scholarly journals Lost in transportation: Information measures and cognitive limits in multilayer navigation

2016 ◽  
Vol 2 (2) ◽  
pp. e1500445 ◽  
Author(s):  
Riccardo Gallotti ◽  
Mason A. Porter ◽  
Marc Barthelemy
Keyword(s):  
New York ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Traditional View ◽  
Multimodal Transportation ◽  
Large Cities ◽  
Amount Of Information ◽  
Information Measures ◽  
Transportation Modes ◽  
Metropolitan Networks

Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder whether it is possible to quantitatively characterize our difficulty navigating in them and whether such navigation exceeds our cognitive limits. A transition between different search strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of a limit associated with cognitive overload and caused by a large amount of information that needs to be processed. In this light, we analyzed the world’s 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the “Dunbar number,” which represents a limit to the size of an individual’s friendship circle, our cognitive limit suggests that maps should not consist of more than 250 connection points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks. In large cities such as New York, Paris, and Tokyo, more than 80% of the trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and, consequently, the traditional view of navigation in cities has to be revised substantially.

scholarly journals Multiplex networks in metropolitan areas: generic features and local effects

2015 ◽  
Vol 12 (111) ◽  
pp. 20150651 ◽  
Author(s):  
Emanuele Strano ◽  
Saray Shai ◽  
Simon Dobson ◽  
Marc Barthelemy
Keyword(s):  
New York ◽  
Metropolitan Areas ◽  
Transportation Systems ◽  
Urban Systems ◽  
Street Network ◽  
Multilayer Network ◽  
Large Cities ◽  
Planning Decisions ◽  
Negative Side Effects ◽  
The Impact

Most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately: it is however important to understand the impact on urban systems of coupling different modes and we report in this paper an empirical analysis of the coupling between the street network and the subway for the two large metropolitan areas of London and New York. We observe a similar behaviour for network quantities related to quickest paths suggesting the existence of generic mechanisms operating beyond the local peculiarities of the specific cities studied. An analysis of the betweenness centrality distribution shows that the introduction of underground networks operate as a decentralizing force creating congestion in places located at the end of underground lines. Also, we find that increasing the speed of subways is not always beneficial and may lead to unwanted uneven spatial distributions of accessibility. In fact, for London—but not for New York—there is an optimal subway speed in terms of global congestion. These results show that it is crucial to consider the full, multimodal, multilayer network aspects of transportation systems in order to understand the behaviour of cities and to avoid possible negative side-effects of urban planning decisions.

scholarly journals Benefits of Community Gardening on Quality-of-Life Issues

1996 ◽  
Vol 14 (4) ◽  
pp. 204-209 ◽  
Author(s):  
Tina M. Waliczek ◽  
Richard H. Mattson ◽  
Jayne M. Zajicek
Keyword(s):  
Quality Of Life ◽  
New York ◽  
Los Angeles ◽  
Community Gardens ◽  
Nationwide Survey ◽  
Community Gardening ◽  
Large Cities ◽  
Human Needs ◽  
Life Issues

Abstract A nationwide survey of community gardeners found differences in rankings of the importance of community gardens related to quality-of-life perceptions based on Maslow' hierarchy of human needs model. Race, gender, and city sizes affected perceptions. When comparisons were made among the four racial/ethnic divisions, responses to 18 of the 24 questions were found to be statistically different. Community gardens were especially important to African-American and Hispanic gardeners. Male and female gardeners rated quality-of-life benefits from gardens similarly in importance. However, women placed higher value on the importance of saving money and the beauty within the garden. Gardeners in small, medium, and large metropolitan cities had similar quality-of-life perceptions with only 4 of the 24 statement responses showing significant differences. Significant differences were found in 10 of the 24 statement responses between gardeners of the two large cities of Los Angeles and New York. In most cases, mean ratings were higher for gardeners in New York than those in Los Angeles.

Multi-modal transportation recommendation with unified route representation learning

2020 ◽  
Vol 14 (3) ◽  
pp. 342-350
Author(s):  
Hao Liu ◽  
Jindong Han ◽  
Yanjie Fu ◽  
Jingbo Zhou ◽  
Xinjiang Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Representation Learning ◽  
Transportation Systems ◽  
Graph Representation ◽  
Dynamic Graph ◽  
Arbitrary Length ◽  
Task Learning ◽  
Semantic Coherence ◽  
Spatio Temporal

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

Performance Measures for Multimodal Transportation Systems

1996 ◽  
Vol 1518 (1) ◽  
pp. 85-93 ◽  
Author(s):  
Richard H. Pratt ◽  
Timothy J. Lomax
Keyword(s):  
Land Use ◽  
Performance Measurement ◽  
Travel Time ◽  
Performance Measures ◽  
System Performance ◽  
Transportation Systems ◽  
Regional Networks ◽  
Multimodal Transportation ◽  
Decision Context ◽  
Multimodal Systems

Transportation systems analyses have been evolving as the decision context for improvement projects and programs has changed. The increased emphasis on the movement of persons and goods, and a recognition of the importance of system performance measures that address the needs and interests of the audiences for mobility information, will result in a very different set of procedures for evaluating transportation and land use infrastructure and policies. Some of the key underlying concerns of performance measurement for multimodal systems are presented. Definitions are included for congestion, mobility, and accessibility that are used to guide the development of performance measures. Travel time–based measures are seen as the most readily understandable quantities, and examples are used to show how mobility can be measured for locations, corridors, transit analyses, and regional networks.

scholarly journals Evaluating the Vulnerability of Time-Sensitive Transportation Networks: A Hub Center Interdiction Problem

2019 ◽  
Vol 11 (17) ◽  
pp. 4614 ◽  
Author(s):  
Ting L. Lei
Keyword(s):  
Linear Programming ◽  
Transit Time ◽  
Preventive Measures ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Worst Case ◽  
Hub And Spoke ◽  
Research Attention ◽  
Perishable Goods ◽  
Critical Facilities

Time-sensitive transportation systems have received increasing research attention recently. Examples of time-sensitive networks include those of perishable goods, high-value commodity, and express delivery. Much research has been devoted to optimally locating key facilities such as transportation hubs to minimize transit time. However, there is a lack of research attention to the reliability and vulnerability of time-sensitive transportation networks. Such issues cannot be ignored as facilities can be lost due to reasons such as extreme weather, equipment malfunction, and even intentional attacks. This paper proposes a hub interdiction center (HIC) model for evaluating the vulnerability of time-sensitive hub-and-spoke networks under disruptions. The model identifies the set of hub facilities whose loss will lead to the greatest increase in the worst-case transit time. From a planning perspective, such hubs are critical facilities that should be protected or enhanced by preventive measures. An efficient integer linear programming (ILP) formulation of the new model is developed. Computational experiments on a widely used US air passenger dataset show that losing a small number of hub facilities can double the maximum transit time.

scholarly journals Does High-Speed Rail Influence Urban Dynamics and Land Pricing?

2020 ◽  
Vol 12 (7) ◽  
pp. 3012 ◽  
Author(s):  
Panrawee Rungskunroch ◽  
Yuwen Yang ◽  
Sakdirat Kaewunruen
Keyword(s):  
Urban Areas ◽  
High Speed ◽  
Technical Information ◽  
Transportation Systems ◽  
Sustainable Transportation ◽  
City Development ◽  
High Speed Rail ◽  
Large Cities ◽  
Urban Dynamics ◽  
Practical Factors

At present, many countries around the world have significantly invested in sustainable transportation systems, especially for high-speed rail (HSR) infrastructures, since they are believed to improve economies, and regenerate regional and business growth. In this study, we focus on economic growth, dynamic land use, and urban mobility. The emphasis is placed on testing a hypothesis about whether HSRs can enable socio-economic development. Real case studies using big data from large cities in China, namely Shanghai province and Minhang districts, are taken into account. Socio-technical information such as employment rate, property pricing, and agglomeration in the country’s economy is collected from the China Statistics Bureau and the China Academy of Railway Sciences for analyses. This research aims to re-examine practical factors resulting from HSR’s impact on urban areas by using ANOVA analysis and dummy variable regression to analyse urban dynamics and property pricing. In addition, this study enhances the prediction outcomes that lead to urban planning strategies for the business area. The results reveal that there are various effects (i.e., regional accessibility, city development plans, and so on) required to enable the success of HSR infrastructure in order to enrich urban dynamics and land pricing. This paper also highlights critical perspectives towards sustainability, which are vital to social and economic impacts. In addition, this study provides crucial perspectives on sustainable developments for future HSR projects.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Urban park soil microbiomes are a rich reservoir of natural product biosynthetic diversity

2016 ◽  
Vol 113 (51) ◽  
pp. 14811-14816 ◽  
Author(s):  
Zachary Charlop-Powers ◽  
Clara C. Pregitzer ◽  
Christophe Lemetre ◽  
Melinda A. Ternei ◽  
Jeffrey Maniko ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Anticancer Agents ◽  
York City ◽  
Urban Park ◽  
Natural Environments ◽  
Large Cities ◽  
Systematic Exploration ◽  
Environmental Bacteria ◽  
City Park

Numerous therapeutically relevant small molecules have been identified from the screening of natural products (NPs) produced by environmental bacteria. These discovery efforts have principally focused on culturing bacteria from natural environments rich in biodiversity. We sought to assess the biosynthetic capacity of urban soil environments using a phylogenetic analysis of conserved NP biosynthetic genes amplified directly from DNA isolated from New York City park soils. By sequencing genes involved in the biosynthesis of nonribosomal peptides and polyketides, we found that urban park soil microbiomes are both rich in biosynthetic diversity and distinct from nonurban samples in their biosynthetic gene composition. A comparison of sequences derived from New York City parks to genes involved in the biosynthesis of biomedically important NPs produced by bacteria originally collected from natural environments around the world suggests that bacteria producing these same families of clinically important antibiotics, antifungals, and anticancer agents are actually present in the soils of New York City. The identification of new bacterial NPs often centers on the systematic exploration of bacteria present in natural environments. Here, we find that the soil microbiomes found in large cities likely hold similar promise as rich unexplored sources of clinically relevant NPs.

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