Modeling and design of smart mobility systems

Fourie, Pieter; Jittrapirom, Peraphan; Binder, Robert B.; Tobey, Michael; Medina, Sergio A. Ordóñez; Maheshwari, Tanvi; Yamagata, Yoshiki

doi:10.1016/b978-0-12-816055-8.00006-3

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…A reconnaissance survey on the study roads revealed that a 200-meter-distance interval was sufficient for capturing relevant information on roadside features, traffic density, land use, transportation infrastructure, and key points of interest. This interval was aligned with the standard practices in related studies [72] and was systematically effective for minimizing data overload. Choosing this interval was practical and feasible in terms of time, budget, and computing resources while allowing for efficient data collection and maintaining the necessary level of detail for analysis.…”

Section: Data Processing: Objective Visual Walkability (Ovw) Street V...mentioning

confidence: 99%

Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh

Biswas,

Roy

2023

GaEE

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Walkability entails measuring the degree of walking activity, a non-motorized mode of active transportation crucial in fast-developing urban settings and combating sedentary lifestyles. While there has been extensive objective research focusing on factors related to the physical environment that influence walkability, there has been a comparatively limited exploration into objectively evaluating a pedestrian’s visual perception. This study in Khulna, Bangladesh, aimed to develop a novel method for objectively measuring walkability based on pedestrian-level visual perception using machine learning. In this research, ResNet, a computer vision model, analyzed 127 panoramic Google Street View images taken at 200-meter intervals from seven major roads. The model, trained with the “deeplabv3plusResnet18CamVid” algorithm, quantified five selected visual features. The results, including walkability rankings, correlation analysis, and spatial mapping, highlighted that greenery and visual enclosures significantly influenced the walkability index. However, the impact of other visual features was less distinctive due to an overall poor streetscape condition. This study bridged the gap between human perception and scientific intelligence, allowing for the evaluation of previously “unmeasurable” streetscape designs. It provides valuable insights for more human-centered planning and transportation strategies, addressing the challenges of modern urbanization and sedentary behavior.

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Section: Data Processing: Objective Visual Walkability (Ovw) Street V...mentioning

confidence: 99%

Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh

Biswas,

Roy

2023

GaEE

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“…It is also the case of autonomous vehicles (AVs), which have been more and more studied. There have been many proposals to incorporate AVs into public transportation systems, models of which have been reviewed and compared in [Berrada and Leurent 2017] and [Fourie et al 2020]. There have been studies of how smart autonomous vehicles (SAVs) could result in competitive advantages with respect to human operated systems, in the form of reduced cost and possibly also added convenience, as discussed in [Liu et al 2019].…”

Section: Introductionmentioning

confidence: 99%

A Transportation System Model Based on Smart Autonomous Vehicles with Distributed Coordination

Oliveira¹,

Bhaya²,

Figueiredo³

2020

Anais Do Workshop De Computação Urbana (CoUrb 2020)

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Public transportation in urban centers is of fundamental importance, being a widely investigated topic. Smart autonomous vehicles (SAVs) present a great potential in revolutionizing transportation systems in urban areas, providing more flexible and efficient solutions. This work proposes a new transportation model based on SAVs that provides a station-based, point-to-point service, with distributed coordination. The model offers two different modes of operation, one with exclusive rides, and the other with ride sharing between clients. A simulator has been developed, through which the system’s characteristics are analyzed, and the two modes of operation compared. It was observed that with the increase in the system client demand over time the ride sharing mode gets more efficient than the mode with exclusive rides, both in terms to the average time required to deliver clients and the total distance traveled.

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Co-designing transport models as a heuristic planning tool

Maheshwari,

Fourie

2024

Phil. Trans. R. Soc. A.

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Recently the transportation sector has witnessed several new technologically driven disruptions that have amplified the complexity of city planning and policymaking. Traditional well-established processes of decision-making in urban planning and transportation are proving insufficient to deal with this degree of complexity and uncertainty. This paper proposes an alternative approach, combining qualitative and normative urban design, with quantitative and predictive transport modelling. This requires urban designers and transport modellers to co-create goal-driven and agile transport models that act as a heuristic tool to guide planning decisions in early design stages. Heuristic modelling is informed by design optioning and vice versa in an iterative loop. A case study is presented to demonstrate how this approach is operationalized to study the impacts of automated vehicles on urban planning. Design workshops are used as a method to elicit responses from stakeholders, which are used to co-create the simulation models. This collaborative process grounds the research in real-world practice and enhances the communication of design proposals and research findings across disciplines. By integrating design thinking methods with agent-based transport simulations, this approach provides a better understanding of emergent effects in complex urban systems and improves stakeholder engagement in the planning process. This article is part of the theme issue ‘Co-creating the future: participatory cities and digital governance’.

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Modeling and design of smart mobility systems

Cited by 4 publications

References 33 publications

Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh

Measuring Objective Walkability from Pedestrian-Level Visual Perception Using Machine Learning and GSV in Khulna, Bangladesh

A Transportation System Model Based on Smart Autonomous Vehicles with Distributed Coordination

Co-designing transport models as a heuristic planning tool

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