Mobility insights through consumer data: a case study of concessionary bus travel in the West Midlands

Current transport facilities are often built around efficiency and meeting the needs of the commuting population. These can therefore struggle to provide services suited to some of the most vulnerable members of society. In order to achieve an inclusive transport system, it is vital that transport authorities have access to detailed insights into the mobility needs and demands of different groups of the population. Increasingly, these transport authorities are making use of smart technologies and the resulting data to gain greater insight into transport users, and in turn inform decision making and policy planning. These smart technologies include automated fare collection (AFC) systems, which produce large volumes of detailed transport and mobility data from smart card transactions. To a lesser extent, retail datasets, such as loyalty card transaction data, have also been utilised. The spatiotemporal components of these data can provide valuable insight into the activity patterns of cardholders that may not be captured in traditional transport data. This thesis presents an exploration of these two forms of consumer data, with a focus on the older population in the West Midlands. Firstly, this thesis demonstrates how smart card data can be processed and analysed to provide detailed insights into the mobility patterns of concessionary bus users and how these relate to long-term changes in bus patronage recorded in the study area. Secondly, the extent to which loyalty card transaction data can be employed to understand retail behaviours and activity patterns is explored, with a focus on how these insights can be used to supplement and enhance the understanding of mobility gained from the smart card data. Finally, these insights are discussed in terms of the capacity of the current transport network to meet the mobility needs of the older population and the potential of consumer data for future transport-related research

Urban Form, Daily Travel Behaviour and Transport CO2 Emission: Micro-level Analysis and Spatial Simulation

Developing low carbon cities is a key goal of 21st century planning, and one that can be supported by a better understanding of the factors that shape travel behaviour, and resulting carbon emissions. Understanding travel based carbon emissions in mega- cities is vital, but city size, and often a lack of required data, limits the ability to apply linked land use, transport and tactical transport models to investigate the impact of policy and planning interventions on travel and emissions. Using Beijing as a case study, this thesis develops a new bottom-up methodology to provide improved transport CO2 emission from people‟s daily urban travel in Beijing from 2000 to 2030. It combines spatial microsimulation approach from geography and activity travel research from the transport field and applies this in a developing country for a long period, where detailed data to undertake fine scale analysis of phenomena such as transport CO2 emissions generated by travel behaviour is very scarce. On the basis of an activity diary survey and demographic data from the 2000 and 2010 population censuses, this research first employs spatial microsimulation to simulate a realistic synthetic populations‟ daily travel behaviour and estimate their transport CO2 emission at a fine geographical resolution (urban sub-district) between 2000 and 2010 for urban Beijing. It compares and analyses the changes in travel behaviour and transport CO2 emissions over this decade, and examines the role of socio-demographics and change in urban form in contributing to the modelled trend. The transport CO2 emission from people‟s daily travel behaviour in urban Beijing is then simulated and projected at disaggregate level to 2030 under four scenarios, to illustrate the utility of this bottom-up approach and modelling capability. The four scenarios (transport policy trend, land use and transport policy, urban compaction and vehicle technology, and combined policy) are developed to explore travel behaviour and transport CO2 emission under current and potential strategies on transport, urban development and vehicle technology. The results showed that, compared to the trend scenario, employing both transport and urban development policies could reduce total passenger CO2 emission to 2030 by 24%, and by 43% if all strategies were applied together. This research reveals the potential of microsimulation in emission estimation for large cities in developing countries where data availability may constrain more traditional approaches, and provides alternative urban development strategies and policy implications for CO2 emission mitigation targets set by the national and local governments

Disaggregating heterogeneous agent attributes and location

a b s t r a c t The use of micro-models as supplements for macro-models has become an accepted approach into the investigation of urban dynamics. However, the widespread application of micro-models has been hindered by a dearth of individual data, due to privacy and cost constraints. A number of studies have been conducted to generate synthetic individual data by reweighting large-scale surveys. The present study focused on individual disaggregation without micro-data from any large-scale surveys. Specifically, a series of steps termed Agenter (a portmanteau of ''agent producer'') is proposed to disaggregate heterogeneous agent attributes and locations from aggregate data, small-scale surveys, and empirical studies. The distribution of and relationships among attributes can be inferred from three types of existing materials to disaggregate agent attributes. Two approaches to determining agent locations are proposed here to meet various data availability conditions. Agenter was initially tested in a synthetic space, then verified using the acquired individual data, which were compared to results generated using a null model. Agenter generated significantly better disaggregation results than the null model, as indicated by the proposed similarity index (SI). Agenter was then used in the Beijing Metropolitan Area to infer the attributes and location of over 10 million residential agents using a census report, a household travel survey, an empirical study, and an urban GIS database. Agenter was validated using micro-samples from the survey, with an average SI of 72.6%. These findings indicate the developed model may be suitable for using in the reproduction of individual data for feeding micro-models

Author's personal copy Disaggregating heterogeneous agent attributes and location

This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. The use of micro-models as supplements for macro-models has become an accepted approach into the investigation of urban dynamics. However, the widespread application of micro-models has been hindered by a dearth of individual data, due to privacy and cost constraints. A number of studies have been conducted to generate synthetic individual data by reweighting large-scale surveys. The present study focused on individual disaggregation without micro-data from any large-scale surveys. Specifically, a series of steps termed Agenter (a portmanteau of ''agent producer'') is proposed to disaggregate heterogeneous agent attributes and locations from aggregate data, small-scale surveys, and empirical studies. The distribution of and relationships among attributes can be inferred from three types of existing materials to disaggregate agent attributes. Two approaches to determining agent locations are proposed here to meet various data availability conditions. Agenter was initially tested in a synthetic space, then verified using the acquired individual data, which were compared to results generated using a null model. Agenter generated significantly better disaggregation results than the null model, as indicated by the proposed similarity index (SI). Agenter was then used in the Beijing Metropolitan Area to infer the attributes and location of over 10 million residential agents using a census report, a household travel survey, an empirical study, and an urban GIS database. Agenter was validated using micro-samples from the survey, with an average SI of 72.6%. These findings indicate the developed model may be suitable for using in the reproduction of individual data for feeding micro-models