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: The recent policy discussions about information technology in transport and traffic demand management have increased the interest in activity-based approaches to the analysis of travel behaviour, in particular in the modelling of household activity scheduling which is at the core of many of the required changes in travel behaviour. The paper is a state-of-the-art review of conceptualizations and models of activity scheduling with special regard to issues raised by the new policy instruments. In the course of the review, the validity of behavioral assumptions are critically examined and several needs for future research identified. Abstract : They present an integrated activity based discrete choice model system of an individual’s daily activity and travel schedule, intended for use in forecasting urban passenger travel demand. The system is demonstrated using a 1991 Boston travel survey and level of service data. The model system activities and associated travel as a daily activity pattern overarching a set of tours. The daily activity pattern includes (a) the primary activity of the day, with one alternative being to remain at home for all the day’s activities; (b) the type of tour for the primary activity, including the number, purpose and sequence of activity stops; and (c) the number and purpose of secondary tours. Tour models include the choice of time, destination and mode of travel, and are conditioned by the choice of a daily activity pattern. The choice of daily activity pattern is influenced by the expected maximum utility derived from the available tour alternatives. Bhat, Abstract : Since the beginning of civilization, the viability and economic success of communities have been, to a major extent, determined by the efficiency of the transportation infrastructure. To make informed transportation infrastructure planning decisions, planners and engineers have to be able to forecast the response of transportation demand to changes in the attributes of the transportation system and changes in the attributes of the people using the transportation system. Travel demand models are used for this purpose; specifically, travel demand models are used to predict travel characteristics and usage of transport services under alternative socio-economic scenarios, and for alternative transport service and land-use configurations. The need for realistic representations of behavior in travel demand modeling is well acknowledged in the literature. This need is particularly acute today as emphasis shifts from evaluating long-term investment-based capital improvement strategies to understanding travel behavior responses to shorter-term congestion management policies such as alternate work schedules, telecommuting, and congestion-pricing. The result has been an increasing realization in the field that the traditional statistically-oriented trip-based modeling approach to travel demand analysis needs to be replaced by a more behaviorally-oriented activity-based modeling approach. Computational-process of household travel geographical Abstract: Household travel behavior entails interdependent deliberate decisions, as well as the execution of routines not preceded by deliberate decisions. Furthermore, travel decisions are dependent on choices to participate in activities. Because of the complexity of the decision making process in which individuals are engaged, computational process models (CPMs) are promising means of implementing behavioral principles, which, unlike other disaggregate modeling approaches do not rely on a utility maximizing framework. A conceptual framework is proposed as the basis of a CPM interfaced with the geographical information system Arc/Info. Abstract: Recent questioning of assumptions underlying current theory and practice in studies of urban travel behavior is continued. The focus here is on the assumption that the individual’s day-to-day travel is habitual and that therefore a one-day record of behavior constitutes a sufficient data base for theory and for model building. A rationale for examining the day-to-day variation in an individual’s travel is established; then some of the field procedures that can contribute to making longitudinal data suitable for studying this issue are discussed mid, by using the Uppsala Household Travel Survey data as an example, the efficacy of these procedures is tested. Next several techniques are described for measuring travel patterns so that day-to-day variability can be detected, and an approach to the measurement problem is outlined with illustrative examples from the Uppsala data, which consist of travel diaries collected over 35 consecutive days. The results of the empirical analysis are preliminary, but they indicate that (a) the quality of longitudinal travel-diary data need not deteriorate over the survey period. (b) both employed men and nonworking women exhibit a great deal of repetition in their daily travel-activity patterns, so that (c) days with similar travel patterns can be identified and grouped. Abstract . This paper is a review and assessment of the contributions made by “activity-based approaches” understanding and forecasting of travel In their brief a decade, activity-based analyses have extensive interest. This work has led to an accumulation of empirical evidence and new insights and has made substantial contributions toward the better understanding of travel behavior. However, practical applications of the approach in transportation planning and policy development have been scarce. Based on an analysis of the inherent characteristics of the activity-based approach, a review of recent (after the 1981 Oxford conference) developments, and a synthesis of the findings from past empirical studies, this study attempts to evaluate the contribution made by activity-based analyses and determine the reasons for the limited practical application. Recommendations are made for the future development of activity-based analysis as a science of travel behavior and as a tool in the practice of transportation planning and policy development. 14(1): Abstract : It is often implicitly assumed by researchers that their readers understand what cognitive map and cognitive mapping are, and their justification for study. This paper differs in this respect by explaining explicitly the ‘what’ and ‘why’ questions often asked, demonstrating cognitive mappings multidisciplinary research worth. First, it examines questions concerning what cognitive maps are, the confusion inherent from the use of the term ‘map’, and the usage and reasons for alternative expressions. Second, it examines the theoretical applications or conceptual research, concerning cognitive maps role in the influencing and explaining spatial behaviour; spatial choice and decision making; wayfinding and orientation; and the cognitive maps utility and role as a mnemonic and metaphorical devise; a shaper of world and local attitudes and perspectives; and for creating and coping with imaginary worlds. Third, it discusses cognitive mappings practical and applied worth, concerning the planning of suitable living environments; advertising; crime solving; search and rescue, geographical educational issues, cartography and remote sensing-, and in the designing and understanding computer interfaces and databases, especially Geographical Information Systems (GISs). (1978). “Modelling 2: 129-153. Abstract : A person's cognitive map, or knowledge of large-scale space, is built up from observa-tions gathered as he travels through the environment. It acts as a problem solver to find routes and relative positions, as well as describing the current location. The TOUR model captures the multiple representations that make up the cognitive map, the problem-solving strategies it uses, and the mechanisms for assimilating new information The representations have rich collections of states of partial knowledge, which support many of the performance characteristics of common-sense knowledge. Abstract: Abstract : This resource paper is intended to give a historical account of the development of the methodology of disaggregate behavioral travel demand analysis and its connection to random utility maximization (RUM). It reviews the early development of the subject, and major methodological innovations over the past three decades in choice theory, data collection, and statistical tools. It concludes by identifying some topics and issues that deserve more work, and fearlessly forecasting the future course of research in the field. 14: Abstract: Day-to-day variability in individuals’ travel behavior (intrapersonal variability) that been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual’s travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior. Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodemographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record Abstract : In this paper an activity-based travel demand model called AMOS is described. The model system is capable of simulating changes in individual activity and travel behavior that may be brought about by a change in the transportation system. These simulations may then be used to predict the impacts of various transportation policies on regionwide travel characteristics. A rule-based activity-scheduling algorithm is at the heart of AMOS. The algorithm simulates changes in activity and travel patterns while recognizing the presence of constrain