Analysis of public transport movement duration on routes of Lviv city
Students Name: Kovalyshyn Volodymyr Mykolaiovych
Qualification Level: magister
Speciality: Cargo and Transport Management
Institute: Institute of Mechanical Engineering and Transport
Mode of Study: part
Academic Year: 2020-2021 н.р.
Language of Defence: ukrainian
Abstract: Providing accurate information on the duration of public transport and the time of arrival of buses makes it possible to reduce the waiting time for passengers and attract more people to use public transport. The authors proposed a new segmental approach for forecasting travel time by public transport. The real data of bus traffic, which are defined in different segments of bus routes, are used. As the number of vehicles in urban areas is constantly growing, traffic congestion has become an important problem in many cities. The development of intelligent transport systems (ITS) is one of the solutions to reduce pollution in transport. Estimating the duration of public transport travel is an important component of ITS, as it provides accurate information about passengers’ travel in real time. An intelligent system can reduce passenger waiting times instead of predefined schedules. It can also improve the quality of system maintenance by instantly adjusting the shipment schedule when unforeseen events occur. Among the various public transport systems, the bus network is the most complex and widespread in modern cities, which can use the existing road infrastructure and has lower running costs. Therefore, to improve the quality of public transport services, it is necessary to focus on the following problem - how to determine the exact time of travel or arrival of the bus. Determining bus travel time in real time has many advantages, including passenger travel planning [1], where trips are planned by calculating the duration of the trip to each destination and optimizing the route [2-5]. With this information, system operators can design and adjust the data according to the estimated duration of the trip. Based on the above, many existing studies predicting the duration of a bus trip have achieved varying degrees of success [2-5]. However, the continuation of these studies will make it possible to more accurately predict the duration of travel by bus. Since bus routes are relatively long, existing models are usually used to analyze a certain part (when dividing the entire route into sections). Popular route construction methods divide bus routes based on important road junctions and bus stops, called race and stop models, respectively. However, the time the bus stays at stops is not considered independent of the duration of the journey. The duration of the bus is the periods of time when the buses wait at the stops, and the travel time, which is the duration of the bus between each two stops. The duration of the bus significantly affects the number of passengers who get on and off, which differs from the travel time, which depends on local traffic conditions. The time the bus stops is more unstable and has various causes. It has a significant impact on the overall travel time of the bus. Therefore, it is necessary to study the time spent by buses at stops, taking into account the time of their movement to this stop, to increase accuracy in estimating the duration of the bus. The object of research is the public transport route #3a in the city of Lviv. The subject of the study is the duration of urban shuttle buses on public transport routes. The purpose of this master’s thesis is to develop a new segmental approach to forecasting travel time by public transport, using a model of real data on bus traffic, which are defined in different segments of bus routes. Evaluate the approach with the help of real trajectories collected in Lviv. As a result of the conducted researches the average duration of movement of buses is defined. Key words - public transport route, passenger traffic, duration of movement. References. Zhu T., Ma F., Ma T., Li C. The prediction of bus arrival time using global positioning system data and dynamic traffic information. In: Wireless and Mobile Networking Conference (WMNC), 2011 4th Joint IFIP. IEEE, 2011. P. 1–5. Gurmu Z.K., Fan W.D. Artificial neural network travel time prediction model for buses using only gps data. J. Public Transport. 17, 2014. P.3. Yang M., Chen C., Wang L., Yan X., Zhou L. Bus arrival time prediction using support vector machine with genetic algorithm. Neural Network World 26, 2016. 205 P. Yu B., Yang Z.Z., Wang J. Bus travel-time prediction based on bus speed. In: Proceedings of the Institution of Civil Engineers-Transport. Thomas Telford Ltd., 2010. P. 3–7. Bai C., Peng Z.R., Lu Q.C., Sun, J. Dynamic bus travel time prediction models on road with multiple bus routes. Comput. intell. Neurosci, 2015. P. 63. Xu H., Ying J. Bus arrival time prediction with real-time and historic data. Clust. Comput. 20, 2017. P. 3099–3106. Comi, A., Nuzzolo, A., Brinchi, S. and Verghini, R. 2017b. Bus dispatching irregularity and travel time dispersion. 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), DOI: 10.1109/MTITS.2017.8005632, pp. 856-860.