Analysis of Bus Travel Time Variability using Automatic Vehicle Location Data

Harsha, M. M.; Mulangi, Raviraj H.; Kumar, H D Dinesh

doi:10.1016/j.trpro.2020.08.123

Cited by 13 publications

(11 citation statements)

References 16 publications

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“…In indoor positioning of motion platform, communication signal mode plays a key role in information exchange. Due to the characteristic of indoor radio wave transmission [1] of different modes of communication signal, the working mode can be automatically changed to adjust the channel when the communication [2] is not smooth. The positioning access point AP can identify the signal source on the sports platform to strengthen the positioning and tracking.…”

Section: Introductionmentioning

confidence: 99%

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Chen

Liu

Fan

2021

Advances in Mathematical Physics

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In order to solve the problem of large signal acquisition error caused by radio wave multipath effect in indoor environment, firstly, the signal source carried on the motion platform is collected for spectrum signal, and the signal processed by wavelet threshold denoising algorithms extracted and stored for spectrum feature extraction. Then, after data training and identification, the signal source is input into the system in random mode for identification. The experimental results show that the improved fuzzy clustering algorithm (FCA) is 12.7% higher than the spectrum envelope extraction method (SEEM) in the recognition rate of spectrum characteristics of different modes of signal source.

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Section: Introductionmentioning

confidence: 99%

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Chen

Liu

Fan

2021

Advances in Mathematical Physics

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“…Taking into consideration all previously stated parameters, it is possible to calculate travel time of the bus (real time of cycle time) which functions in mixed traffic conditions. e model represents a significant contribution to the research of travel time in the mixed traffic flow, since, according to the review of the literature in their paper in 2020, Harsha et al [8] concluded that most research studies are carried out in the countries which have almost homogeneous traffic conditions. e developed analytical model was tested with real data in the work conditions of one typical city line.…”

Section: Discussionmentioning

confidence: 99%

“…[19] consider the information on passengers boarding, a significant factor which affects the travel time. Different studies connected travel time to different distributions, such as normal distribution, log-normal distribution, gamma distribution, logistic distribution, and loglogistic distribution [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Knowledge of these changes and their influence on the functioning of PT vehicles enables better planning of work elements of PT vehicles, which would be in accordance with real traffic conditions. So far, there have been few research studies on variability of travel time of PT vehicles in mixed traffic flow [8]. For this reason, there is a need for carrying out further research in these conditions.…”

Section: Introductionmentioning

confidence: 99%

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The Model of the Optimal Number of Public Transport Vehicles in Mixed Traffic Flow Conditions: A Case Study

Simeunović

Bogdanović

Simeunović

et al. 2021

Discrete Dynamics in Nature and Society

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Bus passenger transport is very important for sustainable urban mobility, and due to the characteristics of the street network, it is usually realized in the conditions of mixed traffic flow. Disturbances and sudden changes of traffic flow parameters occurring in the street network in a mixed traffic flow affect the disruptions in schedule and travel time of all vehicles in the traffic flow, including public transport (PT) vehicles. In order to keep to the planned schedule in the peak hours of PT lines carried out in the conditions of mixed traffic flow, the operators introduce new vehicles or reduce the vehicles’ dwell time at terminuses, which is often impossible to do. The use of a larger number of public transport vehicles increases the fuel consumption, pollutants’ emission, and the operating costs. In this paper, a network optimization model was developed for defining the influence of the change of traffic flow parameters in a mixed traffic flow on travel time of PT vehicles. The model takes into consideration uncertain time unevenness of the change of traffic flow parameters, which enables determining the optimization of travel time and defining the necessary number of public transport vehicles for the purpose of keeping to the planned schedule. In order to develop the transport model, counting and analysis of the characteristics of traffic flow at 61 intersections on the city territory were carried out. The model was tested on bus line number 4 of PT in Novi Sad. The model showed that it is possible to achieve certain savings regarding the number of vehicles with the unchanged headway, that is, the unchanged level of service which is offered to the users. With the application of the model in real traffic conditions, significant savings, as well as operating and external costs’ reduction, can be achieved, which contributes to the sustainability of public bus transport in urban environments.

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“…Several probability distributions are proposed in the existing literature to characterize the variability of link travel times [34] such as shifted log-normal, log-normal, normal [36], gamma, Weibull, Burr Type XII [37], generalized extreme value [38], etc. Numerous real-life studies [39,34,40,41] choose the former, which shows a probability density of zero when the value of the random variable falls below a threshold (which would be the free-flow link travel time) and can adequately fit asymmetric, positively skewed data; and that for many links is the function that most likely describes how travel times are distributed. A 2017 study conducted on global positioning system (GPS) data from taxis during the morning peaks of 5 weekdays in Wuhan (China) [42] found that link travel times may be best represented by log-normal, gamma or normal distributions (on 50 %, 30 %, and 20 % of the analyzed links, respectively) and opted, to avoid computationally intractable calculations, to assume that travel times along a path can be approximated by normal distributions.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Automated Vehicle Location, Fare Control, and Schedule Data for Improved Public Transport Trip Definition

et al. 2021

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Analysis of Bus Travel Time Variability using Automatic Vehicle Location Data

Cited by 13 publications

References 16 publications

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

Research on Spectrum Feature Identification of Indoor Multimodal Communication Signal

The Model of the Optimal Number of Public Transport Vehicles in Mixed Traffic Flow Conditions: A Case Study

Integration of Automated Vehicle Location, Fare Control, and Schedule Data for Improved Public Transport Trip Definition

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