Operating Time Division for a Bus Route Based on the Recovery of GPS Data

Wang, Jian; Cao, Yang

doi:10.1155/2017/1321237

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Globalisation and technology had led towards industrial revolution (IR) 4.0, in which every angle of the daily life is connected with Internet of Things (IOT) [34]. The government should assist key players in bus industries to develop and implement a new technology in bus services to improve its quality of for bus performance, such as actual time bus services [34], designing a significant feeder transit [32], optimisation of bus schedules [31], and systematic departure and arrival of buses [28]. By adapting this technology, trustworthiness and passenger and user perceptions towards bus services can also be increased as operators can deliver services of good quality [24].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Quality of Service of Bus Performance in Johor Bahru

Bakar

Norhisham

Abudeyab

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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The increase in living standard would also be a major factor for rapid urban development, which is caused by the increase in the daily traffic volume. Therefore, traffic congestion can occur daily, especially during peak hours in the morning and late afternoon to cover the huge number of people movement. Moreover, the quality of bus services should be assessed on operational aspects provided by urban operators. Therefore, this paper is to determine the quality of service (QOS) of bus performance in Johor Bahru, Johor. The study was conducted in April 2019. Service quality data were taken at Terminal JB Sentral, Johor Bahru. The Transit Capacity and Quality of Service Manual (TCRPM) was adopted as the specific guidelines and key indicators to determine and analyse the QOS of bus performance in the selected area. The study focused on four specific areas, such as service hour, passenger load factor, on-time performance and service frequency. The study concluded that the passenger load factor was classified as QOS of Class B since passengers could still choose their seats during the journey. On the other hand, on-time performance was classified as QOS of Class E, as most passengers made one round trip per weekday with a transfer experience of not-on-time vehicles every day. The overall QOS for bus services in Johor Bharu was classified as QOS of Class D, which was considered as moderate because several issues on QOS should be addressed and rectified by stakeholders. Some improvement on these three attributes should be made to increase the QOS. The study outcome should be used as an indicator for operators to measure their bus services. Since Johor Bahru is a major city in Malaysia, authorities and the government can use the outcome as inputs to measure the effectiveness of public transportation in urban areas.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Quality of Service of Bus Performance in Johor Bahru

Bakar

Norhisham

Abudeyab

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…This indicates that at a higher spatial aggregation level the variations are normalized, and the predictions are better. There is also a possibility that the variations and the delays experienced at one segment by the buses are compensated by the driver during traversing [11] the other segments. For applications like route travel time predictions and timetable [28] generation, predicting travel times at route level spatial aggregation is needed and the proposed model is recommended.…”

Section: Model Performancementioning

confidence: 99%

“…If the travel time is forecasted more than 60 minutes ahead of the current time, it is a long-term, else a short-term prediction. Long-term prediction aid the decisions for operations planning of the buses [11], while short-term assists the passenger information systems [12], bus routing, fine-tuning schedules, and identifying bus bunching [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid travel time estimation model for public transit buses using limited datasets

Ashwini¹,

Sumathi²,

Sudhira³

2023

IJ-AI

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A reliable transit service can motivate commuters to switch their traveling mode from private to public. Providing necessary information to passengers will reduce the uncertainties encountered during their travel and improve service reliability. This article addresses the challenge of predicting dynamic travel times in urban areas where real-time traffic flow information is unavailable. In this perspective, a hybrid travel time estimation model (HTTEM) is proposed to predict the dynamic travel time using the predicted travel times of the machine learning model and the preceding trip details. The proposed model is validated using the location data of public transit buses of, Tumakuru, India. From the numerical results through error metrics, it is found that HTTEM improves the prediction accuracy, finally, it is concluded that the proposed model is suitable for estimating travel time in urban areas with heterogeneous traffic and limited traffic infrastructure.

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“…The use of information on passengers boarding and alighting from vehicles to know how to avoid overcrowding in the transport network is proposed in [8]. In practice, a bus driver may deliberately speed up or slow down on route to follow the predetermined timetable, in [9] a methodology to calculate the real bus travel time is proposed. A new metric to evaluate the service punctuality is presented in [10].…”

Section: Related Workmentioning

confidence: 99%

Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

et al. 2018

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Travel Time plays a key role in the quality of service in road-based mass transit systems. In this type of mass transit systems, travel time of a public transport line is the sum of the dwell time at each bus stop and the nonstop running time between pair of consecutives bus stops of the line. The aim of the methodology presented in this paper is to obtain the behavior patterns of these times. Knowing these patterns, it would be possible to reduce travel time or its variability to make more reliable travel time predictions. To achieve this goal, the methodology uses data related to check-in and check-out movements of the passengers and vehicles GPS positions, processing this data by Data Mining techniques. To illustrate the validity of the proposal, the results obtained in a case of use in presented.

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Operating Time Division for a Bus Route Based on the Recovery of GPS Data

Cited by 7 publications

References 14 publications

Evaluating the Quality of Service of Bus Performance in Johor Bahru

Evaluating the Quality of Service of Bus Performance in Johor Bahru

Hybrid travel time estimation model for public transit buses using limited datasets

Using Data Mining to Analyze Dwell Time and Nonstop Running Time in Road-Based Mass Transit Systems

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