Profile-speed data-based models to estimate operating speeds for urban residential streets with a 30 km/h speed limit

Dinh, Do Duy; Kubota, Hisashi

doi:10.1016/j.iatssr.2012.06.001

Cited by 35 publications

(18 citation statements)

References 12 publications

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“…One design objective of the steering assistance system is to enable hands-free steering wheel rotation to prevent decreased steering portability when a vehicle travels at low speeds of 30 km/h or less or at parking speeds near or equal to 0 km/h (Dinh and Kubota, 2013;Sharp and Granger, 2003). Handsfree rotation also meets the design objective to reduce the risk of Figure 1 Overall steering assistance control design shoulder injury resulting from rapid two-handed steering (Pandis et al, 2015).…”

Section: Design Of the Steering Assistance Systemmentioning

confidence: 99%

A surface electromyography controlled steering assistance interface

Nacpil

Zheng

Kaizuka

et al. 2019

JICV

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Purpose Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy. Design/methodology/approach A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel. Findings Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn. Originality/value Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

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Section: Design Of the Steering Assistance Systemmentioning

confidence: 99%

A surface electromyography controlled steering assistance interface

Nacpil

Zheng

Kaizuka

et al. 2019

JICV

View full text Add to dashboard Cite

show abstract

“…Previous research has shown that traffic flow, mean speed, traffic control parameters and geometric design features may influence speed variation (Figueroa and Tarko, 2005;Dinh and Kubota, 2012). However, relationships observed among these variables have differed in these studies.…”

Section: Introductionmentioning

confidence: 87%

Speed variation during peak and off-peak hours on urban arterials in Shanghai

Wang

Fan

et al. 2016

Transportation Research Part C: Emerging Technologies

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Increased speed variation on urban arterials is associated with reductions in both operational performance and safety. Traffic flow, mean speed, traffic control parameters and geometric design features are known to affect speed variation. An exploratory study of the relationships among these variables could provide a foundation for improving the operational and safety performance of urban arterials, however, such a study has been hampered by problems in measuring speeds. The measurement of speed has traditionally been accomplished using spot speed collection methods such as radar, laser and loop detectors. These methods can cover only limited locations, and consequently are not able to capture speed distributions along an entire network, or even throughout any single road segment. In Shanghai, it is possible to acquire the speed distribution of any roadway segment, over any period of interest, by capturing data from Shanghai's 50,000+ taxis equipped with Global Positional Systems (GPS). These data, hereafter called Floating Car Data, were used to calculate mean speed and speed variation on 234 road segments from eight urban arterials in downtown Shanghai. Hierarchical models with random variables were developed to account for spatial correlations among segments within each arterial and heterogeneities among arterials. Considering that traffic demand changes throughout the day, AM peak, Noon off-peak, and PM peak hours were studied separately. Results showed that increases in number of lanes and number of access points, the presence of bus stops and increases in mean speed were all associated with increased speed variation, and that increases in traffic volume and traffic signal green times were associated with reduced speed variation. These findings can be used by engineers to minimize speed differences during the road network planning stage and continuing through the traffic management phase.

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“…High speed reduces the control of the vehicle, increases the fatality risk in traffic and the negative impact of high speed on road safety has been confirmed by many researches (e.g. Dinh, Kubota, 2013). What motivates drivers to exceed the allowed speed, despite being aware of the risk of accidents with increasing vehicle speed?…”

Section: Introductionmentioning

confidence: 99%

The Extended Theory of Planned Behavior in Context of Risky Driving – Speeding

2015

Emirates Research Publishing Nov. 25-26, 2015 Paris (France)

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Profile-speed data-based models to estimate operating speeds for urban residential streets with a 30 km/h speed limit

Cited by 35 publications

References 12 publications

A surface electromyography controlled steering assistance interface

A surface electromyography controlled steering assistance interface

Speed variation during peak and off-peak hours on urban arterials in Shanghai

The Extended Theory of Planned Behavior in Context of Risky Driving – Speeding

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