Service quality analysis of signalized intersections from the perspective of bicycling

Beura, Sambit Kumar; Kumar, Kondamudi Vinod; Suman, Shakti; Bhuyan, Prasanta Kumar

doi:10.1016/j.jth.2020.100827

Cited by 12 publications

(10 citation statements)

References 7 publications

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“…Link-based: bicycle stress level (Sorton & Walsh, 1994) and BLOS (Botma, 1995;Jensen, 2007), and BLOS for protected bike lanes (Foster, Monsere, Dill, & Clifton, 2015); . Node-based: BLOS (Jensen, 2013), bicycle intersection safety index (Carter, Hunter, Zegeer, & Stewart, 2007), and QOS analysis for intersections (Beura, Kumar, Suman, & Bhuyan, 2020); . Network-based: level of traffic stress (Mekuria et al, 2012), network evaluation for bike system (Klobucar & Fricker, 2007) and low-stress network connectivity (Lowry, Furth, & Hadden-Loh, 2016) Another classification can represent BLOS based on sharing policy such as (HCM, 2016):…”

Section: Blos Researchmentioning

confidence: 99%

From bike to electric bike level-of-service

Kazemzadeh

Ronchi

2021

Transport Reviews

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The evaluation of electric bike (e-bike) riders' perception of comfort can lead to a better understanding of user requirements. This can be performed through Level-of-service (LOS) studies. To date, the e-bike LOS (ELOS) concept is scarcely developed and research concerning e-bike travel behaviour characteristics is relatively sparse. In this paper, we use bike LOS (BLOS) studies as a foundation to identify the knowledge gap for ELOS. Along with BLOS, e-bike riding comfort and the distinction between bikes and e-bikes characteristics were scrutinised. Travel behaviour, and e-bike modal substitution research were also reviewed to provide a better picture of e-bike riders' requirements. Based on these domains, we propose a preliminary conceptual framework for the development of ELOS. The results suggest that there is a limited number of studies that whether explicitly evaluate ELOS or consider the e-bike in the BLOS analysis. Also, the extent of substitution of cars, public transport, and bikes by e-bikes can range from partial to complete replacement, thus potentially affecting ELOS developments. The specification of this substitution contributes to a deeper understanding of the ELOS concept in relation to the adaptation of LOS indices used for other transport modes. Finally, it appears evident that ELOS developments would require further research on e-bike interaction analysis in shared mobility in which vulnerable road users are present. The findings of this study help researchers and policy-makers assessing the knowledge gap in ELOS and provide them a preliminary conceptual framework for ELOS development.

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Section: Blos Researchmentioning

confidence: 99%

From bike to electric bike level-of-service

Kazemzadeh

Ronchi

2021

Transport Reviews

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“…This issue is associated with the need for research assessing which assumptions can be adopted for e-bikes from those related to bicycle-pedestrians interactions. Research in respect to BLOS studies is mostly based on transport components, including segments [91,[167][168][169][170][171][172], nodes [63,[173][174][175] and networks [5,[176][177][178][179]. However, network-based BLOS studies compared to link and node studies require more extensive research [180][181][182][183][184].…”

Section: Discussionmentioning

confidence: 99%

Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature

et al. 2020

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Research into the bicycle level-of-service (BLOS) has been extensively conducted over the last three decades. This research has mostly focused on user perceptions of comfort to provide guidance for decision-makers and planners. Segments and nodes were studied first, followed by a network evaluation. Besides these investigations, several variables have also been utilized to depict the users’ perspectives within the BLOS field, along with other cycling research domains that simultaneously scrutinized the users’ preferences. This review investigates the variables and indices employed in the BLOS area in relation to the field of bicycle flow and comfort research. Despite general agreement among existing BLOS variables and the adopted indices, several important research gaps remain to be filled. First, BLOS indices are often categorized based on transport components, while scarce attention has been paid to BLOS studies in trip-end facilities such as bicycle parking facilities. The importance of these facilities has been highlighted instead within research related to comfort. Second, the advantages of separated bike facilities have been proven in many studies; however, scarce research has addressed the challenges associated with them (e.g., the heterogeneity within those facilities due to the presence of electric bikes and electric scooters). This issue is clearly noticeable within the research regarding flow studies. Furthermore, network evaluation (in comparison to segment and node indices) has been studied to a lesser extent, whereas issues such as connectivity can be evaluated mainly through a holistic approach to the system. This study takes one step toward demonstrating the importance of the integration of similar research domains in the BLOS field to eliminate the aforementioned shortcomings.

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“…Communities are increasingly interested in promoting bicycling for their potential health and sustainability benefits. For example, multiple studies showed that those who ride bikes are less likely to have diabetes or hypertension (Beura et al, 2020;Dill, 2009;Fishman, 2016aFishman, , 2016bHong et al, 2019;Pettit et al, 2016) and have a lower illness related to sedentary behaviors (Kriit et al, 2019) compared to people who shuttle in motor vehicles. Moreover, cycling helps to address traffic congestion and air pollution (Ogilvie et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Quantifying changes in bicycle volumes using crowdsourced data

Al-Ramini

Takallou

Piatkowski

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

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Service quality analysis of signalized intersections from the perspective of bicycling

Cited by 12 publications

References 7 publications

From bike to electric bike level-of-service

From bike to electric bike level-of-service

Expanding the Scope of the Bicycle Level-of-Service Concept: A Review of the Literature

Quantifying changes in bicycle volumes using crowdsourced data

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