scholarly journals The Effect of Travel Time Information, Reliability, and Level of Service on Driver Behavior Using a Driving Simulator

2017 ◽  
Vol 109 ◽  
pp. 34-41 ◽  
Author(s):  
Zohreh Rashidi Moghaddam ◽  
Mansoureh Jeihani
Keyword(s):  
Travel Time ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Level Of Service ◽  
Information Reliability ◽  
Time Information

Understanding Driver Behavior after Concussion: A Machine-Learning Approach

2020 ◽  
Vol 64 (1) ◽  
pp. 1911-1915
Author(s):  
Maryam Daniali ◽  
Dario D. Salvucci ◽  
Maria T. Schultheis
Keyword(s):  
Machine Learning ◽  
Task Performance ◽  
Verbal Memory ◽  
Driving Simulator ◽  
Cognitive Impairments ◽  
Driver Behavior ◽  
Memory Task ◽  
Learning Approach ◽  
Behavioral Task ◽  
Machine Learning Approach

Concussions are common cognitive impairments, but their effects on task performance in general, and on driving in particular, are not well understood. To better understand the effects of concussion on driving, we investigated previously gathered data on twenty-two people with a concussion, driving in a virtual-reality driving simulator (VRDS), and twenty-two non-concussed matched drivers. Participants were asked to per-form a behavioral task (either coin sorting or a verbal memory task) while driving. In this study, we chose a few common metrics from the VRDS and tracked their changes through time for each participant. Our pro-posed method—namely, the use of convolutional neural networks for classification and analysis—can accu-rately classify concussed driving and extract local features on driving sequences that translate to behavioral driving signatures. Overall, our method improves identification and understanding of clinically relevant driv-ing behaviors for concussed individuals and should generalize well to other types of impairments.

scholarly journals The Impact of Transit Oriented Development Apartment Building to Pedestrian's Level of Service in Pondok Cina Station Using Microscopic Modeling

2019 ◽  
Vol 278 ◽  
pp. 05003
Author(s):  
Randy Asad Pradana ◽  
R. Jachrizal Sumabrata
Keyword(s):  
Travel Time ◽  
Positive Impact ◽  
Service Level ◽  
Field Analysis ◽  
Level Of Service ◽  
Transit Oriented Development ◽  
Total Change ◽  
Result Show ◽  
Microscopic Modeling ◽  
The Impact

The construction of the TOD apartment at the Pondok Cina Station will have an impact on the level of service at the venue. This has a positive impact because there is an increase in KRL users, but it also has the potential to cause problems due to the increased volume. This study aims to analyze the impact of TOD station Pondok Cina apartment development on station service level in 2022 condition and find the best solution to improve service level. The station model is created using PTV VISWALK 10. Validation testing is needed to determine the model is acceptable or not by comparing the model results and actual conditions in the field. Analysis of service level using HCM as a reference. There are several models performed, such as the condition of existing year 2018, condition year 2022 without apartment, condition 2022 with apartment, and alternative condition. Alternative conditions of total change in Pondok Cina station. After the simulation, see the performance of all models based on service level and travel time. The result show given the influence of the apartment, if nothing is done then the level of service worsens from LOS B to LOS E while travel time increases drastically from 78 seconds to 429 seconds by 2022.

Evaluation of cooperative systems on driver behavior in heavy fog condition based on a driving simulator

2019 ◽  
Vol 128 ◽  
pp. 197-205 ◽  
Author(s):  
Xin Chang ◽  
Haijian Li ◽  
Lingqiao Qin ◽  
Jian Rong ◽  
Yao Lu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Cooperative Systems

Advanced Techniques for Travel Time Data Collection

1996 ◽  
Vol 1551 (1) ◽  
pp. 51-58 ◽  
Author(s):  
Shawn M. Turner
Keyword(s):  
Data Collection ◽  
Travel Time ◽  
Real Time ◽  
Cellular Phone ◽  
License Plate ◽  
Time Data ◽  
Video Imaging ◽  
Advantages And Disadvantages ◽  
Travel Time Data ◽  
Time Information

Travel time information is becoming more important for applications ranging from congestion measurement to real-time travel information. Several advanced techniques for travel time data collection are discussed, including electronic distance-measuring instruments (DMIs), computerized and video license plate matching, cellular phone tracking, automatic vehicle identification (AVI), automatic vehicle location (AVL), and video imaging. The various advanced techniques are described, the necessary equipment and procedures are outlined, the applications of each technique are discussed, and the advantages and disadvantages are summarized. Electronic DMIs are low in cost but typically limited to congestion monitoring applications. Computerized and video license plate matching are more expensive and would be most applicable for congestion measurement and monitoring. Cellular phone tracking, AVI, and AVL systems may require a significant investment in communications infrastructure, but they can provide real-time information. Video imaging is still in testing stages, with some uncertainty about costs and accuracy.

scholarly journals Travel Behavior Analysis Using 2016 Qingdao’s Household Traffic Surveys and Baidu Electric Map API Data

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ge Gao ◽  
Zhen Wang ◽  
Xinmin Liu ◽  
Qing Li ◽  
Wei Wang ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Field Study ◽  
Behavior Analysis ◽  
Travel Time ◽  
Travel Behavior ◽  
High Accuracy ◽  
Travel Distance ◽  
Distance Analysis ◽  
Trip Time ◽  
Time Information

Household traffic surveys are widely used in travel behavior analysis, especially in travel time and distance analysis. Unfortunately, any one kind of household traffic surveys has its own problems. Even all household traffic survey data is accurate, it is difficult to get the trip routes information. To our delight, electric map API (e.g., Google Maps, Apple Maps, Baidu Maps, and Auto Navi Maps) could provide the trip route and time information, which remedies the traditional traffic survey’s defect. Thus, we can take advantage of the two kinds of data and integrate them into travel behavior analysis. In order to test the validity of the Baidu electric map API data, a field study on 300 taxi OD pairs is carried out. According to statistical analysis, the average matching rate of total OD pairs is 90.74%, which reflects high accuracy of electric map API data. Based on the fused data of household traffic survey and electric map API, travel behavior on trip time and distance is analyzed. Results show that most purposes’ trip distances distributions are concentrated, which are no more than 10 kilometers. It is worth noting that students have the shortest travel distance and company business’s travel distance distribution is dispersed, which has the longest travel distance. Compared to travel distance, the standard deviations of all purposes’ travel time are greater than the travel distance. Car users have longer travel distance than bus travelers, and their average travel distance is 8.58km.

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