Bus passenger counts using Wi-Fi signals: some cautionary findings

Paradeda, Diego Benites; Kraus, Werner; Carlson, Rodrigo Castelan

doi:10.14295/transportes.v27i3.2039

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…In the literature, the majority of studies examining the potential for tracking individual devices throughout a system (with Wi-Fi, Bluetooth, or cellular connections) focus on ridership estimation (5)(6)(7)(8)(9)(10)(11), with a handful of studies examining its potential for measuring other aspects of travel throughout the system such as comfort, route choice, and reliability (6,(12)(13)(14). Some of these studies report mixed results, and some offer specific words of caution about the potential pitfalls of Wi-Fi data, especially in relation to ridership estimation.…”

Section: Measuring Individual Journey Experience With Automated Datamentioning

confidence: 99%

“…Some of these studies report mixed results, and some offer specific words of caution about the potential pitfalls of Wi-Fi data, especially in relation to ridership estimation. Paradeda et al (11) suggest that detection systems may be relatively inaccurate when compared with survey data, largely because of delays in detection of Wi-Fi signals from ''off the shelf components used with open source software.'' Ryu et al (8) show more promising accuracy on a small scale using a sliding window detection algorithm.…”

Section: Measuring Individual Journey Experience With Automated Datamentioning

confidence: 99%

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Using Wi-Fi Connection Data to Analyze Performance of the Subway System in Toronto, Canada

Grenville,

Klumpenhouwer,

Shalaby

2023

Transportation Research Record: Journal of the Transportation R

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Typical performance measurements of public transit operations make use of vehicle-based data such as automated vehicle location data or passenger-based data at specific fare collection points. Ideally, the performance of a transit system from a reliability perspective and according to passenger experience should be measured through individual passenger journeys. The growing prevalence of smartphones provides one potential source for this analysis, because passive data collection methods such as obtaining Wi-Fi, cellular, and Bluetooth connection data allow us to observe devices as they move throughout the system. In this study we present a collection of methods and performance measures for using Wi-Fi connection data to measure various aspects of customer experience and reliability, including methods for detecting train arrivals at platforms, estimating wait times, measuring origin–destination travel time variation, and developing profiles of various journey types for comparison. In contrast with many other advances toward passenger-based measures, these methods do not require the combination of diverse data sets to generate useful results. These methods are applied to data from the Wi-Fi service in the subway system in Toronto, Canada.

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Section: Measuring Individual Journey Experience With Automated Datamentioning

confidence: 99%

Section: Measuring Individual Journey Experience With Automated Datamentioning

confidence: 99%

Using Wi-Fi Connection Data to Analyze Performance of the Subway System in Toronto, Canada

Grenville,

Klumpenhouwer,

Shalaby

2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…The majority of studies examining the potential of tracking individual devices throughout a system (with WiFi, Bluetooth, or cellular connections) focus on ridership estimation (Aguiléra et al, 2014, Hakegard et al, 2018, Myrvoll et al, 2017, Nitti et al, 2020, Paradeda et al, 2019, Ryu et al, 2020, Wang et al, 2011, with a handful of studies examining its potential for measuring other aspects of travel throughout the system such as comfort, route choice, and reliability (Aguiléra et al, 2014, Asim et al, 2022, Gu et al, 2021. Some of these studies report mixed results, and some offer specific words of caution about the potential pitfalls of WiFi data, especially around ridership estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these studies report mixed results, and some offer specific words of caution about the potential pitfalls of WiFi data, especially around ridership estimation. Paradeda et al (2019) suggest that detection systems may be relatively inaccurate when compared with survey data, while Ryu et al (2020) show more promising accuracy on a small scale using a sliding window detection algorithm. In an attempt to reconstruct passenger trajectories throughout a subway system, Gu et al (2021) estimated that only 5% of trajectories could be completely identified from origin to destination in the Shanghai metro system without the use of additional analysis to fill in the gaps.…”

Section: Introductionmentioning

confidence: 99%

Methods for Analyzing System Performance and User Experience Using WiFi Connection Data

Grenville¹,

Klumpenhouwer²,

Chui³

et al. 2023

Preprint

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Typical performance measurements of public transit operations make use of vehicle-based data such as automated vehicle location data, or passenger-based data at specific fare collection points. Ideally, the performance of a transit system from a reliability and passenger experience should be measured through individual passenger journeys. The growing prevalence of smartphones provides one potential source for this analysis, as passive methods such as WiFi, cellular, and Bluetooth connection data allow us to observe devices as they move throughout the system. In this study we present a collection of methods and performance measures for using WiFi connection data to measure various aspects of customer experience and reliability, including methods for detecting train arrivals at platforms, estimating wait times, measuring origin-destination travel time variation, and developing profiles of various journey types for comparison. In contrast with many other advances towards passenger-based measures, these methods do not require combining diverse datasets to generate useful results. These methods are applied to data from the WiFi service in the subway system in Toronto, Canada.

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“…However, the parameters that .ilter the packets coming from smartphones inside the bus from outside noise are con.igured empirically, usually by setting arbitrary thresholds. This makes the application of such approaches problematic due to the complexity of estimating such limits and possible collateral effects (Oransirikul et al, 2019;Paradeda et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

UAI-FI: using artificial intelligence for automatic passenger counting through Wi-Fi and GPS data

Roriz

Medrano²,

Almeida³

2022

TRANSPORTES

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An important piece of information for planning public transportation is the number of passengers using the system. Several initiatives have started to explore the Wi-Fi packets generated by passengers’ smartphones as means to obtain this information. A sensing device located inside the bus can intercept and collect these packets. By applying filters, e.g., verifying if the signal strength is higher than a threshold, the sensor can infer passengers' presence/absence. However, such limits are set arbitrarily, leading to errors, for example, when close to bus stops. To address this issue, this article proposes a method (UAI-FI) based on an artificial intelligence technique (Support Vector Machine) to classify the origin of packets as inside or outside the bus. To validate UAI-FI, we applied and compared our approach to other methods in a bus line in Goiânia/Brazil. The results suggest that UAI-FI outperformed existing methods. Furthermore, it successfully classified the packet’s origin, obtaining 83.3% and 88.5% of the total number of passengers boarding and alighting the line. Despite the overall similarity, we highlight that UAI-FI’s counting curve presented a delay compared to the manual count indicating that the frequency that Wi-Fi packets are sent can cause the presence/absence of passengers to be perceived at different stops.

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Bus passenger counts using Wi-Fi signals: some cautionary findings

Cited by 13 publications

References 13 publications

Using Wi-Fi Connection Data to Analyze Performance of the Subway System in Toronto, Canada

Using Wi-Fi Connection Data to Analyze Performance of the Subway System in Toronto, Canada

Methods for Analyzing System Performance and User Experience Using WiFi Connection Data

UAI-FI: using artificial intelligence for automatic passenger counting through Wi-Fi and GPS data

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