A study of acceptable trip distances using walking and cycling in Bangalore

Rahul, T.M.; Verma, Ashish

doi:10.1016/j.jtrangeo.2014.05.011

Cited by 67 publications

(19 citation statements)

References 35 publications

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“…Additionally, travel distance is an important characteristic associated with public bicycle travel. Studies have shown that the acceptable travel distance for public bicycle transport is between 1 km and 5 km [24], the average travel distance of bicycle users is about 3.7 km [25] and the critical travel time for cycling is 24 min [26]. The majority of travelers will choose other public transport options if their travel distance is more than 3.5 km [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China

Cheng

2018

Sustainability

134

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In recent years, free-floating bike sharing (FFBS) has become a significant travel mode to satisfy urban residents' travel demands in China. This paper was designed to better understand the characteristics and influential factors of different travel patterns in FFBS. Firstly, travel patterns were divided into three categories: Origin to Destination Pattern (ODP), Travel Cycle Pattern (TCP) and Transfer Pattern (TP). Then, the characteristics of these patterns were analyzed based on a survey of 4939 valid questionnaires in Nanjing, China. A multinomial logit (MNL) model was established to explore the influential factors associated with the three patterns. The results showed the following. (1) Employees and students were more inclined to choose TP and ODP, and the selection probability of employees was larger than that of students. (2) The evening peak was more significant than the morning peak. (3) Residents with short travel distances were more likely to choose TCP and ODP, and when the travel distance reached 4 km, there was a significant transfer to TP. (4) Price had an impact on residents' travel patterns, with residents showing an inclination toward FFBS when making short distance trips, if they were quickly found. Malfunctioning bicycles were an important factor restricting FFBS development. Several policy recommendations are proposed based on these results, for government and FFBS businesses to improve their management of FFBS systems.

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Section: Literature Reviewmentioning

confidence: 99%

Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China

Cheng

2018

Sustainability

134

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“…Caminar y montar en bicicleta produce menos contaminación medioambiental, menos ruido ambiental, menos enfermedades, menos costos de infraestructura, y en los países en vía de desarrollo aumenta las oportunidades transporte en los grupos de bajo nivel socioeconómico 40 , la adaptación de espacios públicos para la práctica de actividades físicas garantiza que las personas se sientan seguras, existiendo un respeto por parte del tráfico vehicular y una percepción de seguridad ante el crimen que permita un disfrute de la actividad física 5,41 ; se ha observado que la percepción de inseguridad durante la noche disminuye la actividad física de las personas, principalmente en mujeres y en quienes habitan en barrios de bajos niveles socioeconómicos 42,43 .…”

Section: Discussionunclassified

Relación entre el transporte y la actividad física en universitarios, Santa Marta, Colombia, 2013: estudio de corte transversal

Ceballos

Miño

Arguello

et al. 2015

archmed

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Objetivo: observar la asociación entre el medio de transporte, la actividad física y el programa de estudios en estudiantes de salud de la Universidad del Magdalena. Materiales y métodos: 302 estudiantes de salud fueron seleccionados aleatoriamentey se les aplicó el Cuestionario Mundial sobre Actividad Física. Se realizó análisis estadístico de asociación por X2. Resultados: se observa asociación estadísticamente significativa entre el medio transporte y el sexo (X2 = 7,2; p = 0,027), del total de 193(63,9 %) estudiantes que caminan o montan en bicicleta, el 52,3 % son mujeres y el 47,6 % son hombres. Se encuentra asociación estadísticamente significativa entre las variables medio de transporte y programa de estudios (X2 = 14,4; p = 0,024): deltotal de 193 estudiantes que caminan o montan en bicicleta por programa de estudios el 34,7 % son estudiantes de medicina y el 30 % son estudiantes de odontología. Igualmente se encuentra asociación entre el sexo y la inactividad física (X2 = 8,9; p = 0,002), 247 (81,8 %) estudiantes realizan menos de 150 minutos de actividad física por semana. Conclusiones: en relación con el medio de transporte, tanto hombres como mujeres en su mayoría caminan o montan en bicicleta, siendo los estudiantes de medicina y odontología los que más tienden a transportarse de esta manera. En relación con la actividad física, cerca del 82 % de los estudiantes realizan menos de150 minutos de actividad física por semana.

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“…In general, the docked bicycle sharing usage rate is higher on weekdays than on weekends; on weekdays the passenger flow would have a peak in the morning and one in the evening [20], indicating that commuting is the main purpose for using the docked bicycle sharing on weekdays [21]. A travel distance of 1 km to 5 km is acceptable for docked bicycle sharing [10,21], and the critical travel time for cycling is within half an hour [22]. Several studies also investigated the factors affecting the docked bicycle sharing user demand.…”

Section: Travel Patterns Of Bicycle Sharingmentioning

confidence: 99%

“…However, the prevalence of different purposes may be influenced by gender and temporal variables, such as time of the day and day of the week [16,17]. In general, the docked bicycle sharing usage rate is higher on weekdays than on weekends; on weekdays the passenger flow would have a peak in the morning and one in the evening [20], indicating that commuting is the main purpose for using the docked bicycle sharing on weekdays [21]. A travel distance of 1 km to 5 km is acceptable for docked bicycle sharing [10,21], and the critical travel time for cycling is within half an hour [22].…”

Section: Travel Patterns Of Bicycle Sharingmentioning

confidence: 99%

Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach

Cao

Jin

2019

Information

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The main objective of this study is to explore the spatiotemporal activities pattern of bicycle sharing system by combining together temporal and spatial attributes variables through clustering analysis method. Specifically, three clustering algorithms, i.e., hierarchical clustering, K-means clustering, expectation maximization clustering, are chosen to group the bicycle sharing stations. The temporal attributes variables are obtained through the statistical analysis of bicycle sharing smart card data, and the spatial attributes variables are quantified by point of interest (POI) data around bicycle sharing docking stations, which reflects the influence of land use on bicycle sharing system. According to the performance of the three clustering algorithms and six cluster validation measures, K-means clustering has been proven as the better clustering algorithm for the case of Ningbo, China. Then, the 477 bicycle sharing docking stations were clustered into seven clusters. The results show that the stations of each cluster have their own unique spatiotemporal activities pattern influenced by people's travel habits and land use characteristics around the stations. This analysis will help bicycle sharing operators better understand the system usage and learn how to improve the service quality of the existing system.

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A study of acceptable trip distances using walking and cycling in Bangalore

Cited by 67 publications

References 35 publications

Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China

Better Understanding the Characteristics and Influential Factors of Different Travel Patterns in Free-Floating Bike Sharing: Evidence from Nanjing, China

Relación entre el transporte y la actividad física en universitarios, Santa Marta, Colombia, 2013: estudio de corte transversal

Spatiotemporal Clustering Analysis of Bicycle Sharing System with Data Mining Approach

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