The factors influencing bicycling in the Bangalore city

Verma, Meghna; Rahul, T.M.; Reddy, Peesari Vamshidhar; Verma, Ashish

doi:10.1016/j.tra.2016.04.006

Cited by 39 publications

(25 citation statements)

References 30 publications

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“…An investigation on factors influencing bicycling in an Indian city, namely Bangalore, concludes that segregated bicycle lanes and signals at intersections are two essential requirements of a safe bicycling environment [16]. Recent investigations on bicycle operations under heterogeneous traffic flow conditions have concluded that the quality of bicycling is largely influenced by vehicular traffic volume [17,18].…”

Section: Review Of Literaturementioning

confidence: 99%

“…In order to assess the prediction precision of the proposed model, several statistical parameters between observed and predicted probabilities are estimated and shown in Table 6 (Rows: [14][15][16][17]. These parameters include the correlation coefficient (R), the average absolute error (AAE), the root-mean-square error (RMSE), and the maximum absolute error (MAE).…”

Section: Observed Predicted Observed Predicted Observed Predicted Obsmentioning

confidence: 99%

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Urban road segment level of service based on bicycle users’ perception under mixed traffic conditions

2017

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During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets (13,624 effective ratings in total) were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a predictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from 'excellent' to 'worst' (A-F).

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Section: Review Of Literaturementioning

confidence: 99%

Section: Observed Predicted Observed Predicted Observed Predicted Obsmentioning

confidence: 99%

Urban road segment level of service based on bicycle users’ perception under mixed traffic conditions

2017

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“…The impediments to bicycling include factors like long trip distances of commuters, harsh weather conditions, greater physical effort, the difficulty of carrying loads while cycling, infrastructure unavailability, a lack of health and environment consciousness among people, extreme traffic conditions that lead to the risk of an accident [23] and, outside urban areas, travelling more slowly than motorized transport. Factors such as physical effort and speed also limit the distance that a cyclist can travel [24].…”

Section: Factors That Prevent Bicycle Transport Usage and The Ways Tomentioning

confidence: 99%

Cycling intellectualization in Smart Cities

Makarova¹,

Boyko²,

Shubenkova³

2017

EAI Endorsed Transactions on Smart Cities

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Intellectualization is the basis for managing Smart Cities. This involves infrastructure development and design of vehicles equipped with intelligent modules, which provide the control ability. Along with it, the transition to "green", safe and sustainable modes of transport, such as bicycle, should be realized. The widespread use of environmentally friendly bicycles is constrained by a number of reasons. The first is the absence of models designed for physically untrained people and the second is that almost half of all deaths on the world's roads are among pedestrians and cyclists. We propose to solve these problems in two ways: development of an information system for bicycle infrastructure planning and modelling and creating control system of Smart Bike with adaptive electric drive that turns on when it's necessary. Functional requirements for the proposed control system, its algorithm and the conceptual scheme of interaction between system's modules are presented in the article.

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“…In the absence of proper sustainable urban transport polices or initiatives, it may lead to an increase in environmental, economic and social challenges. Successful implementation of sustainable transport policies is attributed by positive public attitudes [9]- [11]. Research done in Australia on attitudes to traffic revealed that even though the public was aware of the negative effects of car usage, their actions did not match their modal choice [12].…”

Section: Introductionmentioning

confidence: 99%

Guidelines for Sustainable Urban Transport in Selected Areas of the City of Tshwane, South Africa

Duri

Zyl

2018

WIT Transactions on the Built Environment

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The purpose of this study was to investigate public attitudes towards sustainable urban transport in the City of Tshwane in order to develop guidelines for sustainable urban transport. The number of households owning private cars increased approximately from 22.9% in 2003 to 28.5% in 2013. Geographical location is used for quota sampling so that all the seven regions in the Tshwane could be represented. The total sample size of the study is (N=418). Primary data is gathered by a structured questionnaire while the Likert scale method was employed to get public attitudes regarding sustainable urban transport. The City of Tshwane in South Africa had the largest increase in population using private cars between 2003 and 2013 of approximately 11.3%, hence there is a need to shift from private cars to sustainable modes of transport. The findings of the research revealed that private cars are the most frequently used mode of transport in the City of Tshwane. The results also indicated that the majority of the residents of the City of Tshwane had a negative attitude towards sustainable transport modes and it was attributed to safety, reliability and convenience concerns. Finally, the paper provides recommendations on how the city planners need to implement transport initiatives that can improve the service quality and safety features of sustainable transport modes. The findings presented in this study provide useful insights to the city planners to secure sustainable urban transportation in the City of Tshwane.

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The factors influencing bicycling in the Bangalore city

Cited by 39 publications

References 30 publications

Urban road segment level of service based on bicycle users’ perception under mixed traffic conditions

Urban road segment level of service based on bicycle users’ perception under mixed traffic conditions

Cycling intellectualization in Smart Cities

Guidelines for Sustainable Urban Transport in Selected Areas of the City of Tshwane, South Africa

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