Travel context : development of a model to measure destination brand performance across different travel situations

Purpose: There is a lack of theory relating to destination brand performance measurement in the destination branding literature, which emerged in the late 1990s (see for example Dosen, Vransevic, & Prebezac, 1998). Additionally, there is a lack of research about the importance of travel context in consumers’ destination decision making (Hu & Ritchie, 1993). This study develops a structural model to measure destination brand performance across different travel situations. The theory of planned behaviour (TpB) was utilised as a framework to underpin the consumer-based brand equity (CBBE) hierarchy to develop a model of destination brand performance. Research approach: A proposed model of destination brand performance was developed through a review of the literature. The first study was used to identify destination image attributes (the core construct) using an analysis of the literature, a document analysis, and personal interviews using the Repertory Test qualitative technique. Underpinned by Personal Construct Theory (PCT), the Repertory Test enables the elicitation of attributes consumers use to evaluate destinations when considering travel. Data was examined in the first study to i) identify any attribute differences in travel contexts and ii) create a scale for use in a questionnaire. A second study was conducted to test the proposed model using a questionnaire with eight groups of participants to assess four destinations across two travel contexts. The model was tested utilising structural equation modelling. Findings: The first study resulted in a list of 29 destination image attributes for use in a scale index. Attributes were assessed across travel contexts and few differences were identified. The second study assessed the congruence of destination brand identity (the destination marketing organisation’s desired image) and destination brand image (the actual perceptions held by consumers) using importance-performance analyses. Finally, the proposed model of destination brand performance was tested. Overall the data supported the model of destination brand performance across travel contexts and destinations. Additionally, this was compared to consumers’ decision sets, further supporting the model. Value: This research provides a contribution to the destination marketing literature through the development of a measurement of destination brand performance underpinned by TpB. Practically; it will provide destination marketing organisations with a tool to track destination brand performance, relative to key competing places, over time. This is important given the development of a destination brand is a long term endeavour.

Travel writing

This chapter introduces techniques that are used in travel writing to create a strong sense of place and a meaningful, engaging narrative of a journey. It raises and defines terms of modern rhetoric to show that a distinctive and enduring feature of travel writing lies in the ways it mixes modes of writing. Towards the end, the chapter offers ways of effectively unifying elements of travel writing.

Self-proxy agreement and weekly school travel behaviour in a sectarian divided society

Proxy reports from parents and self-reported data from pupils have often been used interchangeably to identify factors influencing school travel behaviour. However, few studies have examined the validity of proxy reports as an alternative to self-reported data. In addition, despite research that has been conducted in a different context, little is known to date about the impact of different factors on school travel behaviour in a sectarian divided society. This research examines these issues using 1624 questionnaires collected from four independent samples (e.g. primary pupils, parent of primary pupils, secondary pupils, and parent of secondary pupils) across Northern Ireland. An independent sample t test was conducted to identify the differences in data reporting between pupils and parents for different age groups using the reported number of trips for different modes as dependent variables. Multivariate multiple regression analyses were conducted to then identify the impacts of different factors (e.g. gender, rural–urban context, multiple deprivations, and school management type, net residential density, land use diversity, intersection density) on mode choice behaviour in this context. Results show that proxy report is a valid alternative to self-reported data, but only for primary pupils. Land use diversity and rural–urban context were found to be the most important factors in influencing mode choice behaviour.

Rule-based transit signal priority control method using a real-time transit travel time prediction model

An advanced rule-based Transit Signal Priority (TSP) control method is presented in this paper. An on-line transit travel time prediction model is the key component of the proposed method, which enables the selection of the most appropriate TSP plans for the prevailing traffic and transit condition. The new method also adopts a priority plan re-development feature that enables modifying or even switching the already implemented priority plan to accommodate changes in the traffic conditions. The proposed method utilizes conventional green extension and red truncation strategies and also two new strategies including green truncation and queue clearance. The new method is evaluated against a typical active TSP strategy and also the base case scenario assuming no TSP control in microsimulation. The evaluation results indicate that the proposed method can produce significant benefits in reducing the bus delay time and improving the service regularity with negligible adverse impacts on the non-transit street traffic.

Design of open source framework for traffic and travel simulation

For the evaluation, design, and planning of traffic facilities and measures, traffic simulation packages are the de facto tools for consultants, policy makers, and researchers. However, the available commercial simulation packages do not always offer the desired work flow and flexibility for academic research. In many cases, researchers resort to designing and building their own dedicated models, without an intrinsic incentive (or the practical means) to make the results available in the public domain. To make matters worse, a substantial part of these efforts pertains to rebuilding basic functionality and, in many respects, reinventing the wheel. This problem not only affects the research community but adversely affects the entire traffic simulation community and frustrates the development of traffic simulation in general. For this problem to be addressed, this paper describes an open source approach, OpenTraffic, which is being developed as a collaborative effort between the Queensland University of Technology, Australia; the National Institute of Informatics, Tokyo; and the Technical University of Delft, the Netherlands. The OpenTraffic simulation framework enables academies from geographic areas and disciplines within the traffic domain to work together and contribute to a specific topic of interest, ranging from travel choice behavior to car following, and from response to intelligent transportation systems to activity planning. The modular approach enables users of the software to focus on their area of interest, whereas other functional modules can be regarded as black boxes. Specific attention is paid to a standardization of data inputs and outputs for traffic simulations. Such standardization will allow the sharing of data with many existing commercial simulation packages.

The development of a model to measure destination brand performance across travel contexts

The field of destination image has been widely discussed in the destination literature since the early 1970s (see Mayo, 1973). However the extent to which travel context impacts on an individual’s destination image evaluation, and therefore destination choice, has received scant attention (Hu & Ritchie, 1993). This study, utilising expectancy-value theory, sought to elicit salient destination attributes from consumers across two travel contexts: short-break holidays and longer getaways. Using the Repertory Test technique, attributes elicited as being salient for short-break holidays were consistent with those elicited for longer getaways. While this study was limited to Brisbane’s near-home destinations, the results will be of interest to destination marketers and researchers interested in the challenge of positioning a destination in diverse markets.

Average travel time estimations for urban routes that consider exit turning movements

This paper presents a methodology for real-time estimation of exit movement-specific average travel time on urban routes by integrating real-time cumulative plots, probe vehicles, and historic cumulative plots. Two approaches, component based and extreme based, are discussed for route travel time estimation. The methodology is tested with simulation and is validated with real data from Lucerne, Switzerland, that demonstrate its potential for accurate estimation. Both approaches provide similar results. The component-based approach is more reliable, with a greater chance of obtaining a probe vehicle in each interval, although additional data from each component is required. The extreme-based approach is simple and requires only data from upstream and downstream of the route, but the chances of obtaining a probe that traverses the entire route might be low. The performance of the methodology is also compared with a probe-only method. The proposed methodology requires only a few probes for accurate estimation; the probe-only method requires significantly more probes.

Benefits and issues for bus travel time estimation and prediction

Bus travel time estimation and prediction are two important modelling approaches which could facilitate transit users in using and transit providers in managing the public transport network. Bus travel time estimation could assist transit operators in understanding and improving the reliability of their systems and attracting more public transport users. On the other hand, bus travel time prediction is an important component of a traveller information system which could reduce the anxiety and stress for the travellers. This paper provides an insight into the characteristic of bus in traffic and the factors that influence bus travel time. A critical overview of the state-of-the-art in bus travel time estimation and prediction is provided and the needs for research in this important area are highlighted. The possibility of using Vehicle Identification Data (VID) for studying the relationship between bus and cars travel time is also explored.

Bus and car travel time on urban networks : integrating Bluetooth and bus vehicle identification data

Travel time in an important transport performance indicator. Different modes of transport (buses and cars) have different mechanical and operational characteristics, resulting in significantly different travel behaviours and complexities in multimodal travel time estimation on urban networks. This paper explores the relationship between bus and car travel time on urban networks by utilising the empirical Bluetooth and Bus Vehicle Identification data from Brisbane. The technologies and issues behind the two datasets are studied. After cleaning the data to remove outliers, the relationship between not-in-service bus and car travel time and the relationship between in-service bus and car travel time are discussed. The travel time estimation models reveal that the not-in-service bus travel time are similar to the car travel time and the in-service bus travel time could be used to estimate car travel time during off-peak hours

Freeway travel time estimation & prediction : a literature review

This report is the second deliverable of the Real Time and Predictive Traveller Information project and the first deliverable of the Freeway Travel Time Information sub-project in the Integrated Traveller Information research Domain of the Smart Transport Research Centre. The primary objective of the Freeway Travel Time Information sub-project is to develop algorithms for real-time travel time estimation and prediction models for Freeway traffic. The objective of this report is to review the literature pertaining to travel time estimation and prediction models for freeway traffic.

Arterial travel time estimation & prediction : a literature review

This report is the fourth deliverable of the Real Time and Predictive Traveller Information project and the first deliverable of the Arterial Travel Time Information sub-project in the Integrated Traveller Information research Domain of the Smart Transport Research Centre. The primary objective of the Arterial Travel Time Information sub-project is to develop algorithms for real-time travel time estimation and prediction models for arterial traffic. The objective of this report is to review the literature pertaining to travel time estimation and prediction models for arterial traffic.

Fundamental understanding on the use of Bluetooth MAC scanner for arterial travel time estimation

This report is the eight deliverable of the Real Time and Predictive Traveller Information project and the third deliverable of the Arterial Travel Time Information sub-project in the Integrated Traveller Information research Domain of the Smart Transport Research Centre. The primary objective of the Arterial Travel Time Information sub-project is to develop algorithms for real-time travel time estimation and prediction models for arterial traffic. Brisbane arterial network is highly equipped with Bluetooth MAC Scanners, which can provide travel time information. Literature is limited with the knowledge on the Bluetooth protocol based data acquisition process and accuracy and reliability of the analysis performed using the data. This report expands the body of knowledge surrounding the use of data from Bluetooth MAC Scanner (BMS) as a complementary traffic data source. A multi layer simulation model named Traffic and Communication Simulation (TCS) is developed. TCS is utilised to model the theoretical properties of the BMS data and analyse the accuracy and reliability of travel time estimation using the BMS data.

Empirical evaluation of public transport travel time variability

Although transit travel time variability is essential for understanding the deterioration of reliability, optimising transit schedule and route choice; it has not attracted enough attention from the literature. This paper proposes public transport-oriented definitions of travel time variability and explores the distributions of public transport travel time using the Transit Signal Priority data. First, definitions of public transport travel time variability are established by extending the common definitions of variability in the literature and by using route and services data of public transport vehicles. Second, the paper explores the distribution of public transport travel time. A new approach for analysing the distributions involving all transit vehicles as well as vehicles from a specific route is proposed. The Lognormal distribution is revealed as the descriptors for public transport travel time from the same route and service. The methods described in this study could be of interest for both traffic managers and transit operators for planning and managing the transit systems.

Mining temporal and spatial travel regularities for transit planning

Smart Card data from Automated Fare Collection system has been considered as a promising source of information for transit planning. However, literature has been limited to mining travel patterns from transit users and suggesting the potential of using this information. This paper proposes a method for mining spatial regular origins-destinations and temporal habitual travelling time from transit users. These travel regularity are discussed as being useful for transit planning. After reconstructing the travel itineraries, three levels of Density-Based Spatial Clustering of Application with Noise (DBSCAN) have been utilised to retrieve travel regularity of each of each frequent transit users. Analyses of passenger classifications and personal travel time variability estimation are performed as the examples of using travel regularity in transit planning. The methodology introduced in this paper is of interest for transit authorities in planning and managements