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.

scholarly journals Demand Elasticities for Mobile Telecommunications in Austria

2008 ◽  
Vol 228 (1) ◽  
Author(s):  
Ralf Dewenter ◽  
Justus Haucap
Keyword(s):  
Dynamic Panel Data ◽  
Demand Elasticity ◽  
Mobile Telecommunications ◽  
Elasticity Of Demand ◽  
Demand Elasticities ◽  
Long Run ◽  
Short Run ◽  
Telecommunications Services ◽  
Using Data ◽  
Business Customers

SummaryThis paper analyses price elasticities in the Austrian market for mobile telecommunications services using data on firm specific tariffs in the period between January 1998 and March 2002. As a novelty compared to existing studies dynamic panel data regressions are used to estimate short-run and long-run demand elasticities for business customers and for private consumers with both postpaid contracts and prepaid cards.We find that business customers have a higher elasticity of demand than private consumers, where postpaid customers tend to have a higher demand elasticity than prepaid customers. Also demand is as expected more elastic in the long run. In addition, the paper also provides estimates for firm-specific demand elasticities which range from -0.47 to -1.1.

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 Dynamic Analysis for Measuring the Impact of Remittance Inflows on Inflation: Evidence From Georgia

2021 ◽  
Vol 12 (1) ◽  
pp. 339
Author(s):  
Azer Dilanchiev ◽  
Aligul Aghayev ◽  
Md. Hasanur Rahman ◽  
Jannatul Ferdaus ◽  
Araz Baghirli
Keyword(s):  
Policy Making ◽  
Inflation Rate ◽  
Explanatory Variable ◽  
Critical Role ◽  
Quarterly Data ◽  
Independent Variables ◽  
Long Run ◽  
Short Run ◽  
The Impact ◽  
The Relationship

Remittance plays a critical role for small economies like Georgia as an unusual means of financing. In policy-making decisions, an understanding of the essence of the relationship between the amount of money exchanged and inflation is important. The paper studies the impact of remittance inflows, using quarterly data spanning a period (2000-2018), on the inflation rate in Georgia. The paper revealed that all independent variables have an effect on the long-run inflation rate; long-run inflation is positively associated with the leading explanatory variable remittance, and no relation is found in the short-run between remittance and inflation. The paper found that inflation's adjustment level to its equilibrium is 12% annually.

scholarly journals Stabilization Policies and Technological Shocks: Towards a Sustainable Economic Growth Path

2019 ◽  
Vol 11 (1) ◽  
pp. 205 ◽  
Author(s):  
Anelí Bongers ◽  
Carmen Díaz-Roldán
Keyword(s):  
Economic Growth ◽  
Economic Policy ◽  
Policy Making ◽  
Sustainable Growth ◽  
Economic Policies ◽  
Long Run ◽  
Short Run ◽  
Stabilization Policies ◽  
Development Goals ◽  
Sustainable Economic Growth

The purpose of this paper is to explore the extent to which traditional economic policies can be oriented by sound practices. It is becoming widely accepted that sustainable economic growth (and not only economic growth) is the final target of economic policies, but some economic policies are applied just looking to the short-run without taking in account the long-run perspective. Our aim is to show how a sustainable economic policy-making would be possible, making compatible the stabilization of the economy in the short-run with a sustainable economic growth in the long-run. We confront the design of economic policies with the 17 goals of the 2030 Agenda. We argue that all sustainable development goals can be attained by the design and implementation of sustainable economic policies. Finally, to illustrate this point, we will conduct a simulation exercise to show under which combinations of demand policies technological shocks would promote a path of sustainable growth. Our results will provide a reference framework for a sustainable economic policy-making.

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.

A Review of Pseudo Panel Data Approach in Estimating Short-run and Long-run Public Transport Demand Elasticities

2014 ◽  
Vol 34 (1) ◽  
pp. 102-121 ◽  
Author(s):  
Chi-Hong (Patrick) Tsai ◽  
Corinne Mulley ◽  
Geoffrey Clifton
Keyword(s):  
Panel Data ◽  
Public Transport ◽  
Demand Elasticities ◽  
Long Run ◽  
Pseudo Panel ◽  
Short Run

Dynamic adjustments in the United States and Canadian construction industries

1991 ◽  
Vol 21 (3) ◽  
pp. 326-332 ◽  
Author(s):  
Brett Gellner ◽  
Luis Constantino ◽  
Michael Percy
Keyword(s):  
United States ◽  
Decomposition Analysis ◽  
Adjustment Costs ◽  
The United States ◽  
Demand Substitution ◽  
Demand Elasticities ◽  
Long Run ◽  
Factor Demand ◽  
Wood Panels ◽  
Short Run

A factor demand dynamic model is estimated for the Canadian and United States construction industries using quarterly data from 1979 through 1986. The model allows for the existence of adjustment costs in the industry, related for example, to the innovative nature of some products. The demand for nonveneered structural wood panels is consistent with the behavior of an innovative product in the United States but not in Canada. A labor–capital composite input is not quasi-fixed in either country. Short-run adjustments, long-run demand elasticities, and biases of technical change are also derived. A decomposition analysis is used to investigate factors underlying the demand substitution of nonveneered structural wood panels for plywood.

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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