Measuring and Visualising Schedule Adherence

Schedule or timetable adherence is one of the indicators to measure transit service reliability. Measuring schedule adherence is a valuable management tool, because good schedule adherence demands both realistic schedules and good operational

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control. The schedule adherence indicator measures the schedule adherence for transit vehicles at bus stops (Strathman, J., et al., 1998). Adherence to the published schedule is calculated by subtracting the scheduled arrival time from the actual arrival time. A negative value for the arrival time indicates that a vehicle has arrived early. Likewise, a positive value for the arrival time means that a vehicle is late.

To indicate transit vehicle schedule adherence, it is better to visualise their adherence status. (e.g. on-time, ahead, or delayed). A colour-coding–based visualisation technique is used to display vehicle adherence. Measuring and visualising the timetable adherence of transit vehicles helps transit operators in managing transit services. The red colour is used when buses arrive late to a bus stop, while the green colour indicates on-time arrival and the orange colour is used when buses are ahead of their timetable. Figure (4-21) illustrates the use of coloring to indicate buses adherence status to published timetables.

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Figure 4-21 The use of colour coding to indicate the adherence of buses on map- based and tabular-based views.

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The adherence to the published schedule can also be visualised on other various diagrams. A bus route progress diagram, as we can see in Figure (4-22), shows the current location of a transit vehicle, with a colour-code indicating its adherence to the timetable. The Figure also shows the transit vehicles that are out of service by the black colour.

Figure 4-22 The current location of a transit vehicle, with the green coloured boxes indicating on time arrival

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Figure (4-22) is useful for passengers who are not familiar with maps as they can easily locate where transit vehicles are at any given time. For example, the bus number 254 is at the station (Grange Road/Kingscate Drive) is colored in green to indicate on-time arrival. The rest of the buses at the bottom of Figure colored black to signify they are out of service at that time.

A real-time report for transit vehicle adherence can be generated for monitoring the adherence to the time table and detecting any deviation from the planned schedule. As we can see in Figure (4-23), the schedule adherence monitoring reports are generated in real-time. In these reports the system measures the adherence to timetables and assigns a specific colour based on the adherence status (on-time, ahead or late). For example, the bus-adherence reports indicate vehicle number, the actual arrival times, scheduled arrival time, time difference in minutes and schedule adherence status.

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Figure 4-23 Real-time schedule adherence monitoring

Instead of using a tabular format to compare transit vehicles’ actual performance against the timetable, visualising this comparison may be of more use in this context. Figure (4-24) shows the visualisation of vehicle behaviour and a comparison with the timetable, showing adherence and deviation.

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Figure 4-24 Visualisation of vehicle behaviour and a comparison with the timetable, showing the adherence and the deviation

Figure 4-24 illustrates the daily spatial-temporal “behaviour” of transit vehicles (vehicle locations in term of bus stops passed against time) as a series of line-graphs.

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Buses move along the route to complete their journeys, this has been represented by a blue line which shows bus location and time to be compared to the timetable (in red) showing the adherence deviation. Figure 4-24 provides insight into the relationship between actual and scheduled service on a per trip basis. This method of data visualisation can help to show the regularity of the service and indicate irregularity. For example, in this figure the line graph in red represents the timetable while the line graph in blue represents the actual running of a transit vehicle. The figure shows two cases where in the first one the transit vehicle follows the timetable, while in the second case a deviation from the timetable can be detected easily where the difference between the timetable and the actual performance of the bus has been represented using a line plot. This way of visualisation shows the difference between the space and time data for both the transit vehicle and timetable.

Factors that influence adherence to the timetable can be classified into two categories: internal and external factors. Internal factors such as variations in passenger demands, route configuration and driver behaviour, and external factors such as traffic congestion, weather conditions, bus bunching and others can effect transit vehicle operations and make it difficult for them to adhere to the published timetable (Zolfaghari, S. et al., 2002). Our system provides pictorial and tabular views that depict adherence to the timetable. Figure (4-25) shows use of color-codes in a real-time updated report for the number of times transit vehicles were on time, early, or delayed at timing points. In this figure, we used a bar graph to represent transit vehicle adherence. As we can see in the bar-graph transit vehicle numbers are depicted on the x-axis, while the number of times the vehicle was on time, delayed or ahead are depicted on the y-axis. We used colour-coding for transit vehicle adherence as we discussed in Section (4.6.2). The table in the figure shows time points and vehicle number as well as number of times these vehicles adhered to the timetable. For example, vehicle number 254 was 12 times on time and 3 times early and zero times in its arrival at bus stops.

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Figure 4-25 Adherence table shows time points and number of time transit vehicles were on time, early or delayed

Schedule adherence can be described as the percentage of departures that were in a defined on-time window, or perhaps as the percentage that were ahead, on time, and delayed. Figure (4-26) shows a distribution of schedule deviations in full detail. Such a distribution allows operators to vary the “ahead” and “delayed” threshold depending on the timetable difference.

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Figure 4-26 A distribution of schedule deviation

A profile of schedule deviations along the line is a valuable tool, showing how both the mean and spread of schedule deviation changes from stop to stop. The heavy blue line indicates mean schedule deviation. Thin lines represent individual observed trips. How close the mean deviation is to zero indicates whether the scheduled running time is realistic. If the mean deviation suddenly jumps, it suggests that the allowed segment time is unrealistic. Our system can help in measuring the vehicle’s timetable adherence in detail and provide the distribution of the time difference between the actual performances and the timetable. As we can see in Figure (4-27) the time distribution shows how transit vehicles act against the timetable. Most of the transit vehicles appear to adhere to the timetable, where the distribution is concentrated around zero. We also note cases where the deviation from the timetable is high such as 10 to 12 minutes. This evaluation of the transit vehicles’ performance can be measured for any given vehicle at any given time along the route and helps to maintain and control the schedule and enhance the accuracy and reliability of transit services.

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Figure 4-27 Transit vehicle adherence and time distribution

By examining the shape of the distribution, it is possible to compare proportions of on-time, early and late services and obtain a general idea of the transit services. This can be useful to compare transit vehicle adherence over different time intervals (hours, days, months).