Congestion Analysis with Historical Travel Time Data in Seoul

Transcription

1 Congestion Analysis with Historical Travel Time Data in Seoul Yohee Han Department of Traffic Engineering, University of Seoul, Seoul, South Korea Youngchan Kim Department of Traffic Engineering, University of Seoul, Seoul, South Korea Abstract The era of high-quality Big Traffic Data is coming together with the 4th Industrial Revolution. This paper develops the Congestion Index for traffic management using the link travel time from the GPS probe vehicle data. This Congestion Index can evaluate the performance of an arterial network under oversaturated condition. The model is composed of congestion intensity and congestion duration unlike existing congestion index. The link average travel speed which can be collected all the time with wide range is selected to develop the congestion index after investigating the current big traffic data. This paper uses Extra Travel Time, which is the additional travel time further than the travel time at the reference speed, as a new MOE. The congestion criterion was set to Extra Travel Time at LOS F with the purpose of monitoring oversaturated conditions. After classifying the LOS F, LOS FF and LOS FFF as oversaturated conditions, the congestion level is substitute as the congestion intensity coefficient. SCRIN is defined as congestion intensity and congestion duration. We analyzed SCRIN to monitor the traffic congestion of Seoul City using the historical data of Seoul TOPIS travel speed in SCRIN model can be used to monitor the traffic congestion under oversaturated conditions on urban network as a performance index in the traffic management field. KEYWORDS: Congestion Index, Link Average Travel Speed, Extra Travel Time Introduction Traffic congestion generally means that the traffic volume exceeds the capacity of the road. The definition of traffic congestion affects the direction of traffic improvement plan. ECMT (2007) defined three types of traffic congestion from various perspectives. The first is a situation in which demand for road space exceeds supply. The second is the impedance vehicles impose on each other, due to the speed-flow relationship, in conditions where the use of a transport system approaches its capacity. The third is essentially a relative phenomenon that is linked to the difference between the roadway system performance that users expect and how the system actually performs. The definition depends on the object; as like administrators, engineers, users. In order to solve the traffic congestion, it is necessary to quantitatively determine the degree of traffic congestion. Congestion index is a measure of quantitative traffic congestion. There are a lot of indicators; speed-based indicators, temporal based indicators, spatial indicators, service level indicators, reliability indicators and economic indicators. Of these indicators, the traffic engineer considers congestion indicators in terms of traffic management; operational approach. Traffic engineers have sought to deliver technically optimal roadway performance. They want to manage traffic congestion to improve roadway performance. To manage traffic congestion, it is necessary of congestion index for operation and traffic management. There are several congestion indicators that can be used for this purpose. KOTI (2015) has developed the NCI (Network Congestion Intensity) by the project of constructing traffic congestion map DB. This index is the ratio of the total travel time traveled at congestion to the total travel time experienced. FHWA (2015, 2016) uses three congestion indicators - hours of congestion, travel time index, planning time index - in the report of Urban Congestion Trends. Hours of congestion is average duration of daily congestion. Travel time index is peak-period versus off-peak travel times. Planning time index is unreliability of travel. INRIX (2010, 2016) and TomTom (2016) also use indicators such ad FHWA for the concept of additional travel time to free flow travel time. Grant et al. (2011) classified the characteristics of congestion indicator. They include congestion intensity, congestion duration and congestion extent. That indicator can be measured based on traffic volume, travel time and level of service E. From this point of view, KOTI s indicator means congestion duration. FHWA s indicators are similar of the concept of congestion intensity. There are no indicators that have both congestion s characteristics - 1 -

2 among existing indicators. These existing indicators are also difficult to use in terms of operation and traffic management. They focus on how much longer it takes versus free speed for the purpose to provide traffic information. In operation and traffic management, traffic congestion is judged based on capacity rather than free flow speed standard. The capacity is considered LOS E in capacity analysis. On the other hand, existing indicators (i.e. Travel Time Index, congestion hours) determine the traffic congestion including the full range of LOS. It is hard to know how much the traffic congestion should improve through the value. Usually the traffic engineer judge congestion as oversaturated conditions to manage traffic congestion. Prior indicators have limitations in monitoring only oversaturated conditions. Also, congestion indicators should be able to monitor more diverse temporal (i.e. from small time units to aggregate time units) and spatial ranges (i.e. from link units to network units) to find traffic improvement points. This paper aims to develop congestion index for traffic management purposes. This basically represents the oversaturated condition of the individual links; expands to be applicable to intersection units, corridor units, and network units. The index value should be derived in 15 minutes units, so that it can be monitored in units of one hour and one day. This congestion index is defined as SCRIN (Spatiotemporal Congestion Recognition Index). SCRIN can simultaneously express congestion intensity and congestion duration among the coefficients of congestion characteristics. Congestion management using SCRIN is modeled to enable continuous monitoring over a wide area. It is possible to compare and analyze the congestion in oversaturated conditions on a small region (i.e. network units), or to find the congested link in oversaturated conditions within the analysis range. Congestion intensity and congestion duration are monitored macroscopically on a daily unit basis. The congestion intensity should be monitored in detail on an hourly basis. This can analyze traffic congestion characteristics and help to set direction for improvement of traffic operation. To create SCRIN model, it is important to measure the oversaturated conditions. A common measure of effectiveness is delay. Delay generally means average delay per vehicle. Traffic volume is essential to calculate delay. Since the traffic volume detector is not installed in a wide area, the traffic volume is calculated through onsite investigation if necessary. It is difficult to use the traffic based MOE to develop congestion index the ability to monitor congestion at all time over a wide area. It is necessary new MOE based on traffic data can be collected at all time over a wide area instead of traffic based delay. Fortunately, we can use Big Traffic Data that is stored along with the fourth industrial revolution. It is travel time information that can be used as Big Traffic Data in Korea at present. This paper has decided to use Extra Travel Time as a new MOE. Extra Travel Time means the additional travel time further than the travel time at the reference speed. The congestion criterion was set to Extra Travel Time at LOS F with the purpose of monitoring oversaturated conditions. To monitor the congestion in more detail, LOS F was subdivided into three stages (i.e. LOS F, LOS FF, LOS FFF). Thresholds of Extra Travel Time for each congestion level were set to determine macroscopically with the three congestion levels in oversaturated conditions. It is better to use the trajectory data of probe vehicles close to the raw data in order to monitor more detailed oversaturated conditions. In fact, Seoul city collects probe data - the location data of individual vehicle is collected by communication between On-board Unit and Global Navigation Satellite System (i.e. Global Positioning System). However, only link average travel speed is stored as historical data in the center DB. Advanced traffic information such as probe trajectory data is only used during processing and then discarded. So that, link average travel speed is used as a basis for SCRIN model. Also, there is only a node link system built to providing traffic information in Korea. It has a limitation of analyzing congestion with the travel speed of the combined link system rather than the travel speed of the detailed link system. In this paper, we propose SCRIN model that simultaneously indicate congestion intensity and congestion duration. For the macroscopic congestion analysis, each daily value of index is plotted as a scatter plot. In the microscopic congestion analysis, the hourly congestion intensity can be confirmed by time zone. For various congestion comparative analysis, we derived the SCRIN value using the historical travel speed data on 2016 of Seoul city. Link Average Travel Speed Seoul Metropolitan Government collects location data through OBU installed in a taxi registered in Seoul city. 10,000 corporate taxis collect location data every 2 seconds using an external GPS unit; transfer data to the KSCC (Korean Smart Card Center) every 50 seconds. 12,000 corporate taxis and 50,000 private taxis collect location data every 10 seconds using DTG (Digital Tachograph); transfer data to the KSCC every 150 seconds. Seoul TOPIS - 2 -

3 receives this raw data from KSCC in real time to process travel speed in Seoul. Number of taxis in service is 35,000 vehicles per hour during the week; 32,000 vehicles per hour on weekends. The data size of taxi to process travel speed is 19,000 vehicles per hour during the week; 18,000 vehicles per hour on weekends. This is because only the actual taxi data on board the passenger is used at the time of processing the travel speed information. Correction is performed when missing data occurs, through historical data or linkages with external organizations, for example, SK T-map. Table 1. Calculation of Link Average Travel Speed Collector Processing Outputs GPS of Corporate Taxi (10000 vehicles) DTG of Corporate Taxi (12000 vehicles) DTG of Private Taxi (50000 vehicles) Map Matching of individual vehicle Link travel speed of individual vehicle Link average travel speed of each server Correction and integration of link average travel speed The travel speed in Seoul was examined in each processing step, in order to identify data that can actually be used in SCRIN model development. TOPIS goes through three processing steps from the raw data, which is the location data of the taxi, until calculating the link average travel speed of Seoul city. We classify and define each data by stages of data processing from raw data to link average travel speed. There are 4 data TOP 0 as raw data, TOP 1 as map matching data, TOP 2 as individual travel speed, and TOP 3 as link average travel speed. Fig. 2 Kinds of Travel Time Data through Data Process (Seoul TOPIS Data base) TOP 0 is the raw data that the position data of individual taxis which is stored based on the longitude, latitude, altitude, and azimuth angle by communication with GPS. Table 2. Characteristics of TOP 0 Taxi ID Longitude Latitude Altitude Recorded Time Heading Instantaneous Speed Unique Number Boarding TOP 1 is position information on the road that converts GPS data to a road link. At this stage, link information of the road is newly generated; only the taxi information on boarding is extracted. This data is the same as the trajectory of probe vehicle that we common know. Table 3. Characteristics of TOP 1 Taxi ID Conversion Longitude Conversion Latitude Link ID Altitude Recorded Time Heading Instantaneous Speed Boarding

4 TOP 2 is the link travel speed of the individual vehicle. The travel speed for each individual vehicle is generated from the position data on the road link. In this processing step 2, we can get the directional information (i.e. through, turn left, turn right) of the individual vehicle. Table 4. Characteristics of TOP 2 Recorded Time Taxi ID Link ID Data Process Server SVR0 SVR0 SVR0 SVR0 Travel Distance[m] Travel Time[s] Travel Speed[kph] Direction Processing Time TOP 3 is the link average travel speed which is currently provided by TOPIS in real time. The travel speed of the vehicle passing through the same link is calculated based on the link ID. Table 4. Characteristics of TOP 3 Year Month Day Hour Minute Link ID Travel Speed[kph] Among these four information, the data that is continuously stored and managed by TOPIS center is TOP 3. In this paper, the SCRIN model is constructed based on link average travel speed because of these circumstances. SCRIN Model SCRIN is the spatiotemporal congestion index which calculated from link average travel speed, for example, Seoul TOPIS data. SCRIN can monitor macroscopically the oversaturated condition in the urban network. The measure of effectiveness to determine oversaturated conditions is the extra travel time of each link, which can be produced from link average travel speed and reference speed. After classifying the LOS F, LOS FF and LOS FFF as oversaturated conditions, the congestion level is substitute as the congestion intensity coefficient. The index and variables of the SCRIN model are defined as follows. Table 3. Notations Symbol Definition Unit Symbol Definition Unit FI Congestion Cumulative Intensity - u Travel Speed m/s FT Congestion Duration Hour RS Reference Speed m/s S Congestion Average Intensity - Congestion Intensity Factor - ETT Extra Travel Time Sec LD Link Distance m Congestion Duration Factor i-th Individual Analysis Time Zone in 15- minute Increments - - SCRIN is defined as congestion intensity and congestion duration. Congestion Average Intensity (S) is close to 1, which means that congestion is very severe as like LOS FFF. The opposite (closer to 0) means that congestion level is LOS F. Congestion Duration (FT) refers to hours of oversaturated conditions over 24 hours a day

5 Fig. 1 Physical Meaning of Congestion Index Before calculate Congestion Average Intensity and Congestion Duration, the congestion level should be judged basically by the extra travel time of each link on every 15 minutes. The extra travel time is calculated using the spatial mean travel speed and the reference speed in units of 15 minutes., = (1) In this paper, the SCRIN model is constructed by limiting to the urban network with signalized intersections. The determination of oversaturated condition using extra travel time is based on the KHCM s reference value the control delay on signalized intersections. It is assumed that there is no influence into the middle of the link, so that replace the extra travel time of the link with the control delay on signalized intersections. The congestion level was set to thee levels to monitor the oversaturated condition macroscopically. The congestion intensity factor at each congestion level was set at 0.4, 0.7 and 1.0. The congestion intensity factor is the average delays of the queue-based model; the minimum and maximum values of the congestion level were regarded as the waiting time of the individual vehicle, and the average delay time during the analysis time was calculated. 0.4, 100 < , 220 <340, = (2) 1.0, 340 0, h Fig. 3 Estimating Control Delays on SCRIN FF The congestion intensity factor calculated in basic 15-minute unit is converted by time unit and accumulated by a daily basis. We defined this value as the congestion cumulative intensity. (3) = (0.25, ) If the congestion intensity factor is greater than 0, the congestion time factor is given as 1. Otherwise, it is defined as 0., = 1, h,, >0 (4) 0, h - 5 -

6 The congestion duration is a value calculated by a daily basis after converting the congestion time factor into a time unit. = (0.25, ) (5) The average congestion intensity is the ratio of the cumulative congestion intensity to the congestion duration. = (6) As shown in the figure, if there is the extra travel time in 15-minute unit, the congestion level can be determined according to each threshold value. It is possible to monitor the oversaturated condition macroscopically by processing it as a day s congestion intensity and congestion duration. Since SCRIN used two congestion characteristic coefficients congestion intensity and congestion duration, it differs from other existing indicators. Currently, IOC (Intensity of Congestion) and TTI (Travel Time Index) are used as traffic congestion index in Seoul for traffic management. IOC represents the concept of congestion duration, and TTI reflects congestion intensity. Moreover, SCRIN is optimal for oversaturated condition monitoring because the TTI exhibits congestion level A to F. Congestion Analysis Fig. 4 Threshold Comparison of TTI and SCRIN We analyzed SCRIN with the historical data of Seoul TOPIS travel speed in 2016 to verify the applicability as Congestion Index. SCRIN was analyzed by each direction on arterial corridors Gangnam-daero (main arterial road), Eonju-ro, Yeongdong-daero and Teheran-ro. The data were extracted monthly January, April, August and November - by season to confirm seasonal changes. We used two congestion distributions to derive the congestion of Seoul city macroscopically. One is the SCRIN distribution with Congestion Average Intensity (S) and Congestion Duration (FT). The other is the expanded SCRIN distribution with Congestion Cumulative Intensity (FI) and Congestion Duration (FT). Congestion Average Intensity and Duration The vertical axis is set to Congestion Average Intensity; the horizontal axis is set to Congestion Duration; and the SCRIN value per day is expressed as a scatter plot

7 Fig. 5 Meaning of SCRIN Scatter Plot The more scattered plot is to the right, the longer the congestion lasts; and the higher the scatter plot, the more severe the congestion will be. It can be inferred that peak congestion occurs when the data are located at (a) and (b). As a result of SCRIN distribution, the biggest difference was found in the analysis results of Gangnam-daero and Yeongdong-daero. It was found that the congestion intensity of Gangnam-daero was frequently observed up to 16 hours. The congestion average intensity of northbound rather than southbound was more severe. Fig. 6 SCRIN Scatter Plot of Gangnamdaero On the other hand, the congestion duration of Yeongdong-daero was about 4 hours the congestion pattern of normal peak time was shown. Fig. 7 SCRIN Scatter Plot of Yeongdongdaero In order to examine seasonal congestion changes, the analysis results of Eonju-ro s southbound were shown. The congestion duration was the longest in January up to 14 hours and shorter up to 10 hours in November. The congestion average intensity was the most severe in January and August, and relatively low in April

8 Fig. 8 SCRIN Monthly Scatter Plot of Eonju-ro For the comparative analysis of congestion by day of the week, we selected the northbound of Gangnam-daero. This arterial is closely related to weekend leisure activities, and congestion on Saturday was severe; congestion average intensity is high and congestion duration is long as like weekdays. Fig. 9 SCRIN Daily Scatter Plot of Gangnamdaero The SCRIN distribution can be segmented and analyzed by each link in the target arterials. As a result of analyzing the distribution of each link on Teheran-ro s eastbound, it was found that congestion under oversaturated condition occurs in the section from the second link to the sixth link. Especially, the 3rd and 6th links have long congestion and the 4th and 5th links have very high congestion. Fig. 10 SCRIN Sectional Scatter Plot of Teheranro - 8 -

9 The SCRIN distribution can be used to segment the most congested sections in the analyzed section according to the analysis purpose. We can find the most congested day of the week; the most extreme sections; links that start congestion. Congestion Cumulative Intensity and Duration We constructed the expanded SCRIN distribution with Congestion Cumulative Intensity (FI) and Congestion Duration (FT). The vertical axis is set to Congestion Cumulative Intensity; the horizontal axis is set to Congestion Duration; and the SCRIN value per day is expressed as a scatter plot. The larger the slope of the trend line of scatter plot, the greater the intensity of congestion. The longer the trend line, the longer the congestion is experienced. The following figure is the result of directional distribution on Eonju-ro for 4 months. It can be seen that the congestion average intensity of the northbound is higher than that of the southbound through the slope of the trend line. It can be inferred that the northbound experiences longer congestion than the southbound through the length of the trend line. Fig. 11 SCRIN Distribution Chart of Eonju-ro The statistics of the expanded SCRIN distribution can be used to calculate the degree of congestion under oversaturated condition during the analysis period. Conclusions In link average travel speed acquisition environment based on individual vehicle location, we propose the SCRIN model that can analyze the oversaturated condition on the urban network. In the urban network of signalized intersections, the SCRIN model has an ability to macroscopically monitor the congestion characteristics congestion intensity and congestion duration. It is aimed to be used as a basic data to judge whether the congested section needs to be improved through traffic demand management or to be improved through traffic operation management such as traffic signal operation. More specifically, the congestion duration is short and the congestion intensity is high, there is a possibility of mitigating congestion by improvement of traffic signal operation. Whereas, long congestion duration should focus on traffic demand management. Congestion analysis on four main arterials in Seoul showed that the SCRIN can explain the congestion characteristics macroscopically. SCRIN model can be used to monitor the traffic congestion under oversaturated conditions on urban network from the traffic management point of view. SCRIN model based on Big Traffic Data can predict the areas and time zones under very severe congestion. This paper aims to show the possibility of macroscopic congestion analysis with SCRIN model for traffic management purpose using big data which can be used now. In the future, we want to extend the SCRIN analysis of individual link units to the spatial aggregation model. Also, due to the current limit of the use of data, we want to extend the analysis by link unit by direction. It is expected that SCRIN model can be used as a performance index in the traffic management field. Acknowledgment - 9 -

10 This research was supported by a grant(17tlrp - B ) from Development of Cooperative Automated driving Roadway Systems based on LDM and V2X Program funded by Ministry of Land, Infrastructure and Transport of Korean government References Cambridge Systematics, Inc. (2005) Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation, FHWA, p.2-1. Dion, F. et al (2004) Comparison of Delay Estimates at Under-Saturated and Over-Saturated Pre-Timed Signalized Intersections, Transportation Research Part B, 38, pp European Conference of Ministers of Transport (2007). Managing Urban Traffic Congestion, OECD Publishing, pp FHWA (2016) 2015 Urban Congestion Trends: Communicating Improved Operations with Big Data, Federal Highway Administration. FHWA-HOP , pp.2-4. FHWA (2017) 2016 Urban Congestion Trends: Using Technology to Measure, Manage, and Improve Operations, Federal Highway Administration, FHWA-HOP , pp.2-4. Grant, M., Bowen, B., Day, M., Winick, R., Bauer, J., Chavis, A. and Trainor, S. (2011) Congestion Management Process: A Guidebook, Federal Highway Administration, pp Han, Y. and Kim, Y. (2017). A study of measuring traffic congestion for urban network using average link travel time based on DTG Big Data. J. Korea Inst. Intell. Transp. Syst., KITS, Vol. 16, No.1, pp , DOI: /kits He, F., Yan, X., Liu, Y. and Ma, L. (2016) A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index, Procedia Engineering, Elsevier Ltd., pp INRIX (2010) INRIX National Traffic Scorecard: 2010 Annual Report, INRIX, pp.3-14, INRIX (2016) Traffic Scorecard 2015, INRIX, pp Kang, Y. (2000) Delay, Stop and Queue Estimation for Uniform and Random Traffic Arrivals at Fixed-Time Signalized Intersections, PhD Thesis, Virginia Polytechnic Institute and State University, USA. KOTI (2015) National Traffic Survey and DB Project for Traffic congestion map DB construction, Ministry of Land, Transport and Maritime Affairs, pp Ministry of Land, Transport and Maritime Affairs (2000) Korean Highway Capacity Manual -Final Report, pp Ministry of Land, Transport and Maritime Affairs (2013) Korean Highway Capacity Manual, pp , Mousa, R.M. (2002) Analysis and Modeling of Measured Delays at Isolated Signalized Intersections, J. Transp. Eng., 128(4), pp Quiroga, C.A. et. al (1999) Measuring Control Delay at Signalized Intersections, J. Transp. Eng., 125(4), pp Seoul Metropolitan Government (2017) Seoul Travel Speed Report in 2016, pp TomTom, Transportation Research Board (2010) HCM 2010 Highway Capacity Manual, TRB, Vol.3, Chapter 18, pp Transportation Research Board (2016) HCM 2016 Highway Capacity Manual, TRB, Vol.3, Chapter 19, pp Ye, L., Hui Y. and Yang D. (2013) Road Traffic Congestion Measurement Considering Impacts on Travelers, J. Mod. Transport., Springer, p