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This paper aims to investigate the start-up delay at signalized intersections in Abu Dhabi (AD) city, UAE. The impact of some external factors that may affect the start-up delay in examined including; left turn phasing sequences (split/lead/lag), movement turning (through/left), intersection location (CBD/non-CBD) and day time (peak/off-peak). The...
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... In [8], the variability of the start response time was linked to saturation flow rates, crucial for signal performance. In [9,10] factors affecting the start-up delays, aiding intersection design, and phasing decisions were identified. In [11], a Mullered Model accurately estimated delays, facilitating traffic analysis. ...
This study explores the influence of a high percentage of motorcycles on the traffic flow and congestion in Marrakech by examining the impact of motorcycle positioning in shaping urban traffic dynamics, in particular, the start-up lost time at signalized intersections. Different motorcycle positioning strategies are analyzed to improve intersection efficiency and safety. A twofold approach was followed to achieve this objective. First, empirical data were collected using computer vision techniques. Second, different strategies were simulated in VISSIM based on the collected data. The approach adopted for data collection was based on mobile phone video recording at a representative signalized intersection in Marrakech, capturing traffic behaviors during four distinct time periods. Then the YOLOv8 algorithm was employed for real-time object detection and analysis, allowing precise monitoring of motorcycle positioning and examining its influence on the start-up lost time. Afterwards, VISSIM simulations were implemented, on the basis of the collected data, to explore various scenarios, such as motorcycles sharing lanes with cars or dedicated motorcycle lanes. The results reveal a compelling correlation between motorcycle proximity to cars and traffic congestion, with closer positioning leading to increased congestion, longer travel times, reduced average vehicle speeds, and extended queue lengths at intersections. On the contrary, scenarios with dedicated motorcycle lanes consistently show reduced congestion and smoother traffic flow.
... The overall vehicle categories were transformed into Passenger Car Units (PCU) in order to quantify each vehicle category in a single unit. The PCU units used in the study were from the recommended values for Sri Lanka as proposed by Kumarage (1996) 2. 4 Data Analysis ...
... The reference list was updated accordingly. 4 Authors are suggested to discuss field data that you have collected. How have you calculated the PCUs? ...
Loss time is a notable criterion which has to be incorporated in designing signalized intersections. The lost time consists of three main components: start-up loss time, end loss time and other loss times. This study is aimed to develop a relationship between the start-up loss time and the average PCU of the vehicles approach at the signalized intersection. The research study was conducted in a four-legged signalized intersection in an urban area in Sri Lanka. The data collection was done using a drone camera. The first five vehicles of each leaving vehicle platoon was considered for the start-up loss time calculation. The start-up loss time was derived from the start response time and the stop line crossing time while six vehicle categories were considered for the average PCU value. A simple nonlinear regression model was developed having the start-up loss time as the dependent variable and the average PCU of the leaving vehicle platoon as the independent variable. The model produced a high R 2 value of 0.5764 and high Pearson correlation of 0.759. Durbin-Watson value of 2.11 and a p-value lower than 0.05 shows the prediction accuracy of the developed model. The attained results outperform the predetermined general startup loss time value of 2s in Highway Capacity Manual (2000). The analytical reasoning explains that start-up loss time varies with platoon average PCU as a logarithmic curve. Thus, up to PCU value with a loss time clustering according to the platoon PCU for seven vehicle categories as Motor bicycles, Three-Wheelers, Cars, Van/Jeep/Pickups, Light Trucks, Busses and Land Vehicles with startup loss times as 1.06s, 2.78s, 3.83s, 4.05s, 4.45s, 4.6s and 4.62s.
... Maurya AK et al. [16] in their research paper provided an understanding of the layout of speed and headway distribution in diverse traffic situations encompassing multiple motorized and non-motorized automobiles with different speeds. Shawky M et al. [17] came up with research on examining the start-up delay at signalized intersections in Abu Dhabi City, UAE. The study further deals with the effect on traffic efficiency and protection of the demonstrating lead/lag phase-in scheme. ...
The rapid growth in the economy has increased personal vehicle usages which is responsible for hefty road traffic congestion in all major cities. In delay variations at a signalized intersection, there is a significant impact on travel time among the number of various other attributes. Even though the impact of the above-mentioned distinctions on the perspective for design, planning, and analysis of signal control is having an important insinuation nevertheless one of the major problems in the urban area at present scenario is because of the delay due to traffic congestion. Measurement of delay depends upon various parameters such as signal cycle, saturation flow rate, arrival and departure of the class of particular automobiles, types of the intersection, the grade of an intersection, amount of traffic volume, the queue length of traffic, composition of vehicles, and type of traffic control system. Vehicle movement related to a signal indicator, non-stopped movement during the green time, stop and queue movement during red time include a delay that might be due to over-saturation flow in traffic or pedestrian flow, etc. The aim of the paper is to measure the delay variability and find out what amount of delay accounts for the three-leg signalized intersection in total travel time. Delay variability helps in predicting total travel time variability and helps in estimating accurate travel time. It is seen that the stopped delay is the primary affecting constraint in the intersection point which is responsible for the long queue length of the street and it is predominantly founded on the position of the vehicle in the queue line and the manual control of the length of the cycle at a signalized intersection.
... Maurya AK et al. [16] in their research paper provided an understanding of the layout of speed and headway distribution in diverse traffic situations encompassing multiple motorized and non-motorized automobiles with different speeds. Shawky M et al. [17] came up with research on examining the start-up delay at signalized intersections in Abu Dhabi City, UAE. The study further deals with the effect on traffic efficiency and protection of the demonstrating lead/lag phase-in scheme. ...
... Factors that affect start-up lost time include intersection location (e.g., central business district [CBD] or non-CBD), signal phase sequencing, leading/lagging left-turn signal phase, intersection geometry (i.e., number of through lanes, and left-turn lanes), grade, percentage of truck volume, weather conditions, queue length, visibility of traffic light, and motorists' driving state (16). ...
... Shawky and Al-Ghafli found that intersection location, phase split, and lead/lag phasing have a significant impact on start-up lost time. However, the study did not find significant differences between peak and off-peak periods (16). Li and Prevedouros conducted detailed observations of saturation headways and start-up lost times (15). ...
Various connected vehicle (CV) technology applications reported in current literature have the potential to solve the challenges faced by the transportation sector. Over the last decade, extensive research efforts have focused on performance evaluation and the benefits of innovative CV applications; findings indicate that CV technology can effectively mitigate the safety, mobility, and environmental challenges experienced on today’s transportation networks. The majority of previous research has evaluated CV technology through simulation studies. However, a field study is the ideal method of assessing CV technology effectiveness. Field observational studies to validate previous research findings have not been conducted. Therefore, a field study to obtain the actual effectiveness of CV technology was warranted, not only to validate previous findings, but to add to the body of knowledge surrounding this topic. This research presents a field study evaluation of the effectiveness of CV smartphone technology on a 1.1-mi segment of State Road 121, containing five intersections, in Gainesville, Florida. Field observations were conducted using a CV application that uses a smartphone application, EnLighten (Connected Signals, Inc., Oregon, United States), to communicate intersection information to a driver’s smartphone, which serves as a vehicle on-board unit. Traffic operation performance was evaluated using a start-up lost time and discharge distribution model. Findings showed that the CV smartphone technology improved intersection performance with a reduction in start-up lost time of approximately 86%. The CV technology had no impact on the distribution model of position-dependent discharge headways.
