RESUMEN
Despite the international efforts to improve pedestrian safety in different regions of the world, pedestrian fatalities still account for around one-third of annual road traffic deaths. Residential areas are commonly characterized by high pedestrian flows, making pedestrian safety a highpriority public health issue. Different types of treatments such as ITS-based, road markings, and physical treatments have been implemented to improve pedestrian safety, however, their efficiency varies between different locations worldwide. Therefore, this study aims to compare different innovative crosswalk treatments and to investigate their impacts on driving behavior in residential areas using a driving simulator. In our study, we juxtapose five different types of treatments with the untreated control condition. The treatments included two ITS-based solutions [LED pavement lights (ITS_LED) and Variable Message Sign (ITS_VMS)]; two different road markings [yellow zigzag marking (Marking_zigzag) and white road narrowing marking with the word SLOW written in the middle (Marking_narrowing)] and a physical road narrowing treatment (Physical_narrowing). Each of the tested conditions (control condition and treatment conditions) was tested with a Yield/Stop-controlled marked crosswalk located in a residential area with a posted speed limit of 50 km/h for two different situations. In the first situation, there was no pedestrian at the crosswalk (Situation PA), while in the second situation, a pedestrian was present at the crosswalk (Situation PP). Sixty-one volunteers possessing a valid Qatari driving license participated in the experiment. The study results showed that compared to the control condition, Physical_narrowing, ITS_VMS, and Marking_narrowing conditions helped to improve the yielding rates by 12.7 %. In terms of drivers' speed behavior, even though a pedestrian was not present at the crosswalk in Situation PA, physical_narrowing performed best by lowering the mean travel speed by around 10.1 km/h at the conflict location compared to the control location. Finally, the participants rated the physical_narrowing condition highest among the tested conditions. Based on the findings of the study, we conclude that the physical_narrowing treatment outperform other studied treatments since it significantly limits the freedom of drivers to maneuver, forcing them to slow down and give priority to crossing pedestrians.
Asunto(s)
Conducción de Automóvil , Peatones , Humanos , Accidentes de Tránsito/prevención & control , Planificación Ambiental , Seguridad , CaminataRESUMEN
Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination ( R 2 ) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.
RESUMEN
Ever since the beginning of 2020, the mobility of people and goods all over the world has been extremely limited as a result of movement restrictions imposed by local authorities as part of several other efforts to stop the wide spread of COVID-19 pandemic. The Supreme Committee for Crisis Management in the State of Qatar adopted the policy of incremental preventive measures that were adjusted based on the pandemic status. These actions involved several restrictions, aiming a balance between reducing the pandemic spread and the typical daily activities disturbance. This paper assesses the impact of pandemic response measures on traffic mobility by quantifying the holistic impact of the incremental measures at different stages on traffic volumes and traffic safety. Daily traffic counts from 24 intersections were collected every 15-min for several days, representing the traffic before and after implementing each preventive measure. Besides, a screenline was used to represent the traffic entering and leaving the Central Business District (CBD) in the City of Doha. The results show that the daily traffic demand distribution over the course of day was not affected by those preventive measures. However, an overall demand reduction of 30% in baseline traffic was observed for all studied intersections and the screenline when all preventive measures were active. Moreover, the analysis of traffic violations and the total crashes indicated a drop of 73% and 37% respectively. The results from this assessment will assist decision and policy makers, and planners to prioritize traffic management actions for future needs. Further, the findings can also be utilized for mega-event traffic management in the post-COVID era, such as FIFA World Cup 2022 and 2030 Asian Games.
RESUMEN
The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every "thing" to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.
RESUMEN
Objectives: Over half of pedestrians killed and seriously injured in Great Britain in 2015 were involved in crashes at junctions. This study investigates the nature of these crashes. Methods: A study was conducted into pedestrian casualty crashes at priority controlled junctions, roundabouts and signalised junctions in England between 2005 and 2015 using information from the UK STATS19 accident database, the UK National Travel Survey and the UK National Census. Consideration was given to coding frequencies of contributory factors, exposure (in terms of miles walked or driven) as well as age, gender and the resident deprivation index of the road users involved. Results and Conclusions: In terms of indicative blame, the coding frequencies of subjectively determined pedestrian actions and behaviour factors which might have contributed to pedestrian casualty crashes were found to be between 1.6 and 2.8 times the frequencies of driver actions and behavioural factors. Substantial social gradients were found in pedestrian casualty rates per miles walked and in the driver involvement rates per mile driven with those from the most deprived quintile having higher rates. In addition, it was found that female pedestrians, aged 60 years and over, had higher pedestrian casualty rates, per billion miles walked, for all three junction types, when compared with males and females under the age of 60 years, apart from male pedestrians aged 16 years and younger at priority controlled junctions.