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International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

A Study on Traffic Signal System at Lacson- Espaňa Intersection for Optimization

De Jesus, Carlo Adrine B.1, Dela Cruz, Anna Natalie Y.1, Reguyal, Ardreen B.1, Romero, Ericka Patricia D.1, Nestor R. Ong1 1Department of Industrial Engineering, Faculty of Engineering, University of Santo Tomas ,España Boulevard, Manila, Philippines 2 School of Microelectronic Engineering, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis.

Abstract The worsening traffic congestion has a major impact on the day to day activities in Manila, Philippines. The existing traffic signalization systems used in the Lacson – Espaňa, intersections are fixed-time control and it faces traffic congestions during peak time. Hence, in this work, an assessment was conducted to evaluate whether the current traffic system is optimal since the intersection is observed to be congested. The traffic assessment was done at Lacson – Espaňa intersections in Sampaloc, Manila during their peak hours of 7:00AM – 8:00AM and 1:00PM – 2:00PM.Oversaturation Severity Index (OSI) was utilized to analyze the traffic congestion. Results showed that lanes in the AM session were under the case of residual queues which contributed to the congestions.

Keywords: Traffic congestion; Lascon- Espana; oversaturation severity index (OSI)

1. Introduction

For the development of a country’s economy, transportation plays a significant role. Currently at the Philippines, the most common mode of transportation within their country is by land. Metro Manila which is the center of Philippines is experiencing heavy traffic which is caused by the volume of vehicles and commuters. According to the study conducted by Japan International Cooperation Agency (JICA) in July 2013, Philippine government loses P2.6 billion daily because of traffic in the metro, which is projected to be a loss of P18,928,000,000,000.00 (trillion) in a year. This cost is inevitable but can be mitigated if the traffic signalization system if properly set up. Philippines is now currently on the 5th spot in the list of countries with the worst traffic across the globe (Montenegro, 2015). Traffic congestion may be caused by the following conditions: trip making behavior by travelers, incidents, road crashes and vehicle breakdowns especially along the roads with road works and flooding from heavy rains (Regidor, 2012. However, it has been reported that the capacity of road in Metro Manila can only handle 424 vehicles per 1 kilometer of road (Zurbano, 2015). According to the National Economic Development Authority last 2014, the traffic demand in Metro Manila is at 12.8 million trips. Despite the fact that 69% of these total trips is done using public transport, only 22% of the road space is occupied by public vehicles while the 78% of road space is taken by private vehicles. On the other hand, a study about the cost of traffic in Metro Manila was done by the Japan International Cooperation Agency sums up to P2.4 Billion a day and will grow to P6 Billion in 2030 if no improvements will be done (Mabasa, 2014). Since España Boulevard belongs to the list of the busiest roads in Metro Manila (Regidor, 2012), intersections within and connected to this road are considered busy as well. This is why the proponents decided to focus on, España – Lacson intersections. Thus, the objective of this study is to analyze traffic signal system at Lacson- Espaňa intersection and assess whether the lanes in an intersection have smooth flow of traffic by calculating the traffic intensity. In addition, the causes (spillover or residual vehicles) traffic congestion in the intersections will be studied using the Oversaturation Severity Index (OSI).

2. Literature Review

Indeed, traffic signals can contribute a lot to the existing traffic congestion when poorly timed. Presently, there are 155 intersections in the Metro Manila that has traffic signals. Maslekar et al. (2012) has stated that the existing traffic controls, which are static in nature, are the main cause of traffic congestion as the ISSN: 2005-4238 IJAST 1422 Copyright ⓒ 2020 SERSC

International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

systems are set with pre timed timing tactics. In addition, the characteristic of some system such as inaccurate traffic data and insufficient setting contributes to the difficulty of modeling and controlling the system (Hernández et al., 2013). In addition, the traffic systems are employed with predefined control parameters such as cycle length and phase splits which are not related to the real time varying traffic conditions (Zheng, & Recker, 2013). Over the past 5 decades, numerous techniques have been developed to address the traffic control with various level of flexibility (Dujardin et al., 2015). One method that can determine the optimal green time for each cycle would be the “fixed- time control” that assumes stationary traffic demand and kept fixed for a certain time period specially rush hours or peak periods (Lämmer, & Helbing, 2010). In addition, car-following models are utilized to analyze the details of driver behaviors and their collective effects during queuing and traffic congestion (Li, & Zhang, 2015). Quantifying oversaturation may reduce traffic congestion through the residual queue at the end of each cycle of the traffic (Wu et al., 2010). It has also been reported that by utilizing master computer, the cycle length, splits and offsets of the traffic signals can be adjusted based on the vehicular traffic (Zheng, & Recker, 2013). The inclusion of an adaptive algorithm for “self-adjusting” traffic system is encouraging (Hernández et al., 2013). Ardiyanto, Adji & Asmaraman (2018) reported that Histogram of Oriented Gradient (HOG) seemed to be very effective in pedestrian detection. Nonetheless, prior to implementing a smart city concept, the city not only has to be ready in terms of technological aspects, such as infrastructure and infostructure but also needs to be seen the readiness of non-technological aspects, namely suprastructure (Achmad et al., 2018).

3. Methodology

3.1. Study Design España Boulevard belongs to the list of busiest roads in Metro Manila. Because of this, the researches chose to focus on España – Lacson intersections. Assessment, analysis, and improvement of traffic signals was done using the Queuing Theory (Sundrapandian, 2009) and the Oversaturation Severity Index (OSI) model. Observations were done along the said intersections where the total queue, original queue, residual, time needed for the residual to pass, spillover, green time, excess green time, and red time were obtained. Observations were done during the peak hours of7:00AM to 8:00AM and 1:00PM to 2:00PM.

3.2. Subjects and Study Site Since España Boulevard belongs to the list of busiest roads in Metro Manila, this study is focus on España – Lacson intersection. This intersection was chosen as this intersection does not have any existing technology that can count the number of cars present in the road instantaneously. The proponents studied the vehicles that are passing along the Espaňa-Lacson intersection. Observations were done randomly during the AM and PM peak hours of each intersection. The proponents did 30 observations per lane during the stated time period.

3.3. Data Instrumentation and Mode of Analysis The data gathered through the observation along the Espaňa-Lacson intersection as shown in Figure 1.In analyzing the data, the researchers computed the arrival rate, service rate and traffic intensity by using the gathered data on green time, total number of vehicles that were able to pass through the intersection, the red time, and the number of vehicles in the original queue. In addition, the researchers also analyzed the severity of lanes experiencing oversaturation by using the Level of Service (LOS) criteria of DPWH. the solved data such as service rate, arrival rate, were analyzed by the researchers to be able to come up with a recommendation considering all the constraints regarding traffic congestion along the intersection was analyzed in this study.

International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

Figure 1. Espaňa-Lacson intersection

4. Result And Discussion

Based on the data gathered by the researchers, they were able to determine the traffic intensity within the España-Lacson Intersectionduring its peak hours from 7:00am to 8:00am & from 1:00pm to 2:00pm according to the data from MMDA. Table 1 shows the service rate and arrival rate at Espanya-Lacson Intersection. There are two lanes within the said intersection that have traffic intensities lower than 1. The lowest traffic intensity is along the lane from Quezon Avenue to España (UST) in the morning and the other traffic intensity lower than 1 is the lane from Lacson (UST) to Nagtahan in the afternoon. Thus, traffic intensity lower than 1 means that the traffic along that certain lane is stable and still manageable. However, there are few lanes within the España- Lacson Intersection having traffic intensities greater than 1 and one lane that is greater than 2. Obviously, the lanes having such intensities greater than 1 have higher arrival rates than service rates. This means that the system may be saturated or may experience light to moderate traffic congestion causing residual queue or spillover queue. In line with this, the researchers shall quantify the severity of the saturation of the lanes having traffic intensities greater than 1 through the Oversaturation Severity Index (OSI). Then, the researchers shall come up with the computed green time and red time based on the data gathered for the proper allocation of time on each lane to somehow stabilize its traffic condition.

Table 1. España – Lacson Intersection Data Espana-Lacson Intersection Location Sessions Service Rate Arrival Rate Traffic (vehicles/min) (vehicles/min) Intensity Lacson (UST)- Nagtahan AM 11.37 16.04 1.41 Lacson (UST)- Nagtahan PM 15.55 13.36 0.86 Nagtahan- Lacson (UST) AM 12.26 13.85 1.13 Nagtahan- Lacson (UST) PM 13.29 14.97 1.13 Q. Ave-Espanya (UST) AM 50.48 23.84 0.47 Q. Ave-Espanya (UST) PM 11.47 14.16 1.23 Q. Ave-Espanya AM 7.15 12.17 1.70 (UST)(Left) Q. Ave-Espanya PM 12.00 21.88 1.82 (UST)(Left) Espanya(UST)-Q.Ave AM 19.90 20.59 1.03 Espanya(UST)-Q.Ave PM 8.38 19.99 2.39

After assessing the lanes in España - Lacson intersection using the LOS Criteria followed by DPWH, the researchers arrived with the data presented in the Table 2. ISSN: 2005-4238 IJAST 1424 Copyright ⓒ 2020 SERSC

International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

Table 2. LOS Grades of lanes in Espanya-Lacson Intersection Espana-Lacson Intersection Location Session Traffic Intensity Level of Description (PROBABILITY Service DIST) (LOS) (UST)- Nagtahan AM 1.41 F Forced flow; stop and go Lacson (UST)- PM 0.86 E Heavy traffic Nagtahan Nagtahan- Lacson AM 1.13 F Forced flow; stop (UST) and go Nagtahan- Lacson PM 1.13 F Forced flow; stop (UST) and go Q. Ave-Espanya AM 0.47 B Free flow traffic (UST) Q. Ave-Espanya PM 1.23 F Forced flow; stop (UST) and go Q. Ave-Espanya AM 1.70 F Forced flow; stop (UST)(Left) and go Q. Ave-Espanya PM 1.82 F Forced flow; stop (UST)(Left) and go Espanya(UST)-Q.Ave AM 1.03 F Forced flow; stop and go Espanya(UST)-Q.Ave PM 2.39 F Forced flow; stop and go

Thus, based on the results in Table 1 and Table 2 it was found out that majority of the lanes in an intersection were not having a smooth flow or has a traffic intensity ρ>1, with Espaňa-Lacson Intersection, where two lanes namely the Lacson (UST)- Nagtahan PM and Q. Ave-Espaňa (UST) AM have the smooth flow, the rest of the other lanes in the intersection had the traffic intensity ρ>1. The proponents computed the OSI of each intersection in order to distinguish whether a residual queue or spillover causes traffic congestion in each intersection. Column A represents the time in seconds used in order to free residual cars. Column B is the seconds wasted due to spillover. Green time for each lane was represented in Column D which was based on the mode of the green time observations. Red timing in Column E was based on the observed Green time. Temporal Oversaturation Severity Index or TOSI caused by residual queue was solved by dividing time used for discharging residual queue divided by the green time as shown in Column F. Spatial Oversaturation Severity Index or SOSI is the quotient of the spillover time over the red time. SOSI were shown in Column G. The TOSI and SOSI for Espanya-Lacson Intersection is shown in Table 3. Table 3. TOSI and SOSI for Espana –Lacson Intersection Espaňa-Lacson Intersection Location AM/PM A B C D E=A/C F=B/D G Lacson (UST)- AM 36.63 0.00 33.00 92.00 1.11 0.00 0.00 Nagtahan Lacson (UST)- PM 0.00 0.00 53.00 87.00 0.00 0.00 28.07 Nagtahan Nagtahan- AM 19.07 0.00 34.00 92.00 0.56 0.00 0.00 Lacson (UST) Nagtahan- PM 18.27 3.63 54.00 87.00 0.34 0.04 0.00 Lacson (UST) Q. Ave-Espaňa AM 0.00 0.00 92.00 34.00 0.00 0.00 67.47 (UST) Q. Ave- PM 25.50 4.63 87.00 54.00 0.29 0.09 0.00 Espanya (UST) Q. Ave-Espaňa AM 89.03 0.00 58.00 73.00 1.54 0.00 0.00 (UST)(Left) ISSN: 2005-4238 IJAST 1425 Copyright ⓒ 2020 SERSC

International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

Q. Ave- PM 91.37 0.00 37.00 107.00 2.47 0.00 0.00 Espanya (UST)(Left) Espaňa(UST)- AM 5.23 0.00 39.00 92.00 0.13 0.00 0.73 Q.Ave Espaňa(UST)- PM 159.33 0.00 53.00 91.00 3.01 0.00 0.00 Q.Ave

In order to solve traffic congestion due to residual queues and spillovers, the proponents classified lanes based on these scenarios. Scenario 1 is when TOSI > 0 and SOSI = 0 which can be solved by extending the green time. In contrast, Scenario two is solved by red extension which occurs when TOSI = 0 and SOSI > 0. Scenario 3 occurs only when both SOSI and TOSI are greater than 0. There are two ways of solving Scenario 3. When TOSI < SOSI Hu, Wu, and Liu (2013), suggested to increase the capacity of the downstream intersection by reducing its red time thus removing the spillover. The second approach is to use both strategies used in Scenario 1 and 2. Table 4 and Table 5 shows the scenario for each lane, the green extensions, red extensions, and the proposed green and red timing. Changes were made in the green and red timing for the intersection.

Table 4. Scenarios for Espana –Lacson Intersection AM Espaňa-Lacson Intersection AM Location Scenar Green Red GREEN RED io ext. ext. Lacson (UST)- 1 36.63 0.00 70.00 92.00 Nagtahan Nagtahan- Lacson 1 19.07 0.00 54.00 92.00 (UST) Q. Ave-Espanya (UST) SMO 0.00 0.00 92.00 34.00 OTH Q. Ave-Espaňa 1 89.03 0.00 148.00 73.00 (UST)(Left) Espaňa(UST)-Q.Ave 1 5.23 0.00 45.00 92.00

Table 5. Scenarios for Espana –Lacson Intersection PM Espaňa-Lacson Intersection PM Location Scenario Green ext. Red ext. GREEN RED Lacson (UST)- SMOOTH 0.00 0.00 53.00 87.00 Nagtahan Nagtahan- Lacson 3 18.27 3.63 73.00 91.00 (UST) Q. Ave-Espanya 3 25.50 4.63 113.00 59.00 (UST) Q. Ave-Espaňa 1 91.37 0.00 129.00 107.00 (UST)(Left) Espaňa(UST)-Q.Ave 1 159.33 0.00 213.00 91.00

The proponents came up with ideal models for each intersection wherein the cycle time for each intersection was not considered as a constraint. This is because the present cycle times for the intersections were insufficient based on the computed ideal model. Green and red time extensions were added directly to the present signalization models thus resulting to different cycle times between intersections. Tables 6 and Table 7 show the present and proposed timing models for the intersection.

International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

Green Red (secs) Observation - Observation – (secs) Green (secs) Red (secs) Lacson (UST)- 33.00 92.00 70.00 92.00 Nagtahan Nagtahan- Lacson 34.00 92.00 54.00 92.00 (UST) Q. Ave-Espanya 92.00 34.00 92.00 34.00 (UST) Q. Ave-Espaňa 58.00 73.00 148.00 73.00 (UST)(Left) Espaňa(UST)-Q.Ave 39.00 92.00 45.00 92.00

Table 7. Espana –Lacson Intersection PM Timing Model Espaňa-Lacson Intersection PM Timing Model Location MMDA MMDA Actual Actual Green Red Observation Observation (secs) (secs) - Green – Red (secs) (secs) Lacson (UST)- 53.00 87.00 53.00 87.00 Nagtahan Nagtahan- Lacson 54.00 87.00 73.00 91.00 (UST) Q. Ave-Espanya 87.00 54.00 113.00 59.00 (UST) Q. Ave-Espaňa 37.00 107.00 129.00 107.00 (UST)(Left) Espaňa(UST)-Q.Ave 53.00 91.00 213.00 91.00

Thus overall, in the case of the Espaňa-Lacson Intersection, lanes in the AM session were under the case of residual queues except for the Q. Ave-Espaňa (UST). Spillover time was not evident for the fact that these lanes are huge enough to accommodate the upstream vehicles and the visibility of traffic enforcers negate drivers to beat the red light. However in the PM session, Nagtahan- Lacson (UST) and Q. Ave-Espaňa (UST) had spillover time and residual queues. It was observed that this happened because of the jeepneys coming from Nagtahan stopping after the intersection causing the downstream to be clogged. These also affect the cars making the right turn from Q. Ave-Espaňa (UST) lane.

5. Conclusion

In this work, an assessment was conducted to evaluate whether the current traffic system is optimal since the intersection is observed to be congested. The traffic assessment was done at Lacson – Espaňa intersections in Sampaloc, Manila during their peak hours of 7:00AM – 8:00AM and 1:00PM – 2:00PM. Overall results showed that Most of the lanes in the intersections surrounding UST are indeed oversaturated and experience moderate-forced flow (stop and go) traffic. The proposed timing model will enhance the traffic flow at the mentioned intersection.

Acknowledgement

The authors gratefully acknowledge the support given by University of Santo Tomas in conducting this research.

International Journal of Advanced Science and Technology Vol. 29, No. 6s, (2020), pp. 1422-1428

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