This presentation was delivered as part of the Institute for Transport Studies (ITS) research seminar series: https://environment.leeds.ac.uk/events/6/transport . Introduction by Professor Greg Marsden. Abstract: The large religious events in India have always thrown un-imaginable challenges of managing super dense pedestrian movements within a limited area. Kumbh Mela is a mass Hindu pilgrimage of faith in which Hindus gather to bathe in a sacred river. It is considered to be largest peaceful gathering in the world with estimated 100 million people visiting during the Kumbh Mela in 2013 in Allahabad, India and an estimated 75 million people visiting the Kumbh Mela recently held in 2016 in Ujjain, India. The safe mobility of pedestrians and crowd management during these events has always been a matter of concern for organizers and government authorities. The problem is compounded due to absence of reliable and real-time data to track movement of pedestrians and pedestrian density in designated areas. With the aim to gain more fundamental understanding of pedestrian dynamics in mass gatherings and using that knowledge to make such events safer, a major international collaborative research project called Kumbh Mela Experiment (KME) has been recently undertaken. The Kumbh Mela Experiment is an ongoing Indo-Dutch collaborative research project funded jointly by Department of Electronics and Information Technology (DeitY), Govt. of India and Netherlands Organization for Scientific Research (NWO), Netherlands. The Transportation Engineering (TE) Lab. at Indian Institute of Science (IISc) Bangalore is the lead partner of this project. The aim of the project is to use big data and Internet of Things (IOT) for understanding crowd dynamics in mass gatherings and develop crowd management solution particularly focusing on crowd risk. The project aims to deliver the core components of an entire crowd management solution, all the way from designing and building personnel devices for tracking movement (or more precisely human-to-human interaction) to developing advanced computational models to help predict how the crowd may evolve. This leads to a vision of a living simulation where various data sources (personal devices, video cameras, smart phones, monitoring drones) can be fused into models and executed on an advanced compute infrastructure capable of providing both a prioi planning, as well as real-time reactive decision-making. As the name clearly suggests, the data collection was done during the recently held Kumbh Mela (Simhasth-2016) in Ujjain, M.P., India during 22nd April to 21st May 2016. An "Indo-Dutch Collaborative Research Camp" was set up in a farm land of about 27,000 Sq.ft in the Kumbh Mela Area for the months of April and May 2016 to carry out data collection and experimentation activities during Simhasth-2016 for the Kumbh Mela Experiment project. A core team of 45 researchers from India, Netherlands, Russia, and Singapore worked in the research camp on about 20 different experiments defined under the project. Besides this, about 120 internship students from Engineering, Management, and Planning institutes across India were recruited to work under this project and carried out their activities from the camp. The primary data sources/devices used were; about 540 wearable tracking devices, 3000 wearable lanyard devices, Go Pro cameras, drones, questionnaire survey etc. The secondary data sources includes; Police CCTV camera data and drone data, Mahakal Temple CCTV data, etc. This presentation on KME will explain the scientific aspects of the project and the experiences of data collection.

This seminar was delivered as part of the Institute for Transport Studies research seminar series. The presentation describes the networking activities and key findings of a GCRF sponsored project, which purposefully draws together academics, policymakers and practitioners to transcend the hitherto ‘siloed’ transport planning and development studies domains. The INTALInC (International Network for Transport and Accessibility in Low Income Communities) project seeks to promote ‘research into practice’ to highlights the transport and mobility needs of urban poor and socially vulnerable populations in Global South cities and to identify more effective solutions for meeting their travel needs. The eight deliberative workshops that formed the backbone of the research process have identified that women, children, older people and people with disabilities are the most severely affected by transport poverty. Overwhelmingly, the urban poor rely on walking as their main mode of transport, often travelling long distances to get from the peripheral urban slums where they live to their employment and other activities in the city centre. Badly designed transport infrastructures combined with high-levels of largely unregulated traffic expose them to numerous safety and health risks. Their restricted accessibility to job, markets, education, healthcare and other welfare activities severely reduces their livelihoods and life chances. These wider social consequences of inequitable and unsustainable transport systems in Global South cities have been largely ignored by transport planners and social policy professional alike.

This presentation was delivered as part of the Institute for Transport Studies research seminar series. Mobile phone data has recently emerged as a very promising source of data for developing mobility models. Previous transport applications of mobile phone data have however focused primarily on extracting travel patterns and trends. This research aims to extend the application of mobile phone data to mainstream travel behaviour modelling and policy analysis by augmenting the data with information from other sources. This comes along with significant challenges arising from the anonymous and noisy nature of the data. Consequently, novel modelling frameworks have been developed in the context of trip generation, route choice and departure time choice modelling to address these challenges. These have been tested using different types of mobile phone data from Switzerland, Senegal and Bangladesh, providing us with rich insights into the strengths and weaknesses of the different data types. In all the cases, the model estimation results are consistent with the expected travel behaviour. Moreover, validation exercises with respect to hold-out samples and/or valuation metrics from other sources (such as the value of travel time) produce satisfactory results, thereby demonstrating the potential of mobile phone data for transport policy analysis. The findings are expected to be useful to transport practitioners - particularly those working in contexts where traditional data sources for transport modelling are scarce.

This presentation was delivered as part of the Institute for Transport Studies (ITS) research seminar series: https://environment.leeds.ac.uk/events/6/transport. Abstract:This seminar will report on methodological work that explores multiple ways to represent and manipulate network data. A key problem posed by spatial networks is that they have an inherent duality: they are simultaneously graph entities (like the famous Königsberg Bridge problem) and spatial entities. In other words, they are spatial networks. Both representations are important in multiple branches of transport, including traffic, air pollution, mode shift, and road safety modelling. This talk will show several pitfalls in common spatial representations of network data, demonstrate various ways of representing transport networks using recently developed open source software, some of which we have developed, and reports preliminary findings: by breaking up networks into segments of similar sizes, we can better model crash hotspots to inform road safety and sustainable transport policies. Bio: Andrea is a 3rd year PhD student in the department of Statistics at the University of Milan. He will be visiting ITS, Leeds in October and November 2019 to collaborate with Robin Lovelace on new representations of spatial networks. The aim is to review existing packages for spatial network analysis in R (including dodgr, sfnetworks and stplanr), improve support for spatial networks in the ITS-developed R package stplanr, and apply the methods to road safety research in Italy, based on a national dataset of crashes that Andrea is using for his PhD. More info at https://twitter.com/a_gilardi5 and (for code) https://github.com/agila5.

This presentation was delivered as part of the Institute for Transport Studies (ITS) research seminar series: https://environment.leeds.ac.uk/events/6/transport. Introduction by Professor Stephane Hess. Decision field theory (DFT), although popular in mathematical psychology, has only recently made the transition to choice modelling for consumer choices. One of the key differences that DFT has from standard choice models is that it has preference values for each alternative that update over time. This results in a different probability of picking each alternative depending on how long a decision-maker considers their alternatives. Theoretically, this provides a perfect model for explaining and modelling decisions where attributes of possible alternatives change (i.e, gap sizes in lane changing and gap acceptance choices). However, computational complexities of calculating the probability of alternatives in DFT models have resulted in simplifications. Typically, the number of deliberation timesteps has been set to a high value and thus the stochastic process is averaged out. This consequently ignores and fails to utilise the dynamic nature of DFT. This talk discusses the steps towards building a dynamic DFT. First, we demonstrate that DFT can be used effectively to model choices from typical SP and RP transport choice datasets. Second, we consider how the number of deliberation timesteps in a DFT model can be estimated as a function of choice response time. Finally, we discuss the remaining steps required to make DFT a properly dynamic model that can incorporate dynamic data.

This presentation was delivered as part of the Institute for Transport Studies (ITS) research seminar series: https://environment.leeds.ac.uk/events/6/transport. Introduction by Dr Zia Wadud, ITS, University of Leeds. Abstract: Disconnected urban street networks, which I call “street-network sprawl,” are strongly associated with increased vehicle travel, energy use and CO2 emissions. Moreover, since street layouts are essentially permanent, today’s choices on the connectivity of streets will restrict future resilience and lock in car dependence for a century or more. Using data on all 46 million km of mapped streets worldwide and a satellite-derived time series of urbanization, I present the first systematic and globally commensurable measures of street-network sprawl. Latin America, Japan, South Korea, much of Europe and North Africa stand out for their low levels of street-network sprawl, while the highest levels are found in south-east Asia, the United States and the British Isles. Streets in new developments in 90% of the most populous countries have become less connected since 1975, and the same period saw a near doubling in the frequency of a street-network type typical of gated communities. I also identify persistence in street-network sprawl, indicative of path-dependent processes. Specifically, cities and countries with low connectivity in recent years also had relatively low pre-existing connectivity. The lack of comparative, cross-national analysis has been identified as a major research gap in the literature on urban sprawl, and this assessment provide the foundation for future work to understand urban processes, predict future pathways of transportation energy consumption and emissions, and identify effective policy responses. Bio: My research bridges urban planning and environmental economics, and addresses some of the key challenges in transportation, energy and climate change policy. My current work examines global patterns of urban sprawl and car ownership, the effectiveness of local climate planning efforts, and the design of carbon trading programs. I also have broad interests in transportation planning and policy, particularly parking management programs to reduce vehicle travel and emissions. Before coming to UC Santa Cruz, I was an assistant professor in the Department of Geography and McGill School of Environment, McGill University. I hold a PhD in Environment and Resources from Stanford University, and was formerly a Principal with transportation planning firm Nelson\Nygaard Consulting Associates.