Wael Alhajyaseen

Modeling pedestrian crossing speed profiles considering speed change behavior for the safety assessment of signalized intersections.

Pedestrian safety is one of the most challenging issues in road networks. Understanding how pedestrians maneuver across an intersection is the key to applying countermeasures against traffic crashes. It is known that the behaviors of pedestrians at signalized crosswalks are significantly different from those in ordinary walking spaces, and they are highly influenced by signal indication, potential conflicts with vehicles, and intersection geometries. One of the most important characteristics of pedestrian behavior at crosswalks is the possible sudden speed change while crossing. Such sudden behavioral change may not be expected by conflicting vehicles, which may lead to hazardous situations. This study aims to quantitatively model the sudden speed changes of pedestrians as they cross signalized crosswalks under uncongested conditions. Pedestrian speed profiles are collected from empirical data and speed change events are extracted assuming that the speed profiles are stepwise functions. The occurrence of speed change events is described by a discrete choice model as a function of the necessary walking speed to complete crossing before the red interval ends, current speed, and the presence of turning vehicles in the conflict area. The amount of speed change before and after the event is modeled using regression analysis. A Monte Carlo simulation is applied for the entire speed profile of the pedestrians. The results show that the model can represent the pedestrian travel time distribution more accurately than the constant speed model.

Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

Road crashes and related forms of accidents are a common cause of injury and death among the human population. According to 2015 data from the World Health Organization, road traffic injuries resulted in approximately 1.25 million deaths worldwide, i.e. approximately every 25 seconds an individual will experience a fatal crash. While the cost of traffic accidents in Europe is estimated at around 160 billion Euros, driver drowsiness accounts for approximately 100,000 accidents per year in the United States alone as reported by The American National Highway Traffic Safety Administration (NHTSA). In this paper, a novel approach towards real-time drowsiness detection is proposed. This approach is based on a deep learning method that can be implemented on Android applications with high accuracy. The main contribution of this work is the compression of heavy baseline model to a lightweight model. Moreover, minimal network structure is designed based on facial landmark key point detection to recognize whether the driver is drowsy. The proposed model is able to achieve an accuracy of more than 80%.

Pedestrians' Crossing Behavior at Marked Crosswalks on Channelized Right-Turn Lanes at Intersections

Abstract: Unsignalized marked crosswalks are problematic locations from pedestrians’ safety perspective. Although the priority rule at such locations is clear; pedestrians have the absolute right of way over vehicles, driver often compete with pedestrians over the right of way which risks pedestrian safety and impose extra delays on pedestrians. In developing countries, as concluded in many previous studies, vehicles usually do not give right of way to pedestrians, leaving them with the only choice to wait until an accepted gap is available. In Gulf Cooperation Council (GCC) countries where vehicles are the predominant mode of travel, pedestrians are receiving lesser priority. Drivers usually hijack the right of way from pedestrians which often causes safety threats. Therefore, pedestrians loose the reason for crossing at these designed locations pushing them to cross at arbitrary locations increasing their safety risk. This paper investigates influencing factors on the crossing behavior of pedestrians at marked crosswalks located on dedicated right-turn lanes. A study site from Doha, Qatar was selected for video recording and data analysis. A sample of 235 pedestrian observations was used for waiting behavior, crossing speed, accepted gaps, and driver yielding behavior analysis. The results showed that the waiting behavior was independent of pedestrian characteristics and relied only on the traffic characteristics. In addition, the average crossing speed was 1.43m/s and the gender, distraction, and group significantly affected the crossing speed. Beside, the distracted pedestrians and pedestrians crossing in groups accepted significantly larger gaps compared to undistracted and individual pedestrians. Moreover, about 15% of drivers yielded for pedestrians, yielding was irrespective of gender and mainly affected by the crossing direction. Consequently, innovative strategies in terms of engineering measures and awareness are needed to improve pedestrian safety at these locations.

Pedestrians’ Speed Analysis at Signalized Crosswalks

Abstract Pedestrian speed is essential for designing signal timings as well as for understanding pedestrian safety issues. This paper presents analysis of three types of pedestrian speeds (entry speed, crossing speed, and exit speed) at three signalized crosswalks in the State of Qatar. Pedestrian movements were tracked using TrafficAnalyzer software and data were analyzed to determine the effect of signal indications, crosswalk length, and crossing direction on pedestrian speeds. The results of this analysis showed that only 23.69% pedestrians crossed legally, during pedestrian green or pedestrian flashing green interval. Moreover, the pedestrian entry speeds were significantly affected by the pedestrian signal indications. Furthermore, the crossing speeds were positively correlated with crosswalk length for pedestrians crossing on green and red indications while pedestrian exit speeds were independent of crosswalk length but significantly affected by crossing direction.

Changes in Driving Behavior Across Age Cohorts in an Arab Culture: the Case of State of Qatar

Abstract In this investigation, we aimed to examine the structure of the Driving Behavior Questionnaire (DBQ) across age cohorts spanning 14 to 55 years in the State of Qatar. In addition, we aimed to examine variations in driving behavior across age cohorts. Participants were divided by age (in years) into five groups: not yet old enough to drive (under age), 18 to 25; 26 to 35; 36 to 45, and 46 to 55. The DBQ was administered to a sample of 1126 drivers, 50.30% of whom were female. Results are contrary to previous studies: factor analysis showed three pure factors rather than the four factors previously identified. Results also indicate that Qatari drivers share in their approach to driving even though there is great cultural diversity as well as varied language skills and educational levels. Significantly fewer driving aberrations were reported by female participants compared to those who were male. Furthermore, young men (

Relationship between Chauffeurs’ Demographics, and Knowledge of Traffic Signs in the Kingdom of Saudi Arabia

Abstract Authorities in the Kingdom of Saudi Arabia (KSA) are investing heavily on developing Intelligent Transport Systems, which includes installations of Variable Message Signs (VMS) for the road users. However, it is challenging to design the VMS messages since the population possess different cultural, linguistic, and social backgrounds. To aid in the process of VMS design, this paper presents results from a questionnaire survey, conducted on chauffeurs involved in crashes in the Kingdom to understand their knowledge of traffic signs. A sample of around 800 chauffeurs, who were involved in crashes, was collected by conducting personal interviews. The relationship between chauffeurs’ demographic characteristics, such as nationality, age group, language skills, and knowledge of traffic signs were assessed. The chauffeurs’ language skills were assessed for Arabic, English, and native language. Chauffeurs’ knowledge of five traffic signs namely speed limit, no entry, no overtaking, stop sign, and roundabout were tested. The results from descriptive analysis indicated that more than 60% drivers were able to understand traffic signs written in Arabic and English language. The least understood sign was no entry and the most understood sign was stop sign. Nationality of chauffeur was related to the knowledge of traffic signs and language skills. These results suggest that more research should be undertaken before designing VMS especially in a heterogeneous driver population like that of the Kingdom of Saudi Arabia.

An overview of pedestrian signal setting and implementation in the State of Qatar

Abstract: Pedestrian signal control is a main influencing factor on the Level of Service of crossing facilities and on the safety performance considering conflicts with vehicles. In signal design, pedestrian clearance time is a key design parameter for ensuring safe pedestrian crossing at signalized crosswalks. The performance of pedestrian signal control and its impact on pedestrian behavior in the Gulf Cooperation Council (GCC) Countries is rarely addressed in the literature. The characteristics of population, cultural diversity, as well as the extreme hot weather conditions, may lead to significantly different pedestrian behavior in terms of crossing manoeuvres (path and speed), compliance with signal control and interaction with vehicular traffic. This study reviews the pedestrian signal design practices in various countries and investigates the current signal settings along with their adequacy in the State of Qatar using empirical observations. The empirical analysis showed that the 85 th percentile crossing times were longer than the provided Pedestrian Flashing Green (PFG) intervals at the observed crosswalks. Study sites are characterized with unrealistically long Buffer Intervals (BIs) which, from one side, provide sufficient time for pedestrians who started crossing during PFG to complete the crossing. On the other side, they encourage pedestrians to continue crossing after the end of PFG. Additionally, the speed analysis indicated that the observed 15th percentile speed was 1.22 m/s which is similar to the assumed design speed by the Qatar Traffic Control Manual QTCM (2015). Further, the analysis showed that pedestrian crossing speed during PFG or BI was significantly higher than that during PG.