scholarly journals Analyses of Mechanism on Roadside Pollution by On-Board Emissions Measurement System (Characteristics of NOx Emissions in Vehicles with and without EGR System in Real Traffic Conditions)

2005 ◽  
Vol 71 (703) ◽  
pp. 1024-1031
Author(s):  
Yutaka TAKADA ◽  
Tomio MIYAZAKI ◽  
Norimasa IIDA
Keyword(s):  
Measurement System ◽  
Nox Emissions ◽  
Traffic Conditions ◽  
Roadside Pollution ◽  
System Characteristics ◽  
Real Traffic

Study to Compare CO2 Emissions from M1 Bharat Stage VI Passenger Vehicles at Chassis Dynamometer and Indian Real Traffic Conditions

2021 ◽  
Author(s):  
KIRAN THAKARE ◽  
ABHAY SINGH ◽  
OBAID ASHRAF Shah ◽  
REVANTH KUMAR bathina ◽  
ASHISH KULKARNI
Keyword(s):  
Co2 Emissions ◽  
Chassis Dynamometer ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
Real Traffic

scholarly journals The application of the CO2MPAS model for vehicle CO2 emissions estimation over real traffic conditions

2020 ◽  
Author(s):  
Caterina Mogno ◽  
Georgios Fontaras ◽  
Vincenzo Arcidiacono ◽  
Dimitrios Komnos ◽  
Jelica Pavlovic ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Traffic Conditions ◽  
Real Traffic

Use of Performance Measurement System Data to Diagnose Freeway Bottleneck Locations Empirically in Orange County, California

2005 ◽  
Vol 1925 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Robert L. Bertini ◽  
Aaron M. Myton
Keyword(s):  
Performance Measurement ◽  
Measurement System ◽  
Orange County ◽  
Sensor Data ◽  
Performance Measurement System ◽  
Clear Understanding ◽  
Time And Space ◽  
Peak Period ◽  
Traffic Conditions ◽  
Morning Peak

To improve freeway modeling and operations, it is important to understand how traffic conditions evolve in both time and space. The widespread availability of freeway sensor data makes detailed operational analysis possible in ways that were not available in the past. This study, inspired by several other studies of a 6-mi segment of Interstate 405 in Orange County, California, describes the evolution of traffic conditions over one morning peak period by using inductive loop detector data, including vehicle count and lane occupancy measured at 30-s intervals. With cumulative curves of vehicle count and occupancy, transformed in ways that enhanced their resolution, 10 bottleneck activations were identified in time and space over one morning peak period. At bottleneck activation, queue propagation was observed in generally predictable ways. Bottleneck outflows were carefully measured only while the bottlenecks were active, that is, while queued conditions persisted upstream and unqueued (freely flowing) conditions prevailed downstream. When bottlenecks were activated immediately following freely flowing conditions, outflow reductions were observed at queue formation. These reductions were consistent with those in previous studies. The study was limited in that only one day's data were analyzed and ramp data were not available on the day analyzed. Future research will include further analysis of the same site by using more recent data now that ramp counts are available in the California Performance Measurement System database. Understanding the mechanisms that lead to bottleneck activation is a critical step toward improving the understanding of how freeways function and is necessary for addressing operational issues. This clear understanding provides a foundation for determining ramp metering rates and addressing the freeway characteristics that cause bottlenecks to form.

scholarly journals Simultaneous Localization and Map Change Update for the High Definition Map-Based Autonomous Driving Car

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3145 ◽  
Author(s):  
Kichun Jo ◽  
Chansoo Kim ◽  
Myoungho Sunwoo
Keyword(s):  
Evidence Theory ◽  
Autonomous Driving ◽  
High Definition ◽  
Driving System ◽  
The Real ◽  
Traffic Conditions ◽  
Autonomous Cars ◽  
Vehicle Position ◽  
Autonomous Driving System ◽  
Real Traffic

High Definition (HD) maps are becoming key elements of the autonomous driving because they can provide information about the surrounding environment of the autonomous car without being affected by the real-time perception limit. To provide the most recent environmental information to the autonomous driving system, the HD map must maintain up-to-date data by updating changes in the real world. This paper presents a simultaneous localization and map change update (SLAMCU) algorithm to detect and update the HD map changes. A Dempster–Shafer evidence theory is applied to infer the HD map changes based on the evaluation of the HD map feature existence. A Rao–Blackwellized particle filter (RBPF) approach is used to concurrently estimate the vehicle position and update the new map state. The detected and updated map changes by the SLAMCU are reported to the HD map database in order to reflect the changes to the HD map and share the changing information with the other autonomous cars. The SLAMCU was evaluated through experiments using the HD map of traffic signs in the real traffic conditions.

scholarly journals Comparing Speed Estimations from a Moving Vehicle in Different Traffic Scenarios: Absence versus Presence of Traffic Flow

2006 ◽  
Vol 9 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Ángela Conchillo ◽  
Miguel Ángel Recarte ◽  
Luis Nunes ◽  
Trinidad Ruiz
Keyword(s):  
Traffic Flow ◽  
Experimental Studies ◽  
Speed Estimation ◽  
Estimation Task ◽  
Driving Experience ◽  
Moving Vehicle ◽  
Traffic Conditions ◽  
Traffic Environment ◽  
Subject Variables ◽  
Real Traffic

The aim of this research was to study the performance in a speed estimation task of a passenger travelling in a real car in different scenarios: a closed track used in previous experimental studies was compared with interurban traffic environment involving a secondary road and a highway. At the same time, the effect of sex and driving experience on speed estimation was analyzed. Thirty-six participants (18 male and 18 female, half of each group being drivers and half non-drivers) estimated the speed of the car in which they travelled as passengers. The actual speed values varied in the range of 40-100 km/h for the secondary road, 70-120 km/h for the highway condition, and 40-120 km/h for the track. The results obtained for the track in previous studies (Recarte & Nunes, 1996; Recarte, Conchillo, & Nunes, 2004, 2005) were replicated in the same condition and were also verified for the secondary road scenario. However, a different pattern of errors was found for the highway. From the viewpoint of psychophysics, the participants were more accurate on the without-traffic track than in real traffic conditions, considered as a whole. The differences found between road and highway are discussed. No effect was found for between- subject variables, sex, and driving experience.

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