Assessing the consistency between observed and modelled route choices through GPS data

In traffic engineering, different assumptions on user behaviour are adopted in order to model the traffic flow propagation on the transport network. This paper deals with the classical hypothesis that drivers use the shortest possible path for their trip, pointing out the error related to using such approximation in practice, in particular in the context of dynamic origin-destination (OD) matrix estimation. If this problem is already well known in the literature, only few works are available, which provide quantitative and empirical analysis of the discrepancy between observed and modelled route sets and choices. This is mainly related to the complexity of collecting suitable data: to analyse route choice in a systematic way, it is necessary to have observations for a large period of time, since observing trajectories for the single user on a specific day could not be enough. Information is required for several days in order to analyse the repetitiveness and understand which elements influence this choice. In this work the use of the real shortest path for a congested network is evaluated, showing the differences between what we model and what users do. Results show that there is a systematic difference between the best possible choice and the actual choice, and that users clearly consider route travel time reliability in their choice process.In traffic engineering, different assumptions on user behaviour are adopted in order to model the traffic flow propagation on the transport network. This paper deals with the classical hypothesis that drivers use the shortest possible path for their trip, pointing out the error related to using such approximation in practice, in particular in the context of dynamic origin-destination (OD) matrix estimation. If this problem is already well known in the literature, only few works are available, which provide quantitative and empirical analysis of the discrepancy between observed and modelled route sets and choices. This is mainly related to the complexity of collecting suitable data: to analyse route choice in a systematic way, it is necessary to have observations for a large period of time, since observing trajectories for the single user on a specific day could not be enough. Information is required for several days in order to analyse the repetitiveness and understand which elements influence this choice. In this work the use of the real shortest path for a congested network is evaluated, showing the differences between what we model and what users do. Results show that there is a systematic difference between the best possible choice and the actual choice, and that users clearly consider route travel time reliability in their choice process

Choice and the composition of general practice patient registers

Choice of general practice (GP) in the National Health Service (NHS), the UKs universal healthcare service, is a core element in the current trajectory of NHS policy. This paper uses an accessibility-based approach to investigate the pattern of patient choice that exists for GPs in the London Borough of Southwark. Using a spatial model of GP accessibility it is shown that particular population groups make non-accessibility based decisions when choosing a GP. These patterns are assessed by considering differences in the composition of GP patient registers between the current patient register, and a modelled patient register configured for optimal access to GPs. The patient population is classified in two ways for the purpose of this analysis: by geodemographic group, and by ethnicity. The paper considers choice in healthcare for intra-urban areas, focusing on the role of accessibility and equity

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables

Classification algorithms for Intelligent Transport Systems

Intelligent Transport Systems (ITS) consists in the application of ICT to transport to offer new and improved services to the mobility of people and freights. While using ITS, travellers produce large quantities of data that can be collected and analysed to study their behaviour and to provide information to decision makers and planners. The thesis proposes innovative deployments of classification algorithms for Intelligent Transport System with the aim to support the decisions on traffic rerouting, bus transport demand and behaviour of two wheelers vehicles. The first part of this work provides an overview and a classification of a selection of clustering algorithms that can be implemented for the analysis of ITS data. The first contribution of this thesis is an innovative use of the agglomerative hierarchical clustering algorithm to classify similar travels in terms of their origin and destination, together with the proposal for a methodology to analyse drivers’ route choice behaviour using GPS coordinates and optimal alternatives. The clusters of repetitive travels made by a sample of drivers are then analysed to compare observed route choices to the modelled alternatives. The results of the analysis show that drivers select routes that are more reliable but that are more expensive in terms of travel time. Successively, different types of users of a service that provides information on the real time arrivals of bus at stop are classified using Support Vector Machines. The results shows that the results of the classification of different types of bus transport users can be used to update or complement the census on bus transport flows. Finally, the problem of the classification of accidents made by two wheelers vehicles is presented together with possible future application of clustering methodologies aimed at identifying and classifying the different types of accidents

Examining the Active Transportation - Built Environment Relationship in London, Ontario

Research on the relationship between the built environment and active transportation has accelerated and expanded over the past 20 years. This growth is in large part due to continuing evidence of rising rates in obesity and Type-2 diabetes that coincides with decreasing rates of physical activity across all ages in the post-industrial world. Walking more is a simple solution to increasing rates of physical activity. While for most people walking is possible throughout the day, there has been a decrease in the use of walking as a means of transportation. This study examines environmental determinants of active transportation from two perspectives: 1) working adults and 2) elementary school children. It adopts multiple methodologies for identifying travel corridors in geographic information systems (GIS) analysis and tests a novel technique by applying a hexagonal grid to extract built environment measures. Results from this research suggest global positioning system (GPS) tracking is a viable method to capture built environment measures, especially for children. As in previous studies, this study found distance between origin and destination to be the most important determinant to active travel with socio-economic status also playing a key role for adults and children. Results from this research are concurrent with previous literature while employing hexagons as a geographic unit. Examining the active transportation/built environment relationship through the use of GPS and a hexagonal areal unit is a new approach that deserves serious consideration for further research

Building a large-scale micro-simulation transport scenario using big data

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources

Exploring cross-sectional associations between unhealthy food outlet exposure and BMI z-score in elementary school children in London, Canada

The food environment has been implicated in the continuing epidemic of childhood obesity in Canada. The purpose of this thesis is to examine associations between the food environment, childhood weight, and unhealthy diets using data collected by the Spatial Temporal Environmental and Activity Monitoring (STEAM) project conducted among children (N=852) aged 9 to 14 years in Southwestern Ontario between 2010 and 2013. Global Positioning System (GPS) monitors and Geographic Information Systems (GIS) were used to determine the time children spent within 100m of an unhealthy food outlet on weekdays. Structural equation modeling was used to assess the effect of exposure to fast food and variety stores on children’s weight, mediated by unhealthy dietary intake, stratified by sex. There were no significant associations between food outlet exposure and weight for males or females, nor was unhealthy diet a significant mediator of this relationship. Future work and public health implications are discussed

Improving our understanding of the influences of the environment on physical activity

Physical activity reduces the risk of many chronic diseases, and much of the population would benefit from being more active. Public health strategies increasingly identify the environment as an important influence on activity and therefore a target for intervention. However, it is difficult to draw conclusive evidence from the existing literature as the range of environments and spaces individuals are exposed to are rarely accounted for. This thesis aims to further develop our understanding of the ways in which environmental characteristics influence physical activity. The first part examines cross-sectional associations between environmental characteristics such as street connectivity, air pollution, and deprivation, and self-reported and objective measures of physical activity. Data are used from the national multicentre UK Biobank study. The findings suggest environmental characteristics have the potential to contribute to different physical activities but interventions which focus on a single environmental attribute may not have the greatest benefits. The UK Biobank study uses measures of environments around residential addresses which may not capture all locations where participants are active. The second part of my thesis provides a more representative picture of environments that people are exposed to by focusing on activity spaces: locations accessed by an individual as a result of their daily activities. I systematically review literature which uses the activity space concept, and discuss research questions that have been answered, the spatial and temporal methods used, and the implications for causal inference. Included studies used variable methods to assess the features of spaces themselves (such as shape or size) or features within spaces (such as density of destinations). Informed by the conceptual work of the review, the latter section of the thesis uses quantitative and qualitative data from the Commuting and Health in Cambridge study to understand how new transport infrastructure might give rise to changes in use of space. The development of a replicable process to clean and prepare GPS data is presented and findings show how the infrastructure provides a new space for physical activity. The final project explores the applicability of different spatial methods for assessing population levels of activity and how changes in the location of physical activity might contribute to overall levels of activity over time. By developing and applying scalable methods to show how the spatial patterning of behaviour and physical activity changes in the context of an intervention, this thesis provides methodological and scientific contributions to the field of physical activity and public health. Future research in this topic area should aim to strengthen the basis for causal inference and develop evidence to effectively inform public health policy and action.Funding was provided by the Medical Research Council (MC_UU_12015/6) and Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from the British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research (NIHR), and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged (grant numbers 087636/Z/08/Z, ES/G007462/1, MR/K023187/1)

Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set

The value of slow travel: An econometric method for valuing the user benefits of active transport infrastructure

Transport infrastructure investments are typically justified largely on the basis of their ability to increase travel speeds. However, new bicycle facilities, such as separated cycleways, may result in slower journeys. Economic appraisals of proposed bicycle facilities therefore tend to focus on the social benefits, in particular, improvements in public health resulting from increased physical activity. Yet, some welfare benefit must also accrue to the users of the new facilities, given they willingly choose to use them over faster alternatives. This thesis explores how discrete choice modelling can be used to analyse the trade-offs people make when choosing how they travel, and thereby (a) forecast changes in travel demand resulting from bicycle network improvements, and (b) quantify and monetise the resulting benefits to users. Despite the theory having been established in the 1970s, there have been few practical applications of this methodology, and it is yet to be used to value the user benefits of new bicycle facilities in a car-centric city. This thesis also assesses the short-term reliability of such assessments, by analysing changes in travel demand and preferences following an actual infrastructure intervention. It is found that bicycle network improvements offer substantial welfare benefits to users, in terms of improved accessibility, comfort, perceived safety, and transport choice – even though their journeys may end up being slower. Furthermore, these benefits amplify when links are connected into a network. By ignoring such benefits in project appraisal, bicycle facilities may be significantly undervalued, and transport investment decisions inadequately informed