Uber Economics: Evaluating the Monetary and Travel Time Trade-Offs of Transportation Network Companies and Transit Service in Chicago, Illinois

2019 ◽  
Vol 2673 (4) ◽  
pp. 295-304 ◽  
Author(s):  
Joseph P. Schwieterman
Keyword(s):  
Travel Time ◽  
Public Transit ◽  
Transportation Network ◽  
Cost Effective ◽  
Service Levels ◽  
Metropolitan Regions ◽  
Transit Service ◽  
Trade Offs ◽  
The U.S ◽  
Transportation Network Companies

The potential diversion of passengers from public transit to transportation network companies (TNCs) is attracting considerable attention in metropolitan regions. Despite this, relatively little microeconomic analysis has been made available to explore how service attributes affect choices between the services offered by TNCs and public transit. To fill this shortfall, this study evaluates prices and service levels for Lyft, Lyft Line, UberX, UberPool, and Chicago Transit Authority (CTA) services in Chicago. Analysis of 3,075 fares and estimated travel times for 620 trips in the 4- to 11-mile range shows TNCs tend to be relatively costly when expressed in relation to the additional amount spent per unit of time saved. The average traveler using these four TNC services, across the entire sample, spends the equivalent of $42–$108 per hour saved—well above the $14.95/hr. the U.S. Department of Transportation (U.S. DOT) recommends assigning to the average transit passenger when conducting analyses about the value of time. However, for travelers on business and those between locations poorly served by transit, including trips between neighborhoods with less transit service than the downtown district, the analysis shows a significant share of passengers will likely find TNCs cost-effective options based on the U.S. DOT standard. The approach taken illustrates how the mobility benefits and competitive issues associated with TNCs can be systematically evaluated by reviewing the price and travel time characteristics of each trip.

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).

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.

Impacts on Vehicle Occupancy and Airport Curb Congestion of Transportation Network Companies at Airports

2018 ◽  
Vol 2672 (23) ◽  
pp. 52-58 ◽  
Author(s):  
Karina Hermawan ◽  
Amelia C. Regan
Keyword(s):  
Los Angeles ◽  
San Francisco ◽  
Public Transit ◽  
Transportation Network ◽  
Light Rail ◽  
International Airport ◽  
Using Data ◽  
Standard Service ◽  
Transportation Network Companies

How does the growth of transportation network companies (TNCs) at airports affect the use of shared modes and congestion? Using data from the 2015 passenger survey from Los Angeles International Airport (LAX), San Francisco International Airport (SFO), and Oakland International Airport (OAK), this research analyzes TNCs’ relationship with shared modes (modes that typically have higher vehicle-occupancy and include public transit such as buses and light rail, shared vans or shuttles) and the demand for their shared vs. standard service at the airport. Because TNCs both replace shared rides and make them possible, the research also measured the net effects at these airports. The results suggest that in 2015, TNCs caused 215,000 and 25,000 passengers to switch from shared to private modes at SFO and OAK, respectively. By 2020, the increase is expected to be about 840,000 and 107,000 passengers per year, respectively.

Evolution of Transportation Network Companies and Taxis through 2013–2018 in Chicago

2020 ◽  
Vol 2674 (7) ◽  
pp. 385-397
Author(s):  
Sneha Roy ◽  
Anurag Komanduri ◽  
Kimon Proussaloglou
Keyword(s):  
Transportation Network ◽  
Temporal Distribution ◽  
Level Of Service ◽  
Large Urban Area ◽  
On Demand ◽  
The City ◽  
Compare And Contrast ◽  
The U.S ◽  
Travel Distances ◽  
Transportation Network Companies

The objective of this paper is to highlight important differences between taxis and transportation network companies (TNCs) in a large urban area. We analyze the publicly available dataset from Chicago which includes taxi and transportation network company (TNC) utilization and the level of service measures from five months in 2013–2014 and the same five months in 2018–2019. We compare and contrast the data from these two points in time to document utilization of taxis and TNCs and to measure differences in travel times, travel distances, fares, destinations served, and the spatial and temporal distribution of these trips. Travel to and from airports has been evaluated separately owing to the exceptionally high number of trips they generate. Striking differences between pooled and unpooled TNC trip volumes and other travel metrics have been assessed to highlight their operational diversity despite being considered as the same mode. The exploratory analysis has been carried out across the shared-ride, time, and mode dimensions. The study revealed both similarities and differences in taxi trip characteristics between the two evaluation periods and also outlined how the ridehailing market has grown over the years despite the near stagnation in population and employment in the city. We believe that assessing how taxis have fared through this time and highlighting the intrinsic differences between how the old and new mode of on-demand ride services coexist is important. This study aims to help understand how new-age mobility services are impacting transportation in one of the largest cities in the U.S.

Bilevel Formulation for Optimal Traffic-Information Dissemination

2003 ◽  
Vol 1836 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Fan Yang ◽  
Henry X. Liu ◽  
Rachel R. He ◽  
Xuegang Ban ◽  
Bin Ran
Keyword(s):  
Travel Time ◽  
System Performance ◽  
Information Dissemination ◽  
Transportation Network ◽  
Information Services ◽  
Traffic Information ◽  
Total System ◽  
Dynamic Traffic ◽  
Trade Offs ◽  
Upper Level

With the fast-growing telematics market and maturing traffic-information services, telematics devices provide a feasible means with which to manage traffic more efficiently. The provision of traffic information to travelers usually involves different parties that have distinctive objectives: travelers are concerned with benefits of travel-time savings at an affordable service charge, private information service providers (ISPs) seek to provide marketable information services from which they can derive a profit, and traffic management centers (TMCs) have the responsibility to maintain and improve system performance, especially to minimize the total system travel time. How transportation system managers can analyze the trade-offs among these objectives and adjust this new traffic-information flow diagram to improve system performance remains an open question. The trade-offs needed among the conflicting multiple objectives of different parties are studied, and traffic system performance is analyzed. The complex traffic network is formulated as a bilevel program. The upper level can be formulated by using various objective functions, such as the objectives for ISP and TMC. The lower level is a multiclass dynamic traffic-assignment model, which determines dynamic traffic flows in the network by considering the information dissemination strategies provided by the upper-level model. Numerical results of a small network are provided to illustrate the behavior of this model, and they prove that when there is congestion in the dynamic transportation network, appropriate subscribed rates benefit both all travelers and system performance, while the ISPs’ information influences little without congestion in the transportation network.

scholarly journals Investigation of Performance Indicator for Public Transit Connectivity in Multi-Modal Transportation Network of NCT Delhi

2021 ◽  
Vol 9 (VII) ◽  
pp. 3181-3196
Author(s):  
Abhishek Upadhyay
Keyword(s):  
Public Transportation ◽  
Public Transit ◽  
Transportation Network ◽  
Centrality Measures ◽  
Network Centrality ◽  
Theoretic Approach ◽  
Transit System ◽  
Multimodal Transportation ◽  
Transit Service ◽  
Transit Connectivity

Centrality plays a crucial role as agencies at the federal and state level focus on expanding the public transit system to meet the demands of a multimodal transportation system. Transit agencies have a need to explore mechanisms to improve connectivity by improving transit service. This requires a systemic approach to develop measures that can prioritize the allocation of funding to locations that provide greater connectivity, or in some cases direct funding towards underperforming areas. The concept of centrality is well documented in social network literature and to some extent, transportation engineering literature. However, centrality measures have limited capability to analyze multi-modal public transportation systems which are much more complex in nature than highway networks. In my study area, we propose measures to determine Network centrality from a QGIS SOFTWARE which is based on graph theoretic approach for all levels of transit service coverage integrating routes, schedules, socioeconomic, demographic and spatial activity patterns. The objective of using Network centrality as an indicator is to quantify and evaluate transit service in terms of prioritizing transit locations for funding; providing service delivery strategies, especially for areas with large multi-jurisdictional, multi-modal transit networks; providing an indicator of multi-level transit capacity for planning purposes; assessing the effectiveness and efficiency for node/stop prioritization; and making a user friendly tool to determine locations with highest connectivity while choosing transit as a mode of travel. The proposed analysis offers reliable indicators that can be used as tools for determining the transit connectivity of a multimodal transportation network.

The Role of Computer Modeling in Combined Sewer Overflow Abatement Planning

1992 ◽  
Vol 26 (7-8) ◽  
pp. 1831-1840 ◽  
Author(s):  
L. A. Roesner ◽  
E. H. Burgess
Keyword(s):  
System Modeling ◽  
Cost Effective ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
New Techniques ◽  
Hydraulic Simulation ◽  
Continuous Simulation ◽  
Combined Sewer ◽  
The U.S

Increased concern regarding water quality impacts from combined sewer overflows (CSOs) in the U.S. and elsewhere has emphasized the role of computermodeling in analyzing CSO impacts and in planning abatement measures. These measures often involve the construction of very large and costly facilities, and computer simulation during plan development is essential to cost-effective facility sizing. An effective approach to CSO system modeling focuses on detailed hydraulic simulation of the interceptor sewers in conjunction with continuous simulation of the combined sewer system to characterize CSOs and explore storage-treatment tradeoffs in planning abatement facilities. Recent advances in microcomputer hardware and software have made possible a number of new techniques which facilitate the use of computer models in CSO abatement planning.

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