scholarly journals Equity Implications of Ride-Hail Travel during COVID-19 in California

2021 ◽  
pp. 036119812110372
Author(s):  
Anne Brown ◽  
Rik Williams
Keyword(s):  
Transportation Systems ◽  
Neighborhood Characteristics ◽  
Work From Home ◽  
Transit Agencies ◽  
Transit Use ◽  
The U.S

COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and—for those who cannot work from home—to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode—ride-hailing—fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.

scholarly journals Do transportation network companies increase or decrease transit ridership? Empirical evidence from San Francisco

2021 ◽  
Author(s):  
Gregory D. Erhardt ◽  
Richard Alexander Mucci ◽  
Drew Cooper ◽  
Bhargava Sana ◽  
Mei Chen ◽  
...  
Keyword(s):  
San Francisco ◽  
Transportation Network ◽  
Transportation Systems ◽  
Light Rail ◽  
Transit Ridership ◽  
Application Programming ◽  
Transit Agencies ◽  
Using Data ◽  
Transit Use ◽  
Transportation Network Companies

AbstractTransportation network companies (TNCs), such as Uber and Lyft, have been hypothesized to both complement and compete with public transit. Existing research on the topic is limited by a lack of detailed data on the timing and location of TNC trips. This study overcomes that limitation by using data scraped from the Application Programming Interfaces of two TNCs, combined with Automated Passenger Count data on transit use and other supporting data. Using a panel data model of the change in bus ridership in San Francisco between 2010 and 2015, and confirming the result with a separate time-series model, we find that TNCs are responsible for a net ridership decline of about 10%, offsetting net gains from other factors such as service increases and population growth. We do not find a statistically significant effect on light rail ridership. Cities and transit agencies should recognize the transit-competitive nature of TNCs as they plan, regulate and operate their transportation systems.

Comparing a Machine Learning Predictive Model with Federal Transit Administration (FTA)’s Default Useful Life Benchmark to Predict Replacement Costs for Revenue Vehicles

2020 ◽  
Vol 2674 (2) ◽  
pp. 181-190
Author(s):  
Dilip Mistry ◽  
Jill Hough
Keyword(s):  
Machine Learning ◽  
Service Life ◽  
Predictive Model ◽  
Learning Algorithm ◽  
The State ◽  
Decision Makers ◽  
Machine Learning Algorithm ◽  
Useful Life ◽  
Transit Agencies ◽  
The U.S

A predictive model is developed that uses a machine learning algorithm to predict the service life of transit vehicles and calculates backlog and yearly replacement costs to achieve and maintain transit vehicles in a state of good repair. The model is applied to data from the State of Oklahoma. The vehicle service lives predicted by the machine learning predictive model (MLPM) are compared with the default useful life benchmark (ULB) of the U.S. Federal Transit Administration (FTA). The model shows that the service life predicted by the MLPM provides relatively more realistic predictions of replacement costs of revenue vehicles than the predictions generated using FTA’s default ULB. The MLPM will help Oklahoma’s transit agencies facilitate the state of good repair analysis of their transit vehicles and guide decision makers when investing in rehabilitation and replacement needs. The paper demonstrates that it is advantageous to use a MLPM to predict the service life of revenue vehicles in place of the FTA’s default ULB.

Influences of Neighborhood Characteristics and Personal Attitudes on University Commuters’ Public Transit Use

2015 ◽  
Vol 2500 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Mi Namgung ◽  
Gulsah Akar
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Public Transit ◽  
Principal Component ◽  
Neighborhood Characteristics ◽  
Single Family ◽  
Mixed Use ◽  
Personal Attitudes ◽  
Intersection Density ◽  
Transit Use

This study examined the links between attitudes, the built environment, and travel behavior on the basis of data from the Ohio State University's 2012 Campus Transportation Survey. The analysis results indicated that attitudes might have explained travel behavior better than the built environment. Survey respondents were asked questions about their attitudes on public transit use, and their answers were grouped into new attitudinal factors by using principal component analysis. Then, new neighborhood categories were created by K-means cluster analysis by means of built-environment and land use variables (population density, employment density, housing density, median age of structures, percentage of single-family housing, and intersection density). As a result of this analysis, discrete neighborhood categories, such as urban high-density and residential neighborhoods, and urban low-density and mixed-use neighborhoods, were created. Then, differences in attitudes toward public transit were analyzed across these new neighborhood categories. Binary logit models were estimated to determine the influence of these neighborhood categories as well as personal attitudes on public transit use after sociodemographic characteristics were controlled for. The results indicated that attitudes were more strongly associated with travel behavior than with neighborhood characteristics. The findings of this study will aid in the formation of a better understanding of public transit use by highlighting the effects of attitudes and neighborhood characteristics in transit use as well as differences in attitudes between neighborhood types.

Escalator Risk and Assessment of Safety Review

2018 ◽  
Author(s):  
Thomas Bress ◽  
Eugenia Kennedy ◽  
Robert Kupkovits
Keyword(s):  
Emergency Department ◽  
Performance Index ◽  
Transportation Systems ◽  
Consumer Product Safety Commission ◽  
Safety Review ◽  
Development History ◽  
Time Period ◽  
Safety Code ◽  
History Of ◽  
The U.S

Escalators are common mechanical vertical transportation systems that move an estimated 245 million people daily in the U.S. on the more than 33,000 escalators [1]. It has been estimated that about 10,000 escalator-related injuries per year result in an emergency department treatment in the U.S. [2]. A study of escalator injuries published in 2001 concluded that injuries were primarily the result of falls or entrapment at the bottom or top of an escalator or between a moving stair and escalator sidewall [3]. Regarding sidewall entrapment, the 2000 edition of the ASME A17.1, “Safety Code for Elevators and Escalators,” introduced periodic tests for both new and existing escalators to evaluate the potential for sidewall entrapment [4]. The development history of the step/skirt performance index is presented and current requirements in the ASME A17.1 and A17.2, “Guide for Inspection of Elevators, Escalators and Moving Walkways,” codes regarding the index are reviewed. Injury data from the U.S. Consumer Product Safety Commission (CPSC) for escalator riders are analyzed from the timeframe of 1998 to 2017 to seek trends in escalator entrapments during the time period between the introduction of the index and the present.

Use of Planning Support Software in Transit Services

2005 ◽  
Vol 1927 (1) ◽  
pp. 126-136
Author(s):  
Zhong-Ren Peng ◽  
Sarah Hawks ◽  
Kate West
Keyword(s):  
General Trend ◽  
Daily Activities ◽  
Transit Planning ◽  
Planning Support ◽  
Transit Services ◽  
Customer Services ◽  
Transit Agencies ◽  
Support Software ◽  
In Transit ◽  
The U.S

Many U.S. transit agencies have been using planning support software to assist in daily planning, operation, and customer services. However, the literature is not clear about the extent to which transit agencies are using planning support software programs for daily activities. To determine the state of the practice in the use of planning support software in the U.S. transit agencies, a survey was conducted. The survey found that the use of planning support software confirms the general trend in the use of information technology: that is, its use is directly related to the size of the transit agencies. Larger transit agencies tend to use more planning support software, while small agencies do not use that much. Probably one of the most important findings is that many smaller transit agencies consider the purchase and use of planning support software in transit planning, operation, and marketing as unnecessary, especially given the difficulties in obtaining funding, training staff, and hiring and retaining technical support personnel. However, those difficulties are mainly caused by constraints in budgeting and technical staffing issues rather than the undesirability of or the unproved or unrealized benefits related to the use of planning support.

Map-Matching Algorithm for Applications in Multimodal Transportation Network Modeling

2015 ◽  
Vol 2537 (1) ◽  
pp. 62-70 ◽  
Author(s):  
Kenneth Perrine ◽  
Alireza Khani ◽  
Natalia Ruiz-Juri
Keyword(s):  
Public Transportation ◽  
Transportation Network ◽  
Data Representation ◽  
Transportation Systems ◽  
Data Sets ◽  
The Public ◽  
Standard Data ◽  
Planning Models ◽  
Transit Agencies ◽  
Public Transportation Systems

Generalized Transit Feed Specification (GTFS) files have gained wide acceptance by transit agencies, which now provide them for most major metropolitan areas. The public availability GTFSs combined with the convenience of presenting a standard data representation has promoted the development of numerous applications for their use. Whereas most of these tools are focused on the analysis and utilization of public transportation systems, GTFS data sets are also extremely relevant for the development of multimodal planning models. The use of GTFS data for integrated modeling requires creating a graph of the public transportation network that is consistent with the roadway network. The former is not trivial, given limitations of networks often used for regional planning models and the complexity of the roadway system. A proposed open-source algorithm matches GTFS geographic information to existing planning networks and is also relevant for real-time in-field applications. The methodology is based on maintaining a set of candidate paths connecting successive geographic points. Examples of implementations using traditional planning networks and a network built from crowdsourced OpenStreetMap data are presented. The versatility of the methodology is also demonstrated by using it for matching GPS points from a navigation system. Experimental results suggest that this approach is highly successful even when the underlying roadway network is not complete. The proposed methodology is a promising step toward using novel and inexpensive data sources to facilitate and eventually transform the way that transportation models are built and validated.

Challenges With Applying Axle Counter Technology in the USA

2017 ◽  
Author(s):  
John Hofbauer
Keyword(s):  
Electromagnetic Compatibility ◽  
Signal System ◽  
Transit System ◽  
Detection Systems ◽  
Train Control ◽  
Signal Systems ◽  
The Usa ◽  
Track Circuits ◽  
Transit Agencies ◽  
The U.S

There is a growing trend for transit agencies to evolve from wayside and cab-based signal systems to Communication Based Train Control (CBTC). With the complexity of CBTC, a failure of CBTC component could bring a transit system to a standstill. Implementing a secondary signal system can serve to minimize the consequences of a CBTC failure. It is paramount for a transit system to continue to operate, and axle counter technology can be a suitable candidate for use as a secondary signal system. Axle Counter technology has not been widely used in the U.S., but has been used for many years in Europe and the rest of the world. This paper will review and analysis the following: 1. Train Detection Systems; Track circuits vs. axle counters and the basic Principles of Axle Counting; check-in and check-out. 2. Implementing Electromagnetic Compatibility and the EMI standards used in European with previous testing of various axle counter systems, and the frequencies that have been selected, and the proper usage of these frequencies. 3. Testing of radiated emissions using existing guidelines and methods to analyze existing wayside and vehicle Electromagnetic Interferences (EMI), environment conditions, and the limitations of installing axle counters in an existing rail or transit system. 4. Recommendations for improving vehicle and wayside specifications and standards within the U.S. for dealing with installation of axle counter equipment and with failures and EMI emissions between railway devices.

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