Browsing by Author "Sharic, AHS"

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item: Conference-Abstract
Development of bus service reliability measures at the stop level
(Department of Civil Engineering, University of Moratuwa, 2019-09) Sharic, AHS; Bandara, JMSJ; Pasindu, HR
Bus service reliability, one of the key performance measures, has become a major concern of both transit operators and users because it significantly affects user experience and service quality perceptions. Schedule adherence has been the most important existing reliability measure for infrequent services that operate with headways of more than 10 minutes. For routes characterized by high frequency service namely less than 10 minutes of headways, headway variability has been the most important existing reliability measure. But these measures do not differentiate between the cost of being early versus late. Different unreliability characteristics that cannot be captured by the existing measures calls for a supplementary measure. This research adopts two indices from (Saberi, et al., 2013) that overcome those issues such as Earliness Index (EI) and Width Index (WI). The Earliness Index is defined as the percentile rank of delay/headway deviation of zero. The percentile rank of a particular delay/headway deviation is the percentage of delay/headway deviations in its frequency distribution that are lower or equal to it. EI ranges between 0 and 1. For frequent services, an EI of 0 represents the “all behind schedule” condition and an EI of 1 represents the “all ahead of schedule” condition. For not frequent services, an EI of 0 represents the “all late” condition and an EI of 1 represents the “all early” condition. For infrequent services, the theoretical ideal distribution lays on the y-axis of the cumulative distribution function. Buses that are early can be treated as being one headway late, because passengers who are arriving near the scheduled departure time would have to wait for the next bus. Therefore, the “all late” condition is expected to be the achievable ideal distribution for non-frequent services to avoid early departures. Note that the above statement is true only when the theoretical ideal distribution (all “on-time” condition) is not achievable. The closer the EI is to 0, the more reliable is the service. For frequent services, one cannot argue similarly, since maintaining a fixed headway with a small deviation is more important than being ahead of or behind the schedule. Thus, another measure is required to capture the variation of headways. To capture the width of the distribution of headway deviations in frequent services, the Width Index (WI) is defined as the 95th percentile of headway deviations minus the 5th percentile of headway deviations divided by the average scheduled headway. .Data needed for theses are as follows. Using the existing time keeper records at the bus stops, a number of measures can be simply calculated. The scheduled headway at a particular stop can be computed as the scheduled stop time for trip i at a stop minus the scheduled stop time for trip i-1 at the same stop: Note that the proposed reliability indices are not suggested as replacements for the existing measures; rather, they are complementary.
item: Conference-Abstract
Development of service quality index for sustainable bus transport
(Department of Civil Engineering, University of Moratuwa., 2018-09) Sharic, AHS; Bandra, JMSJ; Pasindu, HR
Sustainable transport is essential for achieving most, if not all, of the proposed Sustainable Development Goals (SDGs). Improvement of public transport service quality is one of the best alternatives to achieve sustainable transport goals in any part of the world. Transportation agencies can better integrate the concepts of sustainability into their planning, programming, and project development activities through performance measures. The purpose of this paper is to propose a service quality index for sustainable bus transport (SQISBT) which would enable to see how a country or region is progressing towards sustainability in transport. Reliability, convenience, comfort, safety, security and environmental standards have been identified as the main domains of service quality, in public transport. The relevant performance indicators found were waiting time, travel time, walking time, in-bus environment and station environment. Waiting time was used to reflect the domain of punctuality and reliability. Both waiting time and walking time were the indicators to reflect convenience. Comfort, safety, security and environmental standards were reflected by both in-bus environment and station environment. Waiting time refers to the time spent at a bus stop/terminal to get on a bus. Average excess waiting time (AEWT) is proposed as an indicator. AEWT is estimated as the difference between the average of actual waiting time, and scheduled waiting time. Schedules of all the bus routes in operation are to be collected, and a weighted average of a scheduled headway for bus trips are to be calculated. The schedule adherence can be monitored either using Geographical Positioning System (GPS) or bus dispatchers’ records. Average scheduled waiting time is taken as half of the average headway for frequent bus service routes which have one bus at least every twelve minutes. Decreasing score is always positive. Walking time refers to the time taken for the passengers to get access to a bus from their trip origins and the time taken to reach their trip destinations on foot, after taking the bus. This can be measured by the proportion of households within an acceptable (e.g. 500 meters) walking distance to bus stops/terminals from their trip origins. Buffer zones are to be drawn for all the bus stops/terminals and the proportion of population can be averaged for a certain district. Increasing score is always positive and the score would lie between 0 to 100 percent. The travel time means the time taken in bus travel. The average travel time per unit distance will be found out by the GPS or using published schedules, if no vehicle tracking is available for the certain locations. The weighted average travel time based on number of buses operated, is calculated, taking into consideration different routes and different times. Here, the decreasing score is always positive. In-bus environment refers to the level of comfort expected by the passengers inside bus. This can indirectly be measured by the age of the buses. Year of manufacture of buses and number of years in operation are to be collected from all the buses in an area. The weighted average value will be used as an indicator. The decreasing score is always positive. Station environment refers to the needs and expectations of the passengers at the station or halt. This can be measured by the perception on the levels of which these needs are met. Theory of Maslow’s Hierarchy of Needs was used to derive possible levels of passenger needs inside a bus while traveling. Availability of toilets, washroom, availability of seats and shelters, availability of television and entertainment, availability of categories of counters and availability of one room with all these facilities for a passenger to himself/herself are the identified indicators for measuring the perception on station environment that represent the elements of Maslow Hierarchy of Needs such as physiological, safety, love and belonging, esteem and self-actualization respectively. The score will be 1 to 5 for the respective needs. The perception should be collected from a sample of passengers representing various trip purpose, gender, level of income etc. It is proposed to normalize the above scores using weightages for these service quality parameters obtained in a previous study (Sharic,2016) and the following equation, where Z is the normalized indicator value, Xmin is the ‘worst’ value of the indicator in actual units, whereas Xmax is the ‘best’ value. Xi, c are the values to be received for the identified indicators for a certain city. Zi, c=Xi, c-(Xmin, i) Xmax, i-(Xmin, i)*100 Likewise the normalized values for the indicators will be found notated by Zwalking time, Zwaiting time, Ztravel time, Zin=bus environment and Zstation environment respectively. Then the sustainable bus transport service quality index (SBTSQI) for the certain city/village would be found by the following equation. SQISBT=5Zwalking time*Zwaiting time*Ztravel time*Zin bus environment*Zstation environment
item: Conference-Abstract
Impact of railway existence on poverty in Sri Lanka
(Sri Lanka Society of Transport and Logistics, 2019-09) Sharic, AHS; Bulathsinghala, BSDR; Bemindu, KPJ; Perera, JASK; Gunaruwan, TL
Poverty is an issue of major concern in Sri Lanka, from both the economic and social perspectives. Focusing on the transportation sector in Sri Lanka, this study focuses on exploring the relationship between poverty and the existence of a railway transportation system as a possible nexus that can affect the level of poverty in the country. In order to measure railway existence, the district-wise number of passenger trips and number of railway stops have been considered. The poverty headcount index as of 2016 has been used to measure poverty in all districts of Sri Lanka as a standard, and the number of rail passenger trips and number of railway-stops for year 2016 have been received from Sri Lanka Railways headquarters. The dependent variable was the poverty head count index and the independent variables were number of rail passenger trips and number of railways stops. Simple and multiple linear regressions were run. It was found that there was a statistically significant negative relationship between the district-wise number of railways stops and the poverty headcount index in Sri Lanka. It was also found that the district-wise number of passenger trips did not have a statistically significant relationship with the district-wise poverty headcount index.
item: Thesis-Abstract
Performance evaluation frame work for service quality improvements in public bus transport
(2021) Sharic, AHS; Bandara, JMSJ
The aim of this thesis is to develop service quality measurement methods for bus transport considering two main components: passenger expectation and service performance. Existing measures of passenger expectations do not address the heterogenic characteristics of passengers. Existing measures on bus transport service performance lack consideration of possible dynamic elements of bus operations, such as thresholds for service quality factors, stop level, route level, service headway, bus size etc. There is a need for regulators to monitor the progress of achievement in sustainable transport through public transport service quality improvements. The thesis answers the questions about to what extent the levels of service quality are expected by the bus transport passengers and what is the existing level of service performance of the bus transport operation. Walking time, waiting time, travel time, in bus environment, and station environment are identified as important bus transport service quality factors for passengers. Conjoint analysis is used to measure the weight of passenger expectations on bus transport service quality factors using customer satisfaction surveys on service quality attribute levels. The normalization approach was introduced to address the heterogeneity of passenger behavior. The Threshold Enabled Earliness Index was developed to evaluate the reliability of buses (waiting time) at bus stops for both arrivals and departures using data on scheduled and actual arrival and departure times of the buses at stops. The Threshold Enabled Probability value is created using cross tabulation analysis to evaluate bus travel time reliability (travel time). Two Passenger Comfort Level Indexes STPCLI & SEPCLI to capture standing and seating passenger comfort levels are proposed that could be estimated using boarding and alighting counts. These two indexes are capable of capturing comfort levels for the entire route or a part of a route and not confined to a given location. In the absence of route level income information, methods have been developed to estimate bus revenue when individual passenger boarding and alighting data is available, or cumulative boarding and alighting data at the fare section level is available, or when only demand information or the history of demand distribution is available. A computer algorithm for estimating STPCLI, SEPCLI and to estimate fare revenue at route or part of route for different demand levels at different service headways for different bus sizes is developed. This algorithm could capture the number of passengers iii who would miss a bus due to capacity limitations and is useful in situations where limitations on loading need to be imposed especially to maintain social distancing. The study helps identify the bus transport passenger expectations about the quality of the bus transport service and it also helps to evaluate the service performance of the bus transport system in fulfilling those expectations. These measures can be used to compare the level of service quality among different sections of a bus route, for the entire route, among different routes or for an area and among different areas. This study finally contributes to the ongoing debate on the critiques of the operationalization of service quality measurements. Keywords: Service quality, bus transport, passenger expectation, service