Intelligent Transportation Systems

By P. Conroy, S. Shladover, J. Dahlgren, Pravin Pratap Varaiya, W. Recker, S. Ritchie. 2000

1,012 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/abc0292908e1fe4780a2e51780f7ba5956510304

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Citations in the corpus

A Survey of Intelligent Transportation Systems (2011, background): “But since 2003, traffic and congestion information in Japan has been generated increasingly through the use of probe vehicles, specifically by making VICS-enabled vehicles the probe vehicles themselves [4].”
On the continuum approximation of the on-and-off signal control on dynamic traffic networks (2013, background): “Incorporating the binary traffic signal state variables in a signal optimization process usually results in mixed integer mathematical programs; examples include Improta and Cantarella (1984), Lo (1999a) and Lo (1999b).”
Traffic state estimation on highway: A comprehensive survey (2017, methodology): “…knowledge, there is no comprehensive survey on TSE, although more than a decade has passed from the early and influential work on highway TSE by Wang and Papageorgiou (2005) and there have been considerable advances in this field.2 This makes it difficult to assess available approaches,…”
WreckWatch: Automatic Traffic Accident Detection and Notification with Smartphones (2011, background): “Vehicle localization [36] and rapid data acquisition are important to an Intelligent Transportation System (ITS), which utilizes sensor networks to monitor traffic conditions and make adjustments to increase safety and reduce congestion on transportation networks [34].”
A Survey of IoT-Enabled Cyberattacks: Assessing Attack Paths to Critical Infrastructures and Services (2018, background): “Intelligent transportation systems (ITS) [148] involve smart cars and road infrastructures, railway control systems, air traf-ﬁc control systems, and smart maritime surface vessels (see Fig.”
Energy saving potentials of connected and automated vehicles (2018, positive): “With CAV technology the merge can be coordinated much more safely as experimentally shown in (Hafner et al., 2013) resulting in smoother traffic (Letter and Elefteriadou, 2017; Zhou et al.”
Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. (2017, background): “In-vehicle assistive technologies have been categorized as Advanced Driver Assistance Systems (ADAS) (Paul, Chauhan, Srivastava, & Baruah, 2016; Hummel, Kühn, Bende, & Lang, 2011), Intelligent Transport Systems (ITS) (Dimitrakopoulos and Demestichas, 2010; Beresford and Bacon, 2006), and semi-autono”
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2013, methodology): “To avoid this problem, we apply a low-pass filter to the data and resample every 10 minutes, following the approach proposed and evaluated in Rome (Calabrese and Ratti 2006, Calabrese et al. 2011a).”
Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics (2017, background): “The enhancive requirement for fluidit has resulted in great changes in fundamental facilities in transportation [42].”