Application of Online Transportation Mode Recognition in Games

Hedemalm, Emil; Kor, Ah-Lian; Hallberg, Josef; Andersson, Karl; Pattinson, Colin; Chinnici, Marta

doi:10.3390/app11198901

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…Some of the characteristics of the random forest are accuracy at least as good as contemporary classifiers, relative robustness to outliers and noise, quicker computing times compared to Bagging, and provision of internal error (Hedemalm, 2017).…”

Section: Maximum Speedmentioning

confidence: 99%

Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals

Videa,

Wang

2021

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Passenger data such as real-time origin-destination (OD) flows and waiting times are central to planning public transportation services and improving visitor experience. This project explored the use of Internet of Things (IoT) Technology to infer transit ridership and waiting time at bus stops. Specifically, this study explored the use of Raspberry Pi computers, which are small and inexpensive sets of hardware, to scan the Wi-Fi networks of passengers’ smartphones. The process was used to infer passenger counts and obtain information on passenger trajectories based on Global Positioning System (GPS) data. The research was conducted as a case study of the Streamline Bus System in Bozeman, Montana. To evaluate the reliability of the data collected with the Raspberry Pi computers, the study conducted technology-based estimation of ridership, OD flows, wait time, and travel time for a comparison with ground truth data (passenger surveys, manual data counts, and bus travel times). This study introduced the use of a wireless Wi-Fi scanning device for transit data collection, called a Smart Station. It combines an innovative set of hardware and software to create a non-intrusive and passive data collection mechanism. Through the field testing and comparison evaluation with ground truth data, the Smart Station produced accurate estimates of ridership, origin-destination characteristics, wait times, and travel times. Ridership data has traditionally been collected through a combination of manual surveys and Automatic Passenger Counter (APC) systems, which can be time-consuming and expensive, with limited capabilities to produce real-time data. The Smart Station shows promise as an accurate and cost-effective alternative. The advantages of using Smart Station over traditional data collection methods include the following: (1) Wireless, automated data collection and retrieval, (2) Real-time observation of passenger behavior, (3) Negligible maintenance after programming and installing the hardware, (4) Low costs of hardware, software, and installation, and (5) Simple and short programming and installation time. If further validated through additional research and development, the device could help transit systems facilitate data collection for route optimization, trip planning tools, and traveler information systems.

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Section: Maximum Speedmentioning

confidence: 99%

Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals

Videa,

Wang

2021

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Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review

Gharbi,

Taia-Alaoui,

Fourati

et al. 2024

Sensors

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Due to increasing traffic congestion, travel modeling has gained importance in the development of transportion mode detection (TMD) strategies over the past decade. Nowadays, recent smartphones, equipped with integrated inertial measurement units (IMUs) and embedded algorithms, can play a crucial role in such development. In particular, obtaining much more information on the transportation modes used by users through smartphones is very challenging due to the variety of the data (accelerometers, magnetometers, gyroscopes, proximity sensors, etc.), the standardization issue of datasets and the pertinence of learning methods for that purpose. Reviewing the latest progress on TMD systems is important to inform readers about recent datasets used in detection, best practices for classification issues and the remaining challenges that still impact the detection performances. Existing TMD review papers until now offer overviews of applications and algorithms without tackling the specific issues faced with real-world data collection and classification. Compared to these works, the proposed review provides some novelties such as an in-depth analysis of the current state-of-the-art techniques in TMD systems, relying on recent references and focusing particularly on the major existing problems, and an evaluation of existing methodologies for detecting travel modes using smartphone IMUs (including dataset structures, sensor data types, feature extraction, etc.). This review paper can help researchers to focus their efforts on the main problems and challenges identified.

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Application of Online Transportation Mode Recognition in Games

Cited by 2 publications

References 38 publications

Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals

Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals

Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review

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