Assessing Urban Intersections During Peak Hour Along Jalan Bakri, Muar, Johor

International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 11, November 2020, pp. 1005-1017, Article ID: IJARET_11_11_093 Available online at http://iaeme.com/Home/issue/IJARET?Volume=11&Issue=11 ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.11.11.2020.093

ASSESSING URBAN INTERSECTIONS DURING PEAK HOUR ALONG JALAN BAKRI, MUAR, JOHOR

Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani Universiti Tun Hussein Onn Malaysia, Malaysia

ABSTRACT Arterial provides high degree of mobility while also directly serving abutting land uses in major centres of municipal areas. It is connected to the higher and lower road hierarchy via intersections. The intersections play important roles in dispersing traffic flows which are coming from or towards central business district area. The performance of an intersection has been based on the control delay of approaching vehicles, waiting to continue their trip. According to the Jabatan Kerja Raya standard, level of service of up to D can be considered in accommodating current traffic flow. Otherwise, the traffic might experience longer waiting time which may contribute to other traffic related problems. Bandar Maharani in Muar District is a developing town in Johor State of Malaysia with its arterial aligned toward the centre of the town, creating a spider web look-alike road network. The arterial connect the central business district area of Bandar Maharani from south, north and east bounds. One of the main arterial is Jalan Bakri, connecting Bakri town to Bandar Maharani from south bound. This study was carried out to measure and assess the performance of 10 intersections along Jalan Bakri using the SIDRA Intersection 8.0 during peak hour flow in a weekday. Furthermore, this study analyses the effect of storage length to a pairing intersections network. A 12-hour commuter data acquisition was carried out to determine peak hour through the time-series plot. Later, the peak hour was selected for movement counting at intersections to obtain the most critical traffic flow during weekday. Videotaping method was used to record the movements and extracted manually for analysis purposes. Videotaping method consist of two different devices which are camcorder with 3 meters tripod and drone camera at at least 11 meters height. Based on the commuter data analysis, 70 percent traffic consist of cars, followed by 22 percent motorcycles and 7 and 1 percents of van/medium trucks and heavy trucks/buses, respectively. The highest rate of traffic was recorded between 5.30 to 6.30 PM with computed peak hour factor of 0.94. The individual analysis of intersections found that typical level of service F (worst) were recorded as expected during peak hour, except for INT004, INT005 and INT010 with level of service D, E

http://iaeme.com/Home/journal/IJARET 1005 [email protected] Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani

and B, respectively. Findings showed that the longer the storage, the smaller the delay value and the better level of service of the segment. Influence of storage length with respect to assumed delay which was basically referred to level of service of intersection was strongly correlated with Pearson correlation value of -0.94 and statistically significant at 95 percent significant. The availability of longer storage was not only able to accommodate number of traffic but also shown that reduction of flow rate might be a solution to increase the performance of intersection network. The assessment of existing intersections is essential to constantly ascertain current performance for any implementation of sufficient improvement. The overall findings of this study can be useful for planning purposes by the local municipal council to provide better traffic flow for road users. Keywords: Arterial, Delay, Intersection, Level of Service, SIDRA Intersection 8.0. Cite this Article: Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani, Assessing Urban Intersections during Peak Hour along Jalan Bakri, Muar, Johor, International Journal of Advanced Research in Engineering and Technology, 11(11), 2020, pp. 1005-1017. http://iaeme.com/Home/issue/IJARET?Volume=11&Issue=11

1. INTRODUCTION Arterial provides high degree of mobility while also directly serving abutting land uses in major centres of municipal areas [1]. It is connected to the higher and lower ranks systems via intersections. The intersections play important roles in dispersing traffic flows which are coming from or towards Central Business District (CBD) area. The performance of an intersection has been based on the control delay of approaching vehicles, waiting to continue their trip [2]. According to JKR standard, level of service (LOS) of up to D can be considered in accommodating current traffic flow [3]. If worse than that, the traffic might experience longer waiting time which may contribute to other traffic related problems. Therefore, it is essential to continuously identify current performance of existing intersections in order to implement sufficient improvement. ‘Do nothing’ technique for certain intersections may also be an option to minimize financial implication, as well as to be a reference to other improved intersections [4]. In this study, Bandar Maharani was selected for study location since it has many arterials aligned toward the CBD area. These arterials connect CBD area to many places around Muar such as Bakri, Parit Jawa, Bukit Pasir, Melaka, and Tangkak. Each arterials have their own rate of development based on land-uses along the roads. The arterials consist of various type of signalised and unsignalised intersections which have been the important connectors to the whole road networks. This paper will report the analysis of 10 intersections’ performance along Jalan Bakri based on their level of service and the effect of storage length to the pairing intersections network. The findings may contribute to determination of suitable improving techniques for the owner of the road which is the Muar Municipal Council. This paper consists of introduction, related works, methodology, and findings as well as discussions. Every section explains different information that may assist reader to understand the overall research flows.

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2. RELATED WORKS Many researchers used the Sidra software as a tool to evaluate LOS of intersections due to its reliability and comprehensive outputs from tables and graphical displays [5 - 7]. The SIDRA INTERSECTION software (older versions known as SIDRA or aaSIDRA was developed by Rahmi Akcelik since 1975 up to the latest version of the SIDRA INTERSECTION 8.0 which was released in April 2018 [8]. In this study, the analysis were carried out using the latest version of this software. The U.S HCM approach was implemented since SIDRA version 3.1, starting from the United States Highway Capacity Manual (U.S. HCM) 1984 to the current U.S HCM 2010 [9]. According to the Transportation Research Board (TRB), LOS for signalised and unsignalised intersections are determined by control delay per vehicle (second/vehicle) value for every movement at an approach. Table 1 shows the range of control delay values with respect to the LOS for signalised and unsignalised (two-way stop- controlled (TWSC)) intersections, respectively [9].

Table 1 LOS criteria for signalised and unsignalised intersection [9] Control Delay per Vehicle (s/veh) LOS Signalised Unsignalised (TWSC) A ≤ 10 0 - 10 B > 10 - 20 > 10 - 25 C > 20 - 35 > 15 - 25 D > 35 - 55 > 25 - 35 E > 55 - 80 > 35 - 50 F > 80 > 50

These determination of LOS have been used as one of the methods in the SIDRA INTERSECTION software.

3. METHODOLOGY The essential part in the study is the field data collection. It consists of 12-hour commuter data and turning movement counts at intersections along Jalan Bakri, Bandar Maharani. The commuter data was collected using manual counting by four enumerators from 7.00 a.m. to 7.00 p.m. during weekday. Then, the turning movement counts at selected intersections were only started after a peak hour was identified from the time-series analysis. Instead of turning movement data to be taken for two to three hours in the morning and evening, data collection process can be shortened by identifying a peak hour through commuter count. The performance or LOS analysis of selected intersections were carried out using the U.S. HCM 2010 approach in the SIDRA Intersection 8.0 software. Table 2 lists the intersections and their control types along Jalan Bakri. While, Figure 1 shows the maps of study location which divided into segment I (1 km), II (1 km), III (1 km) and IV (0.8 km).

Table 2 List of intersections along Jalan Bakri Name of Intersection Type of Code (Intersection of Jalan Intersection Segment Bakri - ….) control INT001 Muar Bypass Signalised I INT002 Jalan Haji Kosai Unsignalised I INT003 Jalan Haji Abdullah Signalised II INT004 Jalan Sialin Signalised II INT005 Jalan Haji Jaib Signalised III INT006 Jalan Pesta 2 Signalised III

http://iaeme.com/Home/journal/IJARET 1007 [email protected] Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani

INT007 Jalan Sakeh Signalised III INT008 Jalan Hashim Unsignalised IV Jalan Bentayan/Dato Haji IV INT009 Signalised Hassan Roundabout of Jalan IV INT010 Unsignalised Bakri

INT INT002 INT001

(a)

INT003

INT004

(b)

INT007 INT INT006 INT005

(c)

INT009

INT INT INT010 INT008

(d)

Figure 1 Maps of (a) Segment I, (b) Segment II, (c) Segment III and (d) Segment IV The estimated distance from INT001 to INT010 was 3.8 kilometres. All intersections are signalized intersection except for INT002, INT008 and INT010. There are two pairs of intersections that located closely as if it looks like a staggered intersection, namely INT003- INT004, and INT005-INT006. Manual and videotaping methods were used in volume counting and were carried out using 15-minute time interval with four vehicle classification as shown in Table 3.

Table 3 Four vehicle classification used in this study Class Type of vehicle 1 Motorcycles 2 Cars 3 Vans & Medium Trucks 4 Heavy Trucks & Buses

Data analysis consists of time-series plotting for commuter data and LOS determination using SIDRA Intersection 8.0 for intersections, respectively. Time-series analysis represents the traffic pattern for 12-hour commuter data which reveals an exact time of a peak hour

http://iaeme.com/Home/journal/IJARET 1008 [email protected] Assessing Urban Intersections during Peak Hour along Jalan Bakri, Muar, Johor during study duration. The peak hour usually possesses the worst traffic condition at any facility. SIDRA Intersection 8.0 was also used to analyses the network performance between 2 intersections. Therefore, the effect of storage size or gap between 2 intersections can be also discussed later on.

4. DATA ANALYSIS AND DISCUSSION In this section, the analysis outcomes of 12-hour commuter and turning movement counts were deliberated. The LOS determination of intersections at every segment were shown in graphical output and discussed in detail. Later, the network analysis was presented to identify the effect of storage length between two consecutive intersections.

4.1. Commuter Data Analysis As mentioned in methodology, commuter data was collected by categorising the traffic into four classes to comprehend the traffic composition along Jalan Bakri. Fig. 2 shows the traffic composition at study location for both directions (in and out of Muar Town).

7.2 1.0 22.0 Class 1: Motorcycles

Class 2: Cars

Class 3: Vans & Medium Trucks Class 4: Heavy Trucks & Buses 69.8

Figure 2 Traffic Composition along Jalan Bakri

h 2500 / h

e 2000 v

, e

l 1500 c i

h 1000 Ve

l 500 a t o

T 0

Hour

Figure 3: Time-series analysis of 12-hour commuter data at Jalan Bakri for both directions Referring to Figure 2, 70 percent of traffic flow were dominated by cars, followed by 22 percent of motorcycles. While, small portion of class 3 and 4 vehicles were identified along Jalan Bakri with 7 and 1 percent, respectively. These percentages of traffic composition were almost identical if presented in each direction. Small percentage of busses was identified may due to lack of public transport usage in Bandar Maharani, Muar. This situation can lead to a further debate on public transport issues and by expanding its use, it may be one of the

http://iaeme.com/Home/journal/IJARET 1009 [email protected] Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani solutions to local traffic congestion. Figure 3 shows the time-series analysis of commuter data at Jalan Bakri for both directions. Referring to Figure 3, a peak hour was noticeably identified at 5.30 to 6.30 p.m. with total traffic of 2068 vehicles were passing by along the route. However, this is one of the few peaks shown throughout the 12 hours. Commonly, there are always two clear peak hours during any weekdays which are in the morning and evening. However, due to industrial activities along Jalan Bakri, the situation was otherwise observed. Meanwhile, the off-peak hour with the least number of 1241 vehicles was identified from 9.15 to 10.15 a.m. Based on the data obtained, peak hour factor can be calculated. Peak hour factor (PHF) is the hourly volume during the maximum-volume hour of the day divided by the peak 15-minute flow rate within the peak hour; a measure of traffic demand fluctuations within the peak hour. The peak hour factor is used in the U.S. HCM capacity and level of service analysis to account for the variation in traffic volumes during the peak hour. The computed value of PHF at Jalan Bakri is 0.94. In summary, the turning movement data were then collected at 5.30 to 6.30 p.m. during weekdays at all selected intersections in this study.

4.2. LOS Determination The commuter count analysis was a kick-start for the LOS determination of studied intersections. LOS analysis were usually carried out using data during the most critical traffic condition at intersection in order to identify the ability of intersection to accommodate current traffic flow. Hence, it was expected that during this condition also known as peak-hour, most of the approaches will unsurprisingly obtain poor LOS such as LOS E or LOS F. As mentioned in the methodology, measure of effectiveness for intersection which determine the LOS was control delay. Therefore, some of the approaches at intersection may obtain LOS A because the movement was not subject to traffic light or signal, meaning no waiting time.

4.2.1. Segment I Segment I consists of INT001 and INT002 intersections. Figure 4 shows the LOS of INT001 and INT002 during peak hour. In Figure 4(a), most of the approaches possess the expected poor LOS (E and F) with very long delay, from 40 seconds to up to 17 minutes, especially for the Jalan Bakri (From Bakri town) approach. Bakri town is currently a develop area and Jalan Bakri also has been one of the main routes to Batu Pahat from Muar as well as other travellers from north. Hence, the traffic flow rate has been quite high at every approach and as the result the LOS of this intersection was F. Meanwhile, the degree of saturation at every approach excluding north were already exceeding 1.0 with overall value up to 2.44. Therefore, suggestion on redesigning phase time is not applicable unless with upgrading of the existing intersection geometry or provide a viaduct for through traffic from Bakri to Muar town. Referring to Figure 4(b), as an unsignalised intersection, the through traffic has been the priority at the intersection, hence, it possess the LOS A. While, vehicles from minor approach need to wait for an accepted gap before manoeuvring and merging with major stream. Similar situation goes to the right turning vehicles from major road. Although the intersection’s degree of saturation was already exceeding 1.0, conversion to signalised intersection with proper geometric upgrading may improve the performance of the intersection.

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(a)

(b)

Figure 4: LOS at (a) INT001 and (b) INT002

4.2.2. Segment II This segment consists of 2 intersections namely INT003 to INT004. Figure 5 shows the LOS of both intersections. Referring to Figure 5(a), overall LOS of the intersection was F although LOS of through movement towards Muar CBD was D. The most critical approach was the Jalan Haji Abdullah approach with control delay up to 8 minutes. This may due to the function of the road as a collector road for local streets at several residential areas such as Taman Rozy, Taman Indah, Taman Permata Indah and Taman Junid Perdana. Hence, it accommodates heavy traffic and contributes longer waiting time at the intersection. In Figure 5(b), overall LOS for INT004 was D with the worst LOS F for both right turning from major and minor. Whereas, LOS E was obtained by the through traffic towards Muar CBD, may be caused by incapability of 60 metres storage to accommodate smooth flow for the combined traffic from Bakri bound and Jalan haji Abdullah. Although the area of Sialin Commercial Centre was just around 3172 square metres [10], nonetheless, up to 200 vehicles were entering and exiting it during peak hour.

http://iaeme.com/Home/journal/IJARET 1011 [email protected] Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani

(a)

(b)

Figure 5: LOS of (a) INT003 and (b) INT004

4.2.3. Segment III Segment III consists of INT005, INT006 and INT007. Figure 6 shows the LOS of these intersections. Referring to Figure 6(a), the LOS of the intersection was E with estimated worst delay of 218 seconds at Jalan Haji Jaib approach. This condition may due to high volume of traffic along Jalan Haji Jaib, originally from nearest residential areas such as Taman Orkid and Kampung Kelantan. Fortunately, proper slip lane of left turning was provided for smoother movement at the intersection. In Figure 6(b), the LOS of the intersection was F with the worst delay of up to 18 minutes at Jalan Pesta 2 approach. High traffic were generated and attracted to this area because it has been a develop business centre, so-called the Pesta Baru Business Centre. However, the LOS for through traffic of both directions was C, meaning that the waiting time was shorter for these movements. In Figure 6(c), the LOS of this intersection was also F due to up to 4.5 minutes delay at Jalan Sakeh and Jalan Bakri from Muar CBD approaches. Yet, the through traffic towards Muar CBD was at a comfortable waiting time of 22.5 seconds at the intersection.

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(a)

(b)

4.2.4. Segment IV Segment IV consists of 3 intersections (including roundabout) namely INT008, INT009 and INT010. Figure 7 shows the LOS of these intersections. Referring to Figure 7(a), LOS A (Jalan Bakri from Muar CBD) was obtained due to priority of movement at unsignalised intersection, hence, LOS F were obtained at approaches that need to allow the movement of through traffic. Although, through traffic from Jalan Bakri (from Bakri) has the priority to move but the right turning movement shares the same lane. Therefore, blockage effect has influenced the LOS of the approach. Referring to Figure 7(b), the most critical approach was Jalan Bentayan with LOS E and F for left turning and shared right and through movements, respectively. This situation may be occurred due to small storage or short road length of Jalan Bentayan. However, the longest delay was experienced by the right turning traffic from Muar CBD with up to 11 minutes delay. Since the storage is only 200 metres away, the traffic may have to wait up to three cycle times duration. High volume of traffic was identified for the movement may due to trips to Taman Orkid, Taman Seri Maharani, SMK Dato Sri Amar Di Raja and several other schools, which were located along Jalan Dato Haji Hassan. Referring

http://iaeme.com/Home/journal/IJARET 1013 [email protected] Mohd Erwan Sanik, Joewono Prasetijo, Ahmad Hakimi Mat Nor, Nor Baizura Hamid, Mohammad Ashraf Abdul Rahman, Noor Khazanah A Rahman, Salman Salim, Aslila Abd Kadir, Mardiha Mokhtar, Nor Faizah Razali, Harina Md Amin, Munzilah Md Rohani, Ahmad Raqib Ab Ghani to Figrure 7(c), based on the analysis, this iconic roundabout of Bandar Maharani has recorded high variation of LOS at the approaches. Its large circumference with radius of 55 metres made the roundabout an effective traffic calming facility. Therefore, it allows high volume of traffic to enter the roundabout as well as to reduce cruising speed. Although every approach has short approach distance ranging from 40 to 200 metres, higher traffic volume from Jalan Bakri (from Muar CBD) and Jalan Arab may contribute to poor LOS of F. While, LOS A and B experience by Jalan Perdagangan and Jalan Bakri (from Bakri) approaches.

(a)

(b)

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4.3. Network Analysis Figure 8 shows the LOS at the road segment in pairing network analysis.

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Figure 8 LOS at the road segment in pairing network analysis

40 ay l e D 30

0 100 200 300 400 500 600 700 800 Storage Length

Figure 9: Scatterplot of delay (second) versus storage length (metre)

5. CONCLUSIONS In conclusion, based on the commuter data analysis, 70 percent traffic consist of cars, followed by 22 percent motorcycles and 7 and 1 percents of van/medium trucks and heavy trucks/buses, respectively. The highest rate of traffic was recorded between 5.30 to 6.30 PM with computed peak hour factor of 0.94. The individual analysis of intersections found that typical level of service F (worst) were recorded as expected during peak hour, except for INT004, INT005 and INT010 with level of service D, E and B, respectively. Findings showed that the longer the storage, the smaller the delay value and the better level of service of the segment. The assessment of existing intersections is essential to constantly ascertain

http://iaeme.com/Home/journal/IJARET 1016 [email protected] Assessing Urban Intersections during Peak Hour along Jalan Bakri, Muar, Johor current performance for any implementation of sufficient improvement. The overall findings of this study can be useful for planning purposes by the local municipal council to provide better traffic flow for road users.

ACKNOWLEDGEMENT The authors would like to thank the Research Management Centre, Universiti Tun Hussein Onn Malaysia for the fund given under the Tier 1 Research Grant Code H199.

REFERENCES [1] Federal Highway Administration. Highway Functional Classification: Concepts, Criteria and Procedures. United States Department of Transportation. 2013. [2] Jinjian Li, Mahjoub Dridi, Abdellah El-Moudni. A Cooperative Traffic Control of Vehicle– Intersection (CTCVI) for the Reduction of Traffic Delays and Fuel Consumption. Sensors. 2175(16), pp 1-20, 2016 [3] Jabatan Kerja Raya. Arahan Teknik (Jalan) 13/87: A Guide to the Design of Traffic Signal. 1987. [4] J.A. Bonneson, M. D. Fontaine. NCHRP Report 457: Engineering Study Guide for Evaluation Intersection Improvements. Transportation Research Board. National Academy Press. 2001. [5] Wan Al-Junaidi Jamil, Wan Hashim Wan Ibrahim. An Analysis of Unsignalised Intersection Using aaSIDRA Software, UNIMAS e-Journal of Civil Engineering, 2(4), pp 14-17, 2013 [6] M. R. Haque, M. A. Rahman, M. B. Hossain, M. Roknuzzaman, Capacity Evaluation of Roundabout Intersections in Khulna Metropolitan City by Using SIDRA, Proceedings of International Conference on Planning, Architecture and Civil Engineering, 2017 pp 95 - 100. [7] Ali Mohammed, Hassan Jony, Alaa Shakir and Kamarudin Bin Ambak, Simulation of traffic flow in unsignalization intersection using computer software SIDRA in Baghdad city, in Proc. of Int. Conf. on BuiEng. in BCEE3-2017, vol. 162, 2018, pp. 1-8 [8] Cennetyumlu, Saramoridpour, Rahmi Akçelik. Measuring and Assessing Traffic Congestion: A Case Study. Paper presented at the AITPM2014 National Conference, Adelaide, Australia, 2014. [9] Transportation Research Board. Highway Capacity Manual 2010. Washington D.C. 2010. [10] Sialin Holding Sdn. Bhd., Development of Current Project, Pusat Komersil Sialin. http://www.sialin.my/html/development-project-PusatKomersialSialin-ShopOffices.html (accessed Feb. 1, 2019)

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