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999			_c100835 _d100841
003			MY-KuUP
005			20251125110859.0
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007			ta
008			240529s20232023my a|||fr|||| 000 0 eng d
020			_aTHE0009859 (Local) _qHardback
040			_aUMPSA _beng _cUMP _erda
090			_aPSM .B35 2023 r Bc
100	1		_aSiti Nor Balqis Binti Jafaar, _eauthor.
245	1	0	_aStatistical analysis of rapid bus travel time and ridership data : _ba case study for route 300, kuala lumpur / _c
264		1	_aKuantan Pahang: _bUMPSA, _c2023
264		4	_c©2023
300			_axii, 80 pages : _bIllustration (some colour) ; _c30 cm.+ _e1 CD ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCenter for Mathematical Sciences
502			_aBachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aMalaysia's Rapid Bus system is a modern transportation system designed to provide commuters with efficient transportation. The difference between actual arrive time and actual depart time is defined as travel time. The degree of variation in travel time of a trip repeated under similar conditions over several days is reflected in travel time variability. Users must consider travel time variability when making fundamental travel decisions such as mode, route, and departure time. Ridership takes travel time variability into account when making basic travel decisions such as mode, route, and departure time. The average resident's (per capita) transit ridership can then be measured in terms of weekday boardings or annual boardings. It aims to be on time at every stop, and the variability of travel time is an important factor for users to consider when making travel decisions. However, the system has seen a decline in ridership over the years, which has been attributed primarily to poor service quality, resulting in difficulties in accessing public transport in various regions. This study focuses on the Rapid Bus Kuala Lumpur Route 300 and aims to analyse the relationship between Travel Time and Ridership. Statistical analysis, including graphical representation and hypothesis testing, is used to explore Rapid Bus travel time and ridership characteristics. Predictive analysis using a simple linear regression model is employed to predict the relationship between travel time and ridership on Route 300. This study makes use of Prasarana Malaysia Berhad's Travel Time and Ridership database, Tableau and Microsoft Excel are used as the analysis tools. The findings of the study include an interactive dashboard with useful information that can be used to display Prasarana to others. According to the analysis, Rapid Bus travel time varies significantly throughout the day, with higher travel times during peak hours. Furthermore, ridership has consistently declined over time, indicating decreased use of public transportation. The analysis reveals a positive relationship between Rapid Bus travel time and Route 300 ridership, implying that as travel time increases, so does ridership, possibly due to limited alternative transportation options. The Covid-19 pandemic has had a significant impact on Rapid Bus travel time, resulting in increased congestion and delayed bus arrivals which have contributed to a drop in ridership. Remote work arrangements and public health concerns have also contributed to the drop in ridership during the pandemic. Based on the analysis, recommendations to improve Rapid Bus travel time include optimizing bus routes and utilizing intelligent transportation systems. To increase ridership, the study suggests enhancing service quality, targeting marketing campaigns, and offering incentives like discounted fares or loyalty programs.
610	2	0	_aCenter for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cPSM