Urban Analytics: History, Trajectory, and Critique

Boeing, Geoff; Batty, Michael; Jiang, Shan; Schweitzer, Lisa

doi:10.2139/ssrn.3846508

Cited by 2 publications

(3 citation statements)

References 37 publications

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“…Important advances on multimodal mobility and transportation have been shown to be interdisciplinary and have clearly benefitted from the large variety of scientific fields and practices. Indeed, synergies between disciplines such as urban planning, geoinformatics, computer science, and physics have increased in the last few years, giving rise to new interdisciplinary approaches such as a Science of Cities or Urban Data Science (Boeing et al, 2021;Resch and Szell, 2019). We envision that research on multimodal mobility and transportation to maintain a highly interdisciplinary character also in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, the assumption that individuals have global knowledge of the system and minimize travel time contrast recent findings in spatial cognition, showing that human spatial knowledge and navigation ability is limited (Bongiorno et al, 2021;Gallotti et al, 2016b). For example, a recent study on pedestrian navigation made clear that path choices seem to be affected by the orientation of street segments along the route (Bongiorno et al, 2021). Recent modeling approaches for singlelayer networks (Manley and Cheng, 2018) incorporate these ideas in routing models where agents are characterized by bounded knowledge and limited rationality.…”

Section: Modeling Multimodal Mobilitymentioning

confidence: 99%

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Multimodal urban mobility and multilayer transport networks

Orozco

Alessandretti

Saberi

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modeling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open-source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Modeling Multimodal Mobilitymentioning

confidence: 99%

Multimodal urban mobility and multilayer transport networks

Orozco

Alessandretti

Saberi

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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“…The challenge is that most transportation network companies (TNCs) are not willing to share their data, even though their user privacy can be safely protected (He, 2021). With the rising big data, vehicle GPS data collected all over the world by the private sector have been used to generate traffic conditions for cities (Boeing et al, 2021;Inrix, 2021). In the future, if transportation network companies or GPS data aggregators in the private sector would collaborate with local agencies through mutually agreeable data sharing mechanisms (Viggiano et al, 2020), our method can be widely applied for the public good to detect flood-affected roads.…”

Section: Limitationmentioning

confidence: 99%

Post‐event flood mapping for road networks using taxiGPSdata

Kong

Yang

Qiu³

et al. 2022

J Flood Risk Management

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Dynamically updated and fine-grained flooding maps are critical for situational awareness and decision support. However, traditional methods, such as eyewitness reports, remote sensing, and hydrology models, may fail to correspond with the rapidly changing urban hydrological environment. The presence of crowdsourced data (such as social media data) allows for timely and cost-effective monitoring of flood hazards through collective observations; however, such data can be unreliable due to sample bias and low spatiotemporal resolution. Therefore, new measures to identify flood-affected roads are desirable. In this study, we propose a methodology that leverages taxi GPS data to support post-event flood mapping for the road network. This method can identify whether a significant reduction in the taxi passing rate for a road segment was related to precipitation, and automatically recognize the floodaffected roads based on a logistic regression model. Using taxi GPS data in Shenzhen as an example, we derived the flood map of the road networks, and compared and validated the results. This study demonstrated the usefulness of taxi GPS data in generating high-quality flood maps and the value of incorporating multiple data sources for sensing near real-time flood in the city.

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Urban Analytics: History, Trajectory, and Critique

Cited by 2 publications

References 37 publications

Multimodal urban mobility and multilayer transport networks

Multimodal urban mobility and multilayer transport networks

Post‐event flood mapping for road networks using taxiGPSdata

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