Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. Focussing on pollution reduction, a tool (CONDUITS-DST) integrating the respective KPIs with microsimulation modelling is developed. The paper describes the integration process, including the model chosen for calculating the emissions levels of a number of scenarios, presents the results of the application to a case study in the city of Brussels, and outlines future developments targeted at broadening the integration of the KPIs into decision-makin

Using Key Performance Indicators for multi-criteria traffic management strategic decisions

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. The first stage of the extension focused on pollution reduction, and a novel decision support tool (CONDUITS_DST) integrating the respective KPI with micro-simulation modelling was developed. Case studies executed in Brussels and Zurich demonstrated the usability and viability of the tool. This paper takes the development one step further and reports on the extension of the approach, which moves from single-criterion to a multi-criteria decision support tool through the inclusion of the KPI on traffic efficiency, again based onmicro-simulation modelling outputs

Development and application of an evaluation framework for urban traffic management and Intelligent Transport Systems

The aim of this paper is to present and apply a new evaluation framework for traffic management and Intelligent Transport Systems, to assist urban transport authorities in assessing relevant policies and technologies as to their performance. The principles behind performance measures and indices are outlined, along with a description of theframework development methodology. Two Key Performance Indicators (KPIs) for the topics of mobility and traffic accidents respectively are formulated and operative definitions are presented. Then, the new KPIs are applied to a case study in the city of Paris, involving the introduction of a scheme granting priority to buses at signalised junctions. The results from the before- and after-analysis are reported and interpreted, not only in terms of the case study itself, but most importantly from the standpoint of the applicability of the evaluation framework

Automating a framework to extract and analyse transport related social media content: The potential and the challenges

Harnessing the potential of new generation transport data and increasing public participation are high on the agenda for transport stakeholders and the broader community. The initial phase in the program of research reported here proposed a framework for mining transport-related information from social media, demonstrated and evaluated it using transport-related tweets associated with three football matches as case studies. The goal of this paper is to extend and complement the previous published studies. It reports an extended analysis of the research results, highlighting and elaborating the challenges that need to be addressed before a large-scale application of the framework can take place. The focus is specifically on the automatic harvesting of relevant, valuable information from Twitter. The results from automatically mining transport related messages in two scenarios are presented i.e. with a small-scale labelled dataset and with a large-scale dataset of 3.7 m tweets. Tweets authored by individuals that mention a need for transport, express an opinion about transport services or report an event, with respect to different transport modes, were mined. The challenges faced in automatically analysing Twitter messages, written in Twitter’s specific language, are illustrated. The results presented show a strong degree of success in the identification of transport related tweets, with similar success in identifying tweets that expressed an opinion about transport services. The identification of tweets that expressed a need for transport services or reported an event was more challenging, a finding mirrored during the human based message annotation process. Overall, the results demonstrate the potential of automatic extraction of valuable information from tweets while pointing to areas where challenges were encountered and additional research is needed. The impact of a successful solution to these challenges (thereby creating efficient harvesting systems) would be to enable travellers to participate more effectively in the improvement of transport services

The potential of social media in delivering transport policy goals

Information flow plays a central role in the development of transport policy, transport planning and the effective operation of the transport system. The recent upsurge in web enabled and pervasive technologies offer the opportunity of a new route for dynamic information flow that captures the views, needs and experiences of the travelling public in a timely and direct fashion through social media text posts. To date there is little published research, however, on how to realize this opportunity for the sector by capturing and analysing the text data. This paper provides an overview of the different categories of social media, the characteristics of its content and how these characteristics are reflected in transport-related posts. The research described in this paper includes a formulation of the goals for harvesting transport-related information from social media, the hypotheses to be tested to demonstrate that such information can provide valuable input to transport policy development or delivery and the challenges this involves. A hierarchical approach for categorizing transport-related information harvested from social media is presented. An explanatory study was designed, based on the understanding of the nature of social media content, the goals in harvesting it for transport planning and management purposes and existing text mining techniques. An exploratory case study is used to illustrate the process based on Twitter posts associated with particular UK sporting fixtures (i.e. football matches). The results demonstrate both the volume and pertinence of the information obtained. Whilst text-mining techniques have been applied in a number of other sectors (notably entertainment, business and the political arena), the use of information in the transport sector has some unique features that stem from both day-to-day operational practices and the longer term decisionmaking processes surrounding the transport system – hence the significance and novelty of the results reported here. Many challenges in refining the methodology and techniques remain for future research, however the outcomes presented here are of relevance to a wide range of stakeholders in the transport and text mining fields

Efficacy of Mining Social Media Data for Transport Policy and Practice

The overarching question of whether social media (SM) can produce information of sufficient quality to meet the needs of the transport system planners and operators, policy makers and travellers forms the core of this paper. Three sub themes are investigated, focusing primarily on SM text data and the perspective of transport authorities. A typology of seven primary transport data needs, current data sources and SM sources illustrates advantages of SM data in particular contexts. Following an overview of the text mining process, a review of four main challenges this holds for the transport domain is given. These include issues concerning ontologies, sentiment analysis, location names and measuring accuracy. Finally a review of academic and soft literature has highlighted institutional issues in the use of SM concluding that potential uses of SM information have not yet been explored to their full valu

Transport Policy : Social Media and User-Generated Content in a Changing Information Paradigm

Rapid and recent developments in social media networks are providing a vision amongst transport suppliers, governments and academia of \u2018next-generation\u2019 information channels. This chapter identifies the main requirements for a social media information harvesting methodology in the transport context and highlights the challenges involved. Three questions are addressed concerning (1) The ways in which social media data can be used alongside or potentially instead of current transport data sources, (2) The technical challenges in text mining social media that create difficulties in generating high quality data for the transport sector and finally, (3) Whether there are wider institutional barriers in harnessing the potential of social media data for the transport sector. The chapter demonstrates that information harvested from social media can complement, enrich (or even replace) traditional data collection. Whilst further research is needed to develop automatic or semi-automatic methodologies for harvesting and analysing transportrelated social media information, new skills are also needed in the sector to maximise the benefits of this new information source

Using Key Performance Indicators for multi-criteria traffic management strategic decisions

In recent research work (FP7 CONDUITS) a performance evaluation framework for traffic management and Intelligent Transport Systems was developed. The new framework consists of a set of Key Performance Indicators (KPIs) for the strategic themes of traffic efficiency, safety, pollution reduction and social inclusion, and the last stages of the project saw its validation through its application to four case studies. Following up from this work, this paper presents the extension of the framework for use as a prediction tool enabling urban transport authorities to assess the impacts of relevant policies and technologies before implementing them. The first stage of the extension focused on pollution reduction, and a novel decision support tool (CONDUITS_DST) integrating the respective KPI with micro-simulation modelling was developed. Case studies executed in Brussels and Zurich demonstrated the usability and viability of the tool. This paper takes the development one step further and reports on the extension of the approach, which moves from single-criterion to a multi-criteria decision support tool through the inclusion of the KPI on traffic efficiency, again based onmicro-simulation modelling outputs