Vehicle Speed Determination in Case of Road Accident by Software Method and Comparing of Results with the Mathematical Model

Hoxha, Gëzim; Shala, Ahmet; Likaj, Ramë

doi:10.1515/scjme-2017-0017

Cited by 10 publications

(4 citation statements)

References 4 publications

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“…The vehicle movement feature has the highest contribution on this classification task, followed by pedestrian speed, vehicle speed, left-turn signal type, the total volume of the intersection, total pedestrian volume, signal cycle length, and so forth. Most of the associations between the factors and pedestrian-involved near-crashes have been mentioned in previous studies ( 8 , 16 , 37–39 ). This finding approves the validity of using near-crash data as surrogate data for studying pedestrian safety.…”

Section: Resultsmentioning

confidence: 88%

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

Kong

Das

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This study used a pedestrian-involved near-crash database and adopted an interpretable machine learning framework using SHapley Additive exPlanations (SHAP) to understand the factors associated with critical pedestrian-involved near-crash events. The results indicate that pedestrians with a relatively higher walking speed are more likely to be involved in critical near-crash events. Furthermore, critical pedestrian-involved near-crash events are highly associated with vehicles with driving speeds of less than 10 mph. A higher pedestrian volume is highly associated with critical near-crash events with left-turn vehicles. It is possible that a higher pedestrian volume increases the occurrence of jaywalking behavior or encourages more pedestrians to step into the crosswalk when they should not. By contrast, a higher pedestrian volume is highly associated with non-critical near-crash events with right-turn vehicles. Right-turn vehicles often expect that there will be pedestrians crossing, and a higher volume of pedestrian traffic increases a driver’s awareness and caution while turning. The study also found that a longer signal cycle is highly associated with critical near-crash events when the pedestrian volume is low, while a relatively short signal cycle length is highly associated with critical near-crash events when the pedestrian volume is high. During non-peak hours, pedestrians have less tolerance for a relatively longer signal cycle. Moreover, a relatively shorter signal cycle length at peak hours will limit the number of pedestrians that can cross during a cycle and encourage the possibility of pedestrians jaywalking or stepping onto the crosswalk when they should not.

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Section: Resultsmentioning

confidence: 88%

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

Kong

Das

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…or the initial speed v0 as a function of the total throw distance S and all of the other quantities is obtained. [2] With the help of computer programs, we construct the dynamics of vehicle-pedestrian accident presented in the figures below. The most important part of the paper is the possibility of determining the speed of movement of an accident situation when a vehicle and a pedestrian participate in a crash.…”

Section: Recontructionmentioning

confidence: 99%

“…Based on these elements, the appropriate model should be used to calculate the speed of the vehicle and to give conclusions about collision process. [2] In this paper, the result of mathematical model is compared with the software method for a vehiclepedestrian situation. In the study of accidents where the pedestrians are involved, where the analysis must take into account the combination of all relevant information, such as road characteristics (coefficient of friction, slope, physical elements, traces caused by the accident), meteorological conditions, technical characteristics of the vehicle and morphological characteristics of the pedestrian, it is necessary to use specialized calculation tools in the reconstruction of accidents.…”

Section: Introductionmentioning

confidence: 99%

An example of a vehicle-pedestrian accident reconstruction by Mathematical and Software Model

2023

IJANSER

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During the year 2022, approximately 1165 road accidents occurred in Albania where the number of persons involved in accidents was 1599, while the number of accidents due to the behavior of pedestrians was 167. Referring to the statistics, the increase number of accidents due to the behavior of pedestrian over the year is noticed. In this paper we treat the problem of determining the speed of movement of an accident situation when a vehicle and a pedestrian participate in a crash. As a result of this accident the pedestrian involved remained dead. For determination of speed we focus on the resulting of mathematical simulation and PC Crash software. The aim of this work also is to present a vehiclepedestrian situation where the accident could not be avoided even with the allowed speed of vehicle.

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“…Tyre pressure affects the coefficient of friction, therefore it is worthwhile to look into its effect in the future [12]. Furthermore worthwhile to modelling vehicle speed in case of road accident as well [4]. In this paper, I show an enhanced model which based on following distance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Car Following Model

Mika

2018

Strojnícky Časopis - Journal of Mechanical Engineering

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The vehicle dynamics was a very lot developed in the last twenty years. There is a huge gap in the different vehicle models in this field. Researchers need accurate the car following models because of it. There are several mathematical model, which describe the dynamics and the motion of individual vehicles. This models based on a desired velocity, which is kept by the following vehicle and even small gaps will not induce braking reactions. So this behaviour is not realistic.

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Vehicle Speed Determination in Case of Road Accident by Software Method and Comparing of Results with the Mathematical Model

Cited by 10 publications

References 4 publications

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

An example of a vehicle-pedestrian accident reconstruction by Mathematical and Software Model

Adaptive Car Following Model

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