Exploring Travel Time Reliability Using Bluetooth Data Collection: A Case Study in San Luis Obispo, California

Purser, Krista

doi:10.15368/theses.2016.54

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…Shiravi et al [26] used Wi-Fi scanners for travel time estimation with combination of Bluetooth and Bluetooth increases the reliability of data. Similar applications are noticed in different existing studies [22,25].…”

Section: Related Worksupporting

confidence: 84%

“…Abbott-Jard et al [21] studied the travel time using Bluetooth scanners by detecting MAC of smartphones within its communication range and the vehicle is detected while it passes through the range of a detector. Wi-Fi Scanners are also being used for the detection of vehicles using smart devices [22][23][24][25][26]. Shiravi et al [26] used Wi-Fi scanners for travel time estimation with combination of Bluetooth and Bluetooth increases the reliability of data.…”

Section: Related Workmentioning

confidence: 99%

“…In the very beginning of experiment, 52 participants were detected as missing people. Later, missing people were recorded as 39, 38, 25,19,15, and 0 with elapsing of time and increasing the participants. While reaching zero, all community people were rescued in the MPCS.…”

Section: Index Of Missing Peoplementioning

confidence: 99%

See 2 more Smart Citations

Disaster Resilient Rescue of Coastal Community on Cyclone Warning

Shahin¹,

Akter²,

Debnath³

et al. 2021

Natural Hazards - Impacts, Adjustments and Resilience

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Bangladesh is in the front line of battlefield of disasters due to geographical location and global warming faced over 200 natural disasters in past 40 years and most of the disasters were cyclones. People need to be evacuated and rescued before a cyclone landfall. In current practice, multipurpose cyclone shelter (MPCS) provides short-term safety for the disaster victims in Bangladesh, where people are rescued after disasters which cannot ensure survival of lives. This study aims to develop a method for efficient evacuation and rescue to reduce death tolls in the events of disasters. This study used Wi-Fi scanner and smartphones to detect people. An inbuilt index that includes name, address, mobile number, photo, service set identifier (SSID), and media access control (MAC) of smartphone was developed for 90 registered participants. In this controlled experiment, few new participants turned on hotspot in every five minutes. A new index of people with MAC/SSID was developed in MPCS simulating an emergency. Missing people were detected by comparing inbuilt index and new index, and ordered them self-evacuation. This method captured 100% evacuees. Most importantly, the proposed method will reduce death tools because the people are rescued earlier to a disaster hits a specific area.

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Section: Related Worksupporting

confidence: 84%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Disaster Resilient Rescue of Coastal Community on Cyclone Warning

Shahin¹,

Akter²,

Debnath³

et al. 2021

Natural Hazards - Impacts, Adjustments and Resilience

View full text Add to dashboard Cite

show abstract

“…In addition, errors could happen when the devices fail to function correctly for unidentified reasons or low battery power. In these cases, no data is collected (Purser, 2016).…”

Section: Bluetooth Datamentioning

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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“…Bluetooth signals could travel through glass, but may reflect on surfaces to establish a wireless connection. Errors could occur from a malfunction with the devices for unknown reasons and low battery power which results in no data collection (Purser, 2016).…”

Section: Known Sources Of Errormentioning

confidence: 99%

Estimating Transit Ridership Patterns through Automated Data Collection Technology: A Case Study in San Luis Obispo, California

Kim¹

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Public transportation offers a crucial solution to the travel demand in light of national and global economic, energy, and environmental challenges. If implemented effectively, public transit offers an affordable, convenient, and sustainable transportation mode. Implementation of new technologies for information-harvesting may lead to more effective transit operations. This study examines the potential of automated data collection technologies to analyzing and understand the origin-destination flow patterns, which is essential for transit route planning and stop location placement. This thesis investigates the collection and analysis of data of passengers onboard San Luis Obispo Transit buses in February and March 2017 using Bluetooth (BT) and automatic passenger counter (APC) data. Five BlueMAC detectors were placed on SLO Transit buses to collect Bluetooth data. APC data was obtained from San Luis Obispo Transit. The datasets were used to establish a data processing method to exclude invalid detections, to identify and process origin and destination trips of passengers, and to make conclusions regarding passenger behavior. The filtering methods were applied to the Bluetooth data to extract counts of unique passenger information and to compare the filtered data to the ground-truth APC data. The datasets were also used to study the San Luis Obispo Downtown Farmer's Market and its impact on transit ridership demand. The investigation revealed that after carefully employing the filters on BT data there were no consistent patterns in differences between unique passenger counts obtained from APC data and the BT data. As a result, one should be careful in employing BT data for transit OD estimation. Not every passenger enables Bluetooth or owns a Bluetooth device, so relying on the possession of Bluetooth-enabled devices may not lead to a random sample, resulting in misleading travel patterns. Based on the APC data, it was revealed that transit ridership is 40% higher during the days during which Higuera Street in Downtown San Luis Obispo is used for Farmer's Market-a classic example of tactical urbanism. Increase in transit ridership is one of the aspects of tactical urbanism that may be further emphasized. With rapidly-evolving data collection technologies, transit data collection methods could expand beyond the traditional onboard survey. The lessons learned from this study could be expanded to provide a robust and detailed data source for transit operations and planning.

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Exploring Travel Time Reliability Using Bluetooth Data Collection: A Case Study in San Luis Obispo, California

Cited by 3 publications

References 10 publications

Disaster Resilient Rescue of Coastal Community on Cyclone Warning

Disaster Resilient Rescue of Coastal Community on Cyclone Warning

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

Estimating Transit Ridership Patterns through Automated Data Collection Technology: A Case Study in San Luis Obispo, California

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