Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach

Travel Behaviour Response to Major Transport System Disruptions: Implications for Smarter Resilience Planning

No abstract available

Environmental capacity building through knowledge transfer partnerships

This paper describes the need for organisations to develop adaptive capacity in the face of environmental challenges. It argues that "knowledge transfer" can provide a useful mechanism for developing this environmental adaptive capacity and outlines the experiences of a Knowledge Transfer Partnership between North Tyneside Council and Northumbria University. Initial findings from the partnership suggest that the knowledge and skills transferred to the local authority through knowledge transfer, are already building capacity within the organisation, and beginning to filter down to private sector companies involved with the authority and the communities who they represent

Evaluation of Coordinated Ramp Metering (CRM) Implemented By Caltrans

Coordinated ramp metering (CRM) is a critical component of smart freeway corridors that rely on real-time traffic data from ramps and freeway mainline to improve decision-making by the motorists and Traffic Management Center (TMC) personnel. CRM uses an algorithm that considers real-time traffic volumes on freeway mainline and ramps and then adjusts the metering rates on the ramps accordingly for optimal flow along the entire corridor. Improving capacity through smart corridors is less costly and easier to deploy than freeway widening due to high costs associated with right-of-way acquisition and construction. Nevertheless, conversion to smart corridors still represents a sizable investment for public agencies. However, in the U.S. there have been limited evaluations of smart corridors in general, and CRM in particular, based on real operational data. This project examined the recent Smart Corridor implementation on Interstate 80 (I-80) in the Bay Area and State Route 99 (SR-99, SR99) in Sacramento based on travel time reliability measures, efficiency measures, and before-and-after safety evaluation using the Empirical Bayes (EB) approach. As such, this evaluation represents the most complete before-and-after evaluation of such systems. The reliability measures include buffer index, planning time, and measures from the literature that account for both the skew and width of the travel time distribution. For efficiency, the study estimates the ratio of vehicle miles traveled vs. vehicle hour traveled. The research contextualizes before-and-after comparisons for efficiency and reliability measures through similar measures from another corridor (i.e., the control corridor of I-280 in District 4 and I-5 in District 3) from the same region, which did not have CRM implemented. The results show there has been an improvement in freeway operation based on efficiency data. Post-CRM implementation, travel time reliability measures do not show a similar improvement. The report also provides a counterfactual estimate of expected crashes in the post-implementation period, which can be compared with the actual number of crashes in the “after” period to evaluate effectiveness

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Adapting the community sector for climate extremes

Abstract People experiencing poverty and inequality will be affected first and worst by the impacts of climate change to infrastructure and human settlements, including those caused by increasingly frequent and intense extreme weather events and natural disasters. They have the least capacity to cope, to adapt, to move and to recover. Community service organisations (CSOs) play a critical role in supporting individuals, families and communities experiencing poverty and inequality to build resilience and respond to adverse changes in circumstances. As such, the services they provide comprise a critical component of social infrastructure in human settlements. However, very little is understood about CSOs own vulnerability to – or their role in managing and mitigating risks to their clients and the community from – climate change impacts to physical infrastructure. The Extreme Weather, Climate Change and the Community Sector – Risks and Adaptations project examined the relationship between physical and social infrastructure (in the form of CSO service provision). Specifically, the ways in which the climate-driven failure of CSO service delivery worsens risks to the individuals and communities they serve and, on the other hand, how preparedness may reduce vulnerability to climate change and extreme weather impacts to human settlements and infrastructure.The research comprised a comprehensive and critical scoping, examination and review of existing research findings and an audit, examination and judgment-based evaluation of the current vulnerabilities and capacities of CSOs under projected climate change scenarios. It employed three key methods of consultation and data collection. A literature review examined research conducted to date in Australia and comparative countries internationally on the vulnerability and climate change adaptation needs of CSOs. A program of 10 Community Sector Professional Climate Workshops consulted over 150 CSO representatives to develop a qualitative record of extreme event and climate change risks and corresponding adaptation strategies specific to CSOs. A national survey of CSOs, which resulted in the participation of approximately 500 organisations, produced a quantitative data set about the nature of CSO vulnerability to climate change and extreme weather impacts to infrastructure, whether and how CSOs are approaching the adaptation task and key barriers to adaptation.While the methods employed and the absence of empirical data sets quantifying CSO vulnerability to climate change impacts create limitations to the evidence-base produced, findings from the research suggest that CSOs are highly vulnerable and not well prepared to respond to climate change and extreme weather impacts to physical infrastructure and that this underlying organisational vulnerability worsens the vulnerability of people experiencing poverty and inequality to climate change. However, the project results indicate that if well adapted, CSOs have the willingness, specialist skills, assets and capacity to make a major contribution to the resilience and adaptive capacity of their clients and the community more broadly (sections of which will be plunged into adversity by extreme events). Despite this willingness, the evidence presented shows that few CSOs have undertaken significant action to prepare for climate change and worsening extreme weather events. Key barriers to adaptation identified through the research are inadequate financial resources, lack of institutionalised knowledge and skills for adaptation and the belief that climate change adaptation is beyond the scope of CSOs core business. On the other hand, key indicators of organisational resilience to climate change and extreme weather impacts include: level of knowledge about extreme weather risks, past experience of an extreme weather event and organisational size.Given its size, scope and the critical role the Australian community sector plays in building client and community resilience and in assisting communities to respond to and recover from the devastating impacts of extreme weather events and natural disasters, the research identifies serious gaps in both the policy frameworks and the research base required to ensure the sector’s resilience and adaptive capacity – gaps which appear to have already had serious consequences. To address these gaps, a series of recommendations has been prepared to enable the development and implementation of a comprehensive, sector-specific adaptation and preparedness program, which includes mechanisms to institutionalise knowledge and skills, streamlined tools appropriate to the needs and capacity of a diverse range of organisations and a benchmarking system to allow progress towards resilience and preparedness to be monitored. Future research priorities for adaptation in this sector have also been identified

Microsimulation models incorporating both demand and supply dynamics

There has been rapid growth in interest in real-time transport strategies over the last decade, ranging from automated highway systems and responsive traffic signal control to incident management and driver information systems. The complexity of these strategies, in terms of the spatial and temporal interactions within the transport system, has led to a parallel growth in the application of traffic microsimulation models for the evaluation and design of such measures, as a remedy to the limitations faced by conventional static, macroscopic approaches. However, while this naturally addresses the immediate impacts of the measure, a difficulty that remains is the question of how the secondary impacts, specifically the effect on route and departure time choice of subsequent trips, may be handled in a consistent manner within a microsimulation framework. The paper describes a modelling approach to road network traffic, in which the emphasis is on the integrated microsimulation of individual trip-makers’ decisions and individual vehicle movements across the network. To achieve this it represents directly individual drivers’ choices and experiences as they evolve from day-to-day, combined with a detailed within-day traffic simulation model of the space–time trajectories of individual vehicles according to car-following and lane-changing rules and intersection regulations. It therefore models both day-to-day and within-day variability in both demand and supply conditions, and so, we believe, is particularly suited for the realistic modelling of real-time strategies such as those listed above. The full model specification is given, along with details of its algorithmic implementation. A number of representative numerical applications are presented, including: sensitivity studies of the impact of day-to-day variability; an application to the evaluation of alternative signal control policies; and the evaluation of the introduction of bus-only lanes in a sub-network of Leeds. Our experience demonstrates that this modelling framework is computationally feasible as a method for providing a fully internally consistent, microscopic, dynamic assignment, incorporating both within- and between-day demand and supply dynamic

Green buildings: issues for New Zealand

If the mission of ‘green’ rating tools is to accelerate the transformation of the global built environment towards sustainability then a high priority must be placed on the energy consumed by buildings since energy supplies from various sources are depleting. This paper examines the apparent anomaly that almost all designs of ‘green’ office developments in New Zealand have high proportions of unshaded glazing. They are sealed, lightweight, airconditioned buildings that are dependent on an uninterrupted supply of electricity in order to remain habitable. From an architectural science point of view, these characteristics are not normally associated with sustainability. The paper will investigate the drivers behind the highly glazed buildings recently realized in New Zealand, including those components of ‘green’ rating tools that favour this building type