Experimental Investigation of a Vehicle Behavior Using Different Complex Data Acquisition Systems

Self Cite

The road network in many of the developing cities has remained at a level that allows low-level transport logistics. In this context, and due to the elements of urban development that are currently applied, changes are needed in the road network to meet new road traffic requirements as well as to provide increased road safety. This paper aims to analyse the traffic capacity of the current street network in relation to the introduction of a mall in the study area.

Section: Real Data Set Collection and Gathering Methodsmentioning

confidence: 99%

Traffic analysis for the reconstruction of the street geometries in the case of the construction of shopping centre in crowded cities

Matei

Dumitru

Racilă

et al. 2022

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“…where:  𝜎 is the sensitivity coefficient  𝑣 is the velocity of the vehicle in question  𝑠 is the speed exponent (ranging from -2 to +2)  ∆𝑣 is the velocity of the leading vehicle subtracted from the velocity of the vehicle in question  𝑥 is the distance gap between the leading vehicle and the vehicle in question  𝑑 is the distance headway exponent (ranging from +4 to -1). If 𝑛 𝑖+1 is less than zero, it indicates that the driver will need to slow down his vehicle, which is something that no driver wants to do [6]. In such scenario, the driver wants to switch lanes (behavior change), but if they don't provide their approval, the vehicle will stay in the same lane.…”

Section: Theoretical Considerations With Reference To Model-building ...mentioning

confidence: 99%

Evaluation of different vehicle types by using modelling algorithms in road traffic virtual networks platforms

Gencărău,

Matei,

Dumitru

et al. 2024

With the growing demand for simulating and analyzing road traffic, the scope of project planning, modelling, and execution will no longer be restricted to domain experts. In the future, a significantly larger portion of engineering and traffic management professionals will be required to carry out these activities. In this context, it is necessary to have several modelling and analytic algorithms that are based on the current state of affairs. Currently, the research in this subject is facing issues related to driving style variables and the integration of vehicles with the Intelligent Transportation System (ITS). Recently, there has been a focus on studying the significance of driving style for vehicle classes in connection to traffic flow, enhanced comfort, road safety, and the decrease of chemical and noise pollution. The configuration of traffic patterns is constantly changing, leading to the increasing prevalence of the idea of micro-mobility in recent years. The authors provide a comprehensive overview of the primary mathematical models employed in specific applications, as discussed before.

“…Studies on vehicle dynamics in terms of stability during the braking or starting process are approached in [4]. Experimental determinations have been applied in [5], as well, in order to develop an analysis of vehicle behaviour using different complex data acquisition systems.…”

Section: Introductionmentioning

confidence: 99%

Road safety-related determining of time and space braking under different travel conditions

Oţăt

Victoria

Alexandra

2022

The present paper aims to determine the vehicle kinematic parameters during braking, namely the braking time and space needed. Considering different travel conditions, dry and wet roads, we put forward two methods to determine the braking capability. First, we resort to the analytical method; hence the total braking time needs to be considered as obtained from the necessary braking time until the vehicle stops, or the braking process is completed, i.e. the sum of the time due to physiological delays, the time due to involuntary delays and the actual braking time until the vehicle stops. Next, through the Virtual Crash software platform, we carried out a case study to determine the kinematic parameters of a braking vehicle under different travel conditions. The virtual modelling developed for our case study features braking analyses on a track in alignment. The initial parameters of the vehicle and the travel conditions, i.e. the adhesion coefficient value, are similar as in the analytical method. By processing the numerical results, we developed a comparative analysis of the vehicle kinematic parameters for the two methods used, focusing on validating the performance and effectiveness of the modelling method in terms of the time needed to set new initial parameters.