Project VORTIX will research and develop novel visualisations of real-time passenger choices at transport interchanges. The project involves: developing novel visualisations, including audio-visual decision support (DS) avatars and remote visualisation, for guiding travellers through transport interchanges in real-time (RT); research of location-aware mobile real-time public transport decision support and passenger advisory systems, including integrating real-time vehicle departures; integrating real-time RFID near-field communication (NFC) location sensing and GPS data, with development of a tookit for the transport sector; validation of visualisation functions within live infrastructure in selected London stations; advancing data interface standards; visualisation within Web services and mash-ups. © Copyright 2006 Kizoom, TfL. In Commercial Confidence

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Recent traffic surveys and analysis of road network performance in London show a decline in traffic flows and perversely a decline in speeds and increase in congestion. It is believed that the increases in congestion reflect travellers' responses, both temporary and longer-term, to competition for road network capacity. Continuing adjustments to network capacity in pursuit of mayoral transport priorities, for example, improved safety and amenity, and increased priority for buses, taxis, pedestrians and cyclists, has led to increasing delays for private vehicular traffic. The current annual cost of congestion on London's main roads is estimated to be in the range of 1.8 to 3 billion.Analysis of road network performance is intricate. This is because the road network is essentially an open system with many factors and in which travellers can respond by modifying their choices in many different ways that will affect monitored performance outcomes. The form of these factors, their direction of causality, the fact that some of them interact strongly, and their sheer numbers all contribute to the complexity. These factors have different patterns of influence in both time and space, and analysis of the distinct cause-effect patterns is complicated by the non-linearity of the effects, including the possibility of abrupt growth in congestion once it sets in. Modelling spatial-temporal dependency of the factors is the bottleneck in analysis of the network performance. The challenge is to model dependency in both space and time seamlessly and simultaneously so that the accuracy of analysis can be improved. Another challenge is to fully consider the topology (links and hierarchies) and geometry (distances and directions) of real road networks in the analysis. These are also fundamental challenges in modelling complexity of other types of networks.This research will tackle these challenges. It will be achieved by innovative combination of two chosen novel machine learning methods (Dynamic Recurrent Neural Networks - DRNN and Support Vector Machines - SVM) with the most advanced statistical space-time series analysis (Spatio-Temporal Auto-Regressive Integrated Moving Average - STARIMA) and Geographically Weighted Regression - GWR. These methods are selected because their applications in transport studies are relatively new compared with conventional statistical methods, and, more importantly, they have the potential to improve the representation of the network complexity. The DRNN and SVM can model the non-linearity and non-stationarity existing in most spatio-temporal data which may not be fully accommodated by STARIMA. The STARIMA has the explanatory capability which is missing in DRNN and SVM. The GWR can model the heterogeneity of the networks and improve the understanding of the scales of the networks. Their use in combination will improve the sensitivity and explanatory power of the analysis, to enable the effects of the factors to be assessed separately (isolatable). These methods will also be explored, refined and further developed in the light of experience in this study.The outcome of this research will advance the new and emerging fundamental researches in agent simulations, dynamic network analysis, and computational models and architectures of artificial neural networks, which are widely involved in space-time analysis of social-economic phenomena. It will offer TfL better tools and techniques to manage the road space and mitigate congestion more effectively thereby improving person journey times and overall journey reliability, and in doing so also deliver large economic benefits to London. The benefits of the research will accrue widely to both public and private transport users. The methodology developed here will be transferable to understand the congestion in other big cities around the world with economic, monetary, social and environmental benefits.

Path choices by transport network users depend in part on decisions made in response to foreseeable circumstances at the start of the trip and in part on choices made as the trip unfolds. In the case of transit networks, on-trip choices may depend on factors such as which line arrives first at a particular stop or platform, whereas in the case of traffic networks, on-trip choices may be influenced by factors such as an encountered queue, the state of traffic signals, or information received by the driver. In transit assignment, the effects of on-trip decisions are accommodated through the definition of a set of elemental paths that may be optimal (referred to as a hyperpath) and strategies for on-trip choices. In traffic assignment, the stochastic user equilibrium principle has been used to capture the effects of on-trip choices and different user preferences. However, the usual forms of stochastic assignment (C-logit, path size logit, cross-nested logit or probit) generally place few or ad hoc constraints on the set of feasible paths and assume that all feasible paths have a non-zero probability of use. Choice of path is assumed to be the result of random utility variation rather than the outcome of a choice strategy in the face of unfolding circumstances as the trip takes place. To this extent, the treatment of route choice behaviour is more sophisticated in transit than in traffic assignment. Where congestion and service disruptions are a prominent feature, it is necessary to take time-dependency into account in defining hyperpaths. The objective of this proposal is to extend the concepts of hyperpath and strategy-based decision-making to dynamic transit networks, traffic networks and multi-modal networks and then assess the benefits of so doing.

An efficient transportation system is vital to the economic and social well-being of large cities. The transport demand implied by economic growth, however, requires transport networks to become more and more complex, making their management difficult. Fortunately, modern systems such as the London Underground generate vast amounts of data that can be analysed to better understand passenger behaviour and needs. Besides understanding the typical daily patterns that we can observe on a regular basis, Data Science methods allows us to look into in the less usual events such as unplanned disruptions that are still important to any user, and to also model individualised behaviour instead of only aggregates. In a large system such as the London Underground, signal failures and disruptive events eventually take place, requiring passengers to change plans in a variety of ways. This research provides advanced statistical modelling and machine learning approaches to learn from past events to examine how passengers adapt themselves when a disruption occurs. When a disruption takes place, the model will provide information of likely changes, such as increased number of passengers leaving a station because they could not reach their destination. These models are important for transport authorities to understand the resilience of the system, different combinations of location and time of a disruption, and unusual responses from passengers that may motivate different communication strategies to inform users of better travel adjustments. This research also opens up conceptual ideas to be exploited in the future using new technologies to monitor and adaptively respond to passenger needs in a more optimised and time-effective way.