The maximal covering bicycle network design problem

Ospina, Juan P.; Duque, Juan Carlos; Botero-Fernández, Verónica; Montoya, Alejandro

doi:10.1016/j.tra.2022.02.004

Cited by 17 publications

(10 citation statements)

References 24 publications

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“…However, a centralized initial solution generally reflects the typically centralized mobility flows in cities (Schläpfer et al, 2021), and the algorithm already places the seed points in a way to cover the whole city so that eventually all points will be reached. We consider implementing a non-centralized initial solution conceptually too different and outside of the scope of this paper, but we refer to Ospina et al (2022) who develop a maximum coverage design from the beginning.…”

Section: Discussionmentioning

confidence: 99%

Data-driven micromobility network planning for demand and safety

Folco

Gauvin

Tizzoni

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Developing safe infrastructure for micromobility like bicycles or e-scooters is an efficient pathway towards climate-friendly, sustainable, and livable cities. However, urban micromobility infrastructure is typically planned ad-hoc and at best informed by survey data. Here, we study how data of micromobility trips and crashes can shape and automatize such network planning processes. We introduce a parameter that tunes the focus between demand-based and safety-based development, and investigate systematically this tradeoff for the city of Turin. We find that a full focus on demand or safety generates different network extensions in the short term, with an optimal tradeoff in-between. In the long term, our framework improves overall network quality independent of short-term focus. Thus, we show how a data-driven process can provide urban planners with automated assistance for variable short-term scenario planning while maintaining the long-term goal of a sustainable, city-spanning micromobility network.

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Section: Discussionmentioning

confidence: 99%

Data-driven micromobility network planning for demand and safety

Folco

Gauvin

Tizzoni

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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“…This subsection compares the results obtained from our model with those obtained from the model presented by Ospina et al [8]. Since the case of exposure to traffic-generated air pollution has not been discussed in the network design problem in previous works, we select one of…”

Section: Compare With a Bicycle Network Design Model From The Literaturementioning

confidence: 98%

“…They used a logit model to calculate the cycling utility of choosing a route, the objective of the model was to maximize this utility. Ospina et al [8] presented a model that maximizes the coverage of cyclists while maintaining total network cost at its minimum. Liaw et al [9] proposed a bikeway network design problem in a multi-objective model.…”

Section: Introductionmentioning

confidence: 99%

Cyclists’ exposure to traffic-generated air pollution in multi-modal transportation network design problem

Mortazavi Moghaddam,

Shiran,

Jafarian-Moghaddam

et al. 2023

PLoS ONE

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Moving toward sustainable transportation is one of the essential issues in cities. Bicycles, as active transportation, are considered an important part of sustainable transportation. However, cyclists engage in more physical activity and air intake, making the quality of air that they inhale important in the programs that aim to improve the share of this mode. This paper develops a multi-modal transportation network design problem (MMNDP) to select links and routes for cycling, cars, and buses to decrease the exposure of cyclists to traffic-generated air pollution. The objective functions of the model include demand coverage, travel time, and exposure. The study also examined the effect of having exclusive lanes for bicycles and buses on the network. In the present study, the non-dominated storing genetic algorithm (NSGA-II) solves the upper-level and a method of successive average (MSA) unravels the lower level of the model. A numerical example and four scenarios evaluate the trade-off between different objective functions of the proposed model. The results reveal that considering exposure to air pollution in our model results in a slight increase in travel time (4%) while the exposure to traffic-generated air pollution for cyclists was reduced significantly (47%). Exclusive lanes also result in exposure reduction in the network (60%). In addition, the demand coverage objective function performs well in increasing the total demand in the network by 47%. However, more demand coverage leads to a rise in travel time by 28% and exposure by 58%. The model also showed an acceptable result in terms of exposure to traffic-generated air pollution compared to the model in the literature.

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“…Ospina et al [ 28 ] addressed a similar problem but framed it as a maximal covering bicycle network design problem. The maximal covering bicycle network design problem involves making investment decisions to build a cycling network aimed at maximizing the coverage of cyclists while maintaining a minimum total network cost.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison

Gore¹,

Lynch²,

Jordan³

et al. 2022

JMIR Public Health Surveill

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Background Adding additional bicycle and pedestrian paths to an area can lead to improved health outcomes for residents over time. However, quantitatively determining which areas benefit more from bicycle and pedestrian paths, how many miles of bicycle and pedestrian paths are needed, and the health outcomes that may be most improved remain open questions. Objective Our work provides and evaluates a methodology that offers actionable insight for city-level planners, public health officials, and decision makers tasked with the question “To what extent will adding specified bicycle and pedestrian path mileage to a census tract improve residents’ health outcomes over time?” Methods We conducted a factor analysis of data from the American Community Survey, Center for Disease Control 500 Cities project, Strava, and bicycle and pedestrian path location and use data from two different cities (Norfolk, Virginia, and San Francisco, California). We constructed 2 city-specific factor models and used an algorithm to predict the expected mean improvement that a specified number of bicycle and pedestrian path miles contributes to the identified health outcomes. Results We show that given a factor model constructed from data from 2011 to 2015, the number of additional bicycle and pedestrian path miles in 2016, and a specific census tract, our models forecast health outcome improvements in 2020 more accurately than 2 alternative approaches for both Norfolk, Virginia, and San Francisco, California. Furthermore, for each city, we show that the additional accuracy is a statistically significant improvement (P<.001 in every case) when compared with the alternate approaches. For Norfolk, Virginia (n=31 census tracts), our approach estimated, on average, the percentage of individuals with high blood pressure in the census tract within 1.49% (SD 0.85%), the percentage of individuals with diabetes in the census tract within 1.63% (SD 0.59%), and the percentage of individuals who had >2 weeks of poor physical health days in the census tract within 1.83% (SD 0.57%). For San Francisco (n=49 census tracts), our approach estimates, on average, that the percentage of individuals who had a stroke in the census tract is within 1.81% (SD 0.52%), and the percentage of individuals with diabetes in the census tract is within 1.26% (SD 0.91%). Conclusions We propose and evaluate a methodology to enable decision makers to weigh the extent to which 2 bicycle and pedestrian paths of equal cost, which were proposed in different census tracts, improve residents’ health outcomes; identify areas where bicycle and pedestrian paths are unlikely to be effective interventions and other strategies should be used; and quantify the minimum amount of additional bicycle path miles needed to maximize health outcome improvements. Our methodology shows statistically significant improvements, compared with alternative approaches, in historical accuracy for 2 large cities (for 2016) within different geographic areas and with different demographics.

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The maximal covering bicycle network design problem

Cited by 17 publications

References 24 publications

Data-driven micromobility network planning for demand and safety

Data-driven micromobility network planning for demand and safety

Cyclists’ exposure to traffic-generated air pollution in multi-modal transportation network design problem

Estimating the Health Effects of Adding Bicycle and Pedestrian Paths at the Census Tract Level: Multiple Model Comparison

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