Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning approaches (e.g. deep neural networks) and in short-term traffic demand predictions, relatively few studies have examined this issue using a feature engineering approach to inform model selection. This research extracts novel time-lagged variables describing graph structures and flow interactions from real-world bike usage datasets, including graph node Out-strength, In-strength, Out-degree, In-degree and PageRank. These are used as inputs to different machine learning algorithms to predict short-term bike demand. The results of the experiments indicate the graph-based attributes to be more important in demand prediction than more commonly used meteorological information. The results from the different machine learning approaches (XGBoost, MLP, LSTM) improve when time-lagged graph information is included. Deep neural networks were found to be better able to handle the sequences of the time-lagged graph variables than other approaches, resulting in more accurate forecasting. Thus incorporating graph-based features can improve understanding and modelling of demand patterns in urban areas, supporting bike-sharing schemes and promoting sustainable transport. The proposed approach can be extended into many existing models using spatial data and can be readily transferred to other applications for predicting dynamics in mass transit systems. A number of limitations and areas of further work are discussed

HMIAN: a Hierarchical Mapping and Interactive Attention Data Fusion Network for Traffic Forecasting

© 2022 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/JIOT.2022.3196461With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors such as weather and holidays on traffic is an unsolved problem. This paper proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing functional zones, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into functional zones, calculate the interaction between functional zones and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show that the proposed model better prediction performance compared with other existing approaches in more complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.Peer reviewe

Predicting bicycle arrivals in a Bicycle Sharing System network: A data science driven approach grounded in Zero-Inflated Regression

The adoption of bicycle sharing systems (BSS) is growing in order to improve the way people move around cities, but also to stimulate the development of a more sustainable urban mobility. For the proper functioning of a BSS, it is important to have bicycles permanently available at the stations for users to start their trips, so the literature has undertaken efforts, from the perspective of the service operator, to improve the process of redistribution of bicycles and thus ensure their availability at the different stations. Since the guarantee of available bicycles cannot be assured, this work proposes to develop, from the cyclist's perspective, a proof of concept on the feasibility of informing the user about the possibility of starting a trip in a pre-defined time interval. The main contributions of this work are: (i) the ability to predict how many bicycles will arrive at a given station is a feasible improvement for BSS, (ii) the models developed through the Zero-Inflated Regression approach are a path that can be explored to improve prediction and (iii) unprecedented methodological contribution to the literature on BSS focusing on the end-user's decision power about whether or not it will soon be possible to start a trip.A adoção de sistemas de bicicletas partilhadas (BSS) vem crescendo com o objetivo de melhorar a forma como as pessoas se deslocam pelas cidades, mas também para estimular o desenvolvimento de uma mobilidade urbana mais sustentável. Para o bom funcionamento de um BSS é importante que haja bicicletas permanentemente disponíveis nas estações para os utilizadores iniciarem as suas viagens, pelo que a literatura tem empreendido esforços, sob a ótica do operador do serviço, para melhorar o processo de redistribuição das bicicletas e assim garantir a sua disponibilidade nas diferentes estações. Como a garantia de bicicletas disponíveis não pode ser assegurada, este trabalho propõe-se desenvolver, sob a ótica do ciclista, uma prova de conceito sobre a viabilidade de informar o utilizador acerca da possibilidade de iniciar uma viagem num intervalo de tempo pré-definido. As principais contribuições deste trabalho são: (i) a capacidade de previsão de quantas bicicletas chegarão a uma determinada estação é uma melhoria viável para os BSS, (ii) os modelos desenvolvidos através da aproximação Zero-Inflated Regression são um caminho que pode ser explorado para melhorar a previsão e (iii) contributo metodológico inédito à literatura sobre os BSS com foco no poder decisório do utilizador final sobre se será, ou não, possível iniciar uma viagem em breve

CITIES AND ACCESSIBILITY: THE POTENTIAL FOR CARBON REDUCTIONS AND THE NEED FOR NATIONAL LEADERSHIP

This article begins by outlining the elements that should be included in the framework for understanding how people interact with their built environments. Part II describes how the framework might be made operational through the use of an emerging technique called land-use transportation scenario planning. Part III assesses how well land-use transportation scenario planning fits within the dictates and limits of U.S. transportation law. The analysis ultimately reveals that it holds substantial promise as a tool that could lead to meaningful cuts in carbon emissions

Developing an integrated technology roadmapping process to meet regional technology planning needs: the e-bike pilot study

Smart grid is a promising class of new technologies offering many potential benefits for electric utility systems, including possibilities for smart appliances which can communicate with power systems and help to better match supply and demand. Additional services include the ability to\ud better integrate growing supplies of renewable energy and perform a variety of value-added services on the grid. However, a number of challenges exist in order to achieving these benefits.\ud Many utility systems have substantial regulatory structures that make business processes and technology innovation substantially different than in other industries. Due to complex histories regarding regulatory and deregulatory efforts, and due to what some economists consider natural monopoly characteristics in the industry, such regulatory structures are unlikely to change in the immediate future. Therefore, innovation within these industries, including the development of\ud smart grid, will require an understanding of such regulatory and policy frameworks, development of appropriate business models, and adaptation of technologies to fit these emerging requirements. Technology Roadmapping may be a useful method of planning this type of future development within the smart grid sector, but such technology roadmaps would require a high level of integrated thinking regarding technology, business, and regulatory and policy considerations. This research provides an initial examination of the process for creating such a type of integrated technology roadmapping and assessment process. This research proposes to build upon previous research in the Pacific Northwest and create a more robust technology planning process that will allow key variables to be tested and different pathways to be explored

Measuring the structural determinants of urban travel demand

To be best prepared for tomorrow's cities we need to forecast urban travel demand. To this end, this study calibrates an urban travel demand model, which uses the principal structural variables that have been identified in the literature. It uses a robust econometric method, which has been little applied in the sphere of transportation. The results show that two variables stand out from the others: the user cost of transport - by private car and public transport - and urban density. It is surprising, but explicable with the available data, that the demand functions estimated for a given country are independent from the group of countries to which it belongs.Urban travel ; Demand estimation ; Urban density ; Travel cost