scholarly journals Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City

2020 ◽  
Vol 9 (8) ◽  
pp. 475 ◽  
Author(s):  
Xinxin Zhang ◽  
Bo Huang ◽  
Shunzhi Zhu

The rapid growth of transportation network companies (TNCs) has reshaped the traditional taxi market in many modern cities around the world. This study aims to explore the spatiotemporal variations of built environment on traditional taxis (TTs) and TNC. Considering the heterogeneity of ridership distribution in spatial and temporal aspects, we implemented a geographically and temporally weighted regression (GTWR) model, which was improved by parallel computing technology, to efficiently evaluate the effects of local influencing factors on the monthly ridership distribution for both modes at each taxi zone. A case study was implemented in New York City (NYC) using 659 million pick-up points recorded by TT and TNC from 2015 to 2017. Fourteen influencing factors from four groups, including weather, land use, socioeconomic and transportation, are selected as independent variables. The modeling results show that the improved parallel-based GTWR model can achieve better fitting results than the ordinary least squares (OLS) model, and it is more efficient for big datasets. The coefficients of the influencing variables further indicate that TNC has become more convenient for passengers in snowy weather, while TT is more concentrated at the locations close to public transportation. Moreover, the socioeconomic properties are the most important factors that caused the difference of spatiotemporal patterns. For example, passengers with higher education/income are more inclined to select TT in the western of NYC, while vehicle ownership promotes the utility of TNC in the middle of NYC. These findings can provide scientific insights and a basis for transportation departments and companies to make rational and effective use of existing resources.

Last Subway ◽  
2020 ◽  
pp. 49-72
Author(s):  
Philip Mark Plotch

This chapter assesses the roles played by New York governor Nelson Rockefeller and New York City mayor John Lindsay, as well as William Ronan, in transforming the transportation system. Ronan, Rockefeller, and Lindsay all realized that improving public transportation was critical to strengthening the economy of the city and the region. They were also well aware of the benefits of a Second Avenue subway, since all three of them lived on the Upper East Side. After Lindsay failed to reorganize the transportation agencies, Rockefeller and Ronan developed their own grand vision for the region's transportation network, and in December of 1966, Ronan stepped down from his post as secretary to begin implementing their plan. At the beginning of the state's 1967 legislative session, Rockefeller and Ronan announced their two-pronged approach. First, they proposed integrating the New York City Transit Authority and the Triborough Bridge and Tunnel Authority (TBTA) into the Metropolitan Commuter Transportation Authority (MCTA). In addition, Rockefeller and Ronan would seek voter approval to borrow $2.5 billion that would be dedicated for roadway and public transportation improvements across the state. In 1967, the governor and Ronan obtained the support they needed to transform the transportation network, a feat that Lindsay had not been able to accomplish.


2017 ◽  
Vol 59 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Min Gyung Kim ◽  
Hyunjoo Yang ◽  
Anna S. Mattila

New York City launched a restaurant sanitation letter grade system in 2010. We evaluate the impact of customer loyalty on restaurant revisit intentions after exposure to a sanitation grade alone, and after exposure to a sanitation grade plus narrative information about sanitation violations (e.g., presence of rats). We use a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C, or pending) between-subjects full factorial design to test the hypotheses using data from 547 participants recruited from Amazon MTurk who reside in the New York City area. Our study yields three findings. First, loyal customers exhibit higher intentions to revisit restaurants than non-loyal customers, regardless of sanitation letter grades. Second, the difference in revisit intentions between loyal and non-loyal customers is higher when sanitation grades are poorer. Finally, loyal customers are less sensitive to narrative information about sanitation violations.


2019 ◽  
Vol 26 (1) ◽  
pp. 43-62 ◽  
Author(s):  
Jon Moen ◽  
Ellis Tallman

Before the Panic of 1907 the large New York City banks were able to maintain the call loan market's liquidity during panics, but the rise in outside lending by trust companies and interior banks in the decade leading up the panic weakened the influence of the large banks. Creating a reliable source of liquidity and reserves external to the financial market like a central bank became obvious after the panic. In the call loan market, like the REPO market in 2008, lack of information on the identity of lenders and volume of the market hindered attempts to stop panic-related depositor withdrawals. Our new estimates of who was participating in the call loan market reveal that it did not contract after 1907; while the trust companies became less important, the New York national banks and outside lenders more than made up the difference.


2019 ◽  
pp. 009614421989657
Author(s):  
Jonathan English

New York City witnessed the construction of one of the largest subway systems in the world in the first four decades of the twentieth century. Expansion virtually ceased thereafter, and New York’s public transportation has since relied on a legacy of aging infrastructure. The explanation of this unexpected cessation is key to understanding the city’s current transit problems, and also offers valuable lessons for other cities experiencing infrastructure construction booms. Identifying the 1951 bond issue as a key turning point, this article argues that there are three convergent factors that brought about the end of subway expansion after the Second World War: political leadership distracted by disputes over administration and unable to plan for the long term; financial constraints imposed by construction and labor-cost inflation, the strained municipal budget, and declining ridership; and the New York transit authorities’ indifference to the growing demographic, political, and symbolic significance of the rapidly growing suburbs.


mSphere ◽  
2016 ◽  
Vol 1 (6) ◽  
Author(s):  
Holly M. Bik ◽  
Julia M. Maritz ◽  
Albert Luong ◽  
Hakdong Shin ◽  
Maria Gloria Dominguez-Bello ◽  
...  

ABSTRACT Automated teller machine (ATM) keypads represent a specific and unexplored microhabitat for microbial communities. Although the number of built environment and urban microbial ecology studies has expanded greatly in recent years, the majority of research to date has focused on mass transit systems, city soils, and plumbing and ventilation systems in buildings. ATM surfaces, potentially retaining microbial signatures of human inhabitants, including both commensal taxa and pathogens, are interesting from both a biodiversity perspective and a public health perspective. By focusing on ATM keypads in different geographic areas of New York City with distinct population demographics, we aimed to characterize the diversity and distribution of both prokaryotic and eukaryotic microbes, thus making a unique contribution to the growing body of work focused on the “urban microbiome.” In New York City, the surface area of urban surfaces in Manhattan far exceeds the geographic area of the island itself. We have only just begun to describe the vast array of microbial taxa that are likely to be present across diverse types of urban habitats. In densely populated urban environments, the distribution of microbes and the drivers of microbial community assemblages are not well understood. In sprawling metropolitan habitats, the “urban microbiome” may represent a mix of human-associated and environmental taxa. Here we carried out a baseline study of automated teller machine (ATM) keypads in New York City (NYC). Our goal was to describe the biodiversity and biogeography of both prokaryotic and eukaryotic microbes in an urban setting while assessing the potential source of microbial assemblages on ATM keypads. Microbial swab samples were collected from three boroughs (Manhattan, Queens, and Brooklyn) during June and July 2014, followed by generation of Illumina MiSeq datasets for bacterial (16S rRNA) and eukaryotic (18S rRNA) marker genes. Downstream analysis was carried out in the QIIME pipeline, in conjunction with neighborhood metadata (ethnicity, population, age groups) from the NYC Open Data portal. Neither the 16S nor 18S rRNA datasets showed any clustering patterns related to geography or neighborhood demographics. Bacterial assemblages on ATM keypads were dominated by taxonomic groups known to be associated with human skin communities (Actinobacteria, Bacteroides, Firmicutes, and Proteobacteria), although SourceTracker analysis was unable to identify the source habitat for the majority of taxa. Eukaryotic assemblages were dominated by fungal taxa as well as by a low-diversity protist community containing both free-living and potentially pathogenic taxa (Toxoplasma, Trichomonas). Our results suggest that ATM keypads amalgamate microbial assemblages from different sources, including the human microbiome, eukaryotic food species, and potentially novel extremophilic taxa adapted to air or surfaces in the built environment. DNA obtained from ATM keypads may thus provide a record of both human behavior and environmental sources of microbes. IMPORTANCE Automated teller machine (ATM) keypads represent a specific and unexplored microhabitat for microbial communities. Although the number of built environment and urban microbial ecology studies has expanded greatly in recent years, the majority of research to date has focused on mass transit systems, city soils, and plumbing and ventilation systems in buildings. ATM surfaces, potentially retaining microbial signatures of human inhabitants, including both commensal taxa and pathogens, are interesting from both a biodiversity perspective and a public health perspective. By focusing on ATM keypads in different geographic areas of New York City with distinct population demographics, we aimed to characterize the diversity and distribution of both prokaryotic and eukaryotic microbes, thus making a unique contribution to the growing body of work focused on the “urban microbiome.” In New York City, the surface area of urban surfaces in Manhattan far exceeds the geographic area of the island itself. We have only just begun to describe the vast array of microbial taxa that are likely to be present across diverse types of urban habitats. Podcast: A podcast concerning this article is available.


2005 ◽  
Vol 11 (2) ◽  
pp. 147-156 ◽  
Author(s):  
C. Hembree ◽  
S. Galea ◽  
J. Ahern ◽  
M. Tracy ◽  
T. Markham Piper ◽  
...  

Author(s):  
Anne Halvorsen ◽  
Daniel Wood ◽  
Timon Stasko ◽  
Darian Jefferson ◽  
Alla Reddy

Like many transit agencies, New York City Transit (NYCT) has long relied on operations-focused metrics to measure its performance. Although these metrics, such as capacity provided and terminal on-time performance, are useful internally to indicate the actions needed to improve service, they typically do not represent the customer experience. To improve its transparency and public communications, NYCT launched a new online Subway Dashboard in September 2017. Two new passenger-centric metrics were developed for the dashboard: additional platform time (APT), the extra time passengers spend waiting for a train over the scheduled time, and additional train time (ATT), the extra time they spend riding a train over the scheduled time. Unlike similar existing metrics, NYCT’s new methodology is easily transferable to other agencies, even those without exit data from an automated fare collection system. Using a representative origin–destination matrix and daily scheduled and actual train movement data, a simplified train assignment model assigns each passenger trip to a train based on scheduled and actual service. APT and ATT are calculated as the difference in travel times between these two assignments for each individual trip and can then be aggregated based on line or time period. These new customer-centric metrics received praise from transit advocates, academics, other agencies, and the press, and are now used within NYCT for communicating with customers, as well as to understand the customer impacts of operational initiatives.


Author(s):  
Raymond Gerte ◽  
Karthik C. Konduri ◽  
Nalini Ravishanker ◽  
Amit Mondal ◽  
Naveen Eluru

The concept of shared travel, making trips with other users via a common vehicle, is far from novel. However, a changing technological climate has laid the tracks for new dynamically shared modes in the form of transportation network companies (TNCs), to substantially impact travel behavior. The current body of research on how these modal offerings impact the demand for existing shared modes (e.g., bikeshare, transit) is growing. However, a comprehensive investigation of the temporal evolution of the demand for TNCs and their relationship to other shared modes, is lacking. This research tackles this important limitation by analyzing ridership data for TNCs, taxi, subway, and Citi Bike in New York City using daily ridership data from January 2015 through June 2017. The primary objective was to understand the relationship between TNCs and other shared modal offerings while accounting for the influence of temporal trends and other exogenous factors. A dynamic linear modeling framework was formulated to accommodate time-dependent trends, periodicity, and time-varying exogenous factors on the demand for TNCs. As a preliminary work, the findings of this study reinforce the observed substitution relationship between taxis and TNCs. The results may also indicate a substitutional relationship between TNCs and Citi Bike, and a complementary relationship with subway, however these results still need to be explored further. With potentially impactful findings for planning and policymakers, the predictive model developed in the study can be used to carry out forecasting in support of short- and long-term operations and planning applications.


Sign in / Sign up

Export Citation Format

Share Document