Introducing a Design Framework for a Multi-Modal Public Transportation System, Focusing on Mixed-Fleet Bike-Sharing Systems

Ghamami, Mehrnaz; Shojaei, MohammadHossein

doi:10.1177/0361198118799170

Cited by 7 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research on the design and impact of µM solutions has been reviewed in [25], which focuses on the urban deployment of SBs and ESs. In particular, [26] presents a design framework for a multi-modal public transportation system, including various µM solutions and buses, optimizing user preferences and social costs. Fleet deployment models are analyzed in [27], [28].…”

Section: A Related Literaturementioning

confidence: 99%

Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions

Zardini¹,

Lanzetti²,

Censi³

et al. 2020

Preprint

View full text Add to dashboard Cite

The design of future mobility solutions (autonomous vehicles, micromobility solutions, etc.) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. This requires tools to study such a coupling and co-design future mobility systems in terms of different objectives. This paper presents a framework to address such co-design problems. In particular, we leverage the recently developed mathematical theory of co-design to frame and solve the problem of designing and deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions such as shared bikes and escooters, and public transit, in terms of fleets sizing, vehicle characteristics, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity assumptions and it naturally handles multiple objectives, delivering the rational solutions on the Pareto front and thus enabling policy makers to select a policy. To showcase our methodology, we present a realworld case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess the costs and benefits of interventions, and that such analytical techniques might inform policy-making in the future.

show abstract

Section: A Related Literaturementioning

confidence: 99%

Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions

Zardini¹,

Lanzetti²,

Censi³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Analyzing the behavioral parameters of drivers, pedestrians, and traffic characteristics of accident points have a pivotal role in identifying the effective factors to reduce the number of accidents [8] [9] [10] [11]. Based on accident databases, causes related to vehicle, environment, and road factors affecting the occurrence of accidents have been investigated by researchers through various studies [14]- [23].…”

Section: Problem Importance and Research Goalmentioning

confidence: 99%

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Sheykhfard¹,

Haghighi²,

Soltaninejad³

et al. 2020

JTTs

View full text Add to dashboard Cite

The human factor is the most important cause of road accidents. Investigating the drivers' mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 -65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver's opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers' behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

show abstract

Operations management of shared transport: research status and prospect

Li,

Zhao,

2023

J. of Data, Inf. and Manag.

View full text Add to dashboard Cite

Introducing a Design Framework for a Multi-Modal Public Transportation System, Focusing on Mixed-Fleet Bike-Sharing Systems

Cited by 7 publications

References 25 publications

Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions

Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions

Analyzing Drivers’ Mental Patterns Using Q-Methodology

Operations management of shared transport: research status and prospect

Contact Info

Product

Resources

About