Pedestrians Crossing and Walking Speeds Analysis in Urban Areas under the Influence of Rain and Personality Characteristics

Bargegol, Iraj; Gilani, Vahid Najafi Moghaddam; Hosseinian, Seyed Mohsen; Habibzadeh, Mohammad Reza

doi:10.1155/2022/7768160

Cited by 7 publications

(3 citation statements)

References 25 publications

(21 reference statements)

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“…The width of this range was directly related to the number of available data. According to previous research found in literature [ 16 ], through the Student’s t -Test it was possible to perform a test of statistical hypotheses in order to analyse any correlations between different categories within the same variable. The test was used to determinate whether the average values and the standard deviation of two datasets were significantly different from each other.…”

Section: Methodsmentioning

confidence: 99%

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

Pazzini

Cameli

Lantieri

et al. 2022

IJERPH

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Negative effects of a massive use of cars, such as congestion, air pollution, noise, and traffic injuries, are affecting the cities everywhere. Recently introduced shared vehicles, such as e-scooters and electric bicycles, could potentially accelerate the transition towards sustainable mobility. Although these vehicles are becoming increasingly common and accepted within regulatory frameworks, some local governments are not yet ready to integrate e-scooters into their transport systems. Indeed, the legislation is unclear as it is not easy to determine whether the e-scooter is more like a bicycle or a vehicle. Moreover, it is difficult to predict the impact of e-scooters on road traffic, as well as the type of road infrastructure chosen by e-scooter drivers or the possible interaction of such vehicles with weak road users, such as pedestrians or cyclists. This study showed an analysis of speed and behaviour of e-scooter drivers in the city of Trondheim (Norway) to investigate how to manage this mode of transport. A total of 204 e-scooters were observed on six different roads in the city centre. The speed of e-scooter drivers was measured by a speed tracker (average value 15.4 km/h) and their behaviour recorded by a hidden observer in the field. Gender, age, distance from pedestrians, speed adaptation to the environment, and type of vehicle used were registered for each e-scooter. Through a Binomial Logit analysis, the data obtained were used to analyse the type of road infrastructure preferred by e-scooter drivers. Results showed that the cycle path is more widely used with percentage value from 60% to 90% of users. In addition, the probability of choice depended mainly on the road environment. The aim of this analysis was to assist local authorities in regulating the safe use of e-scooters and developing appropriate policies for their integration into cities.

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Section: Methodsmentioning

confidence: 99%

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

Pazzini

Cameli

Lantieri

et al. 2022

IJERPH

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show abstract

“…Evidence suggests that adverse weather conditions, such as the presence of rain, are associated with a higher likelihood and severity of pedestrian injury in crashes, as jaywalking might be more prevalent [61] and in general pedestrians might exhibit riskier and less compliant crossing behaviour [62]. The presence of rain has also been found to impact pedestrians' walking speed [63,64]. There is some indication that pedestrians' speed can be higher at dusk as a "walk-quickly" strategy might be adopted [65].…”

Section: Weather and Visibilitymentioning

confidence: 99%

Ranking Crossing Scenario Complexity for eHMIs Testing: A Virtual Reality Study

Fratini

Welsh

Thomas

2023

MTI

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External human–machine interfaces (eHMIs) have the potential to benefit AV–pedestrian interactions. The majority of studies investigating eHMIs have used relatively simple traffic environments, i.e., a single pedestrian crossing in front of a single eHMI on a one-lane straight road. While this approach has proved to be efficient in providing an initial understanding of how pedestrians respond to eHMIs, it over-simplifies interactions which will be substantially more complex in real-life circumstances. A process is illustrated in a small-scale study (N = 10) to rank different crossing scenarios by level of complexity. Traffic scenarios were first developed for varying traffic density, visual complexity of the road scene, road geometry, weather and visibility conditions, and presence of distractions. These factors have been previously shown to increase difficulty and riskiness of the crossing task. The scenarios were then tested in a motion-based, virtual reality environment. Pedestrians’ perceived workload and objective crossing behaviour were measured as indirect indicators of the level of complexity of the crossing scenario. Sense of presence and simulator sickness were also recorded as a measure of the ecological validity of the virtual environment. The results indicated that some crossing scenarios were more taxing for pedestrians than others, such as those with road geometries where traffic approached from multiple directions. Further, the presence scores showed that the virtual environments experienced were found to be realistic. This paper concludes by proposing a “complex” environment to test eHMIs under more challenging crossing circumstances.

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“…e Kolmogorov-Smirnov test is utilized to recognize the normality of samples [23]. e comparison of the result of the K-S test called sig.…”

Section: Kolmogorov-smirnov (K-s) Testmentioning

confidence: 99%

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

Habibzadeh

Ameri

Ziari

et al. 2022

Journal of Advanced Transportation

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The aim of the current research was to develop models to predict the severity of accidents on rural roads in Tehran province, Iran. In this regard, using accident data from 2017 to 2020, the machine learning algorithms, including multiple logistic regression, multilayer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) models, as well as statistical methods, including Kolmogorov–Smirnov test, Friedman test, and factor analysis, were implemented to determine the contributory factors in the severity of accidents. Thus, nine variables affecting the severity of accidents were considered in modeling, and then the effect of each variable was calculated. By comparing the results of artificial neural network (ANN) models and the Friedman test, it was indicated that the human factor had a remarkable effect on accident severity. In addition, both machine learning and statistical methods can be served as guidance for safety authorities to provide safety solutions, thereby leading to reducing accidents. Finally, the performances of ANN models were analyzed by other mathematical models built by MATLAB programming.

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Pedestrians Crossing and Walking Speeds Analysis in Urban Areas under the Influence of Rain and Personality Characteristics

Cited by 7 publications

References 25 publications

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

New Micromobility Means of Transport: An Analysis of E-Scooter Users’ Behaviour in Trondheim

Ranking Crossing Scenario Complexity for eHMIs Testing: A Virtual Reality Study

Presentation of Machine Learning Approaches for Predicting the Severity of Accidents to Propose the Safety Solutions on Rural Roads

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