Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

Ji, Yuxiong; Zhao, Jizhou; Zhang, Zhiming; Du, Yong

doi:10.1155/2017/6374858

Cited by 25 publications

(15 citation statements)

References 23 publications

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“…The information that can be detected using WiFi signal sensors includes the following: media access control (MAC) addresses of the mobile device and the access point, frame type, time stamp and signal strength correlated directly with the distance between WiFi sensor and mobile device. Accordingly, by detecting the MAC address at multiple locations over time, a person will be tracked unless he/she turns off WiFi or switches to airplane mode [32]. Table 6.…”

Section: Smart Cities 2020 3 For Peer Review 13mentioning

confidence: 99%

Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

2020

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Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising.

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Section: Smart Cities 2020 3 For Peer Review 13mentioning

confidence: 99%

Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

2020

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“…The most straightforward way to estimate the boarding and alighting stops of a device is to set the boarding stop to the stop closest in time or distance to the first event associated with the device, and the alighting stop to the stop closest in time or distance to the last event associated with the device. An alternative approach is proposed by [11], which is based on the assumption that the time interval between consecutive events follows an exponential distribution. It calculates the probability for each boarding-alighting pair for a device using the timestamps of the events, the number of events, and the bus stop arrival times.…”

Section: Estimation Of Od Matrix Using Wi-fi and Apc Datamentioning

confidence: 99%

Statistical Modelling for Estimation of OD Matrices for Public Transport Using Wi-Fi and APC Data

Håkegård

Myrvoll

Skoglund

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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In this paper, statistical models are proposed to estimate trip level origin-destination (OD) matrices for public transport based on Wi-Fi data traffic. Wi-Fi monitoring equipment installed in 32 buses in Stavanger, Norway, collected Wi-Fi data during several months. The median received signal level of frames transmitted by a device and the time interval between the first and last frame are modelled as statistical distributions, conditional on whether the Wi-Fi device is on the bus or not. Based on these models and using passenger load data from Automatic Passenger Counting (APC) systems installed in the buses, the probability for each detected device being on or off the bus is estimated. When tested on large data sets, the proposed statistical method is more accurate than when hard thresholds for median received signal level and time interval of observation are applied.

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“…Ebadi et al [22] utilize smart card data to construct students' activity-mobility trajectories in time-space dimension. Ji et al [23] propose a Bayesian model to estimate triplevel OD flow matrices utilizing the data collected by Wi-Fi sensors and boarding data provided by automatic vehicle location (AVL) systems. However, compared to the smart card data, the stability of Wi-Fi sensors and the reliability of collected data are relatively low.…”

Section: Introductionmentioning

confidence: 99%

A Method for Bus OD Matrix Estimation Using Multisource Data

Huang

Shen³

et al. 2020

Journal of Advanced Transportation

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The automated fare collection (AFC) system has gained increasing popularity among transit systems worldwide. The AFC system is usually an entry-only system that only records the serial number of the smart card and the transaction time of each use. Neither the AFC data nor the bus global positioning system (GPS) could reveal the passenger’s alighting information, namely, alighting time and station. Hence, the station-to-station origin-destination (OD) trip information cannot be obtained directly from the available data sources. To address this problem, this paper proposes a methodology that estimates the OD matrix by using smart card and GPS data. In this paper, the characteristics of the basic data sources are first analyzed, based on which the bus arrival time is generated using the density-based clustering algorithm and a time correction strategy, based on which the passenger’s boarding station is identified. The alighting stations are inferred based on the characteristics of bus trip chaining, which could identify over 80% of the alighting stations on average. Finally, the proposed methodology is verified by a comprehensive field survey in Suzhou, China, with 100% sample rate.

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Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data

Cited by 25 publications

References 23 publications

Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data

Statistical Modelling for Estimation of OD Matrices for Public Transport Using Wi-Fi and APC Data

A Method for Bus OD Matrix Estimation Using Multisource Data

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