Browse
Submit Data
Databases
API
Help

Dataset Information

17 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Do transportation network companies decrease or increase congestion?

ABSTRACT: This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Between 2010 and 2016, weekday vehicle hours of delay increased by 62% compared to 22% in a counterfactual 2016 scenario without TNCs. The findings provide insight into expected changes in major cities as TNCs continue to grow, informing decisions about how to integrate TNCs into the existing transportation system.

SUBMITTER: Erhardt GD

PROVIDER: S-EPMC6506243 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Do transportation network companies decrease or increase congestion?

Erhardt Gregory D GD Roy Sneha S Cooper Drew D Sana Bhargava B Chen Mei M Castiglione Joe J

Science advances 20190508 5

This research examines whether transportation network companies (TNCs), such as Uber and Lyft, live up to their stated vision of reducing congestion in major cities. Existing research has produced conflicting results and has been hampered by a lack of data. Using data scraped from the application programming interfaces of two TNCs, combined with observed travel time data, we find that contrary to their vision, TNCs are the biggest contributor to growing traffic congestion in San Francisco. Betwe ...[more]

PMID: 31086811

Similar Datasets

College students' use of transportation networking companies: An opportunity to decrease substance-impaired driving.

Project description:Objective: To examine the use of transportation networking companies (TNCs) (eg, Uber) among substance-using students in rural and urban college settings. Participants: Students at two large state universities were randomly selected and screened for substance use. Participants reported use of TNCs generally and after substance use and whether TNC use was on or near campus or in other environments. Methods: Data were evaluated using chi-square test, t-tests, and Fisher's exact tests. Results: Most (85%) participants (n = 99, 61% response rate) had used a TNC. Among students who used TNCs on/near campus, 98% of rural students used them after substance use compared to 85% of urban students (p = .037). We did not detect differences in TNC use by gender or age. Conclusions: Results indicate that TNC use is common after college student substance use and may play a particularly important role in preventing impaired driving for rural campuses where existing transportation options are limited.

| S-EPMC6428618 | biostudies-literature

Large-scale transportation network congestion evolution prediction using deep learning theory.

Project description:Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

| S-EPMC4363621 | biostudies-literature

Congestion in multi-function parallel network DEA.

Project description:Congestion is an economic phenomenon of the production process in which the excessive values of inputs lead to a reduction of the outputs. As the existence of congestion makes to increase costs and decreases efficiency, this issue is not acceptable for decision makers. Hence, many methods have been proposed to detect the congestion in the Data Envelopment Analysis framework (DEA). Most of these methods are designed to deal with the decision making units (DMUs) that have no network structure. However, in most real-world applications, some units are composed of independent production subunits. Therefore, a new scheme is required to determine the congestion of such units. A multi-function parallel system is a more common case in the real world that is composed of the same number of subunits such that each subunit has specific functions. In this paper, considering the operation of individual components of each DMU, a new DEA model is proposed to identify and evaluate the congestion of the multi-function parallel systems. It is shown that the proposed method is highly economical in comparison with the existing black-box view from a computational viewpoint. Then, the proposed model is illustrated using a numerical example along with a real case study.

| S-EPMC10545126 | biostudies-literature

Controlling congestion on complex networks: fairness, efficiency and network structure.

Project description:We consider two elementary (max-flow and uniform-flow) and two realistic (max-min fairness and proportional fairness) congestion control schemes, and analyse how the algorithms and network structure affect throughput, the fairness of flow allocation, and the location of bottleneck edges. The more realistic proportional fairness and max-min fairness algorithms have similar throughput, but path flow allocations are more unequal in scale-free than in random regular networks. Scale-free networks have lower throughput than their random regular counterparts in the uniform-flow algorithm, which is favoured in the complex networks literature. We show, however, that this relation is reversed on all other congestion control algorithms for a region of the parameter space given by the degree exponent ? and average degree ?k?. Moreover, the uniform-flow algorithm severely underestimates the network throughput of congested networks, and a rich phenomenology of path flow allocations is only present in the more realistic ?-fair family of algorithms. Finally, we show that the number of paths passing through an edge characterises the location of a wide range of bottleneck edges in these algorithms. Such identification of bottlenecks could provide a bridge between the two fields of complex networks and congestion control.

| S-EPMC5567293 | biostudies-other

Perceptual Priming Can Increase or Decrease With Aging.

Project description:A decline in declarative or explicit memory has been extensively characterized in cognitive aging and is a hallmark of cognitive impairments. However, whether and how implicit perceptual memory varies with aging or cognitive impairment is unclear. Here, we compared implicit perceptual memory and explicit memory measures in three groups of participants: (1) 59 healthy young volunteers (20-30 years); (2) 269 healthy old volunteers (50-90 years) and (3) 21 patients with mild cognitive impairment, i.e., MCI (50-90 years). To measure explicit memory, participants were tested on standard recognition and recall tasks. To measure implicit perceptual memory, we used a classic perceptual priming paradigm. Participants had to report the shape of a visual search pop-out target whose color or position was varied randomly across trials. Perceptual priming was measured as the speedup in response time for targets that repeated in color or position. Our main findings are as follows: (1) Explicit memory was weaker in old compared to young participants, and in MCI patients compared to age- and education-matched controls; (2) Surprisingly, perceptual priming did not always decline with age: color priming was smaller in older participants but position priming was larger; (3) Position priming was less frequent in the MCI group compared to matched controls; (4) Perceptual priming and explicit memory were uncorrelated across participants. Thus, perceptual priming can increase or decrease with age or cognitive impairment, but these changes do not covary with explicit memory.

| S-EPMC7711047 | biostudies-literature

Renal congestion (Rattus)

Project description:We created a rat renal congestion model and investigated the effect of renal congestion on hemodynamics and molecular mechanisms. The inferior vena cava (IVC) between the renal veins was ligated by suture in male Sprague-Dawley rats to increase upstream IVC pressure and induce congestion in the left kidney only. Left kidney congestion reduced renal blood flow, glomerular filtration rate, and increased renal interstitial hydrostatic pressure. Tubulointerstitial and glomerular injury and medullary thick ascending limb hypoxia were observed only in the congestive kidneys. Molecules related to extracellular matrix expansion, tubular injury, and focal adhesion were upregulated in microarray analysis. Renal decapsulation ameliorated the tubulointerstitial injury. Electron microscopy captured pericyte detachment in the congestive kidneys. Transgelin and platelet-derived growth factor receptors, as indicators of pericyte-myofibroblast transition, were upregulated in the pericytes and the adjacent interstitium. With the compression of the peritubular capillaries and tubules, hypoxia and physical stress induce pericyte detachment, which could result in extracellular matrix expansion and tubular injury in renal congestion.

2018-11-14 | GSE114031 | GEO

An Algorithm for the Mixed Transportation Network Design Problem.

Project description:This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately.

| S-EPMC5023175 | biostudies-literature

Do Co-Worker Networks Increase or Decrease Productivity Differences?

Project description:Do labor mobility and co-worker networks contribute to convergence or divergence between regions? Based on the previous literature, labor mobility contributes to knowledge transfer between firms. Therefore, mobility may contribute to decreasing productivity differences, while limited mobility sustains higher differences. The effect of co-worker networks, however, can be two-fold in this process; they transmit information about potential jobs, which may enhance the mobility of workers-even between regions-and this enhanced mobility may contribute to levelling of differences. However, if mobility between regions involves movement costs, co-worker networks may concentrate locally-possibly contributing to the persistence of regional differences. In this paper, we build an agent-based model of labor mobility across firms and regions with knowledge spillovers that reflects key empirical observations on labor markets. We analyze the impact of network information provided about potential employers in this model and find that it contributes to increasing inter-regional mobility, and subsequently, to decreasing regional differences. We also find that both the density of coworker networks, as well as their regional concentrations, decrease if network information is available.

| S-EPMC8621545 | biostudies-literature

Dynamic co-expression network analysis of lncRNAs and mRNAs associated with venous congestion.

Project description:Venous congestion and volume overload are important in cardiorenal syndromes, in which multiple regulated factors are involved, including long non‑coding RNAs (lncRNAs). To investigate the underlying role of lncRNAs in regulating the development of venous congestion, an Affymetrix microarray associated with peripheral venous congestion was annotated, then a bipartite dynamic lncRNA-mRNA co-expression network was constructed in which nodes indicated lncRNAs or mRNAs. The nodes were connected when the lncRNAs or mRNAs were dynamically co‑expressed. Following functional analysis of this network, several dynamic alternative pathways were identified, including the calcium signaling pathway during venous congestion development. Additionally, certain lncRNAs (LINC00523, LINC01210 and RP11-435O5.5) were identified that may potentially dynamically regulate certain proteins, including plasma membrane calcium ATPase (PMCA) and G protein‑coupled receptor (GPCR), in the calcium signaling pathway. Particularly, the dynamically regulated switch of LINC00523 from co‑expression with PMCA to GPCR may be involved in damage to steady state intracellular calcium. In brief, the current study demonstrated a potential novel mechanism of lncRNA function during venous congestion.

| S-EPMC4991739 | biostudies-literature

Physical Layer Secret-Key Generation Scheme for Transportation Security Sensor Network.

Project description:Wireless Sensor Networks (WSNs) are widely used in different disciplines, including transportation systems, agriculture field environment monitoring, healthcare systems, and industrial monitoring. The security challenge of the wireless communication link between sensor nodes is critical in WSNs. In this paper, we propose a new physical layer secret-key generation scheme for transportation security sensor network. The scheme is based on the cooperation of all the sensor nodes, thus avoiding the key distribution process, which increases the security of the system. Different passive and active attack models are analyzed in this paper. We also prove that when the cooperative node number is large enough, even when the eavesdropper is equipped with multiple antennas, the secret-key is still secure. Numerical results are performed to show the efficiency of the proposed scheme.

| S-EPMC5539482 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data