Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment
By Juan Carlos Herrera, D. Work, Ryan Herring, Xuegang (Jeff) Ban, Quinn Jacobson, Alexandre M. Bayen. 2009
1,048 citations recorded by the source corpus.
Subfields: Building and Construction, Electrical and Electronic Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/74e25ec9777879069e12e6b7486397556a7fd54b
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Citations in the corpus
- Real time queue length estimation for signalized intersections using travel times from mobile sensors (2011, background): “For detailed definitions of VTL and the data that can be collected, one can refer to Hoh et al. (2008), Herrera et al. (2009), and Ban et al. (2009).”
- Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles (2015, background): “The real collected cycles in sample and testing data are from [33] and [34].”
- Urban traffic signal control with connected and automated vehicles: A survey (2019, background): “…the Next Generation Simulation (NGSIM) program (Alexiadis et al., 2004), Safety Pilot program (Henclewood et al, 2014), Mobile Century project (Herrera et al., 2010), the Connected Vehicle Pilot Deployment Program of the United States Department of Transportation (USDOT), and the GAIA open…”
- Driving with knowledge from the physical world (2011, background): “1 Traffic Estimation There are a few projects [1][2][9][11][12][25] aiming to learn historical traffic patterns, estimate real-time traffic flows and forecast future traffic conditions on some road segments in terms of floating car data [23], such as GPS trajectories as well as Wi-Fi and GSM signals”
- Benchmarks for reinforcement learning in mixed-autonomy traffic (2018, methodology): “For microscopic data on human driving behavior there is the NGSIM dataset [3], the 100-car naturalistic driving study [4], and Mobile Millenium [5].”
- Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016, background): “As a result of the increasing number of urban inhabitants, more travel demand have caused various problems such as, traffic congestion, parking difficulties, traffic accidents, public transport inadequacy, and environmental problems [3].”
- Traffic state estimation on highway: A comprehensive survey (2017, background): “, 2015), and some studies have considered mobile data, such as the GPS probe vehicles with some assumptions (Herrera et al., 2010; Seo et al., 2016a) and probe vehicles with additional sensors (Seo et al.”
- Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments (2017, background): “Advancements in traffic state estimation [21, 22, 23] have facilitated high resolution traffic monitoring, through the advent of GPS smartphone sensors [24, 25, 26, 27] that are part of the flow—termed Lagrangian or mobile sensors.”
- Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, background): “detection [23], [24], travel delay measurement [25] and traffic monitoring [26].”
- Understanding Road Usage Patterns in Urban Areas (2012, background): “Traditionally, ODs are costly and difficult to obtain, because they are at best based on travel diaries made every few years, which quickly become obsolete and strongly rely on provided reports 7,8 .”