On-Demand Public Transit: A Markovian Continuous Approximation Model

2021 ◽  
Author(s):  
Daniel F. Silva ◽  
Alexander Vinel ◽  
Bekircan Kirkici
Keyword(s):  
System Performance ◽  
Performance Metrics ◽  
Public Transit ◽  
Continuous Approximation ◽  
Efficient Operation ◽  
Fleet Sizing ◽  
On Demand ◽  
Single Vehicle ◽  
Transit Agencies ◽  
Using Data

With recent advances in mobile technology, public transit agencies around the world have started actively experimenting with new transportation modes, many of which can be characterized as on-demand public transit. Design and efficient operation of such systems can be particularly challenging, because they often need to carefully balance demand volume with resource availability. We propose a family of models for on-demand public transit that combine a continuous approximation methodology with a Markov process. Our goal is to develop a tractable method to evaluate and predict system performance, specifically focusing on obtaining the probability distribution of performance metrics. This information can then be used in capital planning, such as fleet sizing, contracting, and driver scheduling, among other things. We present the analytical solution for a stylized single-vehicle model of first-mile operation. Then, we describe several extensions to the base model, including two approaches for the multivehicle case. We use computational experiments to illustrate the effects of the inputs on the performance metrics and to compare different modes of transit. Finally, we include a case study, using data collected from a real-world pilot on-demand public transit project in a major U.S. metropolitan area, to showcase how the proposed model can be used to predict system performance and support decision making.

Effect of CSI Imperfection on Cognitive Underlay Transmission over Nakagami-m Fading Channel

2015 ◽  
Vol 1 ◽  
pp. 59
Author(s):  
Nguyen Hong Giang ◽  
Vo Nguyen Quoc Bao ◽  
Hung Nguyen-Le
Keyword(s):  
Fading Channels ◽  
System Performance ◽  
Performance Metrics ◽  
Maximal Ratio Combining ◽  
Relay Nodes ◽  
Channel State ◽  
Primary Network ◽  
Secondary Network ◽  
System Designs ◽  
Approximate Expressions

This paper analyzes the performance of a cognitive underlay system over Nakagami-m fading channels, where maximal ratio combining (MRC) is employed at secondary destination and relay nodes. Under the condition of imperfect channel state information (CSI) of interfering channels, system performance metrics for the primary network and for the secondary network are formulated into exact and approximate expressions, which can be served as theoretical guidelines for system designs. To verify the performance analysis, several analytical and simulated results of the system performance are provided under various system and channel settings.

scholarly journals A Skellam regression model for quantifying positional value in soccer

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Konstantinos Pelechrinis ◽  
Wayne Winston
Keyword(s):  
Regression Model ◽  
Video Game ◽  
Soccer Player ◽  
Performance Metrics ◽  
Optical Tracking ◽  
General Managers ◽  
Annual Salary ◽  
Using Data ◽  
English Premier League ◽  
Positional Value

Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.

Partnerships with Technology-Enabled Mobility Companies

2017 ◽  
Vol 2649 (1) ◽  
pp. 106-112 ◽  
Author(s):  
Marla Westervelt ◽  
Joshua Schank ◽  
Emma Huang
Keyword(s):  
Service Delivery ◽  
Public Transit ◽  
Transportation Network ◽  
Public Agencies ◽  
On Demand ◽  
Transportation Network Companies

The rise and the proliferation of the on-demand economy are creating a new mobility marketplace. This research explored how these new options could be synergistic with public transit models and detailed the experiences of two transit operators that entered into service delivery partnerships with a transportation network company and a micro-transit operator. Based on a series of interviews and the experiences of these two public agencies, this research provides a set of key takeaways and recommendations for transit operators exploring the potential of partnering with new mobility services such as transportation network companies (e.g., Uber or Lyft) and microtransit (e.g., Bridj or Via).

Who uses Transport Network Companies?: Characterization of Demand and its Relationship with Public Transit in Medellín

2021 ◽  
Author(s):  
Felipe Bedoya-Maya ◽  
Lynn Scholl ◽  
Orlando Sabogal-Cardona ◽  
Daniel Oviedo
Keyword(s):  
Latin American ◽  
Public Transit ◽  
Descriptive Analysis ◽  
Discrete Choice Models ◽  
Transport Network ◽  
Gender Effects ◽  
Transport Systems ◽  
Factors Affecting ◽  
On Demand ◽  
Latin American Region

Transport Network Companies (TNCs) have become a popular alternative for mobility due to their ability to provide on-demand flexible mobility services. By offering smartphone-based, ride-hailing services capable of satisfying specific travel needs, these modes have transformed urban mobility worldwide. However, to-date, few studies have examined the impacts in the Latin American context. This analysis is a critical first step in developing policies to promote efficient and sustainable transport systems in the Latin-American region. This research examines the factors affecting the adoption of on-demand ride services in Medellín, Colombia. It also explores whether these are substituting or competing with public transit. First, it provides a descriptive analysis in which we relate the usage of platform-based services with neighborhood characteristics, socioeconomic information of individuals and families, and trip-level details. Next, factors contributing to the election of platform-based services modeled using discrete choice models. The results show that wealthy and highly educated families with low vehicle availability are more likely to use TNCs compared to other groups in Medellín. Evidence also points at gender effects, with being female significantly increasing the probability of using a TNC service. Finally, we observe both transit complementary and substitution patterns of use, depending on the context and by whom the service is requested.

Transit Planning Optimization Under Ride-Hailing Competition and Traffic Congestion

2021 ◽  
Author(s):  
Keji Wei ◽  
Vikrant Vaze ◽  
Alexandre Jacquillat
Keyword(s):  
New York ◽  
Traffic Congestion ◽  
Service Providers ◽  
Public Transit ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Transit Planning ◽  
Planning Optimization ◽  
Transit Ridership ◽  
Transit Agencies

With the soaring popularity of ride-hailing, the interdependence between transit ridership, ride-hailing ridership, and urban congestion motivates the following question: can public transit and ride-hailing coexist and thrive in a way that enhances the urban transportation ecosystem as a whole? To answer this question, we develop a mathematical and computational framework that optimizes transit schedules while explicitly accounting for their impacts on road congestion and passengers’ mode choice between transit and ride-hailing. The problem is formulated as a mixed integer nonlinear program and solved using a bilevel decomposition algorithm. Based on computational case study experiments in New York City, our optimized transit schedules consistently lead to 0.4%–3% system-wide cost reduction. This amounts to rush-hour savings of millions of dollars per day while simultaneously reducing the costs to passengers and transportation service providers. These benefits are driven by a better alignment of available transportation options with passengers’ preferences—by redistributing public transit resources to where they provide the strongest societal benefits. These results are robust to underlying assumptions about passenger demand, transit level of service, the dynamics of ride-hailing operations, and transit fare structures. Ultimately, by explicitly accounting for ride-hailing competition, passenger preferences, and traffic congestion, transit agencies can develop schedules that lower costs for passengers, operators, and the system as a whole: a rare win–win–win outcome.

scholarly journals A multidisciplinary approach to health campaign effectiveness

2018 ◽  
Author(s):  
Jessica M. Rath ◽  
Marisa Greenberg ◽  
Ollie Ganz ◽  
Lindsay Pitzer ◽  
Elizabeth Hair ◽  
...  
Keyword(s):  
Tobacco Use ◽  
Performance Metrics ◽  
Audience Response ◽  
Health Campaigns ◽  
Health Campaign ◽  
Prevention Campaign ◽  
Nationally Representative ◽  
Using Data ◽  
Public Health Campaigns ◽  
Use Behavior

Campaign costs are rising, making ad execution testing more critical to determine effectiveness prior to media spending. Premarket testing occurs prior to messages’ airing while in-market testing examines message attributes when messages are aired within a real-world setting, where context plays an important role in determining audience response. These types of ad testing provide critical feedback to help develop and deploy campaigns. Due to recent changes in media delivery platforms and audience tobacco use behavior, this study analyzes two nationally representative youth samples, aged 15-21, to examine if pre-market ad testing is an indicator of in-market ad performance for public health campaigns, which rely on persuasive messages to promote or reduce health behaviors rather than selling a product. Using data from the truth® campaign, a national tobacco use prevention campaign targeted to youth and young adults, findings indicate strong associations between pre-market scores and in-market ad performance metrics.

scholarly journals Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

2019 ◽  
Author(s):  
Mischa Young ◽  
Jeff Allen ◽  
Steven Farber
Keyword(s):  
Travel Time ◽  
Public Transportation ◽  
Ordinary Least Squares ◽  
Gap Filling ◽  
Service Levels ◽  
Transit Service ◽  
Logistic Regressions ◽  
Transit Agencies ◽  
Using Data ◽  
The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤ 15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

scholarly journals Abstract ACO-EEAODR: An Ant-Swarm Inspired Energy-Efficient Ad Hoc On-Demand Routing Protocol for Mobile Ad Hoc Networks

2021 ◽  
Author(s):  
Waqas Shah
Keyword(s):  
Network Lifetime ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Minimum Energy ◽  
Sustainable Growth ◽  
Energy Aware ◽  
On Demand ◽  
Demand Routing

As the world’s economic activities are expanding, the energy comes to the fore to the question of the sustainable growth in all technological areas, including wireless mobile networking. Energyaware routing schemes for wireless networks have spurred a great deal of recent research towards achieving this goal. Recently, an energy-aware routing protocol for MANETs (so-called energy-efficient ad hoc on-demand routing protocol (EEAODR) for MANETs was proposed, in which the energy load among nodes is balanced so that a minimum energy level is maintained and the resulting network lifetime is increased. In this paper, an Ant Colony Optimization (ACO) inspired approach to EEAODR (ACO-EEAODR) is proposed. To the best of our knowledge, no attempts have been made so far in this direction. Simulation results are provided, demonstrating that the ACO-EEAODR outperforms the EEAODR scheme in terms of energy consumed and network lifetime, chosen as performance metrics.

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