Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
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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 277 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 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). 278 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 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