Time Headway Process

2014 ◽  
pp. 79-94 ◽  
Author(s):  
Raffaele Mauro
Keyword(s):  
Time Headway

Analisa Pemilihan Moda Transportasi Umum Rute Tanjung Karang – Bandara Radin Inten II Dengan Stated Preference dan Uji Crame’s V

2018 ◽  
Vol 2 (2) ◽  
pp. 137
Author(s):  
Muhammad Abi Berkah Nadi
Keyword(s):  
Regression Analysis ◽  
Utility Function ◽  
Stated Preference ◽  
Technical Analysis ◽  
Chi Square ◽  
Time Headway ◽  
Independent Variable ◽  
The Relationship ◽  
Selection Of

Radin Inten II Airport is a national flight in Lampung Province. In this study using the technical analysis stated preference which is the approach by conveying the choice statement in the form of hypotheses to be assessed by the respondent. By using these techniques the researcher can fully control the hypothesized factors. To determine utility function for model forecasting in fulfilling request of traveler is used regression analysis with SPSS program. The analysis results obtained that the passengers of the dominant airport in the selection of modes of cost attributes than on other attributes. From the result of regression analysis, the influence of independent variable to the highest dependent variable is when the five attributes are used together with the R square value of 8.8%. The relationship between cost, time, headway, time acces and service with the selection of modes, the provision that states whether or not there is a decision. The significance of α = 0.05 with chi-square. And the result of Crame's V test average of 0.298 is around the middle, then the relationship is moderate enough.

Influences of vehicles’ fuel consumption and exhaust emissions on the trip cost without late arrival under car-following model

2016 ◽  
Vol 27 (01) ◽  
pp. 1650011 ◽  
Author(s):  
Tie-Qiao Tang ◽  
Qiang Yu
Keyword(s):  
Fuel Consumption ◽  
Exhaust Emissions ◽  
Optimal Time ◽  
Fuel Cost ◽  
Total Cost ◽  
Car Following ◽  
Time Headway ◽  
Late Arrival ◽  
Car Following Model ◽  
Open Road

In this paper, we use car-following model to explore the influences of the vehicle’s fuel consumption and exhaust emissions on each commuter’s trip cost without late arrival on one open road. Our results illustrate that considering the vehicle’s fuel cost and emission cost only enhances each commuter’s trip cost and the system’s total cost, but has no prominent impacts on his optimal time headway at the origin of each open road under the minimum total cost.

scholarly journals The Research of Car-Following Model Based on Real-Time Maximum Deceleration

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Driver Model ◽  
Cruise Control ◽  
Maximum Acceleration ◽  
Car Following ◽  
Time Headway ◽  
Asphalt Road ◽  
Car Following Model ◽  
Maximum Deceleration

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.

scholarly journals Do drivers change their manual car-following behaviour after automated car-following?

2020 ◽  
Author(s):  
Tyron Louw ◽  
Rafael Goncalves ◽  
Guilhermina Torrao ◽  
Vishnu Radhakrishnan ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Behavioural Adaptation ◽  
Car Following ◽  
Time Headway ◽  
Driving Behaviour ◽  
Driving Assistance ◽  
Change In Mean ◽  
Lead Vehicle

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

scholarly journals Comparing time to collision and time headway as safety criteria

2021 ◽  
Vol 27 (6) ◽  
pp. 669-675
Author(s):  
Ehsan Ramezani Khansari ◽  
Fereidoon Moghadas Nejad ◽  
Sina Moogehi
Keyword(s):  
Time To Collision ◽  
Time Headway

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

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