Representing mental maps and cognitive learning in micro-simulation models of activity-travel choice dynamics

This paper develops a model, based on Bayesian beliefs networks, for representing mental maps and cognitive learning into micro-simulation models of activity-travel behavior. Mental maps can be used to address the problem that choice sets in models of travel demand are often ad hoc specified. The theory underlying the model is discussed, a specification is derived and numerical simulation is used to illustrate the properties of the model.     

Implicit choice set generation in discrete choice models: Application to household auto ownership decisions

Latent choice set models that account for probabilistic consideration of choice alternatives during decision making have long existed. The Manski model that assumes a two-stage representation of decision making has served as the standard workhorse model for discrete choice modeling with latent choice sets. However, estimation of the Manski model is not always feasible because evaluation of the likelihood function in the Manski model requires enumeration of all possible choice sets leading to explosion for moderate and large choice sets. In this study, we propose a new group of implicit choice set generation models that can approximate the Manski model while retaining linear complexity with respect to the choice set size. We examined the performance of the models proposed in this study using synthetic data. The simulation results indicate that the approximations proposed in this study perform considerably well in terms of replicating the Manski model parameters. We subsequently used these implicit choice set models to understand latent choice set considerations in household auto ownership decisions of resident population in the Southern California region. The empirical results confirm our hypothesis that certain segments of households may only consider a subset of auto ownership levels while making decisions regarding the number of cars to own. The results not only underscore the importance of using latent choice models for modeling household auto ownership decisions but also demonstrate the applicability of the approximations proposed in this study to estimate these latent choice set models.     

Leisure mobility styles in Swiss conurbations: construction and empirical analysis

Leisure travel is the most difficult travel purpose to analyse due to the lack of fixed spatial and temporal referents and the consequent flexibility in patterns. This paper addresses lifestyles, social influence, and the travellers’ social networks, issues that have proved valuable for travel behaviour research in confronting the complexity of leisure travel. An approach for constructing leisure mobility styles, based on orientations towards leisure and mobility, will be presented first and then the hypotheses that transport behaviour can be better explained through analysis of leisure mobility styles will be tested. Multivariate analysis reveals that the leisure mobility style group makes a significant contribution towards clarifying variance for the activities ‘Visiting friends and relatives’, ‘Travel participation’, ‘Mode choice’, and ‘Travel distance for leisure’. The use of leisure mobility styles is most useful for developing practical intervention pointers where the in-group homogeneity of lifestyle should be addressed in greater detail.     

Social networks,social influence and activity-travel behaviour: a review of models and empirical evidence

ABSTRACT

The study of social networks in activity-travel research has recently gained momentum because social activities and social influence were relatively poorly explained in activity-based models of travel demand. Over the last decade, many scholars have shown interest in identifying personal social networks that constitute an important source of explanation of activity-travel behaviour. This paper seeks to review two research streams: social networks and activity-travel behaviour, and social influence and travel decisions. We classify models, summarise empirical findings and discuss important issues that require further research.     

Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars

Recently, policy makers’ expectations about the role of electric cars in reducing emissions have risen substantially. In parallel, academic research on purchase intentions has dramatically increased. Originally, most studies have focused on utility attributes and price. More recently, several hybrid choice models have been estimated to include the impact of attitudes on choice probabilities. In addition, a few studies have caught the attention to social influence. In contributing to this line of research, this paper reports the results of an expanded hybrid choice, which simultaneously estimated all these different effects in a single integrated model of purchase intention. Results indicate that the model performs well. Costs considerations contribute most to the utility of electric cars. Social influence is less important, but there is also evidence that people tend to take it into consideration when there are positive public opinions about electric cars and the market share becomes almost half of friends of their social network. The intention to purchase an electric car is also influenced by attitudes about environmental concerns and technology acceptance.     

Incorporating social impact on new product adoption in choice modeling: A case study in green vehicles

While discrete choice analysis is prevalent in capturing consumer preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social – influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social networks on consumer adoption of new products. By introducing social influence attributes into a choice utility function, social network simulation is integrated with the traditional discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicles as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from the National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social networks may influence hybrid electric vehicle adoption.     

Modeling residential location choice in relation to housing location and road tolls on congested urban highway networks

We present a reformulation of the residential location submodel of the Integrated Model of Residential and Employment Location as a network equilibrium problem, thereby making travel costs by auto endogenous. The location of housing supply is examined as a welfare maximization problem for both user-optimal and system-optimal travel costs using concepts of bilevel programming. Finally, we briefly discuss how the employment submodel can be reformulated, and the entire model solved as a variational inequality problem.     

The effect of location,mobility and socio-demographic factors on task and time allocation of households

This paper investigates the role of location factors in task and time allocation at the household level. It is hypothesized that, if time constraints are less binding as a result of living in an urban area or owning more cars, spouses engage more often and longer in out-of-home activities and schedule their activities more independently. The hypotheses are tested with logistic and Cox regression models of activity participation and time allocation on a data set collected in the Amsterdam–Utrecht region in the Netherlands. Results suggest that the hypotheses are supported with respect to specific household activity scheduling decisions.     

The sequenced activity mobility simulator (SAMS): an integrated approach to modeling transportation, land use and air quality

The persistence of environmental problems in urban areas and the prospect of increasing congestion have precipitated a variety of new policies in the USA, with concomitant analytical and modeling requirements for transportation planning. This paper introduces the Sequenced Activity-Mobility Simulator (SAMS), a dynamic and integrated microsimulation forecasting system for transportation, land use and air quality, designed to overcome the deficiencies of conventional four-step travel demand forecasting systems. The proposed SAMS framework represents a departure from many of the conventional paradigms in travel demand forecasting. In particular, it aims at replicating the adaptative dynamics underlying transportation phenomena; explicitly incorporates the time-of-day dimension; represents human behavior based on the satisficing, as opposed to optimizing, principle; and endogenously forecasts socio-demographic, land use, vehicle fleet mix, and other variables that have traditionally been projected externally to be input into the forecasting process.     

Inference on mode preferences,vehicle purchases,and the energy paradox using a Bayesian structural choice model

Discrete choice modeling is experiencing a reemergence of research interest in the inclusion of latent variables as explanatory variables of consumer behavior. There are several reasons that motivate the integration of latent attributes, including better-informed modeling of random consumer heterogeneity and treatment of endogeneity. However, current work still is at an early stage and multiple simplifying assumptions are usually imposed. For instance, most previous applications assume all of the following: independence of taste shocks and of latent attributes, exclusion restrictions, linearity of the effect of the latent attributes on the utility function, continuous manifest variables, and an a priori bound for the number of latent constructs. We derive and apply a structural choice model with a multinomial probit kernel and discrete effect indicators to analyze continuous latent segments of travel behavior, including inference on the energy paradox. Our estimator allows for interaction and simultaneity among the latent attributes, residual correlation, nonlinear effects on the utility function, flexible substitution patterns, and temporal correlation within responses of the same individual. Statistical properties of the Bayes estimator that we propose are exact and are not affected by the number of latent attributes.     

Assessing the impact of long-term mobility choice motivation and short-term mobility means connotation on the use intention of electric cars in rural areas

In this study several hypotheses comprising a heuristic framework derived from rational-choice (RC) premises and regarding some potentially influencing variables on future use intention of different vehicle types are tested with a rural area sample. Especially the differentiation between long-term vs. short-term as well as functional/rational vs. extra-functional/emotional motivators is assessed. Results suggest a predominance of functional motivators and rational connotations over extra-functional/emotional ones. The models to check whether short-term or long-term effects dominate did not clearly confirm a predominance of long-term factors as hypothesized. In several regression models a moderating effect of rational short-term connotations on different long-term motivators was found, thus contributing notably to the prediction of future vehicle use-intention. The need for further research and theory-driven modeling is briefly discussed.     

Exploring public bicycle network structure based on complex network theory and shortest path analysis: the public bicycle system in Yixing,China

A well-functioning public bicycle system relates not only to its mode of operation, vehicle allocation, rental station layout and vehicle leasing configuration, but also the bicycle network structure and its formation. However, the latter aspects have been widely overlooked in China. Here, we help to further attract more researchers to conduct relevant studies and make suggestions for the development of public bicycle transport in many small and medium-sized cities across the world. We demonstrate how to explore the public bicycle network structure of a county-level Chinese city – Yixing – known for its clay ware and tourism. We show that complex network theory and shortest path analysis technology are useful in characterizing the public bicycle network structure, in aspects such as network topology, the spatial distribution of sub-networks and traffic flows. Finally, the paper proposes relevant urban planning strategies.     

Collecting a multi-dimensional three-weeks household time-use and activity diary in the Bandung Metropolitan Area,Indonesia

This paper describes a comprehensive panel data collection and analysis at household level, including detailed travel behaviour variables and comprehensive in-home and out-of-home activities, individual cognitive habits and affective behaviours, the rate of physical activity, as well as health related quality of life (QoL) information in the Bandung Metropolitan Area (BMA) of Indonesia. To our knowledge, this is the first attempt to collect an individual’s activity diary over an extended period as it captures the multi-tasking activities and multidisciplinary factors that underlie individual activity-travel patterns in a developing country. Preliminary analyses of the collected data indicate that different beliefs, anticipated emotions, support and attachment to motorised modes significantly correlate with different groups of occupation, gender, age, activity participation, multi-tasking activities, and physical health, but not with different social and mental health. This finding highlights the reason why implementing car reduction policies in Indonesia, without breaking or changing the individual’s habits and influencing his/her attitudes have not been fruitful. The results also show that endorsing more physical activities may result in a significant reduction in the individual’s motorised mode use, whilst individuals who demonstrate a tendency to use their spare time on social activities tend to have better social health conditions. Furthermore, undertaking multi-tasking out-of-home discretionary activities positively correlates with better physical health. All these highlight the importance of properly understanding and analysing the complex mechanisms that underlie these fundamental factors that shape individual daily activity-travel patterns in developing countries. This type of multidisciplinary approach is needed to design better transport policies that will not only promote better transport conditions, but also a healthier society with a better quality of life.     

Time-dependent congestion pricing system for large networks: Integrating departure time choice,dynamic traffic assignment and regional travel surveys in the Greater Toronto Area

Congestion pricing is one of the widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a framework for large-scale variable congestion pricing policy determination and evaluation. The proposed framework integrates departure time choice and route choice models within a regional dynamic traffic assignment (DTA) simulation environment. The framework addresses the impact of tolling on: (1) road traffic congestion (supply side), and (2) travelers’ choice dimensions including departure time and route choices (demand side). The framework is applied to a simulation-based case study of tolling a major freeway in Toronto while capturing the regional effects across the Greater Toronto Area (GTA). The models are developed and calibrated using regional household travel survey data that reflect the heterogeneity of travelers’ attributes. The DTA model is calibrated using actual traffic counts from the Ontario Ministry of Transportation and the City of Toronto. The case study examined two tolling scenarios: flat and variable tolling. The results indicate that: (1) more benefits are attained from variable pricing, that mirrors temporal congestion patterns, due to departure time rescheduling as opposed to predominantly re-routing only in the case of flat tolling, (2) widespread spatial and temporal re-distributions of traffic demand are observed across the regional network in response to tolling a significant, yet relatively short, expressway serving Downtown Toronto, and (3) flat tolling causes major and counterproductive rerouting patterns during peak hours, which was observed to block access to the tolled facility itself.