Managing the environmental impacts of transport

Minimising the environmental impacts of the Victorian transport system has been a legislated objective since 2010, but the Department of Transport, Planning and Local Infrastructure has not adequately addressed this when developing the state\u27s strategic transport and land-use planning framework, according to this audit. Overview The environmental impacts of the Victorian transport system are significant and include the production of greenhouse gas emissions, other air pollution and noise. Minimising these impacts has been a legislated objective since 2010, but it is clear that the Department of Transport, Planning and Local Infrastructure (DTPLI) has not adequately addressed this when developing the state\u27s strategic transport and land-use planning framework (the strategic framework)—consisting of Plan Melbourne, Victoria—The Freight State and the state\u27s eight regional growth plans. During the strategic framework\u27s development, DTPLI did not provide the government with any advice: about how proposed strategies would address the environmental impacts of the transport system proposing defined statewide objectives or targets for reducing transport‑related greenhouse gases and other emissions and for limiting the effect of traffic noise. In the absence of DTPLI developing a comprehensive monitoring and reporting framework with clearly defined expected environmental outcomes and performance measures, the framework currently remains aspirational in this regard. Across the three agencies examined, VicRoads has the most comprehensive strategy for managing environmental impacts, and this is a model for what should exist on a portfolio-wide basis. Public Transport Victoria (PTV), on the other hand, does not have a dedicated plan and its performance in this regard has declined since our 2012 audit, Public Transport Performance. Specifically, PTV did not progress options identified by the former Department of Transport on how to improve public transport\u27s energy consumption and greenhouse gas emissions. Further, the quality and availability of publicly-reported information on public transport\u27s environmental performance has declined

Data-driven maintenance analysis of tramway network

One of the proven applications of digitalization in public transport in recent times is the improvement of maintenance strategies using data-driven methods. A robust maintenance strategy ensures the availability of assets to perform its designated operations maximizing the owner’s revenue at minimum costs. This thesis carries out a data-driven analysis of the tram system in Gothenburg with an objective to generate insights on the current maintenance procedures and asset performance which would aid the tram operator in their journey towards digital transformation. The analysis focuses on critical fixed infrastructure assets such as track, switches and catenary and was carried out following CRISP-DM, one of the most common frameworks for data mining. The project analyzed three different data sets – monthly track switching operations, operating restrictions resulting from faulty infrastructure assets and unscheduled maintenance events and historical maintenance records of the fixed assets. Few performance indicators were measured from the switching data such as vehicle passage error rates and rate of manual switching operations. The analysis on operating restrictions focused on identifying the reasons for restriction, its duration, the occurrence of restrictions over time and the associated cost impacts. The most significant part of the analysis was carried out on the past maintenance records available as inspections and work orders. Maintenance performance indicators based on the time incurred to perform such activities were measured. The primary causes of a failure for each asset category were identified. Further, a comparative analysis of inspections done against the standard requirements was also carried out. The analysis found satisfactory performance of switching operations. Regarding track restrictions, a pattern on the number of restrictions over time was observed. The analysis of inspections and work orders pointed out underperformance by maintenance teams and evident shortcomings in data collection. The performance indicators of maintenance teams measured may be used as a benchmark for better monitoring and control. However, they should be subject to scrutiny owing to questionable data quality. Future research should explore the feasibility of employing real-time predictive analytics for maintenance in tram systems based on machine learning

Research Into Options for Reducing Energy Consumption Across the Luas Network

The aim of this research has been to identify the energy consumption requirements of the Luas network, and present practical, cost effective solutions to reducing this energy consumption. To satisfy this, Luas energy consumption data was gathered from a number of sources including the manipulation of existing Luas systems such as PS Scada, the installation of new systems including Powersoft and the specific testing of Luas rolling stock and infrastructure components. Evaluation of this data and the establishment of the Luas energy load allowed for the identification of areas where excessive energy was being consumed. New technologies, industry best practices and efficient operational procedures throughout the European light rail industry were researched and investigated to determine their feasibility for implementation on the Luas light rail network. The energy reduction solutions identified as part of this research include modifications to existing systems such as the Luas passenger saloon heating and ventilation system which has the capacity to save over 1,400,000 kWh of energy and the installation of efficient lighting technologies such as LED’s and Induction lighting which would result in a saving of over 429,667 kWh of energy per year. Specific testing also took place to establish and develop optimal driving styles for Luas vehicles which has the potential to reduce total traction power by 5%. Efficient operational processes including a depot energy management process were devised and implemented during this research and have resulted in energy reductions at both Luas depots of 60%. Long term sustainability solutions such as renewable energy generation and energy storage systems were also consulted and evaluated to determine their suitability for Luas. In total the energy reduction solutions identified as a result of this research have the potential to reduce Luas energy consumption by 3,200,000 kWh, representing a 15% reduction of total Luas energy. The research results and related recommendations have been made to the research partners through this thesis

A dynamic systems approach to risk assessment in megaprojects

Purpose- Megaprojects are large, complex, and expensive projects that often involve social, technical, economic, environmental and political (STEEP) challenges. Despite these challenges, project owners and financiers continue to invest large sums of money in megaprojects that run high risks of being over schedule and over budget. While some degree of cost, schedule and quality risks are considered during planning, the challenge of understanding how risk interactions and impacts on project performance can be modelled dynamically still remains. The consequences learnt from past experiences indicate that there was a lack of dynamic tools to manage such risks effectively in megaproject construction. In seeking to help address these problems, this research put forward an innovative dynamic systems approach called SDANP to risk assessment in megaprojects construction. Design/methodology/approach – The research has developed an innovative SDANP method which involves an integrative use of system dynamics (SD) and analytic network process (ANP) for risk assessment. The SDANP model presented in the thesis has been testified by using data and information collected through a questionnaire survey and interviews from supply-side stakeholders involved in the Edinburgh Tram Network (ETN) project at the Phase One of its construction stage. The SDANP method is a case study risk assessment driven process and can be used against STEEP challenges in megaprojects. Findings – The result of the case study project revealed that the SDANP method is an effective tool for risk assessment to support supply-side stakeholders in decision making in construction planning. The SDANP model has demonstrated its efficiency through case study, and has convinced construction practitioners in terms of its innovation and usefulness. Research limitations/implications – Although the SDANP model has been developed for generic use in risk assessment, data and information used to run the simulation were based on the ETN project, which is in Edinburgh, Scotland. The use of the SDANP model in other megaprojects requires further data and information from local areas. Practical implications – The SDANP method provides an innovative approach to a comprehensive dynamic risk assessment of STEEP issues at the construction planning stage of megaprojects for the first time. It provides an interactive quantitative way for developers to prioritise and simulate potential risks across the project supply network, to understand and predict in advance the consequences of STEEP risks on project performance at the construction stage. Originality/value - The research made an original contribution in quantitative risk assessment with regard to the need for a methodological innovation in research and for a powerful sophisticated tool in practice. The SDANP has shown its advantages over existing tools such as the program evaluation and review technique (PERT) and the risk assessment matrix (RAM)

CISMOB - Cooperative information platform for low carbon and sustainable mobility: baseline assessment report

CISMOB main vision is to promote innovative ways to reduce carbon footprint and increase the sustainability of urban areas by improving the efficiency in the use of urban transport infrastructure through ICT. In a context of increasing availability of sensor technology to monitor and record large amounts of data, a common challenge to policy makers is to identify the best practices to take advantage of these new sources of data and use them to prioritize intervention areas, to manage efficiently current road networks, to inform citizens and motivate them to choose more sustainable mobility options. CISMOB will focus on improving the implementation of regional policies and local mobility programmers by having a thorough understanding about the different transport-related impacts and the main vulnerabilities associated to different zones of the territory. CISMOB partners consider that policy and local mobility programs should not be focused in minimizing a particular parameter (ex. Levels of congestion), but rather to promote holistic approaches capable of responding to the questions: what (to minimize)? why? when? where? and how? Regional and Policy instruments should also provide a framework of indicators to assess and inform the costs and benefits of environmentally effectiveness of different mobility solutions. CISMOB integrates a set of cities and regions of heterogenic characteristics, which are represented by institutions with complementary profiles. All partners will cooperate together in order to learn best practices of sustainable management of urban transport taking advantage of ICT. Workshops, staff exchange-programs and dissemination actions will be carried out with the aim of exchanging local experiences, learning best practices and enhancing the citizen’s participation.CISMOB Project Index Number: PGI0161

Development of a new approach for predicting tram track degradation based on passenger ride/comfort data

These days tram as a type of the public transport system has become popular because of its attractive features such as road usage efficiency, low emission of pollutants, reduction in traffic congestion and efficiency in capital costs and maintenance expenses compared to private cars. For the case study, the Melbourne tram network, which is the longest tram network in the world, has been targeted. Melbourne tram system consists of 493 trams, 24 routes, and 1,763 tram stops. According to the operator of the Melbourne tram network, the total number of patronage in 2017-2018 was 206.3 million. In parallel with the annual increase in tram demand and patronage, tram infrastructure systems need to bear more stresses and traffic pressure. Track degradation is a common problem in the area of tram track infrastructure. One of the main aspects of track degradation is the presence of irregularity in track geometric parameters. In order to deal with degradation problems, tram track infrastructure maintenance management systems have been developed for design and implementation of maintenance works and renewal activities. Track degradation prediction models are the core and the main part of these management systems. Without accurately predicting the future condition of tram tracks, designing and providing preventive maintenance strategies are not feasible. In this research, the collected data which cover six sequential years (2010 to 2015) have been analysed and influencing parameters in tram track degradation have been identified. Gauge and twist were identified as the influencing track geometry parameters in the tram track degradation. Besides that, track surface and rail support as structural parameters were identified as significant parameters in prediction of future track geometry parameters and consequently tram track degradation. In order to develop tram track degradation prediction models and according to the successful experience of the previous studies, three types of prediction models including Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest Regression (RFR) models have been created. According to the results, RFR models provide better predictions in terms of the performance indicators including the coefficient of determination and Root Mean Squared Error (RMSE) compared to the ANN and SVM models. In this research, based on the Melbourne tram track dataset, a new track degradation index has been proposed. Track degradation indices can be used as an indicator of rail condition concerning the risk of damage or failure over a period of time. The index can be applied in establishing a sustainable tram track maintenance management system. The new index composed of two main parts including the mean value of the geometry deviation and the average differential geometry deviation. The proposed index has been compared with three major track geometry degradation indices. For this purpose, the predictability performance of the indices has been considered. In this regard, the Pearson correlation analysis was applied to previous and current values of the indices. According to the results, the correlation coefficient of the proposed index was higher than the other indices. The finding of the evaluation presented that the proposed index can be used as an effective measure for the assessment of the geometric condition of tram tracks. In this research, a new approach has been proposed to predict the tram track degradation were which is cost-effective and can be carried out repeatedly without imposing delay to tram services. Conventional approaches are mainly based on the previous track geometry parameters which have been discussed in this research. In the new approach, passenger ride comfort data or acceleration data has been used to predict the future condition of track geometry parameters which has been represented by the tram track degradation index. For developing the degradation prediction models, the previous models which have been used to predict the degradation based on the track geometry parameters were applied. The future degradation index has been targeted as the target variable and acceleration parameter besides the structural parameters have been used as the explanatory variables. According to the results of the evaluation, the RFR model can predict the future degradation index with approximately 10 percent higher R2 and 9 percent lower prediction error compared to other developed models. In this research two methods for predicting the future tram track degradation index, first was the method based on the previous track geometry parameters and the second was the method based on the acceleration data, have been presented. According to the results of the degradation index prediction based on the previous track geometry parameters, RMSE was 0.35 and R2 value was 0.95. On the other hand, for the prediction based on the acceleration data, RMSE was 1.04 and R2 value was 0.74. The comparison of these methods shows that although the prediction error has been increased and R2 value has been decreased in the latest method, the values of the performance indicators are still in acceptable ranges. These results imply that the prediction of tram track degradation based on the acceleration data can be considered as a reliable method along with conventional tram track degradation prediction method for maintaining tram tracks. The proposed method can provide more predictions of potential future faults by reducing inspection costs and inspection intervals

Comparing blunders in government

Much attention has been paid to government ‘blunders’ and ‘policy disasters’. National political and administrative systems have been frequently blamed for being disproportionately prone to generating mishaps. However, little systematic evidence exists on the record of failures of policies and major public projects in other political systems. Based on a comparative perspective on blunders in government, this article suggests that constitutional features do not play a prominent role. In order to establish this finding, this article (a) develops theory-driven expectations as to the factors that are said to encourage blunders, (b) devises a systematic framework for the assessment of policy processes and outcomes, and (c) uses fuzzy-set qualitative comparative analysis to identify sets of causal conditions associated with particular outcomes (i.e. blunders). The article applies this novel approach to a set of particular policy domains, finding that constitutional features are not a contributory factor to blunders in contrast to instrument choice, administrative capacity and hyper-excited politic

CISMOB - Cooperative information platform for low carbon and sustainable mobility: ICT towards low carbon and sustainable mobility: a multiscale perspective

The transport sector has an important contribution to the economy, employment and to the citizens’ mobility. However, it is also a major contributor to greenhouse gas (GHG) emissions, representing almost a quarter of Europe's GHG emissions, and air pollution, which reduces air quality in cities with direct effects on public health. In particular, road transport is responsible for almost a fifth of total EU emissions and 73% of emissions from transport (European Commission, 2017). Intelligent Transport Systems (ITS) is the application of sensing, analysis, control and communications technologies for the management of the transport process to improve safety, mobility and efficiency, increase security and reduce environment impact. The use of ITS tools in transport has brought significant improvement in transport systems performance and it is a key element in reducing carbon footprint, as well as increasing the sustainability on an urban scale. The increasing road transport volumes in the EU are the primary cause of growing congestion and energy consumption, as well as a source of environmental and social problems (Tafidis & Bandeira, 2017). According to EC, ITS can contribute to the main transport policy objectives by reducing environmental impacts and save energy through better demand management. Therefore, the primary goals for urban transport should be the promotion of cleaner cars and fuels and the reduction of road accidents and traffic congestion. ITS tools can have a significant role to a cleaner, safer and more efficient transport system. EC with the ITS Directive (2010/40/EU) gave the necessary legal framework to their member states to accelerate the implementation of smart technologies in transport sector, giving each country freedom to decide their priorities (Urban ITS Expert Group, 2013). Regarding the mobility sector, there is a clear lack of well-structured policy guidelines that leverage the use of Information and Communication Technologies (ICT), sensing systems and big data to promote a more sustainable use of infrastructures. Specifically, there is still a waste of available resources for estimation real time mobility impacts and an even more obvious inability to use this information to implement sustainable mobility policies. The concept of sustainability in CISMOB concerns not only the carbon footprint but also the local economy and the social dimension, including active transport networks, users and the rest of citizens. Against this background, CISMOB partnership was developed in order to collect new ideas and practical experience.CISMOB Project Index Number: PGI01611, co-funded by the Interreg Europe Programmepublishe

Impact assessment framework to identify sustainable urban mobility solutions (EIT Urban Mobility Innovation Pathway Project - Final Report)

This report describes a new impact assessment framework to identify the policy solutions that enable cities to meet their vision and objectives, taking into account the local context. The framework was developed as a part of the EIT (European Institute of Innovation and Technology) Urban Mobility Innovation Pathway project. The framework consists of three groups of indicators, describing: • the city-level context in which the policy measures are applied • the characteristics of the process through which the policy measures are applied • the likely outcomes and impacts of the policy measur