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

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

Forecasting has long been a challenge, and will remain so for the foreseeable future. But the analytical instruments and data processing capabilities available through the latest technology, and software, should allow much better forecasting than transport ministries, or regulatory agencies typically observe. Privatization brings new needs for demand forecasting. More attention is paid to risk under privatization, than when investments are publicly financed. And regulators must be able to judge traffic studies done by operators, and to learn what strategic behavior influenced these studies. Many governments, and regulators avoid good demand, modeling out of lack of conviction that theory, and models can do better than the"old hands"of the sector. This is dangerous when privatization changes the nature of business. For projects amounting to investments of $ 100-200 million, a cost of $ 100,000-200,000 is not a reason to reject a reasonable modeling effort. And some private forecasting firms are willing to sell guarantees, or insurance with their forecasts, to cover significant gaps between forecasts, and reality. Document type: Book

Abstract

Freight transport. The needs of freight transport companies are assessed through some forty interviews conducted in four European countries and analyzed in the light of innovation diffusion theory. These interviews highlight the operational and economic obstacles to the use of electric vehicles, which are linked to the technology itself but also to its novelty.A quantitative approach completes this study. It is based on a market share prediction model, which quantifies how economic and operational constraints evolve with technological developments. These constraints are measured by two indicators: the vehicle's range adequacy given its use and total cost of ownership (TCO) comparisons. An original feature of the model is that it treats the amount of subsidies for the purchase of an electric vehicle as an endogenous variable that dynamically adapts to market developments.In order to compensate for the lack of available data on commercial vehicle uses, a statistical model has been developed. This model makes the best use of data from a survey on light commercial vehicles in France, conducted by the statistical department of the Ministry of the Environment (SDES).These analyses confirm that the development of the electric vehicle market is not certain and that it is currently extremely dependent on public support. Even in scenarios of continued financial support from public administrations, exponential market growth is unlikely. Rather, the market will grow slowly for many years to come, the time for technology to overcome its8dependence on public financial support. For example, our reference scenario forecasts a 13% market share for electric vans in 2032; Le marché des véhicules électriques est animé par une dynamique très positive. Il s'agit cependant essentiellement d'un marché de niche. Il est donc légitime de s’interroger quant à son avenir.D'une part, cette dynamique est portée par de fortes préoccupations environnementales et bénéficie d'un large soutien des autorités publiques. Les constructeurs automobiles ont ces dernières années fortement investi dans cette technologie, les progrès technologiques sont rapides et offrent des perspectives intéressantes.D'autre part, des subventions conséquentes sont aujourd’hui nécessaires pour permettre aux véhicules électriques d’être compétitifs. Il est inévitable que ces subventions diminuent si le marché grandit. Deux mécanismes opposés sont donc en jeu et rendent incertain le développement du marché des véhicules électriques pour les années à venir.Notre recherche propose d'analyser ces mécanismes pour les véhicules utilitaires légers, et plus particulièrement pour le transport urbain de marchandises. Les besoins des entreprises de transport de marchandises sont évalués à travers une quarantaine d'entretiens, menés dans quatre pays européens et analysés à la lumière de la théorie de la diffusion de l'innovation. Ces entretiens mettent en évidence les obstacles opérationnels et économiques à l'utilisation de véhicules électriques, qui sont liés à la technologie elle-même mais aussi à sa nouveauté.Une approche quantitative complète cette étude. Elle s’appuie sur un modèle de prédiction de parts de marché, qui quantifie la façon dont les contraintes économiques et opérationnelles évoluent avec les développements technologiques. Ces contraintes sont mesurées par deux indicateurs: l'adéquation de l'autonomie du véhicule avec son usage et les comparaisons de coûts totaux de possession (TCO). Une originalité du modèle est qu’il traite le montant des subventions à l’achat d’un véhicule électrique comme une variable endogène, qui s’adapte dynamiquement aux évolutions du marché.Afin de compenser le manque de données disponibles sur les usages des véhicules utilitaires, un modèle statistique a été développé. Ce modèle permet d’exploiter au mieux les données d'une enquête sur les véhicules utilitaires légers en France, menée par le service de la donnée et des études statistiques (SDES) du Ministère de la Transition Écologique et Solidaire