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“未来移动传感器” 为智慧城市改善出行数据
2015年09月30日 08:46 来源: 作者: cyjb  阅读次 上传:管理员 组别:管理组
  
 

来源: 中国科技网  作者:  发布时间: 2015年09月25日  浏览量: 116

中国科技网9月23日报道(张微 编译)墨尔本大学正在测试一个新的智能手机应用程序,用来收集准确的出行和活动数据。

由新加坡—麻省理工学院科研中心(SMART)开发的未来移动传感器(FMS)应用程序,将比传统的纸质日记提供更加详细的出行信息。

墨尔本大学工程学院的教授斯蒂芬 温特教授说,交通规划者要依赖调查数据,了解整个城市的出行情况是怎样的。

在墨尔本,数据是由维多利亚出行和活动综合调查公司(VISTA)提供的。

“完成出行日记调查对于调查对象来说是一个负担。调查信息只有在出行当天才能返回,这就造成了一个问题,如果出发后,出行的习惯发生改变以及出行的地点发生了变动,就很难回答调查者的问题”他说。

一些出行信息可能被忽略或者被调查对象忽视,这会给研究人员带来点麻烦。通过收集描述城市居民出行特征的数据,将为交通政策制订提供信息,并有助于交通政策的执行。

这个应用程序,通过自动追踪用户在一天中的活动,从而简化了数据收集过程。

通过开发各种智能手机传感器,如GPS和加速度计技术,详细描述路径就可以实现。这比传统调查更为优越,传统调查仅限于出发地和目的地的信息。

这个应用程序还能根据出行速度和选择的路径,预测出行方式。每天结束的时候,用户被要求提供应用程序收集的出行数据,确认每个目的地的活动情况。

由于收集大数据和个人隐私之间日益增长的紧张关系,这次收集的所有数据都安全地存储在国家工具与资源研究合作组织的服务器上,在进行分析之前,去除了个人身份列表。

这个项目正在墨尔本大学工程学院的斯蒂芬 温特教授的领导下进行。温特教授详细介绍的未来移动传感器应用程序的优势,他正致力于提高有助于决策者的信息水平。

“通过FMS这样的工具,我们通过城市空间,获得人们活动的细粒度数据。从通勤者每周如何改变他们的出行信息,通过了解去当地商店的优先行人路线,交通规划者就可以了解人们在生活和工作区域的活动情况,”温特教授说。

他还强调,决策者和居民之间需要更好的沟通。

“通过这样的工具,有机会将人们的出行数据与真实体验联系起来。墨尔本人对他们的交通系统非常热衷,我们要找到一些方法,了解在这座大城市出行的喜悦和沮丧。”

'Future Mobility Sensing'—better travel data for a smarter city

A new smartphone application is currently being tested by the University of Melbourne to gather accurate travel and activity data.

The Future Mobility Sensing (FMS) app developed by Singapore-MIT Alliance for Research and Technology (SMART) is expected to provide more detailed travel information than traditional paper-based diaries.

Professor Stephan Winter from the Melbourne School of Engineering said transport planners rely on surveys to understand how, where and when travel is made across the city.

In Melbourne, data is provided by the Victorian Integrated Survey of Travel and Activity (VISTA).

"Completion of a travel diary can be a burden on respondents. Information is returned for a single day of travel only, making it difficult to answer questions around travel habits and travel variability," he said.

Some trips may also be omitted or simply overlooked by respondents, giving researchers a less than accurate picture. By collecting information that describes the broad transport characteristics of the city's residents, it will help to inform and monitor transport policies.

The app simplifies data collection by automatically tracing user movements across the day.

By tapping into various smartphone sensors, such as GPS and accelerometer technology, detailed descriptions of routes can be constructed. This provides a substantial benefit over traditional surveys, which are limited to origin and destination information only.

The app also predicts the method of travel for any trip, based on the travel speed and path taken. At the end of each day, users are asked to review the travel data collected by the app, and confirm the activities undertaken at each destination.

Recognising the growing tension between collection of large datasets and personal privacy, all data is securely stored using servers of the NeCTAR national research cloud and de-identified prior to analysis.

The project is being led by Professor Stephan Winter from the Melbourne School of Engineering. Detailing the advantages of the Future Mobility Sensing app, Professor Winter is keen to highlight the improved level of information that could be made to decision-makers.

"Through tools like the FMS, we will obtain fine-grained data on the movements of people through our urban spaces. From information about how commuters change their travel behaviour across the week, through to knowledge of preferred pedestrian routes in accessing local shops, transport planners can begin to understand how people really engage with their local areas," Professor Winter said.

He also emphasised the need for better communication between decision makers and residents.

"Through tools such as this, there will be opportunities to link travel data with people's actual experiences. Melburnians are passionate about their transport system; we want to look for ways to capture the joys and frustrations of travelling around this great city."

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(本文源自:http://www.ccf.org.cn/sites/ccf/nry.jsp?contentId=2884727073268