Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks

A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows. This is a challenging task due to the different spatial, temporal, social, and economic factors influencing urban mobility flows. These flows, along with the influencing factors, can be modelled as attributed graphs with both node and edge features characterising locations in a city and the various types of relationships between them. In this paper, we address the problem of assessing origin-destination (OD) car flows between a location of interest and every other location in a city, given their features and the structural characteristics of the graph. We propose three neural network architectures, including graph neural networks (GNN), and conduct a systematic comparison between the proposed methods and state-of-the-art spatial interaction models, their modifications, and machine learning approaches. The objective of the paper is to address the practical problem of estimating potential flow between an urban development project location and other locations in the city, where the features of the project location are known in advance. We evaluate the performance of the models on a regression task using a custom data set of attributed car OD flows in London. We also visualise the model performance by showing the spatial distribution of flow residuals across London.Comment: 9 pages, 5 figures, to be published in the Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP 2020

Improving Sustainable Mobility through Modal Rewarding: The GOOD_GO Smart Platform

Private car mobility registers today a h igh accident rate and around 70% of the overall CO2 emissions from transport were generated by road mode split (European Commission, 2016). Moreover, in urban areas they occur 38% of the overall fatalities from road transport, and 23% of the overall CO2 emissions (European Commission, 2013). As a result, a modal shift of at least a part of passenger transport in urban areas, from private car to sustainable transport systems is desirable. This research aims to promote sustainable mobility through two mutually reinforcing "main actions": firstly, there is a r ewarding Open-Source platform, named as GOOD_GO; secondly, there is the SW/HW system connecting to the wide world of private and/or shared bicycles. Through the GOOD_GO platform Web portal and App, a user enters a so called 'social rewarding game' thought to incentive sustainable mobility habits, and gets access to the second item consisting of a system to disincentive bike-theft and based on the passive RFID technology. The low-cost deterrent bike-theft and bike monitoring/tracking system is functional to bring a big number of citizens inside the rewarding game. In 2018, a pilot test has implemented in the city of Livorno (Tuscany, It), and it involved around 1,000 citizens. Results were quite encouraging and today, the cities of Livorno, Pisa and Bolzano will enlarge the incentive system both to home-to-school and home-to-work mobility. The Good_Go platform is an actual M-a-a-S (Mobility-as-a-Service) application, and it becoming a Mobility Management decision system support, jointly with the opportunity of organizing more incentive tenders and rewarding systems types

Validity of Machine Learning in Assessing Large Texts Through Sustainability Indicators

As machine learning becomes more widely used in policy and environmental impact settings, concerns about accuracy and fairness arise. These concerns have piqued the interest of researchers, who have advanced new approaches and theoretical insights to enhance data gathering, treatment and models’ training. Nonetheless, few works have looked at the trade-offs between appropriateness and accuracy in indicator evaluation to comprehend how these constraints and approaches may better redound into policymaking and have a more significant impact across culture and sustainability matters for urban governance. This empirical study fulfils this void by researching indicators’ accuracy and utilizing algorithmic models to test the benefits of large text-based analysis. Here we describe applied work in which we find affinity and occurrence in indicators trade-offs that result be significant in practice to evaluate large texts. In the study, objectivity and fairness are kept substantially without sacrificing accuracy, explicitly focusing on improving the processing of indicators to be truthfully assessed. This observation is robust when cross-referring indicators and unique words. The empirical results advance a novel form of large text analysis through machine intelligence and refute a widely held belief that artificial intelligence text processing necessitates either accepting a significant reduction in accuracy or fairness.Funding for open access charge: CRUE-Universitat Jaume

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents

Global sustainability requires low-carbon urban transport systems, shaped by adequate infrastructure, deployment of low-carbon transport modes and shifts in travel behavior. To adequately implement alterations in infrastructure, it's essential to grasp the location-specific cause-and-effect mechanisms that the constructed environment has on travel. Yet, current research falls short in representing causal relationships between the 6D urban form variables and travel, generalizing across different regions, and modeling urban form effects at high spatial resolution. Here, we address all three gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel based on high-resolution mobility data of six cities across three continents. We show that both distance to city center, demographics and density indirectly affect other urban form features. By considering the causal relationships, we find that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Differences in urban form effects across the cities call for a more holistic definition of 6D measures. Our work is a starting point for location-specific analysis of urban form effects on mobility behavior using causal discovery approaches, which is highly relevant for city planners and municipalities across continents.Comment: 22 pages, 13 figures, 4 table

Reviews and Perspectives on Smart and Sustainable Metropolitan and Regional Cities

The notion of smart and sustainable cities offers an integrated and holistic approach to urbanism by aiming to achieve the long-term goals of urban sustainability and resilience. In essence, a smart and sustainable city is an urban locality that functions as a robust system of systems with sustainable practices to generate desired outcomes and futures for all humans and non-humans. This book contributes to improving research and practice in smart and sustainable metropolitan as well as regional cities and urbanism by bringing together literature reviews and scholarly perspective pieces, forming an open access knowledge warehouse. It contains contributions that offer insights into research and practice in smart and sustainable metropolitan and regional cities by producing in-depth conceptual debates and perspectives, insights from the literature and best practice, and thoroughly identified research themes and development trends. This book serves as a repository of relevant information, material, and knowledge to support research, policymaking, practice, and the transferability of experiences to address challenges in establishing smart and sustainable metropolitan as well as regional cities and urbanism in the era of climate change, biodiversity collapse, natural disasters, pandemics, and socioeconomic inequalities

Origin-Destination Network Generation via Gravity-Guided GAN

Origin-destination (OD) flow, which contains valuable population mobility information including direction and volume, is critical in many urban applications, such as urban planning, transportation management, etc. However, OD data is not always easy to access due to high costs or privacy concerns. Therefore, we must consider generating OD through mathematical models. Existing works utilize physics laws or machine learning (ML) models to build the association between urban structures and OD flows while these two kinds of methods suffer from the limitation of over-simplicity and poor generalization ability, respectively. In this paper, we propose to adopt physics-informed ML paradigm, which couple the physics scientific knowledge and data-driven ML methods, to construct a model named Origin-Destination Generation Networks (ODGN) for better population mobility modeling by leveraging the complementary strengths of combining physics and ML methods. Specifically, we first build a Multi-view Graph Attention Networks (MGAT) to capture the urban features of every region and then use a gravity-guided predictor to obtain OD flow between every two regions. Furthermore, we use a conditional GAN training strategy and design a sequence-based discriminator to consider the overall topological features of OD as a network. Extensive experiments on real-world datasets have been done to demonstrate the superiority of our proposed method compared with baselines.Comment: 10 pages, 8 figure

Understanding public transit patterns with open geodemographics to facilitate public transport planning

Plentiful studies have discussed the potential applications of contactless smart card from understanding interchange patterns to transit network analysis and user classifications. However, the incomplete and anonymous nature of the smart card data inherently limit the interpretations and understanding of thefindings, whichfurther limit planning implementations. Geodemographics, as ‘an analysis of people by where they live’, can be utilised as a promising supplement to provide contextual information to transport planning. This paper develops a methodological framework that conjointly integrates personalised smart card data with open geodemographics so as to pursue a better understanding of the traveller’s behaviours. It adopts a text mining technology, latent Dirichlet allocation modelling, to extract the transit patterns from the personalised smart card data and then use the open geodemographics derived from census data to enhance the interpretation of the patterns. Moreover, it presents night tube as an example to illustrate its potential usefulness in public transport planning