Traffic Delay Studies at Signalized Intersections with Global Positioning System Devices

Transcription

1 Traffic Delay Stuies at Signalize Intersections with Global Positioning System Devices THIS FEATURE PRESENTS METHODS FOR APPLYING GPS DEVICES TO STUDY TRAFFIC DELAYS AT SIGNALIZED INTERSECTIONS. MOST OF THESE INTERSECTION DELAYS ARE DIFFICULT TO MEASURE MANUALLY; WITH GPS-COLLECTED VEHICLE POSITIONING DATA, THEY CAN BE DIRECTLY IDENTIFIED OR INDIRECTLY DERIVED. BY YI JIANG, PH.D., P.E., SHUO LI, PH.D., P.E. AND KAREN QIN ZHU, PH.D. INTRODUCTION As one of the new technologies in transportation engineering, global positioning systems (GPS) have been utilize as an effective tool for measuring various types of traffic ata. GPS evices have been use to conuct work zone stuies, congestion management stuies an carfollowing analyses. 1 3 To further the application of GPS in transportation engineering, a stuy was performe using GPS ata to analyze various types of traffic elays at intersections. In this stuy, vehicle positioning ata were recore with a GPS evice at selecte intersections on several roaway sections in Iniana. At the same time, stoppe-time elays were measure manually by observers at some of the intersections following conventional proceures, so that stoppe-time elays measure both by GPS an manually by observers coul be compare. DATA COLLECTION Data collection was performe on three selecte highway sections in Iniana a section on State Roa 26 (SR-26) in Lafayette, a section on U.S. Highway 52 (US-52) in West Lafayette an a section on U.S. Highway 31 (US-31) in Kokomo. A test vehicle with a GPS evice was use to recor the vehicle positioning ata. In aition, fiel observations were conucte manually to measure stoppetime elays at some of the intersections on the three highway sections. Fiel observations were performe accoring to the manual proceures escribe in the Manual of Transportation Engineering Stuies. 4 Figure 1 illustrates the three roaway segments an the intersections. On the SR-26 section, the eastboun approach of the intersection with Creasy Lane was chosen to measure traffic elays in the southboun irection. This intersection ha a irectional traffic volume of 1,200 vehicles per hour (vph) on SR-26. Both SR-26 an Creasy Lane were four-lane roas with a post spee limit of 40 miles per hour (mph), or 64 kilometers per hour (km/h). On the US-52 test section in West Lafayette, traffic elays on the eastboun approaches at the intersections with Yeager Roa an Salisbury Street were stuie. The intersections were not coorinate. At this location, US-52 was a four-lane highway with a post spee limit of 40 mph (64 km/h) an the two intersecting roas were two-lane streets. The istance between the two intersections was about 0.7 km (0.45 miles). The traffic volume on the eastboun approach at the Yeager Roa intersection was about 70 vph an grew to 820 vph on the segment between Yeager Roa an Salisbury Street. The test section on US-31 in Kokomo was about 15 km (9.5 miles) long. The irectional traffic volume on this four-lane highway was about 1,300 vph an the post spee limit was 45 mph (72 km/h). The northboun approaches of seven intersections (from Lincoln Roa to Sycamore Roa) were inclue in this stuy an the traffic signals at these seven intersections all were coorinate. A limitation of GPS applications is that signal loss may occur in areas with trees, tall builings, or other overhea obstructions along the roaway. This was not a factor in the ata collecte in this stuy because the test sections were in open areas without obstructions from tall trees or other structures. The GPS system use in this stuy consiste of a GPS evice, a laptop computer an a computer program name GPS-Trek. 5 GPS-Trek was evelope 30 ITE JOURNAL / AUGUST 2005

2 specifically for this stuy so that GPSrecore vehicle positioning ata coul be processe automatically an converte into the esire format with values of travel time, istance an spee. Although a test car was use, the GPS metho was ifferent from other methos requiring a test vehicle for conucting travel time an elay stuies, such as the floating car technique an moving vehicle technique. In the floating car technique, the test car is riven by an observer along the test section so that the test car floats with traffic. 6 The river of the test vehicle attempts to pass as many vehicles as those that pass the test vehicle an recors the travel time to traverse the test section. In the moving vehicle technique, the observer makes a roun-trip on a test section an recors the travel time, number of vehicles in the opposite lane, number of vehicles that overtake the test car an number of vehicles that the test car passes. 7 Both of these techniques result in average travel times, however, they o not recor an retain some neee traffic characteristics, such as vehicle spee profile, vehicle eceleration an acceleration an the precise locations of vehicle stops. Moreover, these test car techniques o not provie information on specific intersection elays. IDENTIFICATION OF INTERSECTION DELAYS FROM GPS DATA Traffic elay at an intersection most frequently is quantifie in the following forms: 8 Stoppe-time elay is the time a vehicle is stoppe while waiting to pass an intersection. Approach elay inclues stoppetime elay an the time lost when a vehicle ecelerates from its normal spee to a stop an accelerates from the stop to its normal spee. Travel-time elay is the ifference between the actual time for a vehicle to pass the intersection an the time for the vehicle to pass the intersection at the river s esire spee. Time-in-queue elay is the total time from a vehicle joining an intersection queue to its ischarge across the stop line or curb line. N US-52 Yeager R. Traffic engineers often measure stoppe-time elay in intersection stuies because it is relatively easier to measure manually than other types of elays. Other types, such as approach, traveltime an time-in-queue elay, usually are not measure because there are no commonly acceptable manual methos for irectly measuring these elays. With GPS evices, however, all of these elays can be irectly obtaine or inirectly erive from vehicle positioning ata. Figure 2 illustrates some typical intersection elays on a plot of the istance versus time of a vehicle s traveling positions. As shown in Figure 2, these intersection elays are measure base on the actual an esire path of the vehicle traveling through the intersection. GPS vehicle positioning ata contain the test vehicle s position points at given time intervals. The GPS evice in this stuy was set to recor the test vehicle s positions at each secon, which is the ata points of istance versus time. Therefore, as the test vehicle passes through an intersection, the actual path is being recore. The esire path cannot N US-31 Center R. SR-26 Figure 1. Test segments an intersections. Alto R. US-35 Sycamore R. Marklan Ave. Salisbury St. Hoffer St. 300 N Morgan St. North St. Carter St. Savoy Ave. Blv Ave. Lincoln R. Southway Blv. Nighthawk Dr. be recore irectly with GPS. However, the typical vehicle free-flow spee, or the esire spee, can be measure by riving the test vehicle following the main traffic flow on a fairly istant upstream roaway section of the intersection. This esire spee shoul be measure the upstream intersection is aequately far away from the intersection of interest an when the traffic volume is relatively low. If the upstream intersection is close to the intersection of interest, the test runs for the esire spee shoul inclue those that the test vehicle arrive at the upstream intersection uring the green light an passe it without eceleration. For example, Figure 3 shows a istance versus time plot with GPS-recore test vehicle positioning ata at the intersection of SR-26 an Creasy Lane. In Figure 3, the straight line on the left-han sie is the esire path an is rawn base on a measure esire spee of 77 km/h. The esire spee was measure as the test vehicle approache the intersection at the river s perceive traffic flow spee without interruptions of upstream traffic ITE JOURNAL / AUGUST

3 Distance Desire path Figure 2. Illustration of intersection elays. Distance (meters) D3 D1 Actual path D2 Actual path D1 = Stoppe-time elay D2 = Approach elay D3 = Travel-time elay Time Figure 3. GPS-recore test vehicle istance versus time. Time (secons) Desire path lights an uner light traffic conitions. The curve on the right-han sie is the actual path base on the GPSrecore vehicle positioning ata. With the esire an actual path curves, the stoppe-time elay, approach elay an travel-time elay at the intersection can be measure in Figure 3 in the manner illustrate in Figure 2. It shoul be note that one of the four liste intersection elays, time-in-queue elay is not shown an cannot be obtaine in Figure 2. However, all of the intersection elays, incluing time-inqueue elay, can be obtaine either irectly or inirectly from a GPS-Trek generate ata file without rawing such curves as presente in Figure 2. To emonstrate how to use a GPS-Trek output file to fin intersection elays, part of the GPS-recore ata points that were use to raw the actual path curve in Figure 3 are presente in Table 1. The first four columns are the ata values containe in the GPS-Trek ata file, incluing time, istance, spee an intersection mark. A value of zero in the fourth column inicates the starting point (the curb line) of an intersection. The last two columns of Table 1 are not part of the GPS output file but are ae to the table for explanation. In the fourth column, the length of the stoppe-time elay is represente by the shae area. This elay is simply the time perio for which the spee was zero in front of the intersection. In the first an thir columns, it can be seen that the vehicle spee was zero starting at the 34th secon an ening at the 58th secon. Therefore, the stoppe-time elay for this test run is s = = 24 secons, which is the length of the shae area in the fourth column. Time-in-queue elay is efine as the total time from a vehicle joining an intersection queue to its ischarge across the stop line or curb line. In the GPS output file shown in Table 1, the beginning of the intersection (the curb line) is marke with a zero in the fourth column. The time-inqueue elay then can be obtaine irectly from the ata file. In this example, the test vehicle stoppe or joine the vehicle queue at the 34th secon (starting time of spee = 0) an move across the curb line at the 64th secon. Therefore, the time-in-queue elay is q = = 30 secons, as shown by the shae area in the fifth column. As efine earlier, approach elay is the stoppe-time elay plus the time lost uring eceleration an acceleration as a vehicle passes through an intersection. Unlike stoppe-time elay or time-in-queue elay, approach elay an travel-time elay cannot be rea irectly from the GPS ata file. However, they can be obtaine from the GPS ata file through simple calculations. To obtain the approach elay, the normal spee on the roaway section must be ientifie from the GPS ata. The normal spee shoul be the stable spee of the test vehicle when it is not interrupte by intersection signals. The GPS ata showe that the normal spee was about 53 km/h uring the test run. As shown in Table 1, the test vehicle starte to ecelerate at the 20th secon an the istance point of meters; it resume its normal spee at the 68th secon an the istance point of meters. The actual time spent is T = = 48 secons an the istance travele is D = = meters. 32 ITE JOURNAL / AUGUST 2005

4 The approach elay, therefore, is the ifference between the actual travel time T over istance D an the time that woul be require to travel the same istance at the normal spee. That is, the approach elay can be compute using the following equation: = T 3. 6 (1) a = approach elay, in secons T = time perio from starting eceleration to completing acceleration, in secons D = istance travele uring time T, in meters v n = vehicle s normal spee, in km/h 3.6 = factor to convert time unit to secons With the normal spee of 53 km/h an the ata in Table 1, the approach elay can be calculate as a = 34.5 secons. Similar to Equation 1, travel-time elay can be obtaine using the following equation: a t = T 3.6 D v n D v t = travel-time elay, in secons v = esire spee, in secons T an D are efine as in Equation 1 (2) Table 1. GPS-recore ata an intersection elays. Time Distance Spee Intersection Stoppe Time in (secons) (meters) (km/h) mark elay queue As explaine above, esire spee shoul be measure when approaching traffic flow is relatively low an is not interrupte by traffic signals. On this section of roaway, the esire spee was about 77 km/h uner goo travel conitions. With this value of esire spee, travel-time elay can be compute with Equation 2 as t = 38.7 secons. Therefore, all four types of intersection elays can be obtaine either irectly from the GPS ata file or through calculations with Equations 1 an 2. The values of normal spee an esire spee require in the two equations can be measure with the GPS evice. Normal spee usually can be foun in the spee column of the GPS ata file the spee values are stable or outsie of the eceleration or acceleration perio. Desire spee can be measure with GPS when traffic is smooth an traffic volume is relatively low. DELAY MEASURES WITH GPS AT SIGNALIZED INTERSECTIONS Intersection elays at 10 intersections on three roaway sections were measure with a GPS evice mounte on a test vehicle. Five test runs were mae at the intersection on the SR-26 an US-52 segments an six runs were mae on the US-31 segment. The number of runs was etermine with 5-percent significant level an 2-percent permitte error with the following equation: 9 2 Zα/2 S n = e Stoppe-time elay (24 secons) + ε n = minimum number of test runs neee α = significant level n Time-in-queue elay (30 secons) (3) ITE JOURNAL / AUGUST

5 Table 2. GPS-measure mean intersection elays. Stoppe-time Time-in- Approach Travel-time elay queue elay elay elay Roa Intersection (secons) (secons) (secons) (secons) US-52 Yeager Salisbury SR-26 Creasy US-31 Lincoln Blv Hoffer Savoy Marklan Carter Sycamore Table 3. Measurements of stoppe-time elays at intersections. Difference GPS measure Manually measure = g m Roa Intersection g (secons/vehicle) m (secons/vehicle) (secons/vehicle) US-52 Yeager Salisbury SR-26 Creasy US-31 Blv Hoffer Marklan μ =1.43, S =6.81, μ 1.43 t* = = = S n , t 0.025, 5 =2.571 Z = stanar normal istribution S = estimate of stanar eviation e = permitte error ε n = sample size ajustment Table 2 presents the mean values of the four types of intersection elays measure by GPS at the 10 intersections. Because the seven intersection signals on US-31 are coorinate, the elays at these intersections show not only the performance of iniviual intersections but also the performance of the coorinate intersection system. The relatively long elays at the Lincoln an Marklan intersections coul be an inication that the signal coorination was not as goo as esire an improvement in signal timing esign might be necessary. With GPS ata, the performance of iniviual intersections as well as coorinate intersections can be analyze in terms of intersection elays an spee patterns. Moreover, the total elay at the coorinate intersections also can be erive from the GPS ata with the following equation: total = T T a total = total elay at coorinate intersections, in secons T a = actual time traversing a given istance that covers all coorinate intersections, in secons T = esire travel time traversing the same istance, in secons (4) COMPARISON OF GPS-MEASURED AND MANUALLY MEASURED STOPPED-TIME DELAYS In aition to the intersection elays measure by GPS, stoppe-time elays at six of the 10 intersections were measure manually following the proceure escribe in the Manual of Transportation Engineering Stuies. 10 At each of the six intersections, stoppe-time elay was measure on the same intersection approach as in GPS ata collection. Two observers, each with a stopwatch an an intersection stoppe-time elay fiel sheet, were require at each intersection to recor the number of stoppe vehicles at 15-secon intervals an the number of eparting vehicles uring the 15-minute observation perio. The manual metho assume that each vehicle counte was stoppe for an entire interval, or 15 secons in this stuy. The total count of stoppe vehicles uring all intervals multiplie by the length of the time interval provies the stoppe elay estimate. Diviing this elay estimate by the number of vehicles eparting the approach provies an estimate of stoppe elay per vehicle. Table 3 lists the manually measure stoppe-time elays along with the corresponing values measure by GPS at the six selecte intersections. In this table, g, m an enote the GPS-measure stoppe-time elays, the manually measure stoppe-time elays an the ifference of the two corresponing elay values, respectively. The stoppe-time elays at these intersections were measure simultaneously with GPS an manual methos, so that they woul reflect the same traffic conitions an coul be compare. To statistically compare the ifferences between the two ata collection methos, the stoppetime elay measures at each intersection are treate as a paire observation. Then, a t-test can be performe to etermine if the mean value of, μ, is statistically zero. If μ is statistically zero, it implies that the mean of g an the mean of m are statistically equal, or the GPS an manual methos prouce equal measurements of stoppe-time elays. The test for the paire observations are as follows: 11, 12 If μ is the mean of, S is the stanar eviation of an n is the sample size. The test alternatives are: H 0 : μ = 0 H a : μ 0 The t statistic is calculate as: 38 ITE JOURNAL / AUGUST 2005

6 t* = S μ With a significant level α, If t* t α/2, n-1, conclue H 0 If t* > t α/2, n-1, conclue H a As shown in Table 3, with a significant level of α = 0.05, t* =0.514 an t α/2, n-1 = t 0.025, 5 = Because t* = < t 0.025, 5 =2.571, H 0 is conclue, which means that the measure stoppetime elays from the GPS an manual methos are statistically equal. CONCLUSIONS This feature illustrates methos for intersection elay stuies, incluing the techniques for ientifying various types of intersection elays from GPS-recore ata, graphical representations of intersection elays an performance analyses for iniviual an coorinate intersections. Compare to manually measure stoppe-time elays, the GPS measurements provie the same accuracy with much less requirement for time an labor intensiveness. The stuy results inicate that GPS is an effective tool for traffic elay measurements an analyses. n References 1. Jiang, Y. an S. Li. Measuring an Analyzing Vehicle Position an Spee Data at Work Zones Using Global Positioning System. ITE Journal, Vol. 72, No. 3 (2002): Quiroga, C.A. an D. Bullock. Development of CMS Monitoring Proceures. Louisiana Transportation Research Center, Wolshon, B. an Y. Hatipkarasulu. Results of Car Following Analyses Using Global Positioning System. Journal of Transportation Engineering, Vol. 126, No. 4 (2000): Manual of Transportation Engineering Stuies. Washington, DC, USA: Institute of Transportation Engineers (ITE), Li, S., K. Zhu, B.H.W. van Geler, J. Nagle an C. Tuttle. Improving Efficiency of INDOT Traffic Data Collection Using GPS Devices. Final Report, FHWA/IN/JTRP-2002/19, Garber, N.J. an L.A. Hoel. Traffic an Highway Engineering, Revise 2n Eition. PWS Publishing, Ibi. 8. McShane, W.R., R.P. Roess an E.S. Prassas. Traffic Engineering, 2n Eition. Upper Sale River, NJ, USA: Prentice Hall, Li et al., note 5 above. 10. Manual of Transportation Engineering Stuies, note 4 above. 11. Neter, J., W. Wasserman an M.H. Kutner. Applie Linear Statistical Moels: Regression, Analysis of Variance, an Experimental Designs, Secon Eition. Homewoo, IL, USA: Richar D. Irwin Inc., Walpole, R.E. an R.H. Myers. Probability an Statistics for Engineers an Scientists. New York, NY, USA: The Macmillan Company, YI JIANG, Ph.D., P.E., receive a M.S.C.E. an Ph.D. from Purue University. Prior to joining the Purue University faculty, he worke as a highway engineer at the Iniana Department of Transportation an as an assistant professor at the Tennessee Technological University. He is an associate member of ITE. SHUO LI, Ph.D., P.E., is a transportation research engineer with the Research Division of the Iniana Department of Transportation. His research interests focus on smart technologies for pavement simulation an testing an innovative materials. He also is intereste in acquiring funamental ata for highway infrastructure esign, evaluation an management. KAREN QIN ZHU, Ph.D., is a senior systems analyst with the Division of Research at the Iniana Department of Transportation. She receive her Ph.D. from the University of California San Diego in Her research interests cover the applications of new technologies, such as GIS an GPS, in ata acquisition an ata management. Avertise your company proucts an services by placing a banner a on the ITE Web site! Visit for more information. Placing a banner a is a great way to reach ITE s more than 16,000 members an other Web site guests. You choose your avertisement is place on the site. If you are hiring a new employee you may want to place your banner a within the Employment Center. Have a new prouct line you're looking to promote, look no further than the Technical Information section. Please contact Christina Denekas, Marketing Sales Associate, at ext. 128 or cenekas@ite.org to assist you with all your marketing nees. ITE JOURNAL / AUGUST