Smooth path and speed planning for an automated public transport vehicle

A lot of crashes involving heavy vehicles have been reported over the years in Kerala. Faulty driving behavior is widely regarded as the major cause of such crashes. This study considers the drivers’ perceptions and their socio-demographic background to understand the behavioral variations. The study is done based on a questionnaire survey on public transport operators from which their behavioral characteristics are noted. The study area is divided into three parts namely, North, Central and South Kerala with a fixed sample at each of these regions. The sample size is fixed based on Bill Godden’s method for infinite population. Accordingly, a sample of 384 vehicles was surveyed from each region. Thus, data for a total of 1,152 public transport vehicles were taken from all three regions namely North, South, and Central Kerala. From this sample, the variations in violations, errors and lapses among drivers are noted based on various causative factors. Analyses of these behavioral factors are done to study the influence on aberrant behavior and accident occurrence in public transport vehicle plying in Kerala. A linear regression model is developed to state the importance of causative factors in the occurrence of crashes.

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Studying the regularities in public transport vehicle dwell time required for passenger boarding and deboarding

Bulletin of Kharkov National Automobile and Highway University ◽

10.30977/bul.2219-5548.2021.95.0.252 ◽

2021 ◽

pp. 252

Author(s):

Olha Svichynska ◽

Volodymyr Karpenko

Keyword(s):

Public Transport ◽

Time Distribution ◽

Dwell Time ◽

Mathematical Methods ◽

Passenger Transportation ◽

Distribution Parameters ◽

Forecast Models ◽

Passenger Flows ◽

Transport Vehicle ◽

Time Required

The review of the literature devoted to the research of public transport vehicle (PTV) dwell time required for passenger boarding-deboarding at a stop confirms the importance of taking this time into account when modelling passenger transportation. The data about the dwell time were collected in different time periods for different PTVs in various cities and countries. Thus, there is no general model allowing to define the distribution parameters of dwell time variable and answer the question on the regularities in time values. So, the research of the PTV dwell time at the public transport stops in Kharkiv remains actual. Goal. The search of the regularities in transport system performance indicators including PTV dwell time at a stop will allow to apply relevant mathematical methods for the development of the forecast models which are valuable in the field of organization of passenger transportation and servicing. Methodology. The developed methodology to collect data about PTV dwell time at the stops will allow receiving high-quality survey data. The designed survey sheets enable a surveyor to record all needed information and prepare it for processing. The collected data will allow to define the distribution of the PTV dwell time at a stop. Results. The conducted PTV dwell time survey allowed to collect the sufficient amount of data to estimate the distribution of this variable. During the research, it was determined that the empirical dwell time distribution can be well described with the theoretical gamma distribution. The latter distribution appeared to be applicable for all surveyed PTVs. Originality. The defined distribution of the PTV dwell time at a stop for passenger boarding-deboarding allows receiving the results of passenger flows modelling which are more precise compared to the modelling with no dwell time consideration. The use of the dwell time regularities in the procedure of passenger flows assignment results in the increased precision of flow volumes estimation by up to 14.9 % – from 2.28 % to 1.94 %. Practical value. The received results support the fact that the research of PTV dwell time at a stop is actual, and the solution of the task of dwell time distribution estimation will make it possible to improve passenger flows modelling in public transport route systems.

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Neural network classification for detecting abnormal events in a public transport vehicle

2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM) ◽

10.1109/hnicem.2015.7393221 ◽

2015 ◽

Cited By ~ 2

Author(s):

Cristina P. Dadula ◽

Elmer P. Dadios

Keyword(s):

Neural Network ◽

Public Transport ◽

Neural Network Classification ◽

Transport Vehicle ◽

Abnormal Events

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Public transport vehicle scheduling featuring multiple vehicle types

Transportation Research Part B Methodological ◽

10.1016/j.trb.2014.04.009 ◽

2014 ◽

Vol 67 ◽

pp. 129-143 ◽

Cited By ~ 41

Author(s):

Stephan Hassold ◽

Avishai (Avi) Ceder

Keyword(s):

Public Transport ◽

Vehicle Scheduling ◽

Transport Vehicle

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Detection of Methicillin Resistant Staphylococcus aureus in Public Transportation of Kathmandu Valley, Nepal

Tribhuvan University Journal of Microbiology ◽

10.3126/tujm.v5i0.22312 ◽

2018 ◽

Vol 5 ◽

pp. 51-56 ◽

Cited By ~ 2

Author(s):

Kkadga Bikram Angbuhang ◽

Mukesh Neupane ◽

Aditya Adhikari ◽

Binita KC ◽

Sabina Jha

Keyword(s):

Staphylococcus Aureus ◽

Public Transport ◽

Public Transportation ◽

Methicillin Resistant Staphylococcus Aureus ◽

Bacterial Load ◽

Health Concern ◽

Diffusion Method ◽

Kathmandu Valley ◽

Methicillin Resistant ◽

Transport Vehicle

Objectives: The purpose of this study was to assess microbial load and Methicillin Resistant Staphylococcus aureus from surfaces of public transport vehicle. Methods: The surfaces of public transport vehicle were sampled by swabbing. A total of 56 samples from 28 different vehicles operating in Kathmandu valley were collected and processed according to the standard methodology. The isolates were identified by culture, biochemical tests and subjected to antimicrobial susceptibility testing by modified Kirby-Bauer disk diffusion method following CLSI 2013 guidelines. Methicillin resistant species of Staphylococcus were detected by the virtue of cefoxitin resistance. Results: All 56 samples from the 28 different vehicles were found to have bacterial growth with average bacterial load of 2.47±1.22 x 105 CFU/cm2. The gas vehicles were found to be the most contaminated. Out of 56 samples, 35 (25.9%) were found to be S. aureus growth positive 11 (31.4%) of them being MRSA. Conclusion: The high flow of people with different health conditions in public transport makes the exchange of microorganism more significant. High bacterial load along with MRSA indicates the threats of transmission of infection among travellers. This is of a great public health concern as the mass population of different health condition is in direct exposure and is prone to get infected.

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