Modelling public transport accessibility with Monte Carlo stochastic simulations: A case study of Ostrava

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin-destination (O-D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance-decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.Web of Science1124art. no. 709

An Overview of PIMMS (A Pricing and Investment Model for Multi-Modal Systems): An Areawide Urban Transport Policy Evaluation Model

The resurgence of interest in building better cities provides an opportunity to develop improved land use-transport models; models which are responsive to a wide variety of planning options, in contrast to current urban transport models which are only suitable for evaluating a limited number of major infrastructure options. A wide set of policy tools includes new infrastructure such as private tolled roads, light rail, bus priority systems; travel demand management through road pricing, area licensing and banning of cars in particular locations; and land use incentives/disincentives such as zoning for higher density activity, and more stringent environmental standards. To be responsive to a wide range of policy choices, it is desirable to develop models with a strong foundation in individual behaviour. This paper presents an overview of a project funded by the Australian Research Council. The aim is to develop a computer-based forecasting tool to give planners more flexibility in evaluating strategies designed to improve the performance of cities

Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

Kelionių grandinėmis pagrįsto susisiekimo poreikių modelio kūrimas ir taikymas miestų susisiekimo tinklo planavimui

This thesis is devoted to the analysis of the advanced and innovative tour-based travel demand modelling approach. Tour-based models explicitly recognise traffic as a derived demand for undertaking activities between homes and destinations. Travel demand of urban residents is modelled as trip sequences, which allows precise modelling of trip origin and destination points. The tour-based approach is deemed as a key step forwards towards even more complex agent-based modelling systems. The thesis is structured around three main chapters that can be summarised succinctly as a revision of the state of the practice and research, description of empirical research of travel behaviour, and tour-based model development. The 1st chapter revises the current state of practice and the research on travel demand modelling. All the building blocks that comprise transport models are discussed, and this lays the theoretical foundation for the following chapters. 1st chapter also gives a thorough comparison of trip-based and tour-based model-ling approaches and presents modelling environment. The 2nd chapter defines the process of conducting an empirical research of the travel behaviour patterns of urban residents. The 2nd chapter defines survey methodology and important mobility parameters such as activity sequences and their probabilities of homogeneous urban population segments. The outputs from the 2nd chapter are not only important and interesting on their own, but they also flow into the final third part of the work. The final 3rd chapter defines tour-based travel demand model development steps and showcases their practical application to the real-world scenario. Demand model quality assessment efforts and results are presented and discussed together with necessary explanations for significant deviations from reality. The resulting model is applied to investigate the performance of Siaurine Street in Vilnius, which is to be built in the coming years. At the very end of 3rd chapter a compre-hensive urban travel demand modelling framework is formulated and serves as a best practice guide. General conclusions summarises the whole study. These are followed by an extensive list of references that were mentioned or relied upon to some extent in the work. Finally, separate lists of scientific publications and conference presen-tations conclude the thesis. Overall, there have been five scientific articles published on the topic of the thesis. Four articles were published in scientific journals that are referenced in Clarivate Analytics Web of Science database, and one article was published in a scientific journal that is referenced in other databases

An Investigation of Intra-Household Interactions in Travel Mode Choice

AN INVESTIGATION OF INTRA-HOUSEHOLD INTERACTIONS IN TRAVEL MODE CHOICE This thesis develops a modelling framework to integrate intra-household interactions with tour-based mode choice. The findings provide evidence of intra-household interactions in travel mode choice of each household member and highlight factors associated with joint household activities and shared ride arrangements, with a distinction between weekdays and weekends. The results indicate that household resources, mobility and social constraints, and opportunities to coordinate household members’ activities play an important role in arranging joint household travel. Also, modelling outputs signal the differences that interpersonal interactions make to model elasticities and the implications for transport policy. The originality and the contribution of this research lie in four main areas. First, it tests the relevance of interactions between household members to household mode choice decisions and adds an additional ‘layer of interactions’ to the activity-based modelling framework. The study offers an analysis of household travel decisions embedding context and situation effects, thereby reflecting more realistically the nature of travel decisions. Second, this study offers a typology of joint household tour patterns embedded in a modelling approach which permits a variety of activity-travel patterns amongst all household members together with intra-household interactions. Third, the research provides evidence on the effects of land use factors measured at the micro-level so as to identify which aspects of the built environment are most likely to support policy change for sustainable transport choices. Finally, by separating weekend activity-travel from their weekday counterparts, this study is able to quantify empirically differences which suggest different transport management measures aimed to alleviate traffic congestion and promote public transport use

Calibrating spatial interaction models from GPS tracking data: an example of retail behaviour

Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets

Modélisation spatio-temporelle orientée-objet pour l'étude du comportement de transport basé sur l'activité

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

A systematic literature review

Albuquerque, V., Dias, M. S., & Bacao, F. (2021). Machine learning approaches to bike-sharing systems: A systematic literature review. ISPRS International Journal of Geo-Information, 10(2), 1-25. [62]. https://doi.org/10.3390/ijgi10020062Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction.publishersversionpublishe

Checking Data Quality of Longitudinal Household Travel Survey Data

Ensuring data quality of household travel survey data is often tedious and, thus, time-consuming. To speed up the process of data-checking and to gain an in-depth understanding of the data, data visualization is a practical, fundamental tool. Since 1994, data visualization has been used in the German Mobility Panel (MOP) data-checking process. This paper presents two graphical visualization tools developed for the MOP. Both tools speed up the data checks and ensure high consistency in identifying erroneous data. This paper describes and discusses how the tools provide a continuous data quality assessment

Examining the transport to school patterns of New Zealand adolescents by home-to-school distance and settlement types

Background: Scholarship on active transport to school has largely focused on children, (large) urban areas, the umbrella term of “active transport” which considered walking and cycling together and without taking into account walking and/or cycling distance. This research examined adolescents’ patterns of transport to school in diverse settlement types and in relation to home-to-school distance in the Otago region of Aotearoa New Zealand. Methods: Patterns of transport to school by home-to-school distance, and across school locations, are described for a sample of 2,403 adolescents (age: 15.1 ± 1.4 years; 55% females) attending 23 out of 27 schools in large urban areas (n = 1,309; 11 schools), medium urban areas (n = 265; three schools), small urban areas (n = 652; four schools) and rural settings (n = 177; five schools). Empirical data were collected through an online survey, in which adolescents reported sociodemographic characteristics, travel to school, and perceptions of walking and cycling. Home-to-school distance was measured on the shortest route determined using Geographic Information Systems (GIS)-based network analysis. Results: Transport to school patterns differed significantly by home-to-school distance and across settlement types. Profiles of different transport user groups showed significant variability in sociodemographic characteristics, family factors, average distance to school, self-reported physical activity, and perceived health. Conclusions: Initiatives to promote active transport and reduce reliance on car transport to school, whether to improve health and the environment or to reduce greenhouse gas emissions, need to pay closer attention to the settlement types, distance to school, and characteristics of different transport user modes