“Make no little plans”: Impactful research to solve the next generation of transportation problems

Agatz, Niels; Hewitt, Mike; Thomas, Barrett W.

doi:10.1002/net.22002

Cited by 26 publications

(17 citation statements)

References 145 publications

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“…The results of previous research conducted by Henry Pandopotan Silitonga [5] regarding the effect of leverage on profitability stated that leverage had a significant effect on profitability. However, different results regarding the effect of leverage on profitability were found from research conducted by Afrisa Thomas [6], which stated that leverage did not significantly affect profitability.…”

Section: Introductionmentioning

confidence: 90%

Effect of Liquidity, Leverage, and Working Capital Turn on Profitability

Hidayat

Dewi

2022

ATM

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The purpose of this research is to analyze the effect of liquidity, leverage, and working capital turnover on profitability in coal mining companies listed on the Indonesia Stock Exchange from 2017 to 2020. The data used in this study is quantitative. The data used is secondary data. The Sampling technique used is purposive sampling, and there were 12 firms with research for four years; this obtained 48 observations. The data analysis method used in this study is Panel data regression analysis using reviews 9. The F test result is an F-statistic of 143.6753 > F Table (2.82) and a Prob (F-statistic) value of 0.000000 < 0.05, which means liquidity, leverage, and working capital turnover have a significant effect on profitability. The results of the liquidity t-test are a t statistic (1.198727) < t table (2.01537) and the value of Prob. 0.2371 > 0.05 means that liquidity has no significant effect on profitability. The results of the t-test leverage t statistic (20.47374) > t table (2.01537) and the value of Prob. 0.0000 > 0.05 means that leverage has a significant effect on profitability. The results of the t-test of working capital turnover t statistic (0.986827) < t table (2.01537) and the value of Prob. 0.3291> 0.05 means that working capital turnover has no significant effect on the profitability of coal mining sub-sector companies listed on the Indonesia Stock Exchange.

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Section: Introductionmentioning

confidence: 90%

Effect of Liquidity, Leverage, and Working Capital Turn on Profitability

Hidayat

Dewi

2022

ATM

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“… Tirachini and Cats (2020) discuss factors that influence the contagion risk of COVID-19 in public transit, including vehicle and station occupancy, exposure time, mask use, and hygiene. COVID-19 has led to new challenges for the transportation community, and different examples are given in the survey by Agatz et al (2021) . Gkiotsalitis and Cats (2021b) study the impact of COVID-19 on public transit, and survey methods for public transit planning that can be used to address the changes in demand patterns and reductions in capacity.…”

Section: Related Workmentioning

confidence: 99%

Resiliency of on-demand multimodal transit systems during a pandemic

Auad

Dalmeijer

Riley

et al. 2021

Transportation Research Part C: Emerging Technologies

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During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies. This paper studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. The case study relies on an optimization pipeline that provides an end-to-end ODMTS solution by bringing together methods for demand estimation, network design, fleet sizing, and real-time dispatching. These methods are adapted to work in a multimodal setting and to satisfy practical constraints. In particular, a limit is imposed on the number of passenger transfers, and a new network design model is introduced to avoid the computational burden stemming from this constraint. Real data from the Metropolitan Atlanta Rapid Transit Authority (MARTA) is used to conduct the case study, and the results are evaluated with a high-fidelity simulation. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios.

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“…The results highlighted the significant cost increase when latent demand is not considered as the design under-invested in fixed routes and over-utilized on-demand shuttles. Note also that Agatz et al (2020) highlighted the integration of stakeholder behavior in optimization models as a fundamental theme to address grand challenges in the next generation of transportation systems. This paper proposes a general framework to design ODMTS based on existing and latent demand.…”

Section: Introductionmentioning

confidence: 99%

Capturing Travel Mode Adoption in Designing On-demand Multimodal Transit Systems

Basciftci¹,

Hentenryck²

2021

Preprint

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This paper studies how to integrate rider mode preferences into the design of On-Demand Multimodal Transit Systems (ODMTS). It is motivated by a common worry in transit agencies that an ODMTS may be poorly designed if the latent demand, i.e., new riders adopting the system, is not captured. The paper proposes a bilevel optimization model to address this challenge, in which the leader problem determines the ODMTS design, and the follower problems identify the most cost efficient and convenient route for riders under the chosen design. The leader model contains a choice model for every potential rider that determines whether the rider adopts the ODMTS given her proposed route. To solve the bilevel optimization model, the paper proposes an exact decomposition method that includes Benders optimal cuts and nogood cuts to ensure the consistency of the rider choices in the leader and follower problems. Moreover, to improve computational efficiency, the paper proposes upper bounds on trip durations for the follower problems and valid inequalities that strenghten the nogood cuts.The proposed method is validated using an extensive computational study on a real data set from AAATA, the transit agency for the broader Ann Arbor and Ypsilanti region in Michigan. The study considers the impact of a number of factors, including the price of on-demand shuttles, the number of hubs, and accessibility criteria. The designed ODMTS feature high adoption rates and significantly shorter trip durations compared to the existing transit system and highlight the benefits in accessibility for low-income riders. Finally, the computational study demonstrates the efficiency of the decomposition method for the case study and the benefits of computational enhancements that improve the baseline method by several orders of magnitude.

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“Make no little plans”: Impactful research to solve the next generation of transportation problems

Cited by 26 publications

References 145 publications

Effect of Liquidity, Leverage, and Working Capital Turn on Profitability

Effect of Liquidity, Leverage, and Working Capital Turn on Profitability

Resiliency of on-demand multimodal transit systems during a pandemic

Capturing Travel Mode Adoption in Designing On-demand Multimodal Transit Systems

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