Cv Merkebe Getachew Demissie
Current position: Postdoctoral Researcher at University of Calgary
Thesis Title: Estimation and Prediction of Road Traffic Status with Information from Multiple Sources
Supervisors: Gonçalo Homem de Almeida Rodriguez Correia and Professor Carlos Lisboa Bento (FCTUC)
Thesis abstract: All around the World we experience the trends of the last decades on increased urbanization as more and more people shift their living to cities. However, many cities lack the resources to respond to the magnitude of the change in their urban areas, which forces people to compete for the use of land, roads, public transport, and other urban facilities. As a result of the increasing number of people, cities face an increasing number of private vehicles and commuters which in turn cause various problems such as traffic congestion, parking difficulties, traffic accidents, loss of space for productive activities, public transport inadequacy and undesirable environmental impacts. In the past, public authorities followed approaches that nowadays are financially unsustainable, focused mainly on expanding the road network to alleviate the problem. However, many analysts argue that the solution for these problems is better addressed through intelligent planning and management of the existing urban and transportation systems. Planning of the urban and transportation system traditionally relied on the knowledge of present and future problems that are associated to the urban growth such as how much travel will be generated, where these trips will take place, by which mode and on which routes. Creating such plans requires information regarding the movement of people and vehicles, knowledge of constituents of the urban system, and understanding the nature of activities at different places. There are various traditional methods for gathering the raw data necessary for urban and transportation planning. Although these methods have the advantage of providing detailed information, their limited coverage and expensive costs of implementation often make them insufficient. More recently, the spread of massive sensoring, namely through the generalized use of cellphone, is producing massive amounts of data with spatio-temporal detail about our daily activities and traveling patterns, which could be important to the planning of urban and transportation systems given their pervasiveness, low cost, and real time nature. In this thesis we explore the use of cellphone data for profiling the dynamics of urban activities and characterizing flows of people for planning of urban and transportation systems in cities. Three types of passive mobile positioning data were used: (1) Call Volume, which is the number of calls; (2) Erlang, which is used to measure the equivalent cellphone traffic per hour; and (3) Handover, which is the process of transferring an ongoing call from one base station to another without interruption of service. Our observations are based on hourly aggregated cellphone data obtained from a dataset from a telecom company in Lisbon, Portugal. Though passive mobile positioning data is extracted without incurring additional costs and operational risks for the network, it has two main limitations. Firstly, location acquired by this method is at the granularity of a cell sector, which gives uncertainty on the exact location of the collected variables; secondly, it is only acquired when a phone is engaged in a call or short message service. However, we argue that the aggregate cellphone data used in this study remains useful for our analysis, which is at a scale where the lack of a detailed level of precision is not essential. For validation of our results, we collaborated with other data providers in Lisbon to gather different ground truth datasets that could improve our understanding of urban dynamics such as census data, taxi movement, bus movement, traffic count, points of interest, and presence of people. We proposed new approaches to reflect the potential of passive mobile positioning data for urban and transportation planning. Our approach comprises three stages: (1) exploratory data analysis aimed to discover the kind of relationship that emerges between cellular networks data and urban characteristics, activities, and dynamics at a city-scale; (2) use of cellphone data to detect activities associated to the urban areas in what respects to two aspects of activities: spatial patterns of urban activities, and intensities of urban activities along the hours of a day; and (3) extraction of cellular network data for development of models that predict hourly traffic status. Our results confirm that passive mobile positioning data, taking the advantage of its pervasiveness and availability with reasonably less cost, can provide ways to analyse the dynamics of urban activities at a larger scale. In addition, our approach complements traditional urban data collection methods, which are usually made available less frequently to urban and transportation planners, and is especially useful for developing countries where other approaches are too expensive.
Keywords: cellular network; Erlang; Handover; traffic estimation; transportation planning; urban planning; urban activity; urban dynamics.
1. Demissie, M.G., Correia, G.H., Bento, C., 2015. Analysis of the pattern and intensity of urban activities through aggregate cellphone usage. Transportmetrica A: Transport Science. DOI: 10.1080/23249935.2015.1019591.
2. Demissie, M.G., Correia, G.H., Bento, C., 2014. Extracting urban activities through aggregate cellphone usage. 17th EURO working group on transportation meeting, Seville, Spain, July 2 to 4, 2014.
3. Demissie, M.G., Correia, G.H., Bento, C., 2013. Intelligent road traffic status detection system through cellular networks handover information: An exploratory study. Transportation Research Part C: Emerging Technologies, 32(2013), pp.76-78.
4. Demissie, M.G., Correia, G.H., Bento, C.L., 2013. Exploring cellular network handover information for urban mobility analysis. Journal of Transport Geography 31(2013), pp. 164-170.
5. Demissie, M.G., Correia, G.H., Bento, C.L., 2013. Traffic volume estimation through cellular networks handover information. 13th World Conference on Transportation Research, Rio de Janeiro, Brazil, July 15 to18, 2013.
6. Demissie, M.G., Correia, G.H., Bento, C., 2014. Application of datasets from multiple sources for urban and transportation planning: emphasis on cellular network data. Poster presented at the 4th MIT Portugal Program Conference, Coimbra, Portugal, June 27, 2014.
7. Demissie, M.G., Correia, G.H., Bento, C., 2014. Urban sensing using cellphone data. 11th Annual Transports Study Group Conference, Covilha, Portugal, January 6 to 7, 2014.
8. Demissie, M.G., Correia, G.H., Bento, C.L., 2013. Exploring the relationship between number of bus passengers and cellular networks information. CITTA 6th Annual conference on planning research, Coimbra, Portugal, May 17, 2013.
9. Demissie, M.G., Correia, G.H., Bento, C.L., 2013. Inferring characteristics of places from cellular networks data. CITTA 6th Annual conference on planning research, Coimbra, Portugal, 17th May, 2013.
10. Demissie, M.G., Correia, G.H., Bento, C.L., 2012. Intelligent road traffic status detection system through cellular networks handover information. Poster presented at the 3rd MIT Portugal Program Conference, Guimarães, Portugal, May 28 to 29, 2012.
11. Demissie, M.G., Correia, G.H., Bizarro, P., Bento, C.L., 2012. Estimation and prediction of road traffic status with information from multiple sources. 9th Annual Transports Study Group Conference, Tomar, Portugal, January 5 to 6, 2012.
1. Demissie, M.G., 2014. Combining datasets from multiple sources for urban and transportation planning: emphasis on cellular network data. PhD thesis, Coimbra University, Coimbra, Portugal.
2. Demissie, M.G., 2009. Simulation based performance assessment of mini-roundabout. Master´s thesis. Royal Institute of Technology (KTH), Stockholm, Sweden.
3. Koyrita. A., Demissie, M.G., 2007. Study of property of silk fibre produced in Ethiopia and its post cocoon facilities. Bahir Dar University, Bahir Dar, Ethiopia.