Is the value of travel time savings increasing? Analysis throughout a financial crisis

Rich, Jeppe; Vandet, Christian Anker

doi:10.1016/j.tra.2019.03.012

Cited by 15 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Travel time for car-sharing and taxi stages are assumed to be similar to car stages and are composed of free-flow times and additional delays caused by congestion multiplied by a penalty factor of 1.35 (40). In sum, time spent finding a parking space, and access and egress time is assumed to be similar for the private car and car-sharing as many dedicated car-sharing parking spots in Copenhagen can be assumed to shorten the time needed to find a parking space, while access time can sometimes be longer.…”

Section: Methodsmentioning

confidence: 99%

“…For each of the 131,338 stages (a stage is denoted s in the following), travel time and costs were calculated according to the following formulas, that have recently been applied by Rich and Vandet (40) to similar data using the DNTM: …”

Section: Methodsmentioning

confidence: 99%

“…Consistent with the DNTM and previous Danish studies on the value of travel time savings (VTTS) (39,40), generalized travel costs for each alternative mode m and stage s are then computed using an income-specific VTTS: …”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

Reck

Axhausen

2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Mobility as a service (MaaS) seeks to integrate emerging shared mobility modes with existing public transportation (PT). Decisive to its uptake will be attractive subscription plans that cater for heterogeneous mobility needs. Research on willingness to pay for such plans has commenced, yet remains divided on a central question: how much to include of which mode, and how? Complementing previous research building on stated preference data, in this study revealed preference data is used to analyze the viability of different subscription plan components (PT, car-sharing, bike-sharing, taxi), modes of inclusion (budgets in minutes and season tickets) and subscription cycles (weekly, monthly). PT season tickets are found to be viable for 83% of all respondents. Interestingly, the viability of minute budgets of car- and bike-sharing depends on subscription cycle length. Using a monthly subscription cycle, car-/bike-sharing appears viable to include in a bundle for 35%/31% of all respondents, respectively. Using a weekly subscription cycle, these figures drop to 1.4%/0.4%, respectively, as weekly variation in demand is much higher than monthly variation. In contrast to many current MaaS pilots, taxi use remains too infrequent to include as recurring credit in MaaS plans. Rather, pay-as-you-go is the economically more sensible option for consumers. This research therefore challenges the idea of all-inclusive mobility flat rates and suggests a more modular design.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

Reck

Axhausen

2020

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…The work in regards to travel time-related contexts is vast, varying from distribution fitting (Rahmani et al, 2015;Ma et al, 2017a;Fu et al, 2019;Ma et al, 2016;Westgate et al, 2016;Yang and Wu, 2016;Chalumuri and Yasuo, 2014;Guessous et al, 2014;Woodard et al, 2017); simulation-based studies (Chen and Chien, 2000;Ojeda-Cabral and Chorus, 2016;Zhang et al, 2018;Samandar, Williams, and Ahmed, 2018;Lv et al, 2019); and the value of travel time and travel time reliability (Athira et al, 2016;Truong and Hensher, 1985;Ettema and Timmermans, 2006;Devarasetty et al, 2012;Carrion and Levinson, 2012;Soriguera, 2014;Hensher, 2001;Engelson and Fosgerau, 2016;Kou et al, 2017;Xiao et al, 2017;Rich and Vandet, 2019;Bouscasse and Lapparent, 2019). However, of the travel time-related studies, the most related to the current work is travel time prediction or forecasting through various statistical methods.…”

Section: Related Workmentioning

confidence: 97%

Understanding Factors Affecting Arterial Reliability Performance Metrics

Anderson¹,

Sirupa²,

Kothuri³

et al. 2019

View full text Add to dashboard Cite

The contents of this report reflect the views of the authors, who are solely responsible for the facts and the accuracy of the material and information presented herein. This document is disseminated under the sponsorship of the U.S. Department of Transportation University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof. The contents do not necessarily reflect the official views of the U.S. Government. This report does not constitute a standard, specification, or regulation.

show abstract

“…The increase of the costs, externalities, and penalties of crowding, which are expected to emerge as the occupancy ranks of buses or bus stops, leads users to be willing to pay more to decrease their travel times if, for instance, they ride on a bus with standard occupancy of four travellers per square meter, compared to riding on a bus that has only a handful of passengers, all seated safely [27], [28]. Thus, a connection is expected to exist between the crowding level and the value of travel time savings (VTTS); in some cases, it has been empirically identified by some authors [29], [30].…”

Section: Level Of Crowdingmentioning

confidence: 99%

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

et al. 2020

View full text Add to dashboard Cite

Service quality is a significant concern for both providers and users of public transportation. It is crucial for transit agencies to clearly recognize the causes of unreliability before adapting any improvement strategy. However, evaluation of main causes of bus service unreliability has not been investigated well. Existing studies have three main limitations in context of recognizing causes of service unreliability. First, public transport networks and traffic condition are highly complex systems and most of the existing models are not capable to accurately determine the relationship between service irregularity and impact factors. Second, definition of "Big data" has been neglected and most of the studies only focused on one source of large scale data set to determine the causes of unreliability. Third, bus service unreliability can impact the users" perception toward the public transport, significantly. It has been recommended by number of studies that bus service reliability should be evaluated from both service providers" and users" perspective. However, the impact of service unreliability from passengers" perception is not well investigated, yet. Consequently, we proposed a novel simulation-based sensitivity analysis to evaluating main causes of bus service unreliability using a combination of three different sources of big data. Moreover, for the first time we developed a simulation model in R studio which is an open source and powerful coding environment. According to the results, the level of reliability in Route U32 showed the highest sensitivity to headway variations. Waiting time can be decreased by 61% if only bus operators can reduce the headway variation by 25% of the actual observed data. Big gap and bus bunching could be almost disappeared by decreasing headway variations. Moreover, the terminal departure policy could significantly improve the passenger waiting time. Waiting time can be decreased by 36% when almost all the buses depart the terminal on-time.

show abstract

Is the value of travel time savings increasing? Analysis throughout a financial crisis

Cited by 15 publications

References 11 publications

How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

How Much of Which Mode? Using Revealed Preference Data to Design Mobility As a Service Plans

Understanding Factors Affecting Arterial Reliability Performance Metrics

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

Contact Info

Product

Resources

About