Understanding transport mode choice for commuting: the role of affect

This study examines the relationship between positive and negative user valence and transport mode choice behaviour. We integrate latent attitudes ‘affect’ and ‘salience’ into transport mode choice models using the framework of integrated choice and latent variable modelling and simultaneous maximum likelihood estimation methods. The results are consistent with findings in similar travel behaviour and behavioural economics literature. The study extends the findings of previous research and has demonstrated that user sentiments about public transport mode and salient public transport experiences have a significant impact on travel mode choice behaviour. It was found that private motorised users are more sensitive to overcrowding and anti-social behaviours on PT than active and PT travellers. Key attitudinal indicators influencing individual transport choice behaviour are established to guide public policy. The key indicators of Affect and Salience must be analysed and addressed through public policy to enhance PT user experience and develop services and facilities to increase the utility of PT in-vehicle travel time.     

Semiparametric specification of the utility function in a travel mode choice model

We propose a semiparametric approach that can capture the nonlinearity of deterministic components of the utility functions in discrete choice models and demonstrate it by analyzing travel mode choice behaviour for an interregional trip. The proposed smoothing spline-based specification method can be used to make ex ante evaluations regarding the parametric specifications of the deterministic utility functions in discrete choice models.     

Integrating parking behaviour in activity-based travel demand modelling: Investigation of the relationship between parking type choice and activity scheduling process

Traditionally, the parking choice/option is considered to be an important factor in only in the mode choice component of a four-stage travel demand modelling system. However, travel demand modelling has been undergoing a paradigm shift from the traditional trip-based approach to an activity-based approach. The activity-based approach is intended to capture the influences of different policy variables at various stages of activity-travel decision making processes. Parking is a key policy variable that captures land use and transportation interactions in urban areas. It is important that the influences of parking choice on activity scheduling behaviour be identified fully. This paper investigates this issue using a sample data set collected in Montreal, Canada. Parking type choice and activity scheduling decision (start time choice) are modelled jointly in order to identify the effects of parking type choice on activity scheduling behaviour. Empirical investigation gives strong evidence that parking type choice influences activity scheduling process. The empirical findings of this investigation challenge the validity of the traditional conception which considers parking choice as exogenous variable only in the mode choice component of travel demand models.     

Investigation and analysis of evidence of asymmetric churn in travel demand models

There is a large amount of research work that has been devoted to the understanding of travel behaviour and for the prediction of travel demand and its management. Different types of data including stated preference and revealed preference, as well as different modelling approaches have been used to predict this. Essential to most travel demand forecasting models are the concepts of utility maximisation and equilibrium, although there have been alternative approaches for modelling travel behaviour. In this paper, the concept of asymmetric churn is discussed. That is travel behaviour should be considered as a two way process which changes over time. For example over time some travellers change their mode of travel from car to bus, but more travellers change their mode from bus to car. These changes are not equal and result in a net change in aggregate travel behaviour. Transport planners often aim at producing this effect in the opposite direction. It is important therefore to recognise the existence of churns in travel behaviour and to attempt to develop appropriate policies to target different groups of travellers with the relevant transport policies in order to improve the transport system. A data set collected from a recent large survey, which was carried out in Edinburgh is investigated to analyse the variations in departure time choice behaviour. The paper reports on the results of the investigation.     

A practical approach to identifying the market potential for high speed rail: A case study in the Sydney-Canberra corridor

This paper presents the methodology and selective empirical results from a study of the demand for a high speed rail system serving the Sydney-Canberra corridor currently dominated by air travel for business trips and car travel for non-business trips. We outline the steps involved in the study from problem specification, data needs, development of base year trip tables, model specification and estimation to establish switching behaviour in the presence of a new mode and calculation of induced demand for current travellers. A stated choice heteroskedastic extreme value switching model is used to evaluate the choice of fare type for business and non-business travel given the current mode used in the corridor for each sampled traveller conventional train, charter coach, scheduled coach, plane or car. Starting with the current travel profile, patronage can be predicted under alternative fare regimes, taking into account diverted traffic, induced traffic and growth. Treating fare class as endogenous enhances the real choice context facing potential patrons.     

Choice of parking: Stated preference approach

Over recent years, parking policy has become a key element of transport policy in many countries. Parking policy measures can affect many different dimensions of travel behaviour but are likely to be most significant in terms of travellers' choice of parking type and location. This dimension of travel choice has, to date, received comparatively little attention, yet is of vital importance if we are to properly understand and predict the effects of parking policy measures.This paper presents two studies addressing this issue carried out in the United Kingdom and Germany. Both studies used a stated preference approach in order to collect disaggregate data on travellers responses to changes in parking attributes and used these data to build simple logit models of parking type choice. The studies were designed in order to allow comparable choice models to be estimated from the two datasets. The results obtained strongly indicate the need to separately identify the costs associated with different components of the parking activity (e.g., general in-vehicle time, parking search time, egress time) and also point to the existence of significant differences in the relative valuation of these components across different journey purposes. Where possible, the results of the choice modelling exercises are also compared with existing revealed and stated preference results and are found to be generally in line with prior expectations.     

Market segmentation as a mechanism in allowing for variability of traveller behaviour

A major problem with aggregate transport planning models is the accounting of variability in traveller behaviour when the basic unit of analysis is the geographical traffic zone. In an attempt to allow for this variance, recent attention has been given to the role of socio-economic (user and household) characteristics in systematically identifying a homogeneous grouping of travellers with respect to the issue under study rather than restricting the grouping definition according to physical geographical criteria alone. This homogeneous grouping criterion combined eventually with a necessity to represent travel demand in a spatial context, can assist in improving our ability to explain real travel patterns by the development of an improved aggregation condition. The emphasis is on modelling homogeneous groups of travellers separately, and then relating the individual sets of results to each other to obtain an aggregate prediction of behaviour via a knowledge of the representativeness of each group contained in the total sample. This paper presents a technique to identify the relative homogeneity of travellers in accordance with a specified criterion, and illustrates its use with individual household data for the Sydney Metropolitan Area. The paper concludes with a discussion of the advantages of segmentation in operational transport planning, in particular with reference to the aggregation of disaggregate behavioural travel choice models, or movement from a micro-model of individual choice behaviour to an aggregate model of travel demanu.     

Route Choice Behaviour of Freeway Travellers Under Real-time Traffic Information Provision – Application of the Best Route and the Habitual Route Choice Mechanisms

Abstract

This paper investigates route choice behaviour on freeways between Taipei and Taichung in Taiwan under the provision of real-time traffic information. Two types of route choice selection rules (the best-route and habitual-route) are analysed using ordered probit models to identify the major influences on freeway travellers’ route choice behaviour. The level of service associated with each route is defined as a generalised cost saving (GCS) and specified non-linearly with a threshold inherent to travellers. The marginal (dis)utility thresholds in the ‘best’ and ‘habitual’ behaviour models are identified through a goodness-of-fit grid. The results confirm that the thresholds for changing the inertia behaviour of drivers should be larger than the ones for choosing the best routes. In addition, the drivers are more likely to choose either the best or the habitual routes once the GCS are greater than the identified threshold values.     

Trip chain complexity: a comparison among latent classes of daily mobility patterns

This paper studies the relationship between trip chain complexity and daily travel behaviour of travellers. While trip chain complexity is conventionally investigated between travel modes, our scope is the more aggregated level of a person’s activity-travel pattern. Using data from the Netherlands Mobility Panel, a latent class cluster analysis was performed to group people with similar mode choice behaviour in distinct mobility pattern classes. All trip chains were assigned to both a travel mode and the mobility pattern class of the traveller. Subsequently, differences in trip chain complexity distributions were analysed between travel modes and between mobility pattern classes. Results indicate considerable differences between travel modes, particularly between multimodal and unimodal trip chains, but also between the unimodal travel modes car, bicycle, walking and public transport trip chains. No substantial differences in trip chain complexity were found between mobility pattern classes. Independently of the included travel modes, the distributions of trip chain complexity degrees were similar across mobility pattern classes. This means that personal circumstances such as the number of working hours or household members are not systematically translated into specific mobility patterns.

     

Not All Transfers Are Created Equal: Towards a Framework Relating Transfer Connectivity to Travel Behaviour

Abstract

Walking from origins to transit stops, transferring between transit lines and walking from transit stops to destinations—all add to the burden of transit travel, sometimes to a very large degree. Transfers in particular can be stressful and/or time‐consuming for travellers, discouraging transit use. As such, transit facilities that reduce the burdens of walking, waiting and transferring can substantially increase transit system efficacy and use. In this paper, we argue that transit planning research on transit stops and stations, and transit planning practice frequently lack a clear conceptual framework relating transit waits and transfers with what we know about travel behaviour. Therefore, we draw on the concepts of transfer penalties and value of time in the travel behaviour/economics literature to develop a framework that situates transfer penalties within the total travel generalized costs of a transit trip. For example, value of time is important in relating actual time of waiting and walking to the perceived time of travel. We also draw on research to classify factors most important to users’ perspectives and travel behaviour—transfer costs, time scheduling and five transfer facility attributes: (1) access, (2) connection and reliability, (3) information, (4) amenities, and (5) security and safety. Using this framework, we seek to explicitly relate improvements of transfer stops/stations with components of transfer penalties and changes in travel behaviour (through a reduction in transfer penalties). We conclude that the employment of such a framework can help practitioners better apply the most effective improvements to transit stops and transfer facilities.     

Is car drivers’ response to congestion charging schemes based on the correct perception of price signals?

This paper deals with the question of whether the capability of car drivers to estimate the cost of a new hypothetical, highly differentiated congestion charge influences their decision to change travel behaviour. The analysis makes use of an integrated choice and latent variable model (ICLV) which merges classic choice models with the structural equation approach (SEM) for latent variables. This hybrid model improves the explanatory power considerably compared with a conventional discrete choice model. The results suggest that charge complexity decreases the resistance in considering behavioural changes. Car drivers tend to avoid a travel option where the price is not known beforehand, a phenomenon known as ambiguity avoidance.     

Towards integrated traveller information

Traveller information provision has become a key government priority in the UK following the publication of its Transport White Paper in July 1998. Developments are already underway for the integration of information systems across different public transport modes and between public transport information systems covering different regions. The Highways Agency (which is responsible for managing the strategic road network in the UK) is also advancing in its development of information databases and systems for the motorist. This paper considers the prospect of providing travellers with multimodal information with integration of driver information with public transport information. If such integration can be achieved then travellers, and in particular drivers, have the prospect of being presented with comparable information on travel options across modes. An integrated information service has a great potential to inform and influence travel choices. The paper considers the issue of travel choices and the role that information can play particularly in the context of mode choice. The political (UK) and technological climate for information provision is outlined. The paper identifies issues that will need to be addressed to identify the requirements from, and potential benefits of, integrated information, and the obstacles and challenges likely to be faced in moving towards it. Driver reactions, in particular, to the prospect of integrated information provision are fundamental to the assessment of its potential importance and value.     

Do supra-modal traveller types exist? A travel behaviour market segmentation using Goal framing theory

Travel behaviour market segmentations have become a popular method of identifying different types of car users, bicyclists or public transport users. However, while previous studies have looked at different types of users within single modes, such as the car, little research has explored the existence of traveller types transcending modes. The study presented here is an extension of an earlier segmentation study that distinguished travellers based on their individual preferences, yet did so independent of their current mode choice. The data came from a travel survey at a middle-sized UK university and were analysed using a combination of hierarchical and iterative partitioning methods. Crucially, however, the current study uses a different theoretical framework to previous segmentation research—goal framing theory—which may more adequately explain the findings than models used in the past such as the theory of planned behaviour. The findings supported earlier work, suggesting the presence of seemingly stable traveller types that cut across modes and can be distinguished based on gain, hedonic and normative goals. This has important implications for policies aimed at encouraging mode change which may have been too preoccupied with changing people’s attitudes rather than paying attention to people’s underlying travel preferences.

     

Examining the Impacts of Residential Self‐Selection on Travel Behaviour: A Focus on Empirical Findings

Abstract

Numerous studies have found that suburban residents drive more and walk less than residents in traditional neighbourhoods. What is less well understood is the extent to which the observed patterns of travel behaviour can be attributed to the residential built environment (BE) itself, as opposed to attitude‐induced residential self‐selection. To date, most studies addressing this self‐selection issue fall into nine methodological categories: direct questioning, statistical control, instrumental variables, sample selection, propensity score, joint discrete choice models, structural equations models, mutually dependent discrete choice models and longitudinal designs. This paper reviews 38 empirical studies using these approaches. Virtually all of the studies reviewed found a statistically significant influence of the BE remaining after self‐selection was accounted for. However, the practical importance of that influence was seldom assessed. Although time and resource limitations are recognized, we recommend usage of longitudinal structural equations modelling with control groups, a design which is strong with respect to all causality requisites.     

“I can do perfectly well without a car!”

This article presents the results of a study exploring travellers’ preferences for middle-distance travel using Q-methodology. Respondents rank-ordered 42 opinion statements regarding travel choice and motivations for travel in general and for car and public transport as alternative travel modes. By-person factor analysis revealed four distinct preference segments for middle-distance travel: (1) choice travellers with a preference for public transport, (2) deliberate-choice travellers, (3) choice travellers with car as dominant alternative, and (4) car-dependent travellers. These preference segments differ in terms of the levels of involvement and cognitive effort in travel decision making, the travel consideration-set and underlying motivations. The study showed that for most people there is more to travel than getting from point A to point B, and that there is considerable heterogeneity in middle-distance travel preferences. Policy implications for reducing the need for travel and promoting a modal shift from car to other travel modes are discussed.     

A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice

The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling.     

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.     

Direct versus indirect models for the effects of unreliability

Two distinguishable modelling approaches exist for modelling the attitudes of travellers to the unexpected day-to-day variability of travel times. The direct approach sees the extent of travel time variability (TTV) as the variable that travellers react to, whereas the indirect approach claims that TTV effects are fully explained by trip scheduling considerations. Past research has not yet overcome the issue of which of these concepts is preferable, especially for public transport users. In the current paper, factors affecting bus users’ scheduling behaviour and attitudes to TTV are investigated, based on a survey among bus users in the city of York, England. The survey methodology and its Internet-based design are described. The results confirm that the influence of TTV on bus users is best explained indirectly through scheduling considerations. The penalty placed on early arrival to the destination is found similar to the penalty on travel time itself; late arrivals are much more heavily penalised. Since the common treatment of TTV in practice is through models that ignore the effect of lateness and earliness, we also examine how using the simple approach rather than the correct one affects the economic interpretation of TTV; the results reveal a massive bias.