Techniques to Visualize and Monitor Transit Fleet Operations Performance in Urban Areas

Transcription

1 1 Techniques to Visualize and Monitor Transit Fleet Operations Performance in Urban Areas ABSTRACT Wei Feng, Miguel Figliozzi, Scott Price, Wu-chi Feng and Kristina Hostetler This study focuses on the development of tools to visualize the performance of TriMet s Route 15 which experiences difficulties in terms of schedule adherence and headway regularity. TriMet is the transit provider in the Portland metropolitan region and its Bus Dispatch System (BDS) has been collecting and archiving automated vehicle locator (AVL) and automatic passenger counter (APC) data since The availability of this rich AVL/APC archived data allows for new data visualization techniques for routelevel operations and performance monitoring. This research provides novel ways to summarize and visualize vast amounts of bus route operations data in an insightful and intuitive manner: 1) a route/stop level visualization performance measure framework using color contour diagrams and 2) a dynamic interactive bus monitoring visualization framework based on a Google Maps platform. Visualizations proposed in this study can aid transit agency managers and operators to identify operational problems, better understand how such problems propagate spatially and temporally across routes. INTRODUCTION The ability to accurately and effectively analyze various performance measures is fundamental for a transit agency to determine how well it is adhering to its service standards. Transit agencies have a keen interest in understanding methods of generating and displaying transit operations performance data which contain detailed and accurate information. Such data is not only valuable in identifying potential problem areas along a route; it is also constructive in proposing effective strategies to improve service reliability. Therefore, well-developed visualization methods can prove invaluable by conveying large amounts of data and complex performance measures in a comprehensive format. A number of low-cost surveillance, monitoring and management systems as part of intelligent transportation systems (ITS) programs now exist, enabling transit agencies to collect advanced operational data for testing and analysis of operating efficiency and service reliability (1,2,3). Though the availability of this rich AVL/APC archived data make it possible for transit agencies to generate valuable transit performance measures (TPMs) at different aggregation levels (4, 5, 6); both Berkow et al. (7) and Kimpel (8) explain that due to the sheer volume of information available, a large amount of useful information is underutilized. This vast amount of data obviates the need to explore and employ visualization techniques as a way to comprehensively convey key. Using the transit provider for the Portland metropolitan region, TriMet, as a case study, this paper offers two frameworks for visualizing bus route operations performance measures: 1) A static visualization performance measure framework using color contour diagrams which can disaggregate offline data down to an hourly level for different segments, aggregate over time or space, or provide single stop analysis; and 2) A dynamic, interactive bus monitoring visualization framework using the Google Maps API, demonstrating bus operations performance along a route for any chosen bus. ROUTE CONFIGURATION AND DATA DESCRIPTION Route configuration This study focuses on TriMet s Route 15 which experiences difficulties in terms of schedule adherence and headway regularity. Route 15 is characterized by its long distance and high buffer time to absorb uncertainties. Operations performance data for Route 15 is derived from TriMet s Bus Dispatch System (BDS), which includes automatic vehicle location (AVL) technology and automated passenger counters

2 2 (APC). Route 15 runs east-west; with the east terminal located at the Gateway Transit Center and two terminals located at the west end of the route: 1) Montgomery Park, and 2) NW Thurman & 27 th Avenue. Figure 1 shows the route schematic as well as indicating the high frequency service stops in bold typeface. SE Belmont & 39 th SE Belmont & 60 th SE Morrison & 12 th NW Thurman & 27 th SW Morrison & 17 th SW Washington & 5 th Montgomery Park NW 23 rd & Lovejoy Total SW Salmon & 5 th SE Belmont & 60 th SE Belmont & 39 th SE Belmont & 11 th NW Thurman & 27 th SE Washington & 82 nd SE Stark & 82 nd Eastbound Stop Name Montgomery Park NW 23 rd & Marshall SW 18 th & Morrison SE 103 rd & Washington Gateway TC SE 102 nd & Washington Gateway TC Stop Name Westbound High Frequency Service 6:00 am 10:00 am WB EB High Frequency Service 4:00 pm 7:00 pm Total FIGURE 1 Route 15 map. Names in bold-face indicate high frequency service stops Scheduled stops are depicted by the black dots in the route schematic of Figure 1. For the majority of the day, Route 15 is low frequency service (defined by headways greater than ten minutes) (9). The time periods for high frequency service (defined as headways less than ten minutes) (9) are also shown in Figure 1. High frequency service for westbound weekday travel occurs between the hours of 6:00 am and 10:00 am and between the stops at SE Stark & 82 nd and SW Morrison & 17 th ; for eastbound travel high frequency service occurs between the times of 4:00 pm and 7:00 pm and between the stops at SW Salmon & 5 th Avenue and SE Washington & 82 nd Avenue. Data description

3 3 The studied data period ranges from 14 September 2009 to 26 February 2010 and includes all weekdays (totaling 116 days). TriMet has implemented a BDS that records detailed stop-level data in real time and enables the use of a variety of control actions (4). TriMet also archives stop-level data for later analysis. The archived stop-level data contain rich information including service date, actual departure time, schedule departure time, actual arrival time, dwell time, stop ID, vehicle number, and operator number, passenger movement, bus load, and many more. Whenever a bus arrives at or departs from a stop, a new record is entered. The column leave_time is the actual departure time of that bus at the stop expressed in seconds after midnight; stop_time is the scheduled departure time of that bus at that stop expressed in seconds after midnight; and arrive_time is the actual arrival time for that bus at that stop expressed in seconds after midnight. The dwell time here is recorded as the time (seconds) when the door is opening, therefore, dwell time is usually not equal to actual departure time minus actual arrival time. All the other information shown in Table 1 is self-explanatory. STATIC VISUALIZATION OF BUS OPERATIONS PERFORMANCE MEASURES This section demonstrates the capacity for a systematic analysis approach using color contour diagrams to visualize off-line operations performance data. Such diagrams work as temporal and spatial visualization tools to illustrate route-level operations performance measures as well as providing the capability to concentrate on single-stop characteristics. The frequency of transit service determines which performance measures ought to be analyzed, as there are particular performance measures which are best applied depending on either low or high frequency service. For either case, performance measures were computed hourly, and for different scheduled stops, or segments, for each weekday over a half-year period; and are displayed using color contour diagrams. Examination of the resulting color contour diagrams can reveal whether additional analysis is necessary for a single stop across all hours of a day (temporal analysis); for a single time period across all scheduled stops (spatial analysis); or for just a single stop during a single time period. We can generate the distribution of values, as well as a single mean value for a specified stop, or segment, for a specified hour of day for several performance measures including on-time performance, headway adherence, dwell time, travel time and speed, passenger boarding and alighting, and also lift use. Low Frequency Service Analysis (On-Time Performance, Dwell Time, & Travel Time/Speed) Recall that low frequency service is determined by headways greater than ten minutes. Per the Second Edition Transit Capacity and Quality of Service Manual (9), on-time performance is one of the most popular reliability measures used in the transit industry and is generally best applied for low frequency service. While the TCQSM 2 nd suggests on-time performance as being 0 to 5 minutes late, TriMet defines on-time performance as being no more than 1 minute early to no later than 5 minutes past scheduled departure time. Therefore, the index for on-time performance percentage is calculated using the following formula: (1)

4 4 FIGURE 2 On-time performances for route 15 westbound. To apply this formula, we can calculate the hourly on-time performance for each scheduled stop along the route for both directions, using the half-year s worth of archived BDS data. The results for the westbound

5 5 Route 15 are shown in Figure 2. The Level of Service (LOS) ranges for on-time performance are as follows (9): A B C D E F < 0.75 In Figure 2, on-time performance for nine scheduled stops along the westbound (from right to left) Route 15 were calculated hourly. The on-time performance indexes are shown by color, ranging from red to green to represent Low LOS to High LOS. The on-time performance values and their relative colors are shown on the color bar. The white area in the color contour diagram indicates that there is no data for those time periods. The outlined area in the color contour diagram denotes the high frequency service time periods and stop locations. When considering spatial factors, the changing colors from green to red horizontally across the diagram in Figure 2 suggests that the on-time performance becomes gradually worse from the east terminal to the west end of the route. Temporally, the changing colors vertically indicate the on-time performances are the best at the start of the day from 4:00 am to 6:00 am; and they are the worst in the middle of day from 10:00 am to 1:00 pm. There is a ten-hour period (from 10:00 am to 8:00 pm) when the stops on the west-side of the river generally have very low on-time performances. Because these west-side stops continue to serve relatively densely populated downtown areas, recurrent problems may exist with poor traffic signaling and transit coordination. While the color contour diagram in Figure 2 shows the overall picture of Route 15 s on-time performance for a half-year s worth of BDS data broken down for each hour of a day, it is also worth examining either temporal or spatial points of interest. In this case, the on-time performance for the 11:00 am to noon time period appears to experience all ranges of LOS, thus requiring additional spatial analysis for this time period. If we were curious about the performance behavior of a single stop for all hours of the day, then temporal analysis would be appropriate. Figure 3 illustrates on-time performance data for the 11:00 am to noon time period using a box-plot graph. Each box-plot shows the range of schedule adherence (actual departure time minus scheduled departure time) values for each stop during this time period. Comparing the data range of each box plot to the on-time threshold (1 minute early to 5 minutes late, depicted by the two solid black lines running across the graph) illustrates that the proportion of data that lie within the threshold lines. The data range for the stops towards the west-end of the route lie above the on-time threshold, indicating poor on-time performance, and particularly more late departures.

6 6 FIGURE 3 Box-plots of schedule adherence rata ranges for route 15 (Westbound). (a)

7 7 (b) FIGURE 4 Box-plots for (a) dwell time and (b) travel speeds for route 15 (westbound). It is also worth investigating potential causes of poor on-time performance levels. The BDS data readily offers dwell time data for each scheduled stop and segment, and a straightforward way to generate travel time data. In order to provide comparable analysis between each route segment since each segment is a different length, thus having different travel times we first converted travel time data to speed in miles per hour. Again using box-plots, we examine dwell times (the time difference between door open and close when a bus serving at a stop) and travel speeds to determine any trends or correlations to on-time performance in Figure 4. The dwell time box-plots show curious behavior, particularly at the stop SE Stark & 82 nd Avenue. The vertical stretch of this box-plot suggests high variability and longer duration of dwell times at this particular stop during this 11:00 am to noon time period. It is possible that the bus may arrive early to that particular stop, warranting a longer dwell time in order to get back on schedule (departure time). Though, it cannot be readily concluded that the dwell time performance at this stop directly affects the ontime performance, perhaps the events happening at this stop continue to propagate along the route, causing later issues. For all the downstream scheduled stops, dwell time for each stop alone does not appear to be a key factor for the poor on-time performance for that stop. Figure 4 (b) shows the speed box-plots for the same time for all the scheduled stops, it is shown that the median travel speed between SE Belmont & 39 th Ave and SE Morrison & 12 th Ave, and the travel speed between SE Morrison & 12 th Ave and SW Washington & 5 th Ave are lower than the upstream median speeds. Also, the median travel speeds for the further downstream segments are even lower, which may possibly cause the large amount of late departure for those downstream stops. Therefore, Figure 3 and Figure 4 (a) and (b) indicate that an adjustment of the schedule departure times for the west end scheduled stops around this particular hour. High Frequency Service Analysis (Headway adherence) Just as the on-time performance measure is best applied for low frequency service, headway adherence is used to measure service reliability for high frequency service routes. Bear in mind, high frequency

8 8 service is defined by headways that are generally ten minutes or less. The formula for calculating headway adherence is shown below (9): (2) Where is the coefficient of variation of headways; and headway deviation is the difference between the actual headway and the scheduled headway. The headway adherence values and relative LOS are below: A B C D E F > 0.75 By using the half-year s worth of archived BDS data and applying this formula, we can calculate hourly headway adherence for each scheduled stop along the route for both directions. The results for the westbound Route 15 are shown as another contour diagram in Figure 5. Similar to the previous contour diagram, Figure 5 presents the headway adherence of nine scheduled stops along the westbound (right to left) Route 15 calculated hourly. The headway adherence is again depicted by a color range red to green, representing low LOS to high LOS. The headway adherence values and their corresponding colors are shown on the color bar. Again, the white space in the diagram means there is no data. The outlined area in the diagram indicates the high frequency service time periods and stops; thus, the cells of interest lie within the outlined area. Figure 5 suggests that in general, headway adherence performs at mediocre LOS levels (Grade D or below) for the duration of the high frequency service periods and for most scheduled stops. It is significant to note that this substandard performance happens immediately at the start of the high frequency service at stop SE Stark & 82 nd Avenue. Moreover, the stop at SW Morrison & 17 th Avenue appears to chronically underperform for the duration of its high frequency service period, at LOS Grade E or worse. Again, the contour diagram easily highlights specific problem areas and time periods with low LOS grades for headway adherence. Inspecting the stop at SW Washington & 5 th Avenue again, this time between 8:00 am and 9:00 am (during high frequency service), we see it is, at best, performing at an LOS Grade D for headway adherence. This suggests that headways are not consistent for this stop and there are large deviations from the scheduled headway. Figure 6 (a) and (b) shows the running speeds (this speed is different from the Figure 4 (b) travel speeds in that this running speed excluded the dwell times for stops between each two scheduled stops) and dwell times (these dwell times for each scheduled stops also contain all the upstream unscheduled stops dwell times) for every segments in the westbound route between 8:00 am and 9:00 am.

9 FIGURE 5 Headway adherence performances for route 15 westbound. 9

10 10 (a) (b) FIGURE 6 Box-plots for (a) Running speeds and (b) passenger boarding (Westbound).

11 11 Figure 6 (a) shows a large average passenger boarding and variance between stops SE 102 nd & Washington and SE Stark & 82 nd at this time compared to other stops, Figure 6 (b) shows the running speed between these two stops is relative high. Therefore, the poor headway adherence for stop SE Stark & 82nd between 8 am and 9 am shown in Figure 5 is mainly due to the large amount and uncertainty of passenger demand. in the next two segments, both running speeds are relative high and passenger boarding amounts and variances are small. Therefore, the headway adherences fro stops SE Belmont & 60 th and SE Belmont & 39 th are relative better. However, after stop SE Belmont & 39 th, the running speed is very slow compared to previous segments, meanwhile the passenger boarding amount and variance are much higher than other segments. Therefore, the headway adherence at stop SE Morrison & 12 th between 8 am and 9 am suddenly becomes worse. After this stops, passenger boardings are stable low, running speed first increases in the subsequent segment and then decreases to very low values in the last two segments where the downtown area is. By comparing Figure 5 and Figure 6 (a) and (b), we can find that segment between stops SE Belmont & 39 th and SE Morrison & 12 th is the problematic segment that leads to downstream poor headway adherence, and the reason is that too slow running speed combined with large amount and variance passenger boarding activities. DYNAMIC VISUALIZATION OF BUS OPERATIONS PERFORMANCE One of the issues with static analysis of historical data is the difficulty to understand some of the finer details of what is happening within the system. That is, it tends to provide a macro-level view of the system. Therefore, we argue that both macro-level as well as more detailed micro-level information are necessary components to understand such complex systems. However, the overwhelming amount of micro-level data for such large-scale systems can lead to looking at irrelevant data. For our TriMet Route 15 study, we focus on enabling time-varying display of the collected archived data. Our system consists of three primary components: an SQL database, PHP based web server, and the web-based JavaScript client. We use an SQL database to store the BDS data. The data is accessed through the web-interface. Initially, our implementation had much of the functionality using only the web server; yet we found that the interactions, particularly updates to the visualization, were not scalable and moved towards a client-based approach. Thus, the web server in our implementation responds to single day queries issued by a web client. The server then translates the request into an SQL query and provides the data inline to the client. The client web browser directly handles much of the visualization interaction without having to reconnect with the server. Using this approach allows fairly scalable implementation with much of the work being handled at the client. Route Map Purple = Westbound Green = Eastbound Direction Indicator Distinguished by rear emission Opposite of emission direction Also indicated in pop-up window Table 1 Descriptions of map features Estimated Passenger Loads Shown using window icons Number of black windows represent estimated passenger load: 1. 25% 2. 25% - 50% 3. 50% - 75% 4. >75% Bus capacity = 60 Bus Stop Service Indicator Depicted by background color of bus icon Blue = bus serving stop White = bus running between stops On-Time Performance Indicator Shown by colors of bus icons Red = Late; schedule adherence > 5 Green = Early schedule adherence < -1 Yellow = On time; -1 < schedule adherence < 5 At the client, JavaScript is used to parse additional information, calculations, and to store the main data structures. Once the information is loaded into JavaScript data structures, the Google API is

12 12 called to perform the actual mapping on to the map. We used customized icons (in this case, buses) we designed and loaded them on the Google map as clickable events. The user can then run the visualization of the day s event, looking at a bus s particular location during the day. Clicking on a single bus (see Figure 7) on the map brings up additional information relevant to the particular bus such as schedule adherence, vehicle number, and headway. The user can play the movement of the buses during the day and drill down into a particular bus to see more details. Finally, the icons were designed to provide the user visual information such as bus capacity and performance as will be described shortly. FIGURE 7 Dynamic visualization framework snapshot for TriMet route 15. Figure 7 provides a snapshot of the framework showing all buses that are running along the route at this time. On the upper left corner are options for users to select which day and time period to play. This play function provides both forward and backward moving options for users to observe how bus bunching propagate over time and space. The time period to play can either be a fixed time interval (which should be larger than the load time five seconds in this case) or by an event-based moving interval; i.e., whenever there is a bus arrival or departure activity, reload the map. The information displayed in the pop-up window is pulled directly from the database or by using basic math functions. Table 1 provides further detail of additional map features. All the information shown in the pop-up window is updated every time a bus departs from a stop. The actual and schedule headways are the time difference between the current bus (actual and schedule) departure time and the previous bus (actual and schedule) departure time. The ability to dynamically visualize how buses in a route move spatially and temporally is extremely useful for understanding transit operations performance, particularly to better understand how the impacts of decreased on-time performance and increased headway deviations propagate spatially and temporally and result in effects such as bus bunching. A preliminary version of the dynamic visualization tool is available for use and exploration at the following web address:

13 13 Implementation Issues In the implementation of our visualization system, we had several issues that we needed to deal with. First, the routes of the buses needed to be created in order to have the bus icons follow the appropriate roads on Google Maps. In order to handle this, we manually created the bus routes as piece-wise linear routes. When a bus needs to be displayed between two stops, its icon will follow these routes. Second, the database only records the arrival and departure time of the buses but does not have actual bus location information between stops. To implement where the bus is in between two stops, we take the time required for the bus to reach the next stop and linearly interpolate its location based on the time and the distance along the piece-wise linear route we created. We hope to incorporate the GPS recorded data into the visualization when they become available. CONCLUSIONS AND FUTURE WORK The ability to accurately and effectively generate and analyze operations performance measures is critical for transit agencies to ensure efficient transit operations and management. This research built on previous studies and introduced the aptitude for using TriMet s archived data to create visualization tools which allow analysis disaggregated down to the hourly and stop level. The visualization techniques presented in this paper help improve the generation and display of performance measures in the following ways: 1) well-developed visualized performance measures are inherently easier to understand compared to a tabular output by drawing attention to specific stops or time periods of interest; 2) when compared to other visualization studies, the data visualization techniques demonstrated here not only utilize spatial analysis along a route map, but also show how temporal analysis is possible for different hours of day providing more thorough comprehension of how performance propagates over time and distance; 3) offering the flexibility to change the temporal aggregation level from hourly, to AM-peak, PM-peak, and off- peak can also provide detailed performance measurement for single stop analysis at a selected time period; 4) generating the different visualization diagrams and graphs are automatic, accurate, and applicable to other routes; and finally 5) demonstrating the value of dynamic visualization for route monitoring by animating bus operations in order to see how their performance propagates temporally and spatially. Although this research demonstrated the capacity for visualization tools, limitations were encountered. Namely, the color contour diagrams generated are currently restricted to only route-level analysis and have not yet been developed for network-level examination. Additionally, the dynamic visualization model is currently based on historical data and cannot yet be applied to real-time monitoring. Future work includes incorporation of the static data visualization into the dynamic visualization tool. Preliminary thoughts for this include the capability to generate contour diagrams by including a Performance Measure button as part of the user interface on the dynamic visualization dashboard. By clicking on this button an appropriate contour diagram can be generated in a separate window showing the performance measures relative to the current user selected. Furthermore, the historical bus data is event-based, where buses only record the time that they are arriving or departing a stop. We have currently interpolated the bus location along segments based upon the actual time it took for the bus to travel between two stops. As the BDS database starts archiving all the GPS data, we expect to be able to support more fine-grained interaction with this visualization technique. Ultimately it is our hope that the visualization methods proposed in this study will aid transit agencies to identify operational problematic stops at certain times of day, better understand how such problems propagate spatially and temporally, and be constructive in proposing appropriate solution strategies.

14 14 ACKNOWLEDGEMENTS Funding and support for this project was provided by the Oregon Transportation Research and Education Consortium (OTREC) and TriMet. The authors also thank Steve Callas and David Court of TriMet for providing the data and support to complete this research.

15 15 REFERENCES 1. Furth, G. P., B. J. Hemily, T. H. J. Muller and J. G. Strathman. TCRP Report 113: Uses of Archived AVL-APC Data to Improve Transit Performance and Management. Transportation Research Board of the National Academies, Washington, D.C Furth, G. P., B. J. Hemily, T. H. J. Muller and J. G. Strathman. TCRP Web Document 23 (Project H- 28): Uses of Archived AVL-APC Data to Improve Transit Performance and Management: Review and Potential. Transportation Research Board of the National Academies, Washington, D.C Furth, G. P. et al. TCRP Synthesis 34: Data Analysis for Bus Planning and Monitoring. Transportation Research Board of the National Academies, Washington, D.C Bertini, R. L. and A. M. El-Geneidy. Generating Transit Performance Measures with Archived Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1841, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp Berkow, M., J. Chee, R. L. Bertini, and C. Monsere. Transit Performance Measurement and Arterial Travel Time Estimation Using Archived AVL Data. IEEE Liao, C. and H. X. Liu. Development of a Data Processing Framework for Transit Performance Analysis. In Transportation Research Board 89th Annual Meeting. CD-ROM. Transportation Research Board of the National Academies, Washington, D.C., Berkow, M., A. M. El-Geneidy, R. L. Bertini, and D. Crout. Beyond Generating Transit Performance Measures: Visualizations and Statistical Analysis Using Historical Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 2111, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp Kimpel, T. Data Visualization as a Tool for Improved Decision Making Within Transit Agencies. Transportation Northwest (TransNow), Research Report No. TNW , TCRP Report 100: Transit Capacity and Quality of Service Manual, 2 nd ed. Transportation Research Board of the National Academies, Washington, D.C., 2003.

16 16 AUTHORS Wei Feng*, Miguel Figliozzi Department of Civil and Environmental Engineering Portland State University Scott Price, Wu-chi Feng Department of Computer Science Portland State University Kristina Hostetler Urban and Regional Planning Portland State University *Corresponding author Department of Civil and Environmental Engineering Portland State University P.O. Box 751 Portland, OR