Most transportation agencies stipulate that an important planning goal is to provide equitable and just public transport services. However, who is to be served and the type of service that should be provided has been ambiguous. This paper develops a methodology for examining equity in the provision of public transportation services. An approach for identifying areas in need of public transport is developed based upon the use of socio-demographic and economic information. Public transport need is then related to levels of access to service. This approach makes it possible to establish the degree to which public transport services may be considered equitable in relation to need and suitable access. A detailed analysis of the southeast Queensland region of Australia illustrates how this approach may be used to inform public transport decision making. Copyright 2001 Blackwell Publishers

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This paper describes and evaluates privacy-friendly methods for extracting quasi-social networks from browser behavior on user-generated content sites, for the purpose of finding good audiences for brand advertising (as opposed to click maximizing, for example). Targeting social-network neighbors resonates well with advertisers, and on-line browsing behavior data counterintuitively can allow the identification of good audiences anonymously. Besides being one of the first papers to our knowledge on data mining for on-line brand advertising, this paper makes several important contributions. We introduce a framework for evaluating brand audiences, in analogy to predictive-modeling holdout evaluation. We introduce methods for extracting quasi-social networks from data on visitations to social networking pages, without collecting any information on the identities of the browsers or the content of the social-network pages. We introduce measures of brand proximity in the network, and show that audiences with high brand proximity indeed show substantially higher brand affinity. Finally, we provide evidence that the quasi-social network embeds a true social network, which along with results from social theory offers one explanation for the increases in audience brand affinity.

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Fire and rescue services are among the most critical public services provided by governments to protect people, property and the environment from fires and other emergencies. Efficient deployment of fire stations is essential to ensure timely response to calls for service. Given the geographic nature of such problems, spatial optimization approaches have long been employed in public facility location modeling along these lines. In particular, median and coverage approaches have been widely adopted to help achieve travel-cost and service-coverage goals, respectively. This paper proposes a bi-objective spatial optimization model that integrates coverage and median goals in the service of demand areas. Based on the properties of derived objective functions, we presented a constraint-based solution procedure to generate the Pareto frontier, enabling the identification of alternative fire station siting scenarios. The developed model is applied to an empirical study that seeks to identify the best fire station locations in Nanjing, China. The results demonstrate the value of spatial optimization in assisting fire station planning and rescue resource deployment, highlighting important policy implications.

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