A GPS/GIS method for travel mode detection in New York City

Gong, Hongmian; Chen, Cynthia; Bialostozky, Evan; Lawson, Catherine T.

doi:10.1016/j.compenvurbsys.2011.05.003

Cited by 249 publications

(160 citation statements)

References 14 publications

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“…Note that the high accuracy of .937 obtained in [21] is reached using, besides motion features, the matching of GPS data with the transportation network and with the real time bus location, the latter being hardly available in most applications. However, also other works, e.g., [46], [45], show that map matching has the potential for improved classification accuracy.…”

Section: Identification Of the Transport Modementioning

confidence: 99%

“…In [43], a very large accuracy of .985 is obtained using GPS data and accelerometer measurements which are not available from Moves (notice moreover that in [43] only foot, bike and motorized transport are discriminated). Other work, such as [21], [44], [45], [46], considers more MoTs, reaching accuracies ranging from .771 up to .937, however, they only consider a maximum of 5 MoTs. Moreover, all use high frequency GPS data from which motion features such as acceleration and instantaneous heading change can be derived.…”

Section: Identification Of the Transport Modementioning

confidence: 99%

See 1 more Smart Citation

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Bucher¹,

Cellina²,

Mangili³

et al. 2016

Proceedings of ICT for Sustainability 2016

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Abstract-The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.

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Section: Identification Of the Transport Modementioning

confidence: 99%

Section: Identification Of the Transport Modementioning

confidence: 99%

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Bucher¹,

Cellina²,

Mangili³

et al. 2016

Proceedings of ICT for Sustainability 2016

View full text Add to dashboard Cite

show abstract

“…Although GPS data has been shown to work well for mode detection, certain disadvantages are associated with it. The main difficulty is the drop in accuracy due to signal loss or degradation during warm or cold starts, and in 'urban canyons' (Gong et al 2012;Schuessler and Axhausen 2009;Stopher et al 2008). Warm and cold starts happen when a GPS logger requires between 5 and 30 s more to find enough satellites for accurate location detection after being off (or underground) for a long period of time.…”

Section: Gps Onlymentioning

confidence: 99%

Use of acceleration data for transportation mode prediction

Shafique

Hato

2014

Transportation

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Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in preprocessing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during preprocessing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities.

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“…It is also should be emphasized that the latter research used also supplementary accelerometer data for classification. This type of information is being widely used in recent researches; sometimes together with preliminary knowledge about the transportation network exist in the study-area (Troped et al, 2008;Gong et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Multi-Stage Approach to Travel-Mode Segmentation and Classification of GPS Traces

Zhang

Dalyot

Eggert

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a multi-stage approach toward the robust classification of travel-modes from GPS traces. Due to the fact that GPS traces are often composed of more than one travel-mode, they are segmented to find sub-traces characterized as an individual travel-mode. This is conducted by finding individual movement segments by identifying stops. In the first stage of classification three main travel-mode classes are identified: pedestrian, bicycle, and motorized vehicles; this is achieved based on the identified segments using speed, acceleration and heading related parameters. Then, segments are linked up to form sub-traces of individual travel-mode. After the first stage is achieved, a breakdown classification of the motorized vehicles class is implemented based on sub-traces of individual travel-mode of cars, buses, trams and trains using Support Vector Machines (SVMs) method. This paper presents a qualitative classification of travel-modes, thus introducing new robust and precise capabilities for the problem at hand.

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A GPS/GIS method for travel mode detection in New York City

Cited by 249 publications

References 14 publications

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Use of acceleration data for transportation mode prediction

Multi-Stage Approach to Travel-Mode Segmentation and Classification of GPS Traces

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