Investigating bus travel time and predictive models: a time series-based approach

Comi, Antonio; Zhuk, Mykola; Kovalyshyn, Volodymyr; Hilevych, Volodymyr

doi:10.1016/j.trpro.2020.02.109

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…It is affected by various traffic flows (buses share lanes with other road users) and this affects the time of the bus trip. This is especially typi-cal for routes "to" or "from" the city center due to the high concentration of trips to work or study [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…1): Terminal A -shopping center "King Cross Leopolis" (southern city); Terminal B -square "Rizni" (city center). The analyses synthetized below were performed using AVM data of the bus [13]. The collected data includes the average speed of traffic on the route of all buses during the working days of the week.…”

Section: Bus Average Speed Analysis and Forecastingmentioning

confidence: 99%

“…Data for bus route #3A were collected by monitoring buses on the website http://track.ua-gis.com/ for 10 weeks [13]. The collected data includes the average speed of the buses for the direction of the city center.…”

Section: Bus Average Speed Analysis and Forecastingmentioning

confidence: 99%

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Possibilities of forecasting bus average speed on city route

Zhuk,

Hilevych,

Kovalyshyn

et al. 2024

MATEC Web Conf.

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Average bus speed analysis plays a key role in transit operations planning. Such analysis makes it possible to perform forecasting more accurately, especially taking into account the possibility of automated monitoring of a large database. The paper offers a new approach to forecasting the average bus speed on the city route, taking into account the “skip-stop” method. For the convenience of data processing and comparison, four main time periods were selected. The corresponding array of data was organized for each of time periods in the software environment “Statistica”. The conducted analysis characterizes a significant difference in the values of the speed indicators. It has been established that the change in speed leads to non-compliance with traffic schedules and indicates difficult traffic conditions during peak hours. It was studied and compared the deviation of the speed from the average value during the day. It is proposed a new scenario for the operation of buses with skipping stops in the hour when the speed on the route is lower than the average value. According to the results of speed modeling in peak years, it shows the possibility of reducing the negative effects of speed on the schedule. This forecasting of the average buses speed will enable system operators to more accurately design traffic schedules and minimize disruptions in them.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Bus Average Speed Analysis and Forecastingmentioning

confidence: 99%

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Possibilities of forecasting bus average speed on city route

Zhuk,

Hilevych,

Kovalyshyn

et al. 2024

MATEC Web Conf.

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“…In this paper, a time-series approach is applied to investigate bus travel times; the analysed data refer to the automatic vehicle location (AVL) of some transit lines in the cities of Rome (Italy, [5]) and Lviv (Ukraine, [25]). The first objective is to correlate bus travel time with general traffic patterns and other explanatory variables.…”

Section: Introductionmentioning

confidence: 99%

Bus Travel Time: Experimental Evidence and Forecasting

Comi

Polimeni

2020

Forecasting

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Bus travel time analysis plays a key role in transit operation planning, and methods are needed for investigating its variability and for forecasting need. Nowadays, telematics is opening up new opportunities, given that large datasets can be gathered through automated monitoring, and this topic can be studied in more depth with new experimental evidence. The paper proposes a time-series-based approach for travel time forecasting, and data from automated vehicle monitoring (AVM) of bus lines sharing the road lanes with other traffic in Rome (Italy) and Lviv (Ukraine) are used. The results show the goodness of such an approach for the analysis and reliable forecasts of bus travel times. The similarities and dissimilarities in terms of travel time patterns and city structure were also pointed out, showing the need to take them into account when developing forecasting methods.

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“…For travelers, they can plan their trips based on these information reasonably. Besides, accurate and timely information about the bus speed and time is crucial; it could attract more travelers, reduce the waiting time, and improve their satisfaction [3,4]. erefore, for providing public transportation agencies and travelers with more timely and accurate information, efficient models need to be developed to predict the bus speed more accurately.…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction

Zhai

Cui

Zhang

et al. 2020

Mathematical Problems in Engineering

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For resolving or alleviating the transportation problems, it is necessary to efficiently manage the public transportation and provide public transport services with high quality and advocate green travel, which rely on accurate traffic data. In order to obtain more accurate bus speed in the future, this paper proposed a novel dynamic hierarchical spatial-temporal network model based on Grey Relation Analysis (EGRA), the convolutional neural network (CNN), and the gated recurrent unit (GRU). The proposed model is named the DHSTN; it exploited EGRA to analyze and choose the suitable candidate line sections with high impacts on the target section and, then, construct a multilayer structure based on the CNN, GRU, and attention mechanism to analyze and capture the spatial and temporal dependency, and finally, the extreme learning machine (ELM) is exploited for the fusion of the long-term and short-term dependency to predict the bus speed variation in the next time interval. Comparative experiments indicate that the DHSTN has better performances, the mean absolute error is around 2.6, and it meets the real requirements.

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Investigating bus travel time and predictive models: a time series-based approach

Cited by 8 publications

References 17 publications

Possibilities of forecasting bus average speed on city route

Possibilities of forecasting bus average speed on city route

Bus Travel Time: Experimental Evidence and Forecasting

An Improved Deep Spatial-Temporal Hybrid Model for Bus Speed Prediction

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