scholarly journals Effects of Fog in a Brazilian Road Segment Analyzed by a Driving Simulator for Sustainable Transport: Drivers’ Speed Profile under In-Vehicle Warning Systems

2021 ◽  
Vol 13 (19) ◽  
pp. 10501
Author(s):  
Felipe Calsavara ◽  
Felipe Issa Kabbach ◽  
Ana Paula C. Larocca
Keyword(s):  
Driving Simulator ◽  
Warning System ◽  
Weather Conditions ◽  
Severity Index ◽  
Road Segment ◽  
Transport Systems ◽  
Speed Profile ◽  
Warning Systems ◽  
Intelligent Transport Systems ◽  
Intelligent Transport

Intelligent transport systems enable vehicles to communicate with each other and with the environment, ensuring road safety. Their implementation can help reduce the number of accidents, especially in stretches of s-curves, where speed control is essential to ensure the safety of drivers, and under hazardous weather conditions. Such systems promptly notify drivers about potentially dangerous road conditions, such as fog, so that they can better adapt their driving behavior. This study evaluates the driver’s speed profile in different scenarios (clear weather, fog weather, and fog with an in-vehicle fog warning system) considering the road geometry elements (s-curves). A driving simulator recreated the real scenarios of a principal Brazilian road segment, showing the geometric and weather conditions of a road known for its several s-curves and frequent incidence of fog. A preliminary study identified the most critical curves through a weighted severity index methodology to define the critical segment. The results showed drivers considerably reduced their speed in the scenario with a warning system, thus contributing to the safety of s-curved segments. The implementation of in-vehicle warning systems can avoid or reduce the need for major infrastructure interventions such as geometric design, through investments in new intelligent transport systems.

scholarly journals Speed Profile Prediction in Intelligent Transport Systems Exemplified by Vehicle to Vehicle Interactions

2015 ◽  
Vol 15 (5) ◽  
pp. 63-77 ◽  
Author(s):  
Ivan Bosankic ◽  
Lejla Banjanovic-Mehmedovic ◽  
Fahrudin Mehmedovic
Keyword(s):  
Large Scale ◽  
Traffic Information ◽  
Transport Systems ◽  
Flow Density ◽  
High Temporal Resolution ◽  
Speed Profile ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Vehicle To Vehicle ◽  
Prediction Approach

Abstract Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs of vehicles. The V2VSPP was trained and evaluated by using traffic data provided by the Australian Centre for Field Robotics. It contains vehicle state information, vehicle-to-vehicle communications and road maps with high temporal resolution for large numbers of interacting vehicles over a long time period. The experimental results show that the proposed approach (V2VSPP) has the capability of providing accurate predictions of speed profiles in multi-vehicle trajectories setup.

scholarly journals Analysis of In-Vehicle Warning System for Rail-Road Level Crossings: Case Study in the City of Thessaloniki, Greece

2020 ◽  
Vol 49 (1) ◽  
pp. 42-59
Author(s):  
Anastasios Skoufas ◽  
Socrates Basbas ◽  
Josep Maria Salanova Grau ◽  
Georgia Aifadopoulou
Keyword(s):  
Service Use ◽  
Warning System ◽  
Transport Systems ◽  
Intelligent Transport Systems ◽  
Level Crossing ◽  
Driving Dynamic ◽  
Taxi Drivers ◽  
Intelligent Transport ◽  
The Impact ◽  
The City

The present research has investigated the impact of a Cooperative – Intelligent Transport Systems service for increasing Rail – Road Level Crossing safety, in terms of driving dynamic of the taxi drivers who used the service at the city of Thessaloniki, Greece. The Cooperative – Intelligent Transport Systems service informed drivers when approaching a Rail – Road Level Crossing, through 6 different paths, at the western area of the city of Thessaloniki. The results were yielded after comparing two datasets concerning the use of the Cooperative – Intelligent Transport Systems service by 168 taxi drivers for 28 days and without the use of the Cooperative – Intelligent Transport Systems service by 15 taxi drivers for 25 days. Even if conclusions are contrasting for the different types of the Rail – Road Level Crossing transits, the findings highlight a relation between speed reduction with types of transits whose first road segment is rectilinear, during Cooperative – Intelligent Transport Systems service use, while minor differentiations are noticed for Rail – Road Level Crossing transits with sharp turns and stop signs.

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