Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
Estimation of Intercity Travel Pattern and Impact on Yangon-Pathein Road between Ayeyarwady Region and Yangon Region Using Call Detail Record
Nan Thazin Khine OO a, KYAING b, Ko Ko LWIN c, Yoshihide SEKIMOTOd a,b Department of Civil Engineering, Yangon Technological University, Ministry of Education, Insein 11011, Yangon, Myanmar a Email: [email protected] b Email: [email protected] c,d Institute of Industrial Science, The University of Tokyo, Komaba 4-6-1, Meguro Ku, Japan c Email: [email protected] d Email: [email protected]
Abstract: Intercity trips have been understudied by researchers and public agencies in comparison to routine trips within an urban area. Currently, intercity travel demand are increasing, and a significant portion of total mileage travelled. However, traditional methods to estimate OD demands through household or roadside surveys are time consuming and expensive. An emerging alternative is passive data sources, such as anonymous cellular data. Anonymous cellular data can provide large random samples and provide results much faster and as much lower cost than travel surveys. It also has a low deployment cost, as it does not require any additional equipment. Yangon-Pathein Highway is the only available supply for the travelling between Ayeyarwady region and Yangon city. The purpose of this thesis is to investigate how anonymous cellular data may be used to extract about intercity travel patterns and how the travel pattern have the impact on Yangon-Pathein Highway Road.
Keywords: CDR, Travel Pattern, Traffic Congestion
1. INTRODUCTION
Intercity trips have historically been understudied and largely overlooked by researchers and public agencies. There have been considerable researches on urban trips which make up the majority of traffic congestion compared to more infrequent long-distance trips. Therefore, collection of long-distance travel data is not prioritized and limited in comparison. Although intercity travel occurs less frequently and regularly than urban commute trips, it accounts for a significant portion of traffic congestion. The road transport system is generally the most frequent used for intercity trips. The road transport system can also promote underdeveloped areas by opening up access to those areas to more developed areas and enlarging opportunities. Therefore, the road transport system may be considered as a complex system where a failure of a point in the system could affect the performance of the whole system. Therefore, the need to understand current intercity travel pattern is an essential part of road transport system development. A deep understanding of human-mobility patterns can yield interesting insights into a variety of important societal issues, such as urban planning, road traffic monitoring and forecasting. However, the largest barrier to this advancement in intercity research is limited data on
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intercity travel. Traditional methods of data collection for OD flows through household surveys or roadside surveys is time consuming and expensive. As people do not make long- distance trips routinely, these surveys would require a larger sample size of respondents or a lengthier questioning period to gather enough long-distance trips for the statistical analysis. These surveys are also not able to provide information about the frequency of the long distance trips that users make. As well, long-distance trips are distributed unevenly in the population; some may make long distance trips often and others rarely. According to the 2014 census data of Yangon is 5.24 million which indicated increases in population growth which is as a result of the centre of private and public business. Due to the rapid urbanization, vehicle ownership and traffic volume have significantly increased. Thus can causes accumulations of vehicles on the road and service of roadway is more than its capacity. Traffic congestion has seriously affected not only to the environment at to people’s daily activities. Call Detail Record (CDR), also known as Floating Phone Data (FPD) are position information of mobile phones in the carrier network and are destined to be the next big thing in data used for transport models or to describe daily trip chains and intermodal connectivity. Mobile network operators are beginning to see CDR as an asset that they want to put on the market. Scientists and transport engineers hope for more detailed and comprehensive information on people’s movements and data activists fear worldwide surveillance and a restriction of civil rights. One advantage of mobile phone data over other mobility data sources is its potential for revealing social interaction reflected in people’s calling and messaging behaviour, which can sometimes serve as a good reference to understand human mobility. Based on a literature study on the relations between travel and telecommunication, such relations were explored empirically by comparing the estimated relative OD matrices and the mobile phone interaction matrices. The mobile phone interaction matrices contain the aggregate number of mobile phone interactions (i.e., calls and text messages) of all users from one place to another place, extracted from the provided dataset of communication between antenna towers. The aim of this study is to support the government for strategic planning for highway network. The article is organized as follows. Section 2 presents the previous work with CDR and intercity travel. Study area and method are presented in section 3. In section 4, the discussion about the travel demand and behaviors from CDRs data in township level.
2. LITERATURE REVIEW
In the 2001 National Household Travel Survey (NHTS) from the US, they found that 56% of intercity trips are taken for leisure, followed by 16% for business trips and 13% for commuting purposes (Bureau of Transportation Statistics [BTS], 2003). The predominant mode of these trips is by personal vehicle, accounting for 90% of trips, followed by 7% made by air. The NHTS survey estimated that 1.3 trillion person-miles of long-distance travel was made (BTS, 2003), and from the 1995 American Travel Survey long-distance trips accounted for 25% of total personal vehicle mileage (L. Zhang et al., 2012). This large share of intercity trips shows that these trips contribute significantly to congestion, air delays, energy consumption and emissions (Cho, 2013; Kuhnimhof et al., 2009). For example, researchers in Germany found that even though long-distance trips over 100 km accounted for only 2% of all trips, it was responsible for 62% of climate impact from travel (Aamaas, Borken-Kleefeld, & Peters, 2013).
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Call detail records can be used to estimate the trip volumes between origin and destination. Estimation of origin and destination can be made either between cities or within the city. The OD flows are critical for transportation planning to know whether the infrastructure improvement or investment. The use of cellular network data to understand mobility patterns has been studied almost since these networks became widely available (Steenbruggen et al, 2013). It contains studies in the field from as early as 1994. As algorithms evolve and processing large-scale data becomes easier, cellular network data is on the way to become a natural complement to expensive travel surveys and observations that are typically only available for a much smaller sample than cellular network data (Becker et al., 2011). The information that can potentially be estimated from cellular network data includes not only the travel demand and traffic flows, but also metrics like the daily range of travel (Becker et al., 2011) or the home and workplaces of the users (as in Alexander et al., 2015, Gundlegård et al., 2015 and Isaacman et al., 2011), which can be interesting for analyzing commuting patterns. In order to extract the movements relevant for traffic analysis from the raw cellular network data, most studies perform some kind of trip extraction partitioning the raw data into stationary sections and sections of movement. Due to the fact that cellular network data can contain a lot of noise, there is no obvious definition of what a movement/trip is. Therefore, trip extraction algorithms vary a lot among different authors. Several studies like use a time-window during which a continuous movement has to be detected in order to filter out cell-switching noise between neighbor cells, which can occur even if the user did not physically move (Iqbal et al, 2014, Ming-Heng et al. 2013, Gundlegård et al 2015 and Sohn et al. 2006). Increasing intercity travel demand generates impacts important to recognize. Public agencies need reliable forecasts to make expensive transportation infrastructure or service decisions (L. Zhang et al., 2012). This demand also leads to higher energy consumption and emissions; a better understanding of intercity travel can assist policy makers to shape environmental policies such as ones that encourage demand shift to higher occupancy modes (which has lower emissions per passenger-trip) or invest in emission-friendly alternatives (Haobing Liu, Xu, Stockwell, Rodgers, & Guensler, 2016).
3. METHODOLOGY
3.1 Study Area
The study area of this research is Ayeyarwady region with population more than 6.5 million, which is the most populous of Myanmar’s states and regions. Ayeyarwady region is composed of six districts including 26 townships, 219 wards, 1912 village groups and 11651 villages. But this research focuses on township level. It has an area of 35149 square kilometres. The location of study area is shown in Figure 1.
3.2.Method
The methodology of this research is illustrated in Figure 2. There are two data sources including primary data for validation and secondary data using Call Detail Record. CDR data are provided by one of the largest telecommunication operators in Myanmar, containing more
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than 2.2 billion anonymized mobile phone records for the whole country. The data includes 1st to 7th December
Figure 1. Location Map of Study Area
2015. To deal with the large-scale data, BigGIS-RTX Toolbox, which is specially created for big data processing. The dataset consists of CDR records and location-based data. The CDR records contain the details of a telephone call or other communication transaction which includes the base station's identifier, the calling and called number, and the timestamps corresponding to the transaction. And the location data can be produced when a user hand over from one cell to another. In addition, the geographical position information (latitude, longitude) of most base stations could be regarded as the location of the users. From the CDR and location-based data, the users' stay points can be easily extracted. Primary data were collected on September, 2018 for one day. The duration of data collection starts from 8:00 AM to 4:00 PM. Questionnaire survey were conducted at toll gates, including No.(1) Bo Myat Tun bridge and No.(2) Bo Myat Tun bridge, are located at Nyaungdon Township in Ayeyarwady Region. The OD, number of trips and the number of passengers per vehicle are collected from the questionnaire survey.
3.3. Data Preparation
CDR consists of data files and voice files. At first, these two files are needed to format to the standard data format which is shown in Table 1 and 2. The required fields are extracted by using SQL server in the BigGIS-RTX Tool Box.
Table 1. Formatted CDR Data PID EVENT DTIME DURATION UP DOW CELLID N PID_00D2A 1048 201512055914 446 440 1384 CID_414080360561 15F8C00 PID_02D59 1048 201512011606 0 0 0 CID_414011460561 B5F8B00 58
PID_03D2A 1048 201512011605 64 194 610 CID_414036800561 D5F8B00 54
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PID_03D2A 982 201512011613 74 562 11687 CID_414081201561 D5F8B00 35 4
Data Collections
Secondary Data Primary Data
Raw CDRs Data (Data & Voice) National Census
Formatting the Raw Data
Extract the Data to Days Traffic Volume for particular mode at Toll Gate(Passenger Car, Bus Merge the Data and Voice and Truck)
Order the PID,DTIME Ascending
Vehicle Occupancy Rate OD Trip Generation
Cell ID Geolocator
Add Origin Destination Trip Duration Magnification Factor
Magnified CDRs
Traffic Volume from CDRs
Validation of Traffic Volume Comparison of Traffic Using CDRs Volume between CDRs and Ground Truth
OD Route Generation for both Person Trips and Vehicular Trips
OD Route
Results and Discussion
Conclusion and Recommendations 281
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Fig 2.Implementation Program of Research Flow Table 2. Formatted CDR Voice Data PID EVENT DTIME DURATION CELLID
PID_00D2A15F8C0 1048 201512055914 446 CID_414080360561 0
PID_02D59B5F8B0 1048 20151201160658 0 CID_414011460561 0
PID_03D2AD5F8B 1048 20151201160554 64 CID_414036800561 00
PID_03D2AD5F8B 982 20151201161335 74 CID_414081201561 00
Since the data and voice files include the seven days randomly, it is necessary to extract these two files day by day. BigGIS-RTX Tool Box cannot work without extracting the data into ascending. The extract files are merged to one file. The merged file has the random PID and DTIME. Therefore, this file is extracted to PID and DTIME ascending. This is done with the help of SQL server. The results are shown in Table 3.
Table 3. Ordering CDR Data by PID and DTIME PID DTIME CELLID PID_00D2A15F8C00 201512055914 CID_414080360561
PID_02D59B5F8B00 20151201160658 CID_414011460561 PID_03D2AD5F8B00 20151201160554 CID_414036800561
PID_03D2AD5F8B00 20151201161335 CID_414081201561
Table 4. OD Trip Matrix ORG ORG ORG_TIM _ _ DES_ DES_ PID E DES_TIME ORG_CID DES_CID LON LAT LON LAT
PID_00D2A 201512071 201512071 CID_4140100 CID_4140100 34572 17776 7138. 17726 15F8C00 04924.00 20058.00 60124093 65125332 .515 50 234 23.3
PID_02D59 201512071 201512071 CID_4140100 CID_4140100 43488 18582 40121 18337 B5F8B00 04237.00 83851.00 65125652 65124251 .996 48.2 .943 14.3
PID_03D2A 201512070 201512070 CID_4140100 CID_4140100 57632 17852 7138. 17726 D5F8B00 15722.00 50234.00 60124133 65125331 .208 29.5 234 23.3
PID_03D2A 201512070 201512071 CID_4140100 CID_4140100 7138. 17726 674.7 17754 D5F8B00 50234.00 21733.00 65125331 65125321 234 23.3 7 05.6
3.3.1 OD Trip Computation
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To get the origin and destination of the mobile phone users, the BTS geolocation information are converted to the desired origin longitude and latitude and destination, longitude and latitude by using OD trip generation menu in Big Data-RTX Tool Box. Origin and destination PID ORG_LON ORG_LAT DES_LON DES_LAT ORG_TSP DES_TSP
00D2A15F8C00 34572.515 1777650 7138.234 1772623.3 Tramway Ngaputaw
02D59B5F8B00 43488.996 1858248.2 40121.943 1833714.3 Insein Ngaputaw
03D2AD5F8B00 57632.208 1785229.5 7138.234 1772623.3 Pathein Ngaputaw
03D2AD5F8B00 7138.234 1772623.3 674.77 1775405.6 Maubin Ngaputaw time are obtained from the trip duration. The results of OD Trip Generation is shown in Table 4.
Table 5 OD Matrix with Townships
3.3.2 Cell ID Geolocator
When results of OD Trip Generation are input to the Cell ID Geolocator, the origin and the destination townships will be known as shown in Table 5.
3.3.3. OD Route Assignment
The OD pairs are added to the OD Route Generation Links in BigData RTX V3 together with road network data. The OD route generation links are used shortest path analysis. The results are shown in Table 6. The results are added to the ArcGIS to analyze the data, which shown in Figure 3.
Table 6. OD Route Table
PID RID LENGTH
PID_01D8EC6F1B00 RID_058203 86.94208
PID_01D8EC6F1B00 RID_058204 53.15354 PID_01D8EC6F1B00 RID_058205 8.578697 PID_01D8EC6F1B00 RID_058207 17.02955
Where; PID = Person Identification (Encrypted mobile SIM card number) EVENT = Call events such as incoming call, outgoing call, etc. DTIME = Calling date and time DURATION = Call duration CELLID = Cell Identification Number
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UP = Upload data size DOWN = Download data size SITEID = BTS site identification number SITENAME =BTS site name 3.4. Method of Analysis
3.4.1. OD Trip Flow
It is crucial to understand how and where people move or travel (demand) so that the transportation infrastructure (supply) is able to handle these volumes efficiently. These OD flows are important for strategic planning of transportation networks and to identify where infrastructure improvements are required. OD flows or matrices have been estimated with limitations via surveys or traffic counts. An OD is identified by a sequence of consecutive cell phone records limited by both temporal and spatial constraints. The rules are as follows: when two single points are within twenty four hours interval, the two points will be joined together as one OD point. If people travel within the study area exceeds twenty four hours interval will not be considered in this research. The database filters and identifies all users that had at least one entry within Ayeyarwady region and one entry in the Yangon region. Thus, only the records for users that made an intercity trip between Ayeyarwady and Yangon regions are identified. For a user, the first record in Yangon region or Ayeyarwady region was assigned as the Start Location, where the Last Timestamp in that zone assigned as the trip Start Time. The other city that was not the Start Location city is then assigned as the End Location. The First Timestamp that occurs in the End Location is assigned as the trip End Time.
3.4.2. Route Assignment
Traffic assignment is another mature domain that has been studied extensively by urban and transportation planners. Having estimated OD flows, our next task is to efficiently assign these trips to transportation infrastructure, in this case a road network. For route choice estimation based on spatially sparse CDR data most of the trips will have very few or no intermediate cells in the cell path. This means that the route assignment will rely heavily on information about the road network structure and is often, due to lack of a calibrated model for traffic assignment, simplified with a static shortest-path assignment. During rush hours the route choice due to congestion can differ significantly from the shortest path. However, the filtering of spatially well-defined trips reduces the proportion of trips that is assigned using the shortest path and can give a better estimate of the route flow proportions compared to the case where all trips are used as input to the estimate. A possible improvement would be to combine this approach with a route choice generated using a classic traffic assignment model.
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Figure 3. Route Network between Ayeyarwady and Yangon Region 3.4.3. Magnification Factor
To obtain the actual population from mobile users, the magnification factor including population growth rate and phone growth rate are used. The CDR data, which are used in this research, are from MPT. However there are three mobile operators including Telenor, Ooreedo and MPT. Therefore the market share and penetration rate of MPT are needed to calculate the magnification factor. The magnification factor are computed based on 2014 census population. Therefore 5% of population growth rate are assumed to get the 2018 population. Moreover, this research use 2015 mobile data. There are 50% mobile phone subscribers growth rate are increased from 2015 to 2018.Mgnification Factors for each township are shown in Figure 4.
3.4.4. Vehicle Occupancy Rate
Vehicle occupancy rate is traditionally used to convert the person trips to vehicle trips. So CDR person trips are converted into vehicular trips by vehicle occupancy rate. Vehicle occupancy rate is the number of passengers in a vehicle during a trip. This rate can be expressed as the number of persons per vehicle or by the percentage of occupied seats. The higher the rate means the fewer vehicles are used on the specified road network. Vehicle occupancy rate is determined by the townships in this research. 40 35 30 25 20 15 10 5
0
Yekyi
Magnification Magnification Factor
Zalun
Einme
Bogale
Ingapu
Labutta
Pathein
Maubin
Kyangin
Daydaye
Kyaiklatt
Phyapon
Wakema
Mawleamyek… Danubyu
Hinthada
Thapaung
Kyaungon
Pantanaw
Ngaputaw
Kyonpyaw Myanaung Townships
Figure 4. Magnification Factor for Each Townships
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4. RESULT AND DISCUSSION
4.1 Data Analysis of CDR Data
Trip pattern of incoming trips (from Ayeyarwady to Yangon Region) and outgoing trips (from Ayeyarwady to Yangon Region are discussed.
4.1.1. Analysis of Incoming Trips (from Ayeyarwady Region to Yangon Region)
Figure 5 illustrates the number of trips from each township in Pathein District. Pathein Township has the highest trip number because there are 39 BTS, which is the highest number in Pathein District. Ngaputaw Township has trips lower than 100 for all days. Kyaunggon and Kangyidaunt townships have the similar trip number. And Yekyi Township has the least trips in Pathein District because there are only one BTS in Yekyi Township. People travel the most on Friday in Pathein, Kyonpyaw, Thabaung and Kangyidaunt Townships. People from the rest of the townships in Pathein District travel the most on Saturday. People travel the least on Sunday for all townships in Pathein District.
15000 10000 5000
0 Number of Trips Numberof
Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 5. Incoming Trip Pattern of Pathein District
It can be seen that the trip frequency is the highest in Dedaye Township among any other townships in Pyapon district. Pyapon and Bogale townships in Pyapon district have the similar trip number while Kyaiklat Township is the second highest in trip frequency in Pyapon District. However, the number of BTS is the highest in Pyapon and Bogale Townships and the least in Dedaye and Kyaiklat Townships. People from the all townships are travel the most on Tuesday, which is shown in Figure 6. Trips are the second highest on Wednesday. The rest of the days have the similar trip pattern in all townships.
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Figure 6. Incoming Trip Pattern Pyapon District According to Figure 7, Maubin Township has the highest trips and people travel the least to Yangon region in Danubyu and Pantanaw townships. Although the number of BTS is the highest in Maubin Township, number of BTS are similar in the rest of the townships in Maubin district. People from Maubin Township travel the most on Thursday.
120000
100000
80000
60000
40000 Number of Trips Numberof
20000
0 Maubin Danubyu Nyaungdon Pantanaw Townships Monday Tuesday Wednesday Thursday Friday Saturday Sunday Figure 7. Incoming Trip Pattern of Maubin District Figure 8 shows that the trip frequency is similar between Einme and Wakema Townships although the trips from Einme is highest in Myaungmya District. The number of trips from Myaungmya Township is significantly low compared to the other townships in Myaungmya District. People from Myaungmya District travel the most on Saturday and travel the least on Sunday. Travel pattern of all townships in Myaungmya District for a week is almost similar except for Wednesday.
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8000 7000 6000 5000 4000 3000
2000 Number of Trips Numberof 1000 0 Myaungmya Wakema Einme Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 8. Incoming Trip Pattern of Myaungmya District
There are only two townships in Labutta District, which can be seen in Figure 9. Although Labutta Township has higher BTS number than Mawelamyekyun Township, the trips are similar between Labutta Township and Mawelamyekyun Township.
1000 900 800 700 600 500 400
300 Number of Trips Numberof 200 100 0 Mawleamyekyun Labutta Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 10. Incoming Trip Pattern of Labutta District
In Figure 11, Zalun Township has the highest trips compared to the other townships in Hinthada District. And the second highest trips are occurred in Hinthada Township. Laymyethna and Ingapu townships have apparently no trips going to Yangon Region.
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45000 40000 35000 30000 25000 20000
15000 Number of Trips Numberof 10000 5000 0 Zalun Myanaung Laymyethna Hinthada Ingapu Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 11. Incoming Trip Pattern of Hinthada District
4.1.2. Analysis of Outgoing Trips (from Yangon Region to Ayeyarwady Region)
The number of trips is the highest in Pathein Township with over 20000 trips. Because Pathein is the capital city of Ayeyarwady. The number of trips from Kyaunggon and Kyonpyaw Townships have similar trip pattern with slightly over 5000 trips. Kangyidaunt and Thabaung Townships have the trips lower than 5000 trips. Ngaputaw and Township has almost no trips travelling from Yangon region. Yekyi Township has apparently no trips travelling to Yangon Region. People from all of the townships in Pathein District travel the most on Friday which shown in Figure 12. The number of trips are the second highest in Tuesday. All of the townships in Pyapon District travel the least on Friday.
25000 20000 15000 10000 5000
0
Number of Trips Numberof
Yekyi
Pathein
Thapaung
Ngaputaw
Kyonpyaw
Kyaunggon Kangyidaunt Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 12. Outgoing Trip Pattern of Pathein District
There are nearly 9000 trips travelling from Yangon Region to Dedaye Township on Monday. Trip patterns are similar on Thursday, Friday and Sunday in Dedaye Township. The distance between Dedaye Township and Yangon Region is the shortest compared to the rest of the townships in Pyapon District. The percentage of trips travelling from Kyaiklat and
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Bogale townships from Yangon Region are significantly higher than the percentage of trips from Pyapon Township. People from the Pyapon District travel the least on Saturday. Trip pattern on Wednesday are similar to the trip pattern on Saturday in Pyapon District as shown in Figure 13.
100000 80000 60000 40000 20000
0
Number of Trips Numberof
Phyapon
Bogale
Daydaye Kyaiklatt Townships
Monday Tuesday Wednesday Thursday Friday Saturday
Figure 13. Outgoing Travel Pattern of Pyapon District It is shown in Figure14 that the majority of people from Maubin Township travel the most to Yangon region. The second highest is from Nyaungdon Township. The distance between Maubin and Nyaungdon townships and Yangon region is the shortest compared to other townships Danubyu and Pantanaw Townships to Yangon region have less number of trips compared to Maubin Township as shown in Figure 4.9. Trip pattern for all townships is different for all seven days.
120000 100000 80000 60000 40000
Number of Trips Numberof 20000
0
Maubin
Nyaungdon
Danubyu Pantanaw Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 14. Outgoing Travel Pattern of Maubin District
As shown in Figure 15, Labutta Township is nearer to Yangon Region compared to Mawelamyekyun Township. Therefore, Labutta Township has the higher trips compared the Mawelamyekyun Township.
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8000
7000
6000
5000
4000
3000 Number of Trips Numberof 2000
1000
0 Myaungmya Wakema Einme Townships
Monday Tuesday Wednesday Thursday
Friday Saturday Sunday
Figure 15. Outgoing Travel Pattern of Myaungmya District
Myaungmya district has only three townships. Trip pattern of Einme and Wakema townships have similar trip pattern as the trip distance between Wakema and Yangon and Einme and Yangon are the same as shown in Figure 16.
800 700 600 500 400 300
Number of Trips Numberof 200 100 0 Mawleamyekyun Labutta Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 16. Outgoing Travel Pattern of Labutta District
As shown in Figure 16, Labutta Township is nearer to Yangon Region compared to Mawelamyekyun Township. Therefore, Labutta Township has the higher trips than the Mawelamyekyun Township. In Figure 17, Zalun Township has the highest trips which travelling from Yangon region. All of the townships except Zalun Township have less than 5000 trips. Kyangin Township has apparently no trips travelling from Yangon Region to Kyangin Township. There has
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apparently no trips travelling from Yangon region to Kyangin Township. People travel the most on Friday and Saturday.
25000
20000
15000
10000 Number of Trips Numberof 5000
0
Zalun
Ingapu
Kyangin
Hinthada
Myanaung Laymyethna Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 17. Outgoing Travel Pattern of Hinthada District
4.2. Ranking for Person Trips between Ayeyarwady Region and Yangon Region
Figure 18 shows the ranking of townships based on the number of person trips travelled between Ayeyarwady and Yangon Region. Maubin Township travel the most Ayeyarwady Region to Yangon Region.
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90000
80000
70000
60000
50000
40000
30000
20000
10000
0
Yekyi
Dedaye
Pathein
Maubin
Zalun
Pantanaw
Einme
Bogale
Ingapu
Nyaungdon
Labutta
Kyangin
Kyaiklatt
Phyapon
Wakema
Danubyu
Hinthada
Thapaung
Kyaungon
Ngaputaw
Kyonpyaw
Myanaung
Laymyethna
Myaungmya Kangyidaunt Mawleamyekyun Figure 18. Ranking of Person Trips between Ayeyarwady Region and Yangon Region
4.3. Vehicular Traffic from Call Detail Record
60000 50000 40000 30000 20000 10000
Number of Trips Numberof 0
Zalun
Einme
MAUBIN
Ingapu
Pathein
Maubin
HINTHADA
Kyaiklatt
Phyapon
Danubyu
Thapaung
Kyonpyaw
Laymyethna
Myaungmya
Kangyidaunt Mawleamyekyun Townships
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Figure 19. Vehicular trips from CDR Figure 19 shows that each township has similar trip pattern for all days. Vehicular traffic is converted by using vehicle occupancy rate. Maubin has the highest trips compared to other townships. The second highest number of trips are occupied in Zalun Township.
4.4. Impact of Traffic on Road Segment
4.4.1. Vehicular Traffic on Weekday
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The data will be analysed for week days and weekend with four road segments as shown in Figure 22.
Figure 20. Vehicular Traffic Flow on Weekday
31%
46%
18% 5%
Pathein Ngaputaw Kangyidaunt Thapaung
Figure 21. Percentage of Trips on Road Segment 1 The route of road segment 1 is shown in Figure 21.It is clear that the impact of trips between Pathein Township and Yangon region on road segment 1 is huge because of more than 45% of trips travel on the road segment 1. The second most travel on road segment 1 is Thabaung Township. Kangyidaunt Township has only 15% impact on Road segment 1. Ngaputaw Township has the least impact on road segment 1 with only 5%. Figure 22 shows how many percentage of trips from each township impact on road segment 2. Pathein Township has huge impact on road segment 2 with 29%. Similarly, Einme Township has 28% of trips on road segment 2. Kangyidaunt and Ngaputaw have over 10%
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impact on road segment 2 while Myaungmya and Labutta townships have below 10% on road segment 2.
19% 29%
11%
2% 3% 9%
28%
Einme Labutta Myaungmya Kangyidaunt Ngaputaw Pathein Thapaung
Figure 22. Percentage of Trips on Road Segment 2
0.01% 1% 2% 6% 6% 1% 6% 2% 4%
11% 55% 5% 1% 0.2%
Einme Labutta Myaungmya Kangyidaunt Ngaputaw Pathein Thapaung Kyaungon Kyonpyaw Laymyethna Mawleamyekyun Pantanaw Wakema Yekyi
Figure 23. Percentage of Trips on Road Segment 3
In Figure 23 road segment 3 has the huge impact due to the trips related to Pathein Township. Kyaunggon Townships is the second highest trip percentage on road segment 3. The rest of the townships have almost no impact on road segment 3. According to the Figure 24, all of the townships from Ayeyarwady region have to use th4 segment 4. The segment 4 is the segment which connect between Ayeyarwady and Yangon Region. It can be said that trips between Ayeyarwady and Yangon Region.
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1% 0.003% 4% 2% 2% 1% 1% 6% 14% 24% 14% 0.15% 0.46% 2% 12% 10% 1% 0.10% 0.03% 0% 0.18% 0.06% 2% 0.13% 1% 3% Bogale Danubyu Daydaye Einme Hinthada Ingapu Kangyidaunt Kyaiklatt Kyangin Kyaungon Kyonpyaw Labutta Laymyethna Maubin Mawleamyekyun Myanaung Myaungmya Ngaputaw Nyaungdon Pantanaw Pathein Phyapon Thapaung Wakema Yekyi Zalun
Figure 24. Percentage of Trips on Road Segment 4
4.5. Validation of Vehicular Traffic with Ground Truth Data
16000 50% 14000 40% 12000 30% 20% 10000 10% 8000 0% 6000 -10% 4000 -20% 2000 -30%
0 -40%
Percentage of Error Percentageof No of Vehicular Trips Vehicular of No
Townships
Actual Outgoing CDR Outgoing Actual Trips CDR incoming Ground Truth CDR Percentage of Error
Figure 25 Percentage of Error between CDRs Data and Ground Truth Data
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Figure 26. Comparison of Vehicular Traffic of CDR with Ground Truth Data Maubin and Pantanaw townships have nearly the same data between CDR data and ground truth data as shown in Figure 26 and the percentage of error is shown in figure 25.
5. CONCLUSION
This study analyses traffic congestion of the existing condition, route choice and origin- destination (O-D) of the daily traveller by interviewing where they are originated, which routes they choose and what the purposes of the trip are and also includes the use of GIS technology. It integrates the geodatabase, network analyst tool, and including base maps.
ACKNOWLEDGEMENTS
The authors would like to give their gratitude to the teachers who give support, ideas and advice and also their insightful comments.
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