S Soora Rasouli

Activity-based models of travel demand : promises, progress and prospects

Because two decades have almost passed since the introduction of activity-based models of travel demand, this seems the right time to evaluate progress made in the development and application of these models. This invited paper seeks to discuss the initial promises of activity-based models as an alternative to four-step and tour-based models, summarize progress made and identify still unsolved issues that require further research.

Uncertainty in travel demand forecasting models: literature review and research agenda

Abstract Reasoning why uncertainty analysis will become important in years to come, this paper reviews prior work on uncertainty analysis in travel demand forecasting. Different sources of uncertainty are discussed. Studies examining these various sources of uncertainty are summarized differentiating between four step models, discrete choice models and activity-based models of travel demand. Next, gaps in the literature and avenues of future research are systematically discussed with a special focus on complex activity-based models. The paper is completed with some concluding comments.

Mobile Technologies for Activity-Travel Data Collection and Analysis

As mobile technologies become ever more pervasive in modern society, users find increasingly innovative methods to take advantage of the newest developments and mobile devices. Data mining, in particular, has seen a vast shift as a result of wireless technologies.Mobile Technologies for Active-Travel Data Collection and Analysis concentrates on one particular and fast-growing application of mobile technologies: data acquisition for the tourism industry. This critical reference source provides travel agents, visitors, and hosts with the most advanced data mining methods, empirical research findings, and computational analysis techniques necessary to compete effectively in the global tourism industry.

Incorporating activity-travel time uncertainty and stochastic space–time prisms in multistate supernetworks for activity-travel scheduling

Multistate supernetwork approach has been advanced recently to study multimodal, multi-activity travel behavior. The approach allows simultaneously modeling multiple choice facets pertaining to activity-travel scheduling behavior, subject to space–time constraints, in the context of full daily activity-travel patterns. In that sense, multistate supernetworks offer an alternative to constraints-based time-geographic activity-based models. To date, most research on time-geographic models and supernetworks alike has represented time and space in a deterministic fashion. To enhance the validity and realism of the scheduling process and the underlying space–time decisions, this paper pioneers incorporating time uncertainty in multistate supernetworks for activity-travel scheduling. Solutions based on the concept of the -shortest path are proposed to find the reliable activity-travel pattern with confidence level. An algorithm combining label correcting and Monte-Carlo integration is proposed to finding the-shortest paths in the presence of time window constraints. An example of a typical daily activity program is executed to demonstrate the applicability of the proposed extension.

Incorporating Mechanisms of Social Adoption in Design and Analysis of Stated-Choice Experiments: Illustration and Application to Choice of Electric Cars

The use of stated-choice experiments in travel behavior research has increased dramatically over the past decade. Although considerable progress has been made, overprediction of market shares of new choice alternatives is often reported. This study is based on the contention that such overprediction is partly because experimental designs do not incorporate conditions that reflect how new alternatives are received in the marketplace: social adoption is not experimentally varied. The aim of this paper is to show how social adoption can be incorporated into the design and analysis of stated-choice experiments. The intention to buy an electric car is used for illustration. This paper discusses the design of the choice experiment and summarizes the main findings of the analyses. Results indicate that, although social influence plays a less significant role than attributes of electric cars in the buying process, different elements of social networks exert an influence on peoples buying decisions. These effects vary between friends, relatives, colleagues, and the larger peer group. Moreover, the effects are nonlinear as the particular form of the part-worth utility function depends on the element of the social network. The latent decision to buy a car also depends on socio demographic variables. The results of this novel approach have implications for the design of stated-choice experiments.

Bounded Rational Choice Behaviour: Applications in Transport

The book is an attempt to stimulate development in travel behaviour analysis and provide a basic source of reference to the transportation research community. The aim of the book is to give centre stage to some recent innovative approaches to models of bounded rationality, both under conditions of certainty and uncertainty.

A hybrid choice model with a nonlinear utility function and bounded distribution for latent variables: application to purchase intention decisions of electric cars

ABSTRACT The hybrid choice model (HCM) provides a powerful framework to account for heterogeneity across decision-makers in terms of different underlying latent attitudes. Typically, effects of the latent attitudes have been represented assuming linear utility functions. In contributing to the further elaboration of HCMs, this study suggests an extended HCM framework allowing for nonlinear utility functions of choice alternatives including not only observed but also latent variables. Box–Cox transformations are used to represent the nonlinear utility function. Johnson’s SB distribution is suggested to represent the random term of the latent variables, satisfying the constraint of the Box–Cox transformation. An empirical study using stated choice data about the intention to purchase electric cars is conducted. The empirical results show that the proposed framework can capture nonlinear effects of underlying variables including latent attitudes, thereby enhancing the explanatory power of the choice model.

Exploring Passenger Assessments of Bus Service Quality Using Bayesian Networks

Studies on public transit have emphasized the role of passenger satisfaction with service quality in travel choice decisions and indicated that satisfaction depends on various service attributes. Few studies have, however, systematically examined the underlying relationships among service attributes to assess their influence on passenger overall satisfaction. Therefore, to contribute to this rapidly-emerging literature, this paper applies Bayesian networks to quantify the influence of each service aspect on passenger overall satisfaction with regular bus service quality. This analysis involved 609 passengers who participated in a 2013 regular bus service survey in Nanjing, China. The derived Bayesian network shows the relationships among service attributes and passenger overall satisfaction graphically. In particular, service aspects such as running on schedule, acceptable waiting time, available seats, clean onboard environment, pleasant environment at stations, convenient design for transfers, and air-conditioning were the key determinants of overall satisfaction with bus service.

Bias in random regret models due to measurement error: formal and empirical comparison with random utility model

ABSTRACT This study addresses the so-called uncertainty problem due to measurement error in random utility and random regret choice models. Based on formal analysis and empirical comparison, we provide new insights about the uncertainty problem in discrete choice modeling. First, we formally show how measurement error affects the random regret model differently from the random utility model. Then, random measurement error is introduced into level-of-service variables and the effect of measurement error is analyzed by comparing the estimated parameters of the concerned choice models, before and after introducing measurement error. We argue that although measurement error leads to biased estimation results in both types of models, uncertainty tends to accumulate in random regret models because this model involves a comparison of alternatives. Therefore, input uncertainty tends to lead to larger bias in random regret models. Moreover, since random regret models assume semi-compensatory decision processes, bias in random utility models is homogenous across individuals and alternatives, while bias in random regret models is heterogeneous. Several approaches are discussed to overcome this uncertainty problem in random regret models.

Using emulators to approximate predicted performance indicators of complex microsimulation and multiagent models of travel demand

Abstract The very high run times associated with microsimulation and agent based models of travel demand preclude their application in real time policy development sessions in which discussions require fast turnaround times of model simulation results. This paper suggests and explores the use of emulators to increase the possibilities of using large scale simulation models in real time policy development sessions. An emulator is a statistical approximation of a large scale model, depicting the relationship between a set of input variables and an output variable of a large scale model. To illustrate the approach, an emulator is built for the Albatross model system with special reference to its daily distance traveled per person performance indicator. Results are satisfactory and show that simple main effects plus first order interaction effects of regression model can approximate daily distance traveled per person, simulated by the considerably more complex large scale Albatross model system, very accurately. The paper also examines the impact of the number of simulation runs on the performance of the emulator. Results suggest that the accuracy of the emulator improves, albeit in a non-linear fashion, with an increasing number of simulation runs. Implications and avenues of future research are discussed.