Browsing by Author "Liyanage, TU"

Now showing 1 - 6 of 6

item: Conference-Extended-Abstract
Bus passenger demand analysis in seven major corridors of Western Province
(Sri Lanka Society for Transport and Logistics, 2016-06) Amalan, TP; Liyanage, TU; Gunaruwan, TL
item: Conference-Extended-Abstract
Importance of integrated transport planning for congestion alleviation at major road corridors: a case study
(Sri Lanka Society of Transport and Logistics, 2018-06) Liyanage, TU; Perera, KR; Dalpedadu, G; Gunaruwan, TL
item: Conference-Extended-Abstract
Introduction of an integrated engineering solutions to ease the rapidly increasing traffic condition in main city centres: a case study of traffic simulation at main network with proposed Rajagiriya flyover
(Sri Lanka Society for Transport and Logistics, 2016) Perera, KR; Liyanage, TU; Senevirathne, S; Gunaruwan, TL
item: Conference-Full-text
parking demand and supply behavior of the users in a hilly terrain at Bandarawela city
(Sri Lanka Society of Transport and Logistics, 2022-08) Deivendra, HT; Dalpethado, G; Liyanage, TU; Perera, N; Thibbotuwawa, A
Parking facilities can serve an important role in a city center. Parking demand and supply attribute balance is contributing to the proper traffic management in a city. Bandarawela is a one of major cities in Badulla district with higher topographic variability as the specific attributes to this city. This study has identified several issues that are inheriting to the mountainous topography. Determining the balance of parking demand and supply, most using vehicular modes, type of parking, analyzing the attributes of parking that can be used for future developments in hilly terrain. Series of analysis using statistical analysis techniques is included as the methodology for data analysis. It is expected that the outcomes and recommendations will be useful to predict parking demand and supply behavior of the users in a hilly terrain.
item: Conference-Abstract
Study of mode selection behaviour of passengers across the Western and Southern Provinces at coastal transport corridor
(Sri Lanka Society of Transport and Logistics, 2019-09) Liyanage, TU; Samarakoon, GS; Perera, KR; Gunaruwan, TL
The Coastal Transport Corridor consists of the Coastal Railway Line, Colombo - Galle (A002) Highway, and the Southern Expressway (E01). Between 2013 and 2017 both the Southern Expressway and the Coastal Railway Line were extended up to Matara in order to increase their reach. This research paper attempts to investigate how mode choices of passengers have switched between the Coastal Railway, Galle Road (A002), and Southern Expressway (E01) in the years since 2013 – 2017 by analysing modal splits in 2013 and 2017. The modal splits are determined using empirical data collected from classified vehicle counts, railway passenger counts, and bus volume counts carried out on the Western and Southern Province border for all three routes in 2013 and 2017. The research also attempts to analyse possible reasons for any shifts in modes between the three transport routes. A significant reduction in passenger volumes has been observed over time in rail transport towards Western Province during the morning peak. The number of luxury buses on Galle Road (A002) has reduced, together with the demand for the passenger volumes. However, the bus service on E01 has managed to entice rail and bus passengers away from A002. This suggests that, despite all three routes having the same reach, buses on E01 have managed to capture more commuters travelling from Southern Province to Western Province. A noticeable modal shift has taken place between modes of public transportation (rail and bus) rather than the desired shift from private to public transportation. This shift has also been facilitated by the increasing interconnectivity of Expressways in Sri Lanka (E01 and E02), resulting in a wide range of bus routes originating from Kadawatha, Colombo Fort, Kaduwela, etc. to transport passengers between the Western and Southern provinces. While a majority of passengers use public modes, the popular mode of choice among private modes is car. However, out of all the vehicles that used E01, cars made up 56.81% in 2017. Compared to the percentage of cars in E01 in 2013, this is an increase of 7.39% in just four years. In order to avoid car demand exceeding the capacity of the E01, solutions that entice car users to choose public transportation are recommended. Therefore, more investment is required to promote buses on E01 as an alternative mode of transport that can provide the comfort, reliability, and other attributes of travelling by car. Furthermore, Sri Lanka Railways needs to pay attention to different market segments and fulfil needs of passengers belonging to each sector, in order to entice a modal shift.
item: Thesis-Abstract
Use of electricity consumption for traffic modeling of a suburban area
Liyanage, TU; Kumarage, AS
The history of urban travel demand studies spreads over a period of more than fifty years. Most of them are recorded from developed countries, with just a handful from developing countries. The scarcity of reliable and up-to-date socio-economic data to the required formats, and fewer possibilities of acquiring electronic data bases are the most apparent reasons for this situation. Often, data bases from more than one type of non-related data sources are required to run a complete travel demand forecasting model. This has restrained the calibration and forecasting of travel demand models in developing countries. In particular, little attention has been given to forecasting travel in small and medium communities except for a few instances from developed countries. The primary reason for this is that, forecasting travel for small communities is not considered important, when statewide or national level travel forecasting models have not been developed, and specially due to the limited financial and technical capacities in the respective agencies. National level travel surveys are however not adequately sensitive to small and medium urban centres as they do not represent local travel behaviour adequately. But the need for travel demand forecasting in small communities is great with respect to infrastructure development planning. Many researches have shown that there is a strong relationship between trip generation and the combined income of a household. But it is very difficult to collect the income data in developing countries, and no proper and reliable data sources are available. In this context, more readily available electricity consumption data, for both households and for non-households can be used as a cost effective approach for ascertaining travel demand, given that such data can be easily measured either in terms of disaggregate household or aggregate area level, at a much lesser cost. There are a number of advantages to use electricity consumption as an explanatory variable for travel forecasting. The electronically available disaggregated data sets can be easily used in many forms at the data preparation stage. This helps to use the data in aggregate or disaggregate forecasting according to the user requirements. The monthly updated data can be aggregated into any form of small zones by sorting them with addresses. The spatial location of the user can be geo-referenced and located with these addresses. Therefore, the use of GIS for travel modeling is possible. Since the electricity is accessible to many users in urban areas, variations of the land use changes can be assessed in time with updated data. Generalized functional forms for trip generation, mode selection, and trip distribution in suburban areas using electricity consumption as the main explanatory variable are suggested herein. The trip generation forecasting is explained by electricity consumption at household level with the hypothesis that household electricity consumption behaving as a surrogate variable for the combined income of that household. This model fit has been strengthened by introducing some of the socio-economic variables as well. Mode split models have also been calibrated using household electricity consumption, and functional forms for each mode and are presented separately. Both the trip generation and the mode selection by non-electricity users have been incorporated with category analysis techniques. The concept of traffic attraction to a destination zone based on its economic strength has been used here relating to the non-household electricity consumption level as a surrogate variable for the economic strength of that zone. The assignment of traffic in local road network is suggested with available commercial software popular for small areas to have a complete series of traffic forecasting models. The up-to-date electricity consumption data in electronic format could be obtained from the Lanka Electricity Company Ltd (LECO) or Ceylon Electricity Board (CEB) free of charge or at a nominal fee. Therefore, this approach will give a very economical use of a model that has been calibrated in a state-of-the art method to suit the local traffic environment. The simple and cost effective approach will be especially helpful for the local authorities for infrastructure development and planning.