Browsing by Author "Wickramasinghe, V"

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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
Evaluating the Effectiveness of Speed Humps Related to Speed Profile and Noise Profile
(SLIIT Faculty of Engineering, 2023-03-02) Gamlath, K.G.D; Amarasingha, N; Wickramasinghe, V
Speed humps are an effective traffic calming measure to improve the safety of road users. On the other hand, speed humps have certain drawbacks, such as increasing emergency response time, causing damage to cars, and high noise levels due to excessive traffic. These impacts further vary with different hump profiles. Thus, the primary objective of this research is to investigate how the geometric profile of speed humps affects vehicle speed and noise level. The secondary objective is to find the Level of Service in the presence and absence of a speed hump by using VISSIM microsimulation. In this study, Lake Drive Road, Nawala, was selected with four different speed hump profiles. The Sound Meter smartphone application was used for noise monitoring. A drone camera footage was utilized to capture vehicle flows while speed trajectories of each vehicle were developed using tracking software. The developed speed profiles were used for the simulation purpose. Then, a Multiple Linear Regression (MLR) model was developed and validated to predict the hump height for the desired speed reduction and desired noise level for each selected four-vehicle category. Further, the average noise levels were found to be higher than the Central Environmental Authority's permissible noise level, and it increases with the height of the hump. It was also observed that as the height of the hump increases, vehicle speed decreases. The largest speed reduction, 42.13 %, was observed in passenger cars, while the lowest speed reduction, 23.5 %, was observed in motorcycles. Therefore, speed analysis findings reveal that passenger cars have a significant speed reduction when compared to other categories. However, the average speed reduction for all vehicles was identified as 33.85 %, and VISSIM simulations revealed that the average Level of Service (LOS) drops to LOS C from LOS A due to the presence of the speed hump.
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).
Embargo
Plus Go: Intelligent Complementary Ride-Sharing System
(IEEE, 2019-11-21) Wickramasinghe, V; Edirisinghe, A; Gunawardena, S; Gunathilake, A; Kasthurirathna, D; Wijekoon, J
Currently the world population is gathering to the cities making huge traffic congestion throughout the day. This has drawn serious attention to the society incurred to implement smart solutions for traffic management. One of the prominent problems for traffic congestion is the number of vehicles entering the cities is high. It is a popular fact that the solitary travelers coming to a defined destination make the vehicles underutilized. Therefore, this study proposes a solution to implement a new ride-sharing platform: Plus Go, to reduce this underutilization. Plus Go matches the travelers by considering the designation, traveler preferences, shortest path details, and the ratings of the users. Moreover, Plus Go intelligently estimates the traveling cost based on the fuel consumption of the vehicle, distance traveled, and the time taken to reach the destination. The proposed solution matches the travelers with 98% accuracy ensuring that ride-sharing is an effective solution to reduce the number of vehicles entering the cities.
Open Access
Queue Length Prediction at Un-Signalized Intersections with Heterogeneous Traffic Conditions
(SLIIT, 2022-02-11) Rathnayake, I; Amarasinghe, N; Wickramasinghe, V; Liyanage, K
Increasing queue lengths while reducing average vehicle speeds is a notable criterion in intersections with heterogeneous traffic conditions. Such queue lengths vary with different intersection controls. Thisstudy aimed to estimate the queue length at un-signalized intersections with heterogeneous traffic conditions. The study was done for un-signalized intersections in Peradeniya and Weliwita, Sri Lanka and the data were collected through video recordings. The queue lengths in an un-signalized intersection with mixed traffic conditions have an instantaneous aggressive variation due to the uncontrolled movements. Thus, a time series analysis with the aid of Vector Auto Regression (VAR) model was used in order to estimate the queue length. Variables considered in this study were arrival flow rate, discharge flow rate, number of conflicts for 15 seconds time intervals as independent variables and queue length at the end of each 15 seconds as the dependent variable. For the modelling, the procedure of “Box-Jenkins” method was followed. After the confirmation of the variables are stationary, Cointegration check and Granger causality tests were done to check the cointegration between variables and the granger causality between variables. Then, VAR models were developed using 80% data from the total data set for both locations. The remaining 20% of the data set was used to validate the model using the MAE, MAPE, and RMSE error values between the actual and predicted queues. Among both models, 0.94 of higher R2 value and Durbin Watson value as 2 was obtained for the developed model using raw variables for Weliwita junction. Furthermore, the observed MAE, MAPE, and RMSE values for Weliwita model were 3,5 and 6%, respectively. Thus, the results of this study can be used to reduce traffic congestion while enhancing the safety of the users at un-signalized intersections in Sri Lanka.