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 Public transit network planning in small cites considering safety and convenience

2020 ◽  
Vol 12 (1) ◽  
pp. 168781402090235 ◽  
Author(s):  
Changxi Ma ◽  
Dong Yang
Keyword(s):  
Traffic Congestion ◽  
Public Transit ◽  
Network Planning ◽  
Optimization Method ◽  
Transit Oriented Development ◽  
Transit Planning ◽  
Transit Network ◽  
The Public ◽  
Traffic Generation ◽  
Interval Value

Scientific and rational public transit network planning, not only can effectively alleviate city traffic congestion, but also can reduce the risk of accidents. First, based on the data of residents’ travel survey, this article employs the multiple regression method to forecast the traffic generation and adopts the double-constrained gravity model to forecast the residents’ travel distribution of small cites. Second, by aiming at public transit planning objectives, the unsafe roads for public transit are screened, and the public transit trip-mode sharing rate is set as the interval value. According to the interval value, the public transit trip-mode sharing rate is divided into three cases, and the three alternatives of public transit network are calculated based on the network optimization method and the public transit-oriented development model. Next, the alternatives are evaluated by the set pair analysis method, and the optimal scheme is selected. Finally, this article takes the public transit network planning of Huaiyuan County in Anhui Province as an example, and the results show the proposed method is feasible.

scholarly journals Short-Term Prediction of Demand for Ride-Hailing Services: A Deep Learning Approach

2021 ◽  
Author(s):  
Long Chen ◽  
Piyushimita Vonu Thakuriah ◽  
Konstantinos Ampountolas
Keyword(s):  
New York ◽  
Deep Learning ◽  
Built Environment ◽  
Travel Behavior ◽  
Traffic Congestion ◽  
Service Providers ◽  
Road Traffic ◽  
Travel Behaviour ◽  
Demand Prediction ◽  
Research Areas

AbstractAs ride-hailing services become increasingly popular, being able to accurately predict demand for such services can help operators efficiently allocate drivers to customers, and reduce idle time, improve traffic congestion, and enhance the passenger experience. This paper proposes UberNet, a deep learning convolutional neural network for short-time prediction of demand for ride-hailing services. Exploiting traditional time series approaches for this problem is challenging due to strong surges and declines in pickups, as well as spatial concentrations of demand. This leads to pickup patterns that are unevenly distributed over time and space. UberNet employs a multivariate framework that utilises a number of temporal and spatial features that have been found in the literature to explain demand for ride-hailing services. Specifically, the proposed model includes two sub-networks that aim to encode the source series of various features and decode the predicting series, respectively. To assess the performance and effectiveness of UberNet, we use 9 months of Uber pickup data in 2014 and 28 spatial and temporal features from New York City. We use a number of features suggested by the transport operations and travel behaviour research areas as being relevant to passenger demand prediction, e.g., weather, temporal factors, socioeconomic and demographics characteristics, as well as travel-to-work, built environment and social factors such as crime level, within a multivariate framework, that leads to operational and policy insights for multiple communities: the ride-hailing operator, passengers, third-part location-based service providers and revenue opportunities to drivers, and transport operators such as road traffic authorities, and public transport agencies. By comparing the performance of UberNet with several other approaches, we show that the prediction quality of the model is highly competitive. Further, Ubernet’s prediction performance is better when using economic, social and built environment features. This suggests that Ubernet is more naturally suited to including complex motivators of travel behavior in making real-time demand predictions for ride-hailing services.

The Hangover: Assessing Impact of Major Service Interruptions on Urban Rail Transit Ridership

2017 ◽  
Vol 2648 (1) ◽  
pp. 79-85
Author(s):  
Julene Paul ◽  
Michael J. Smart
Keyword(s):  
New York ◽  
Urban Areas ◽  
Hurricane Sandy ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit System ◽  
Transit Systems ◽  
Service Interruptions ◽  
Transit Ridership ◽  
Transit Agencies

Driven by several factors, transit ridership has increased dramatically in some major U.S. urban areas over the past several years. Developing accurate econometric models of system ridership growth will help transit agencies plan for future capacity. As major weather events and maintenance issues can affect transit systems and have large impacts on the trajectory of ridership growth, this study examined the effect of major and minor service interruptions on the PATH heavy rail transit system in northern New Jersey and New York City. The study, which used PATH ridership data as well as data on weather, economic conditions, and fares for both PATH and competing services, concluded that Hurricane Sandy likely dampened ridership gains. Other major service interruptions, which lasted only hours or days, had little effect on long-term ridership growth. Suggestions for further study of service interruptions, especially in the face of climate change and resiliency issues in coastal regions, are presented.

Empirical Analysis of Long-Run Elasticities and Asymmetric Effects of Transit Demand Determinants

2020 ◽  
Vol 2674 (8) ◽  
pp. 274-284
Author(s):  
Lisa Li ◽  
Dena Kasraian ◽  
Amer Shalaby
Keyword(s):  
Policy Making ◽  
Demand Elasticity ◽  
Transit Planning ◽  
Dynamic Panel Model ◽  
Transit Service ◽  
Demand Elasticities ◽  
Transit Ridership ◽  
Long Run ◽  
Short Run ◽  
Transit Agencies

The effects of transit ridership determinants can be quantified as demand elasticities which are often used to inform transit planning and policy making. This study seeks to determine the impacts of transit service supply, fare, and gas prices on ridership by quantifying the short-run and long-run demand elasticities, as well as test whether transit ridership exhibits an asymmetric response to the rise and fall of these factors using a panel data of 99 Canadian transit agencies over the period of 2002–2016. The results of the dynamic panel model show the effects of transit service and fare to be greater in the long run. The short-run fare elasticity was found to be –0.24 while the long-run elasticity was –1.1. Furthermore, the demand elasticity with respect to service levels was also found to be inelastic (0.28) in the short run but elastic (1.3) in the long run. The cross-elasticity of gas prices was estimated to be 0.17. The existence of asymmetry was analyzed using decomposition techniques to separately estimate the coefficients for the rise and fall in each of the determinants. The equality of these coefficients was tested against each other and it was found that ridership responded more to an increase in transit supply than a decrease. The importance of these results to policy making are then discussed.

Optimizing Public Transit Quality and System Access: The Multiple-Route, Maximal Covering/Shortest-Path Problem

2005 ◽  
Vol 32 (2) ◽  
pp. 163-178 ◽  
Author(s):  
Changshan Wu ◽  
Alan T Murray
Keyword(s):  
Service Quality ◽  
Shortest Path ◽  
Public Transit ◽  
The United States ◽  
Path Model ◽  
Transit System ◽  
Transit Service ◽  
Maximal Covering ◽  
Transit Ridership ◽  
Transit Agencies

Public transit service is a promising travel mode because of its potential to address urban sustainability. However, current ridership of public transit is very low in most urban regions—particularly those in the United States. Low transit ridership can be attributed to many factors, among which poor service quality is key. Transit service quality may potentially be improved by decreasing the number of service stops, but this would be likely to reduce access coverage. Improving transit service quality while maintaining adequate access coverage is a challenge facing public transit agencies. In this paper we propose a multiple-route, maximal covering/shortest-path model to address the trade-off between public transit service quality and access coverage in an established bus-based transit system. The model is applied to routes in Columbus, Ohio. Results show that it is possible to improve transit service quality by eliminating redundant or underutilized service stops.

scholarly journals Uber vs. Pubic transit friend or foe? : an empirical study of ridership trends across Uber cities in the USA

2021 ◽  
Author(s):  
Dominic Tremblay
Keyword(s):  
Public Transit ◽  
Transportation Network ◽  
Municipal Governments ◽  
Policy Makers ◽  
Highly Educated ◽  
Transit Ridership ◽  
The Usa ◽  
Transit Agencies ◽  
Taxi Industry ◽  
High Level

Uber is a disruptive transportation network company (TNC) that is affecting the way people move in cities. While its effects on the taxi industry are increasingly clear, little research has been conducted to study its effect on public transit ridership. This study uses descriptive statistics to establish demographic and socio-economic profiles of cities that Uber has targeted, and a quasi difference-in-difference analysis to explore Uber's effect on transit ridership levels in order to determine whether the service is acting as a complement or substitute to public transit. The results from this high-level study suggest that Uber my be more of a complement to transit over time, and in large dense regions with highly-educated, affluent, productive labour markets that already have very high transit ridership. Finally, recommendations informed by these findings are offered for transit agencies, policy makers, and municipal governments as they continue to determine how to regulate Uber and similar ride sourcing services

scholarly journals The future of emerging technologies in public transit in Greater Toronto Area

2021 ◽  
Author(s):  
Zaiem Haider
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Emerging Technologies ◽  
Public Transit ◽  
Elderly Person ◽  
Advanced Technology ◽  
Greater Toronto Area ◽  
The World ◽  
Transit Agencies

In communities throughout the world, strong and convenient public transportation makes valuable contributions to economic development, increased safety, energy conservation, a cleaner environment, less traffic congestion, and an improved quality of life. Whether it's a disabled person on her way to a doctor appointment, a child on the way to the library, or an elderly person going to buy groceries, rails, buses and vans connect people to their community. While transit serves many purposes, one of the most important of which is to provide critical access and mobility for transit-dependent and lower-income residents country wide, it also reduces the pressure on critical commute corridors by offering a convenient alternative to driving alone. People who are dependent on public transit, the young or the old, the disabled or the low-income, deserve a first-class system. A survey was conducted by City Pulse Toronto (CP 24) and the question they put to the viewers was "Would improved public transit convinces you to give up your car?" The result was amazing that 96% of the people using cars opted for Public transit. In the last decade statistics depict that the cities that have adopted emerging technologies in public transit are reaping the benefits of their increased rider ship by almost three fold. It is disappointing to see that the transit-using trend in Greater Toronto Area (GTA) has decreased in the past five years except in the regions where transit agencies are updating their systems. Throughout the North America and other parts of the world, transit agencies are deploying automatic vehicle location and control fleet management systems, electronic and interactive customer information systems, and contact/contactless fare collection systems to save costs, improve operations and management efficiency and provide better service to customers. In this project an effort is made to depict the extent of adoption of advanced technology in the provision of public transportation service in Greater Toronto Area. The focus is on some of the most innovative or comprehensive implementations, categorized under two types of services/technologies, Automatic Passenger Counting and Electronic Fare Payment. Another objective of this study is to assemble the knowledge on successful applications of advanced technologies, the issues in their implementation, the goals and benefits of Intelligent Transportation System's integration. The study focuses on institutional, operational and technical barriers with the expectation that this will lead to more widespread adoption of ITS systems and techniques.

scholarly journals Do Private Transport Services Complement or Compete against Public Transit? Evidence from the Commuter Vans in Eastern Queens, New York

Urban Science ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 24
Author(s):  
Catherine Musili ◽  
Deborah Salon
Keyword(s):  
New York ◽  
Public Transit ◽  
Wait Times ◽  
Transfer Time ◽  
City Bus ◽  
Transport Services ◽  
Time Savings ◽  
Transit Agencies ◽  
Original Survey ◽  
Insight Into

Do private transport services complement or compete against public transit? As transit agencies scramble to adjust to the new transport landscape of mobility services, this has become an important question. This study focuses on New York’s commuter vans (also known as “dollar vans”), private vans that have operated alongside public transit for decades. We use original survey and observational data collected in the summer of 2016 to document basic ridership characteristics and to provide insight into whether the commuter vans complement or compete against city buses. Commuter van ridership in Eastern Queens is high; it is roughly equivalent to city bus ridership on parallel routes at approximately 55,000 per day. Further, more than 60% of van riders surveyed would have had a free trip on a city bus, through either a transit pass or transfer. Time savings was an important motivation for these riders to pay extra for the vans; the vans are faster than city buses, and van wait times are shorter. These results suggest that New York’s commuter vans complement public transit by serving as a feeder system. This conclusion, however, is highly context-dependent. As private transport services proliferate, continued research is needed to ascertain their relationships with public transit.

scholarly journals Facing the future of transit ridership: which riders bought a car; who is planning on riding less?

2022 ◽  
Author(s):  
Matthew Palm ◽  
Jeff Allen ◽  
Yixue Zhang ◽  
Ignacio Tiznado Aitken ◽  
BRICE BATOMEN ◽  
...  
Keyword(s):  
Public Transit ◽  
Political Support ◽  
Future Research ◽  
Recent Immigrants ◽  
Automobile Ownership ◽  
The North ◽  
Transit Ridership ◽  
Transit Agencies ◽  
Transit Use

Public transit agencies face a transformed landscape of rider demand and political support as the COVID-19 pandemic continues. We explore people’s motivations for returning to or avoiding public transit a year into the pandemic. We draw on a March 2021 follow up survey of over 1,900 people who rode transit regularly prior to the COVID-19 pandemic in Toronto and Vancouver, Canada, and who took part in a prior survey on the topic in May, 2020. We model how transit demand has changed due to the pandemic, and investigate how this relates to changes in automobile ownership and its desirability. We find that pre-COVID frequent transit users between the ages of 18-29, a part of the so-called “Gen Z,” and recent immigrants are more attracted to driving due to the pandemic, with the latter group more likely to have actually purchased a vehicle. Getting COVID-19 or living with someone who did is also a strong and positive predictor of buying a car and anticipating less transit use after the pandemic. Our results suggest that COVID-19 heightened the attractiveness of auto ownership among transit riders likely to eventually purchase cars anyways (immigrants, twentysomethings), at least in the North American context. We also conclude that getting COVID-19 or living with someone who did is a significant and positive predictor of having bought a car. Future research should consider how the experiencing of having COVID-19 has transformed some travelers’ views, values, and behaviour.

Using Smart Farecard Data to Support Transit Network Restructuring: Findings from Los Angeles

2019 ◽  
Vol 2673 (6) ◽  
pp. 202-213 ◽  
Author(s):  
Pragun Vinayak ◽  
Zeina Wafa ◽  
Conan Cheung ◽  
Stephen Tu ◽  
Anurag Komanduri ◽  
...  
Keyword(s):  
Los Angeles ◽  
Time Of Day ◽  
Transit Planning ◽  
Metropolitan Regions ◽  
Trip Purpose ◽  
Transit Ridership ◽  
Market Shares ◽  
Transit Market ◽  
Transit Agencies ◽  
In Transit

Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors.

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