Assessment of different pedestrian communication strategies for improving driver behavior at marked crosswalks on free channelized right turns.

INTRODUCTION: Previous studies have indicated low driver yielding rates to pedestrians in various countries. This study analyzed four different strategies to improve driver yielding rates at marked crosswalks on channelized right turn lanes at signalized intersections. METHOD: A sample of 5,419 drivers was collected for four gestures using field experiments for males and females in the State of Qatar. The experiments were conducted in daytime and nighttime on weekends at three different locations; two sites are located in an urban area and the third is located in non-urban area. The effect of pedestrians' and drivers' demographic characteristics, gestures, approach speed, time of the day, location of the intersection, car type, and driver distractions on yielding behavior is investigated using logistic regression analysis. RESULTS: It was found that for the base gesture, only 2.00% of drivers yielded to the pedestrians, while for hand, attempt, and vest-attempt gestures the yielding percentages were considerably higher, 12.81%, 19.59%, and 24.60%, respectively. The results also showed that females received significantly higher yielding rates compared to males. In addition, the probability of a driver yielding increased 2.8 times when drivers approached at slower speed compared to a higher speed. Further, drivers' age group, accompanied, and distractions were not significant in determining drivers' probability of yielding.

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures.

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.

Transport impacts in Germany and State of Qatar: An assessment during the first wave of COVID-19.

Response measures to contain COVID-19 spread varied from country to country, some imposed a complete lockdown while some imposed partial restrictions. This paper compares the transport impacts of the COVID-19 pandemic for two countries having dissimilar characteristics, Germany and State of Qatar, based on the rates of infection and response measures. Secondary data, obtained from Google mobility reports, and primary data, collected from local agencies, were used for comparison purposes. The secondary data comparison from February 2020 to July 2020 indicated an overall decline in mobility for all commercial activities and an increase was noted for parks and residential locations for Saxony, Germany. For State of Qatar, the mobility was decreased to all places except residential locations. Further, the comparison for traffic volumes and the number of crashes during the first wave of the pandemic indicated that the reduction in traffic volumes, major, and minor crashes was coupled with restrictive measures rather than COVID-19 incidences for both countries. Further, the traffic volumes showed a statistically significant inverse linear relationship with the stringency index for both countries during weekdays as well as weekends. These results suggest that the policy measures are key in governing movement restrictions and containing the spread of pandemic rather than the number of COVID-19 incidences. Further, the authorities should monitor the traffic trends during the pandemic and enforce the traffic rules and regulations as soon as the movement restrictions are lifted.

Quantifying the impact of COVID-19 preventive measures on traffic in the State of Qatar.

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.

Exploring the impacts of COVID-19 on travel behavior and mode preferences.

Various measures were recommended or imposed by the governments to control the spread of COVID-19. Travel behaviors are significantly influenced due to such measures. However, people have various travel needs ranging from grocery shopping to work. This study examines the changes that occurred in travel behavior due to the COVID-19 pandemic. Data were collected through an online questionnaire survey that included questions on trip purpose, mode choice, distance traveled, and frequency of trips before and during COVID-19. 1203 responses were collected from various countries around the world. Results explained that trip purpose, mode choice, distance traveled, and frequency of trips for the primary travel were significantly different before and during the pandemic. Further, the majority of trips were made for shopping during the pandemic. There was a significant shift from public transport to private transport and non-motorized modes. People placed a higher priority on the pandemic related concerns while choosing a mode during the pandemic as compared to the general concerns. Gender, car ownership, employment status, travel distance, the primary purpose of traveling, and pandemic-related underlying factors during COVID-19 were found to be significant predictors of mode choice during the pandemic. Outcomes of this study could be useful in transport planning and policymaking during pandemics based on the travel needs of people. In particular, government authorities could utilize such knowledge for planning smart and partial lockdowns. Service providers, e.g., taxi companies and retailers, could use such information to better plan their services and operations.

Pedestrian crashes at priority-controlled junctions, roundabouts, and signalized junctions: The UK case study.

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.