Bus travel time variability: some experimental evidences

Comi, Antonio; Nuzzolo, Agostino; Brinchi, Stefano; Verghini, Renata

doi:10.1016/j.trpro.2017.12.072

Cited by 32 publications

(26 citation statements)

References 12 publications

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“…At this initial point of the review it should be mentioned that the works published on these prediction methods are generally lacking in their analysis of the input variables used to make TT predictions. We could, however, mention the works of Yetiskul and Senbil [3] and Comi et al [4], which analysed the variables that affect TT behaviour in order to provide useful information for long-term TT planning. The former analysed TT behaviour in the Turkish city of Ankara.…”

Section: Related Workmentioning

confidence: 99%

Bus Travel Time Prediction Model Based on Profile Similarity

Cristóbal

Padrón

Quesada-Arencibia

et al. 2019

Sensors

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In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

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Section: Related Workmentioning

confidence: 99%

Bus Travel Time Prediction Model Based on Profile Similarity

Cristóbal

Padrón

Quesada-Arencibia

et al. 2019

Sensors

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“…Feng et al (2015) examined the impact of traffic signals and traffic volumes on bus stop-to-stop travel time. Comi et al (2017) investigated the relationship between bus traffic and the temporal congestion variability of the relevant background car traffic.…”

Section: Literature Reviewmentioning

confidence: 99%

Determinants of Bus Riding Time Deviations: Relationship between Driving Patterns and Transit Performance

Cats

2019

J. Transp. Eng., Part A: Systems

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Urban bus services are subject to high levels of uncertainty and disturbances. Methods to determine the timetable are designed to absorb variations in riding times between stops by allocating additional travel time. The propagation of service unreliability along the route could be restrained by drivers' adjustment at stops and between stops. This paper analyzes the main determinants of bus riding times deviations based on automatic vehicle location (AVL) data from four trunk lines in Stockholm, Sweden. The analysis indicates that drivers can and do adjust their speeds in response to instantaneous real-time schedule adherence information, although these adjustments depend on the underlying control scheme: locations where the performance is measured. A model for bus riding time deviations was estimated with autoregressive effects, performance indicators, link characteristics, and trip attributes as the explanatory factors. The results can support the development of travel time prediction and real-time control strategies that take drivers' response to operations into account. This highlights the importance of the human factor in designing control schemes and the corresponding transit performance evaluation.

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“…The data obtained was processed using a combination of two optimization models, the Time Series method for travel time analysis and the Linear Programming method to determine the number of buses operating to run optimally. Comi, et al in his research on the variability of bus travel time utilized the Time Series method to see the operational patterns of buses in Rome [1]. The calculation of travel time between terminals/bus stops was used so that the results of the research were more accurate.…”

Section: Optimization Modelmentioning

confidence: 99%

“…This study observed the strategies and technologies that have been applied by the company, the travel time of the buses that operate on Corridor 1, and what factors affect trip duration. By adapting to studies conducted by Benevolo, et al (2016) on Smart Mobility Taxonomy, Comi, et al regarding travel time variability [1], and Berhan, et al regarding optimization of the number of buses operating using the Linear Programming method [2], this research obtained results in the form of the benefits of the implementation of technology and also provided a solution in the form of a bus schedule (time table) that can subsequently be used as a basis for determining the minimum number of buses operating in order to optimize bus utilization and daily operating costs.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Travel Time and Bus Utilization of Corridor 1 Trans Jakarta to Realize the Smart Mobility Concept

Wicaksono¹,

Harahap²

2019

Proceedings of the 12th International Conference on Business and Management Research (ICBMR 2018)

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The concept of Smart Mobility is very closely related to public transportation. Trans Jakarta's operational effectiveness is one of the important factors in realizing Smart Transportation in the province of DKI Jakarta. This research looks at the strategies and technologies that have been applied by the company, the travel time of buses which operate on Corridor 1, and the factors affecting trip duration. The analysis was conducted based on the data obtained, which comprised of both primary and secondary data. Primary data includes interviews and field observations, such as the number of stops, the duration of traffic lights, and areas that point to potential obstacles. Secondary data was obtained from the company, which includes the number of passengers, bus travel history, and bus specifications and capacity. By adapting to the research conducted by Benevolo, et al. [5] regarding Smart Mobility Taxonomy, Comi, et al. [1] regarding travel time variability, and Berhan, et al. [2] regarding the optimization of the number of buses operating using the Linear Programming method, this research obtained results in the form of benefits of the technology implemented, as well as providing a solution in the form of a bus schedule (time table) which will subsequently be used as a basis to determine the minimum number of buses operating in order to optimize bus utilization and daily operating costs.

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Bus travel time variability: some experimental evidences

Cited by 32 publications

References 12 publications

Bus Travel Time Prediction Model Based on Profile Similarity

Bus Travel Time Prediction Model Based on Profile Similarity

Determinants of Bus Riding Time Deviations: Relationship between Driving Patterns and Transit Performance

Analysis of Travel Time and Bus Utilization of Corridor 1 Trans Jakarta to Realize the Smart Mobility Concept

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