Abstract
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.
Highlights
Travel time plays a key role in assuring the reliability and quality of service in a transit system
Given that the roads are congested during peak hours and less congested during nonpeak hours, and, subsequently, travel time is longer in rush hours than in nonpeak hours, time series allow these patterns to be captured
In relation to bus travel time, there are no significant differences in patterns, with high values during morning and afternoon peak hours
Summary
Travel time plays a key role in assuring the reliability and quality of service in a transit system. The use of this variable ranges from operation planning (e.g., for short- and long-term planning) to service monitoring (e.g., for real-time information). In large historical cities (e.g., Rome, Italy), the influence of traffic congestion on travel time variability has been pointed out. It is linked, on the one hand, to the city structure and, on the other hand, with the growing number of private and freight vehicles travelling into the city [1,2,3]. Time series analysis captures this pattern [6]
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