Impact of weather conditions on middle school students’ commute mode choices: Empirical findings from Beijing, China

Ma, Lu; Xiong, Hui; Zhong, Wei; Xie, Kaiqiang

doi:10.1016/j.trd.2018.05.008

Cited by 39 publications

(33 citation statements)

References 53 publications

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“…The GPS data is anonymously produced by many different devices such as vehicle tracking systems, mobile phones etc., therefore, using the GPS data for mobility inference is more practical and non‐trivial. Few studies like [19, 25, 37, 38] use only the GPS data for inference task, but at the same time only using the GPS data is not enough, because there are some hidden patterns that cannot be identified from GPS attributes as human mobility is highly correlated with the climate conditions [31, 39] and the weather data can be easily integrated with the GPS data. Therefore, we use weather attributes in addition to the GPS data, because using the weather data does not incur any cost and is easily available for all locations.…”

Section: Related Workmentioning

confidence: 99%

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Nawaz

Huang

Wang

et al. 2020

IET Intelligent Transport Systems

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Section: Related Workmentioning

confidence: 99%

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Nawaz

Huang

Wang

et al. 2020

IET Intelligent Transport Systems

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“…Second, other factors that may influence mode choice were not considered in this study. These include the built environment factors such as sidewalks, lighting and tree shading along the route [12], weather conditions [65], traffic volume [66] and other perceived factors such as security. Third, only public schools, and no private schools, were considered for analysis in this study.…”

Section: Discussionmentioning

confidence: 99%

School Commuting Mode Shift: A Scenario Analysis for Active School Commuting Using GIS and Online Map API

Liu

Kelobonye

Zhou

et al. 2020

IJGI

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Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the likelihood of driving. In this study, we combine open-access data acquired from Baidu Map application programming interface (API) with GIS (Geographic Information System) technology to estimate the extent to which the present school–home distances can be reduced for public middle schools in Jianye District, Nanjing, China. Based on the policies for school planning and catchment allocation, we conduct a scenario analysis of school catchment reassignment whereby residences are reassigned to the nearest school. The results show that, despite the government’s ‘attending nearby school’ policy, some students in the study area are subjected to excess school–home distances, and the overall journey-to-school trips can be reduced by 20.07%, accounting for 330.8 km. This excess distance indicates the extent to which the need for vehicle travel can be potentially reduced in favor of active school commuting and a low-carbon lifestyle. Therefore, these findings provide important insights into school siting and school catchment assignment policies seeking to facilitate active school commuting, achieve educational spatial equity and reduce car dependence.

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“…Another mode choice study of school-going students in china [13] pointed the differences between the school education scenario in china and rest of the world like provision of one child only, vehicle ownership compulsion for admissions, etc. and found out that car ownership, departure time, parental employment and distance affected the mode choice, another similar study in China [14] analyzed the effect of weather condition on the educational commutes in China and concluded that along with other conventional factors sky condition temperature air quality and humidity effects the mode choice of students.Another educational mode choice study in India [15]analyzed student's mode choice in the Indian city of Kanpur and concluded that family income, vehicle ownership, and gender affects the mode choice. A mode choice study in 2012 [16] Investigated the mode choice of work trips in Kathmandu and found that the factors affecting the choice are waiting and travel time, total income, cost of commute, and age.…”

Section: Previous Workmentioning

confidence: 96%

Revealed Preference Analysis for Mode Choice in Graduate Level Engineering Students of Kathmandu Valley

Chataut

Shrestha

2021

J. Adv. Coll. Engin. Mgt.

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Proper planning which is the key element in ensuring infrastructure efficiency, relies on demand analysis. Among the various trips under the domain of demand analysis educational trips occupy a significant part and hence the knowledge about patterns and attitudes of these trips is important to policymakers and infrastructure planners. The current study analyzes the mode choice of graduate-level engineering students in Kathmandu valley where the current transportation system is facing multiple problems thus requiring a proper planning intervention. This study reveals the educational mode preference among the students of engineering colleges. The reveal preference survey was conducted at the various engineering colleges in Kathmandu valley. The study concludes that the travel distance, number of siblings, and vehicle ownership effects the selection of personal modes of transport and distance effects the selection of public transportation, walking option being base criteria for both the cases. It is recommended that existing walking conditions should be improved within the educational zones while public transport which are preferred options for long journeys be designed considering movement between zones.

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Impact of weather conditions on middle school students’ commute mode choices: Empirical findings from Beijing, China

Cited by 39 publications

References 53 publications

Convolutional LSTM based transportation mode learning from raw GPS trajectories

Convolutional LSTM based transportation mode learning from raw GPS trajectories

School Commuting Mode Shift: A Scenario Analysis for Active School Commuting Using GIS and Online Map API

Revealed Preference Analysis for Mode Choice in Graduate Level Engineering Students of Kathmandu Valley

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