The intention to notice : the collection, the tour and ordinary landscapes

The Intention to Notice: the collection, the tour and ordinary landscapes is concerned with how ordinary landscapes and places are enabled and conserved through making itineraries that are framed around the ephemera encountered by chance, and the practices that make possible the endurance of these material traces. Through observing and then examining the material and temporal aspects of a variety of sites/places, the museum and the expanded garden are identified as spaces where the expression of contemporary political, ecological and social attitudes to cultural landscapes can be realised through a curatorial approach to design, to effect minimal intervention. Three notions are proposed to encourage investigation into contemporary cultural landscapes: To traverse slowly to allow space for speculations framed by the topographies and artefacts encountered; to [re]make/[re]write cultural landscapes as discursive landscapes that provoke the intention to notice; and to reveal and conserve the fabric of everyday places. A series of walking, recording and making projects undertaken across a variety of cultural landscapes in remote South Australia, Melbourne, Sydney, London, Los Angeles, Chandigarh, Padova and Istanbul, investigate how communities of practice are facilitated through the invitation to notice and intervene in ordinary landscapes, informed by the theory and practice of postproduction and the reticent auteur. This community of practice approach draws upon chance encounters and it seeks to encourage creative investigation into places. The Intention to Notice is a practice of facilitating that also leads to recording traces and events; large and small, material and immaterial, that encourages both conjecture and archive. Most importantly, there is an open-ended invitation to commit and exchange through design interaction.

Inside the pleasure dome : cultural tourism on Australia’s Gold Coast

The City of the Gold Coast in Queensland, Australia, will host the Commonwealth Games in 2018. In advance of the Games, the City is beginning to reposition the traditional marketing programs that were based around the four S’s- ‘sun, sand, surf and sex.’ There is a new emphasis on urban sophistication, sport, science, education and the environment. At the same time, local communities are asking for renewed attention to residential issues, particularly relating to recognising the importance of culture to the region. In this paper I explore the development of integrated computer technologies (ICTs) as a way of linking tourism, culture and place in the experience economy of the Gold Coast. The discussion is framed by theories of the post-tourist, contemporary cultural tourism and the role of mobile technologies, and the figure of the ‘referential tourist.’ An examination of stakeholder responses to changing business and social frameworks on the Gold Coast shows how discussions about a range of issues coalesce around cultural tourism. Local communities have the opportunity to engage with the new tourist as they move quickly between leisure and cultural experiences, at once connected to tourist expectations but increasingly self-directed. The Surfers Paradise Nights campaign, which is based around social media, is a case in point. This campaign aims to interest visitors in becoming a part of a familiar third place, an online space, but one that will sustain an emotive connection to the physical location and events. The paper also draws on research carried out in Brisbane, Queensland, in relation to building connections between place and culture on designated, self-directed journeys via iPhone technology. Participant responses indicate the importance of narrative to developing cultural frameworks.

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

Retrieving dynamic origin-destination matrices from Bluetooth data

The Bluetooth technology is being increasingly used, among the Automated Vehicle Identification Systems, to retrieve important information about urban networks. Because the movement of Bluetooth-equipped vehicles can be monitored, throughout the network of Bluetooth sensors, this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. Some of the main challenges inherent to Bluetooth data are, first, that Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be estimated. Second, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold: to give an overview of the issues inherent to the Bluetooth technology, through the analysis of the data available from the Bluetooth sensors in Brisbane; and to propose a method for retrieving the itineraries of the individual Bluetooth vehicles. We argue that estimating these latent itineraries, accurately, is a crucial step toward the retrieval of accurate dynamic Origin Destination Matrices.

Transit passenger segmentation using travel regularity mined from Smart Card transactions data

Transit passenger market segmentation enables transit operators to target different classes of transit users to provide customized information and services. The Smart Card (SC) data, from Automated Fare Collection system, facilitates the understanding of multiday travel regularity of transit passengers, and can be used to segment them into identifiable classes of similar behaviors and needs. However, the use of SC data for market segmentation has attracted very limited attention in the literature. This paper proposes a novel methodology for mining spatial and temporal travel regularity from each individual passenger’s historical SC transactions and segments them into four segments of transit users. After reconstructing the travel itineraries from historical SC transactions, the paper adopts the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm to mine travel regularity of each SC user. The travel regularity is then used to segment SC users by an a priori market segmentation approach. The methodology proposed in this paper assists transit operators to understand their passengers and provide them oriented information and services.

Passenger segmentation using smart card data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.

Retrieving trip information from a discrete detectors network : The case of Brisbane Bluetooth detectors

Automatic Vehicle Identification Systems are being increasingly used as a new source of travel information. As in the last decades these systems relied on expensive new technologies, few of them were scattered along a networks making thus Travel-Time and Average Speed estimation their main objectives. However, as their price dropped, the opportunity of building dense AVI networks arose, as in Brisbane where more than 250 Bluetooth detectors are now installed. As a consequence this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed. Some of these problems stem from the structure of a network made out of isolated detectors itself while others are inherent of Bluetooth technology (overlapping detection area, missing detections,\...). The aim of this paper is threefold: First, after having presented the level of details that can be reached with a network of isolated detectors we present how we modelled Brisbane's network, keeping only the information valuable for the retrieval of trip information. Second, we give an overview of the issues inherent to the Bluetooth technology and we propose a method for retrieving the itineraries of the individual Bluetooth vehicles. Last, through a comparison with Brisbane Transport Strategic Model results, we highlight the opportunities and the limits of Bluetooth detectors networks. The aim of this paper is twofold. We first give a comprehensive overview of the aforementioned issues. Further, we propose a methodology that can be followed, in order to cleanse, correct and aggregate Bluetooth data. We postulate that the methods introduced by this paper are the first crucial steps that need to be followed in order to compute accurate Origin-Destination matrices in urban road networks.