For this, we derived an empirical relation between the standard deviation of travel time, mean delay of travel time and length of route. This has been implemented in the national Dutch model as a post processing module. The new travel time reliability forecasting model will be incorporated in the Dutch guidelines for CBA. Document type: Part of book or chapter of book

Abstract

This book examines the urban transport crisis from an international, comparative perspective. Throughout the industrialized world car ownership and use have grown rapidly over the past few decades while, in contrast, public transport use has either fallen or stagnated. These trends have caused increasingly severe social, economic and environmental problems. The purpose of this book is not just to describe the differences in transport systems, travel behaviour and transport problems but to identify policies which will help solve the ever growing problems of urban transport. The authors examine the problems and solutions experienced by a number of countries and provide a comparative assessment of their policies and future developments. Document type: Book

Abstract

The history of demand modeling for person travel has been dominated by the modeling approach that has come to be referred to as the four step model (FSM) (see Chapter 2). Travel, always viewed in theory as derived from the demand for activity participation, in practice has been modeled with trip-based rather than activity-based methods (as presented in Chapter 4). Trip origin-destination (O-D) rather than activity surveys form the principle database. The influence of activity characteristics decreases, and that of trip characteristics increases, as the conventional forecasting sequence proceeds. The application of this modeling approach is near universal, as in large measure are its criticisms (these inadequacies are well documented, e.g., by McNally and Recker (1986)). The current FSM might best be viewed in two stages. In the first stage, various characteristics of the traveler and the land use - activity system (and to a varying degree, the transportation system) are "evaluated, calibrated, and validated" to produce a non-equilibrated measure of travel demand (or trip tables). In the second stage, this demand is loaded onto the transportation network in a process than amounts to formal equilibration of route choice only, not of other choice dimensions such as destination, mode, time-of-day, or whether to travel at all (feedback to prior stages has often been introduced, but not in a consistent and convergent manner). Although this approach has been moderately successful in the aggregate, it has failed to perform in most relevant policy tests, whether on the demand or supply side. This chapter extends the material in Chapter 2 by providing a concise overview of the mechanics of the FSM, illustrated with a hypothetical case study. The discussion in this chapter, however, will focus on U.S. modeling practice. Transportation modeling developed as a component of the process of transportation analysis that came to be established in the U.S.A. during the era of post-war development and economic growth. Initial application of analytical methods began in the 1950s. The landmark study of Mitchell and Rapkin (1954) not only established the link of travel and activities (or land use) but called for a comprehensive framework and inquiries into travel behavior. The initial development of models of trip generation, distribution, and diversion in the early 1950s lead to the first comprehensive application of the four-step model system in the Chicago Area Transportation Study (see Weiner, 1997) with the model sandwiched by land use projection and economic evaluation. The focus was decidedly highway-oriented with new facilities being evaluated versus traffic engineering improvements. The 1960s brought federal legislation requiring "continuous, comprehensive, and cooperative" urban transportation planning, fully institutionalizing the FSM. Further legislation in the 1970s brought environmental concerns to planning and modeling, as well as the need for multimodal planning. It was recognized that the existing model system may not be appropriate for application to emerging policy concerns and, in what might be referred to as the "first travel model improvement program", a call for improved models led to research and the development of disaggregate travel demand forecasting and equilibrium assignment methods that integrated well with the FSM and have greatly directed modeling approaches for most of the last 30 years. The late 1970s brought "quick response" approaches to travel forecasting (Sosslau et al., 1978; Martin and McGuckin, 1998) and independently the start of what has grown to become the activity-based approach (see Chapter 4). The growing recognition of the misfit of the FSM and relevant policy questions in the 1980s led to the (second, but formal) Travel Model Improvement Program in 1991; much of the subsequent period has been directed at improving the state-of-thepractice relative to the conventional model while fostering research and development in new methodologies to further the state-of-the-art (see Chapter 4). The FSM is best seen as a particular application of transportation systems analysis (TSA), a framework due to Manheim (1979) and Florian et al. (1988), which positions the model well to view its strengths and weaknesses. A brief presentation of this TSA framework introduces the FSM context and leads to a discussion of problem and study area definition, model application, and data requirements. The models that are perhaps most commonly utilized in the FSM are then presented in the form of a sample application. Document type: Part of book or chapter of book

Abstract

The paper explores the transportation-land use policy connection. More specifically, it considers the question, can land use policy be used to alter transportation behavior? The answer is of some importance. If the answer is yes, then there is hope that land use policies can be designed and implemented that will bring some relief to the congestion and complex transportation problems that are facing US metropolitan areas. This is the underlying assumption behind most smart growth policy reforms. If the answer is no, then land use policy may still be important, but is not likely to play an important role in resolving transportation issues. The paper then offers a schematic that identifies necessary conditions for land use policy to play a role in addressing transportation issues. Specifically, the paper argues that for land use policy to play an effective role, three conditions must hold. First, land use must be able to alter transportation behavior; secondly, transportation infrastructure must not fully determine land use; and thirdly, the condition on which the authors consider most extensively, land use policy must significantly and constructively affect land use. After presenting the schematic, the paper considers the evidence on each of these conditions. Based on the review of the evidence, the paper concludes that land use policy can play an effective role in transportation issues, but that the role is likely to be small, often counter productive, and most effective at the neighborhood scale. Document type: Part of book or chapter of book

Abstract

Between 1900 and 2000, the length of paved roads in the United States increased from 240 km to 6,400,000 km (Peat 2002, BTS 2002) with virtually 100% of the U.S. population having almost immediate access to paved roadways. Similarly, in 1830 there were 37 km of railroad in the United States, but by 1920 total track mileage had increased more than ten-thousand times to 416,000 km miles, however since then, rail track mileage has shrunk to about 272,000 km (Garrison 1996, BTS 2002). The growth (and decline) of transport networks obviously affects the social and economic activities that a region can support; yet the dynamics of how such growth occurs is one of the least understood areas in transport, geography, and regional science. This is revealed time and again in the long-range planning efforts of metropolitan planning organizations (MPOs), where transport network changes are treated exclusively as the result of top-down decision-making. Changes to the transport network are rather the result of numerous small decisions (and some large ones) by property owners, firms, developers, towns, cities, counties, state department of transport districts, MPOs, and states in response to market conditions and policy initiatives. Understanding how markets and policies translate into facilities on the ground is essential for scientific understanding and improving forecasting, planning, policy-making, and evaluation. Document type: Part of book or chapter of book

Abstract

Abstract\r This chapter summarizes some of the results from the Working Group “National Travel Surveys” in COST Action 355. All 50 presentations could not be reviewed here; thus, we focus on three crucial topics: \r \r the periodicity of data collection, but also its longitudinal aspects (advantages of continuous surveying, repeated cross-sections vs. panel surveys, etc.),\r \r \r new technologies for improving the efficiency and accuracy of mobility surveys (computer-assisted telephone, Web-based, interviews, GPS, GSM, RDS, etc.),\r \r \r innovative approaches, exemplified by qualitative surveys combined with conventional quantitative ones, and by biographical approaches. Document type: Part of book or chapter of book

Abstract

Technology is changing the way we move and reshaping cities and society. Shared and on-demand mobility represent notable transportation shifts in the 21st century. In recent years, mobility on demand (MOD)—where consumers access mobility, goods, and services on-demand by dispatching shared modes, courier services, public transport, and other innovative strategies—has grown rapidly due to technological advancements; changing consumer preferences; and a range of economic, environmental, and social factors. New attitudes toward sharing, MOD, and mobility as a service (MaaS) are changing traveler behavior and creating new opportunities and challenges for public transportation. This chapter discusses similarities and differences between the evolving concepts of MaaS and MOD. Next, it characterizes the range of existing public transit and MOD service models and enabling partnerships. The chapter also explores emerging trends impacting public transportation. While vehicle automation could result in greater public transit competition in the future, it could also foster new opportunities for transit enhancements (e.g., microtransit services, first- and last-mile connections, reduced operating costs). The chapter concludes with a discussion of how MOD/MaaS partnerships and automation could enable the public transit industry to reinvent itself, making it more attractive and competitive with private vehicle ownership and use.