How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles

2017 ◽  
Vol 54 ◽  
pp. 80-89 ◽  
Author(s):  
Sandip Chakrabarti
Keyword(s):  
Los Angeles ◽  
Public Transit ◽  
Mode Choice

Discrete Choice Elasticities for Elderly and Disabled Travelers Between Fixed-Route Transit and Paratransit

1998 ◽  
Vol 1623 (1) ◽  
pp. 31-36 ◽  
Author(s):  
Joel P. Franklin ◽  
Debbie A. Niemeier
Keyword(s):  
Discrete Choice ◽  
Current Practice ◽  
Public Transit ◽  
Choice Model ◽  
Mode Choice ◽  
The Elderly ◽  
The Public ◽  
The Difference ◽  
Mode Choice Modeling ◽  
Significant Factors

In the current practice of mode-choice modeling, models typically focus on the more traditional choices, such as those between automobile, transit, and nonmotorized transportation. For most travelers these are, indeed, the most relevant modes. However, for some segments of the population, particularly the elderly, the choice is more limited. This study investigates the factors that affect the elderly and disabled travelers’ choice between public transit and paratransit. Data collected from the public transit service, Sacramento Regional Transit, and the paratransit service, Paratransit, Inc., in Sacramento, California, were used to develop a mode-choice model and to calculate elasticities of significant factors. Age was found to have an elastic effect, whereas the difference in fare had an inelastic effect.

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.

scholarly journals Measuring Cost Variability in Provision of Transit Service

2000 ◽  
Vol 1735 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Brian D. Taylor ◽  
Mark Garrett ◽  
Hiroyuki Iseki
Keyword(s):  
Los Angeles ◽  
Cost Allocation ◽  
Public Transit ◽  
Time Of Day ◽  
Allocation Model ◽  
Transit Systems ◽  
Capital Costs ◽  
Transit Service ◽  
Cost Variability ◽  
Time Of Travel

The cost of producing public-transit service is not uniform but varies by trip type (e.g., local or express), trip length, time of travel, and direction of travel, among other factors. However, the models employed by public-transit operators to estimate costs typically do not account for this variation. The exclusion of cost variability in most transit-cost-allocation models has long been noted in the literature, particularly with respect to time-of-day variations in costs. This analysis addresses many of the limitations of cost-allocation models typically used in practice by developing a set of models that account for marginal variations in vehicle-passenger capacity, capital costs, and time-of-day costs. FY 1994 capital and operating data are used for the Los Angeles Metropolitan Transportation Authority (MTA). This analysis is unique in that it combines a number of previously and separately proposed improvements to cost-allocation models. In comparison with the model currently used by the Los Angeles MTA, it was found that the models developed for this analysis estimate ( a) higher peak costs and off-peak costs, ( b) significant cost variation by mode, and ( c) lower costs for incremental additions in service. The focus is on the limitations of the rudimentary cost-allocation models employed by most transit operators and not on the Los Angeles MTA per se. This analysis found that an array of factors addressed separately in the literature can be incorporated simultaneously and practically into a usable cost-allocation model to provide transit systems with far better information about the highly variable costs of producing service.

scholarly journals The Implication of Road Toll Discount for Mode Choice: Intercity Travel during the Chinese Spring Festival Holiday

2018 ◽  
Vol 10 (8) ◽  
pp. 2700 ◽  
Author(s):  
Xiaomei Lin ◽  
Yusak Susilo ◽  
Chunfu Shao ◽  
Chengxi Liu
Keyword(s):  
Chinese Spring ◽  
High Speed ◽  
Public Transit ◽  
Mode Choice ◽  
Mixed Logit ◽  
Negative Effects ◽  
High Speed Rail ◽  
The Public ◽  
Spring Festival ◽  
Intercity Travel

Intercity travel congestion during the main national holidays takes place every year at different places around the world. Charge reduction measurements on existing toll roads have been implemented to promote an efficient use of the expressways and to reduce congestion on the public transit networks. However, some of these policies have had negative effects. A more comprehensive understanding of the determinants of holiday intercity travel patterns is critical for better policymaking. This paper aims to investigate the effectiveness of the road toll discount policy on mode choice behavior for intercity travel. A mixed logit model is developed to model the mode choices of intercity travelers, which is estimated based on survey data about intercity journeys from Beijing during the 2017 Chinese Spring Festival holiday. The policy impact is further discussed by elasticity and scenario simulations. The results indicate that the expressway toll discount does increase the car use and decrease the public transit usage. Given the decreased toll on expressways, the demand tends to shift from car to public transit, in an order of coach, high-speed rail, conventional rail, and airplane. When it comes to its effect on socio-demographic groups, men and lower-income travelers are identified to be more likely to change mode in response to variation of road toll. Finally, policy effectiveness is found to vary for travelers in different travel distance groups. Conclusions provide useful insights on road pricing management.

How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region

2021 ◽  
pp. 036119812110556
Author(s):  
Yiyuan Wang ◽  
Anne Vernez Moudon ◽  
Qing Shen
Keyword(s):  
Travel Behavior ◽  
Public Transit ◽  
Transportation Network ◽  
Mode Choice ◽  
Large Population ◽  
Past Research ◽  
Substitution Effects ◽  
Cross Sectional ◽  
Individual Mode ◽  
Walking And Biking

This study investigates the impacts of ride-hailing, which we define as mobility services consisting of both conventional taxis and app-based services offered by transportation network companies, on individual mode choice. We examine whether ride-hailing substitutes for or complements travel by driving, public transit, or walking and biking. The study overcomes some of the limitations of convenience samples or cross-sectional surveys used in past research by employing a longitudinal dataset of individual travel behavior and socio-demographic information. The data include three waves of travel log data collected between 2012 and 2018 in transit-rich areas of the Seattle region. We conducted individual-level panel data modeling, estimating independently pooled models and fixed-effect models of average daily trip count and duration for each mode, while controlling for various factors that affect travel behavior. The results provide evidence of substitution effects of ride-hailing on driving. We found that cross-sectionally, participants who used more ride-hailing tended to drive less, and that longitudinally, an increase in ride-hailing usage was associated with fewer driving trips. No significant associations were found between ride-hailing and public transit usage or walking and biking. Based on detailed travel data of a large population in a major U.S. metropolitan area, the study highlights the value of collecting and analyzing longitudinal data to understand the impacts of new mobility services.

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