Browsing by Author "Jayatilleke, S"

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Open Access
Estimating the Delay to the Mainstream Traffic due to Jaywalking Pedestrians on Urban Roads
(ASCE, 2021-06) Jayatilleke, S; Wickramasinghe, V; Madushani, H; Dissanayake, S
Growth of road users in urban areas results in consequential higher interactions between pedestrians and vehicles causing delay to the mainstream traffic flow. The delay caused by pedestrians who make random jaywalking along the carriageway is substantial when such behavior exists. Thus, the prime objective of this research was to study the interaction and develop a delay model to estimate the collective delay caused to the mainstream traffic which encounters jaywalking pedestrians. This delay is influenced by the characteristics of the crossing pedestrians and the behavior of on-coming vehicles. The data collection was done in a suburban city near Colombo, Sri Lanka. The data were extracted from video footages taken using a drone camera. Both the movement of the vehicles and crossing pedestrians on the subject lane were tracked using automated software in order to enhance the accuracy of the results. The delay caused to mainstream vehicle was derived using the deceleration and acceleration behavior. The proposed delay model exemplifies that the pedestrian-vehicle gap and the pedestrian speed along with other relevant pedestrian characteristics such as age, pedestrian speed at the start of the vehicle speed drop, and vehicle-related characteristics such as vehicle speed at the start and end, veh-ped gap at the vehicle speed drop, subject lane, and vehicle type are highly significant to the delay of the subject vehicle on the mainstream. The overall R value of 0.63 was observed from the regression analysis of the proposed delay model. The applicability of the proposed model for each pedestrian and vehicle characteristic has been determined and evaluated based on their level of significance.
Open Access
Introduction of a Simple Estimation Method for Lane-Based Queue Lengths with Lane-changing Movements
(Springer, 2022-10-03) Jayatilleke, S; Wickramasinghe, V; Amarasingha, N
Trafc congestions are increased globally due to rapid urbanization and expedited economic developments in many countries. Vehicle queue is a governing aspect of trafc congestion, studied over the past decades. Most of the existing queue estimation approaches are limited to homogeneous trafc conditions. However, the trafc conditions in many developing countries are heterogeneous and are heavily infuenced by mixed vehicle composition, lane changing, and gap-flling behaviours. This study aims to estimate the queue length at signalized intersections having heterogeneous trafc conditions. The heterogeneity was assimilated with the consideration of Passenger Car Units (PCU) in the measurements of the trafc fow and the lanechanging movement within the considered road section. The infuential factors of the queue length were contemplated with the arrival fow, discharge fow, outbound lane change, inbound lane change, and signal confguration. A Vector Auto Regression (VAR) model was developed to estimate queue length, with a lag time of 15 s for each variable. The results have indicated a higher accuracy in the queue estimation as well as the practical application for prediction, constituting the trafc characteristics of the formed vehicle queue. The R squared of the VAR model was 0.97, along with a Mean Absolute Percentage Error (MAPE) of 21.55%. The model estimation results of right turning lanes were well accurate with MAPE ranging from 15 to 17%, whilst for through movement lanes, accuracy was slightly low with MAPE in the range of 23–26%. The study manifests the functionality of the developed methodology for accurate queue estimations, asserting the practical applicability of VAR models in other locations constituting mixed trafc.
Open Access
Introduction of a Simple Estimation Method for Lane-Based Queue Lengths with Lane-changing Movements
(Springer, 2022-12-21) Jayatilleke, S; Wickramasinghe, V; Amarasingha, N
Traffic congestions are increased globally due to rapid urbanization and expedited economic developments in many countries. Vehicle queue is a governing aspect of traffic congestion, studied over the past decades. Most of the existing queue estimation approaches are limited to homogeneous traffic conditions. However, the traffic conditions in many developing countries are heterogeneous and are heavily influenced by mixed vehicle composition, lane changing, and gap-filling behaviours. This study aims to estimate the queue length at signalized intersections having heterogeneous traffic conditions. The heterogeneity was assimilated with the consideration of Passenger Car Units (PCU) in the measurements of the traffic flow and the lane-changing movement within the considered road section. The influential factors of the queue length were contemplated with the arrival flow, discharge flow, outbound lane change, inbound lane change, and signal configuration. A Vector Auto Regression (VAR) model was developed to estimate queue length, with a lag time of 15 s for each variable. The results have indicated a higher accuracy in the queue estimation as well as the practical application for prediction, constituting the traffic characteristics of the formed vehicle queue. The R squared of the VAR model was 0.97, along with a Mean Absolute Percentage Error (MAPE) of 21.55%. The model estimation results of right turning lanes were well accurate with MAPE ranging from 15 to 17%, whilst for through movement lanes, accuracy was slightly low with MAPE in the range of 23–26%. The study manifests the functionality of the developed methodology for accurate queue estimations, asserting the practical applicability of VAR models in other locations constituting mixed traffic.
Open Access
Introduction of a Simple Estimation Method for Lane-Based Queue Lengths with Lane-changing Movements
(Springer, 2023-03) Jayatilleke, S; Wickramasinghe, V; Amarasingha, N
Traffic congestions are increased globally due to rapid urbanization and expedited economic developments in many countries. Vehicle queue is a governing aspect of traffic congestion, studied over the past decades. Most of the existing queue estimation approaches are limited to homogeneous traffic conditions. However, the traffic conditions in many developing countries are heterogeneous and are heavily influenced by mixed vehicle composition, lane changing, and gap-filling behaviours. This study aims to estimate the queue length at signalized intersections having heterogeneous traffic conditions. The heterogeneity was assimilated with the consideration of Passenger Car Units (PCU) in the measurements of the traffic flow and the lane-changing movement within the considered road section. The influential factors of the queue length were contemplated with the arrival flow, discharge flow, outbound lane change, inbound lane change, and signal configuration. A Vector Auto Regression (VAR) model was developed to estimate queue length, with a lag time of 15 s for each variable. The results have indicated a higher accuracy in the queue estimation as well as the practical application for prediction, constituting the traffic characteristics of the formed vehicle queue. The R squared of the VAR model was 0.97, along with a Mean Absolute Percentage Error (MAPE) of 21.55%. The model estimation results of right turning lanes were well accurate with MAPE ranging from 15 to 17%, whilst for through movement lanes, accuracy was slightly low with MAPE in the range of 23–26%. The study manifests the functionality of the developed methodology for accurate queue estimations, asserting the practical applicability of VAR models in other locations constituting mixed traffic. © 2022, The Institution of Engineers (India).