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APPENDIX B FIELD STUDY DATA COLLECTION AND REDUCTION Several analytic models were developed during this study to facilitate the evaluation of interchange ramp teIIIiinal capacity and level of service. This appendix provides a description of the database used to calibrate these models. Specifically, it describes the composition of the database, the field study sites, the methods used to collect the data, and finally, some summary statistics of the reduced data. Subsequent appendices describe the development and calibration of the analytic models as well as Weir application to the interchange evaluation process. B.1 DATABASE COMPOSITION This section describes the traffic flow problems associated with interchange areas, as they relate to the objectives ofthis research. The problems of primary interest are those occuning on the arsenal cross street at or between Me interchange ramp terminals and any adjacent, closely-spaced intersections. Initially, these flow problems are described in the context of models needed to describe Me problem's elect on arterial performance. Then, the variables included in these models are identified in the context of defining a data collection plan. B.~.1 Traffic Flow Problems Associated With Interchange Ramp Terminals The findings from the survey of practitioners (see Appendix A) indicated Mat there were several types of traffic flow problems associated with signalized interchange ramp terminals. The impact of these flow problems was typically amplified by relatively short distances (as measured along Me arsenal cross street) between Me terminals or between a tenninal and an adjacent signalized intersection. These flow problems were broadly categonzed as: (~) midblock turbulence (i.e., weaving) and unbalanced lane volumes Mat stem from high-volume turn movements in the interchange vicinity; and (2) flow restriction or impediment to discharging queues due to a relatively near downstream traffic queue. Four models were developed to facilitate Me evaluation of these flow problems. The vanables included in these models were used to identify the data needed for mode! calibration. The four models include: 1. Capacity Model. This mode! quantifies the effect of downstream traffic conditions on the traffic characteristics used to estimate the capacity of lefr-tum and through movements at interchange ramp terminals arid adjacent intersections. These characteristics include start-up lost time, saturation flow rate, arid clearance lost time. The capacity of an upstream signal phase has been found to be adversely affected by Me close proximity of a downstream queue; particularly when Me queue spills back into He upstream intersection. The form of this mode} is described in Appendix C. 2. Approach Lane Utilization Model. This model quantifies Me extent of unbalanced lane use in multi-lane larle groups. On a cycle-by-cycle basis, marry drivers in the interchange area tend B-!
to use one lane of a multi-lar~e lane group more than the others; they rarely choose the lane wad the fewest vehicles in it. One possible reason for this clustering in interchange areas may be driver desire to 'preposition" for a downstream turn. In this situation, drivers in interchange areas position their vehicles in the appropriate inside (or outside) lane at an upstream ramp terminal or adjacent signalized intersection in anticipation of a turn at the next terminal or intersection. The frequency of this behavior is increased when turn volumes are high or the distance between terminals is too short for "comfortable" lane changing. The form of the lane utilization mode! is descnbed in Appendix C. 3. Queue Length Model. This model can be used to convert a predicted queue length from the number of equivalent passenger cars to units of distance (e.g., meters). This queue length conversion model was found be an essential component of He capacity model. This model was also incorporated into Be queue interaction model developed for this research, as descnbed in Appendix D. 4. Arterial Weaving Motlel. This mode! quantifies the effect of weaving activity on the efficiency Of arsenal traffic flow. The weaving maneuver that is predominate in interchange areas is the off-ramp right-turn movement that weaves across the arsenal to make a left-turn at the downstream signalized intersection. This maneuver has been observed to cause significant turbulence In He arsenal traffic flow resulting In significant increases In travel time and, In some cases, lengthy queues on the off-ramp. The form of this model is described In Appendix E. B.~.2 Database Elements The data needed to calibrate He aforementioned models can be categorized as: (~) basic traffic characteristics (model inputs), (2) traffic performance measures (model outputs), (3) signal controller settings, (4) traffic control features, and (5) geometric data. The elements that comprise the first two categories are dynamic and were collected continuously during the field studies. These elements are listed in Table Be-. The latter three categories represent static data types. They were often measured prior to the start of the study. Elements of each of these three categories are listed below. · Signal Controller Settings. This category includes the traffic signal controller settings and operation. In particular, cycle times, coordination onsets, signal phase sequence, change interval, and phase splits were collected during each study. If the controller had one or more actuated phases, then the individual actuated phase durations were recorded with a computer connected to the signal controller. Traffic Control Features. This category includes speed limit, traffic control signs, and pavement markings. This information was of a general nature and was used to describe the character of the interchange area. B-2
Table By-. Traff~c-related database elements Mo' lel Category ~ Data Type use ~ Lane ~ Queue . Capacity Utilization Length Traffic Discharge Headway Cat haractenstics | Discharge Speed | C l l l Start-Up Lost Time C Count & Location of Cars on Be Downstream Link at He 5~ of Offs'' Queued Driver ' Wing Reaction Time ~| | V | Queued Vehicle Storage Length l l | V l Traffic Demands by Lane and Movement | TravelPa~Mat ix(O-D's) | | V I ; Weave & Non-Weave Vehicle Travel Tune | Weave & Non-1 'cave Flow Rate per Lane l l l Performance Saturation Flow Rate C . Men cures | Lane Utilization Percentage l l V | Queue Length V ~ | Weaving Vehicl Speed l l l l u Notes: 1 - Data collection method: C - computer-monitored tape switches; V - video tape records. Weaving V V V V · Geometric Data. This category includes geometric infonnation along the arsenal and at each intersection. Artenal information includes cross section, distance between intersections, turn bay lengths, and lane assignments. Intersection information includes approach grade, skew angle, and turn radii. B.2 STUDY SITE DESCRIPTION B.2.l Study Site Characteristics A list of desirable characteristics for the field study sites was prepared based on information obtained from the survey of practitioners and the insights obtained while formulating the aforementioned models. These site characteristics are described in the following paragraphs. Interchange Types. The study sites were selected to collectively include Me two basic forms of service interchange commonly used in suburban and urban areas: the diamond and the partial cloverleaf (or parclo) interchanges. Variations of these two interchange forms stem from variations in the distance between We ramp terminals and the routing of the left and right-turn movements (i.e., Trough the signal or via a loop ramp). Further assessment of the correlation between interchange type, the extent of its operational problems, and its frequency of application in B-3
urban areas led to the following six interchange types being identified as the most appropriate candidates for the field studies: Diamond Interchange I. Compressed Diamond 2. Tight Urban Diamond (without frontage roads) 3. Tight Urban Diamond twin frontage roads) 4. Single Point Urban Diamond Partial Cloverleaf (Parclo) 5. Parclo B (2-quad) 6. Parclo AB (2-quad) Study Types. The development of We data collection procedure was strongly influenced by: (~)theneedto optimize the qualify end quantify oftraffic-related characteristics (see Table B-~), and (2) the capabilities of the computer and video data collection equipment available to the study team. Based on a thorough examination of all feasible collection procedures, it was concluded that two different types of field study were needed. One type of study required the equipment to be deployed for the purpose of collecting the capacity and lane utilization model data. The data needs of these models required the concurrent study of one direction of travel along two successive, arterial street segments. These segments included the section between the two ramp terminals and the section between the ramp terminal and adjacent intersection. This study type was referred to as a Capacity Study. A typical data collection setup for this study is shown in Figure B-1 . This figure shows the setup for the diamond interchange form, a similar setup was used for the parclo form. As shown In Figure B-1, there are two possible study "cases" at an interchange. These cases are named the "downstream" and "upstream" cases. The natne of each case identifies the orientation of the adjacent intersection with respect to the travel direction studied. The field studies were designed to include a mixture of both cases. This approach permitted an examination of the effects of the adjacent intersection on bow interchange inflow and outflow. The second type of study required the equipment to be deployed for the purpose of collecting the weaving data. This study focused on the off-ramp right-turn movement weaving across the through traffic to make a left-turn at the adjacent, downstream intersection. To collect this data, the equipment was deployed at each end of the arterial weaving segment. This study type was referred to as a Weaving Stucly. A typical data collection setup for this study is shown in Figure B-2. Queue length arid starting reaction time data, needed for the Queue Length Model, were collected with both study types. The data needs of this model required only a view of the front and rear of the traffic queue. As this type of view was generally available from either study type, a third study type was not developed to collect this data. c' ~ B-4
Downstream Case Zip U pstrea m Case Camera 2 \\ ~Boundary of/ I Study Zone ~'1 NY ~ Camera ~17 . _ _ _ _ ~l ~ ~/ ~ A. . it:: Boundary of / Study Zone LEGEN D "< ond field of view ,~ Tape Switch Sensor 7 Photocell Sensor ~ Tape Switch Speed Trap - Figure B-1. Capacity study data collection setup for a diamond interchange. B-S . l l Camera ~ ... ... ................ ~ . _ _ i., ·.-.- _ -- ~ ~ ~ r - =_ ~ · ^.B. : ~ . = . . ._ . . ..~' 'I . . .~;¢,.. A. .......... =-
an/ ~ - - ~ video Camaro \2 ~ LEGEND Comer Bounds of/ Study Zone f ~e -. If afar ~ co~ deft. B-6 ~ , _ ^ _ , Hi. . ~ ~ . Comes 2
The road segments and corresponding traffic movements considered dunng each study are shown (by number or letter) in Figures B-l and B-2. These figures also show the locations of the tape switch sensors, photocell sensors, and video cameras. The sensors were monitored by a computer, which also served as the data processing and recording device. This computer processed the sensor input and automatically converted it into more desirable fonns such as phase duration and discharge headway. The video cameras were used to record Me vehicle locations on the downstream segments, travel paths, and weaving/non-weav~ng vehicle characteristics. The actual data were manually extracted from videotapes dunog playback in the laboratory, subsequent to the field studies, and processed Into more usefid forms at that time. The specific types of data collected by each collection system are listed In Table By-. Geometric and Traffic Demand Criteria. The selection of specific field study sites (i.e., interchanges) was based on their degree of compliance with the following geometric and traffic demand criteria. Geometric Cntena I. Cross Section: 2. Ramp to Ramp Distance: 3. Ramp to Intersection Distance: 4. Adjacent Land Access: Traffic Demand Criteria I. Arterial AADT: 2. Ramp Terminal v/c Ratio: Traffic Control Cr~tena I. Arterial Speed Limit: 48 to 80 km/in 2. Ramp & Intersection Signalization: pretimed or sem~-actuated/coordinated 3. Ramp & Intersection Cycle Length: preferably the same at both 4. Artenal Left-Turn Phasing: protected 5. 4 or 6 through traffic lanes 60 to 275 meters (stop line to stop line) 60 to 275 meters (stop line to stop line) no parking and preferably no driveways on arterial 20,000 or more 0.7 to I.0 during peak hours Arterial Left-Turn Phasing: Arterial alignment: ~.... . .. · . - preferably less than 2° horizontal curvature, less than 2% grade, and negligible skew In audition to these cntena, the study sites had to have frequent and recharting traffic queues on We arterial during the peak traffic periods. The intent of these criteria was to insure that We database would be representative of Interchanges in urban areas with fairly typical geometries. The requirement of "hequent and recuTnug queues" was a recognized deviation Tom the characterization of "being representative" and "Wpical;" however it was a necessary extension as it produced the _ . . ~ , ., . · ~ ~ . · . · ~ ~ · ~ ~ ~ . . e . ~ - - ~ _ _ number or observations necessary to yield meaningful arid statistically soured models. B.2.2 Study Site Locations In addition to Me aforementioned criteria, there was a need for geographic diversity in the collective list of study sites. Study sites were identified in six geographic regions ofthe U.S. These regions included Me Northwest, Southwest, Upper Midwest, Lower Midwest, Northeast, and B-7
Southeast. Within these regions, highway agencies in Me states with large metropolitan areas were contacted and inquiry was made as to potential study locations. Interchanges that most nearly complied with the desired criteria were identified as candidates for a preliminary site visit. Based on We results of We preliminary visit to the candidate sites, twelve interchanges were identified as being most suitable for field study. Every effort was made to identify two interchanges for each of the six types identified in a previous section; however, this goal could not be achieved In some instances. Table B-2 descnbes We distribution of the twelve study sites, as categonzed by nterchar~ge type arid study location. Table B-2. Interchanges studied by tone and location ~,, ,,, _ l _ l Interchange | Study Location l Type Nebraska Arizona Texas Kansas Compressed Diamond 1 2 right Urban Diamond (no frontage) | | 1 Tight Urban Diamond (Wow homage) Single Point Urban Diamond 7 T ~ RarcloB (2-quad) l l l I _ Rarclo AB (2-quad) ~I T I I = I Total: 1 2 1 3 1 2 1 ~ ~- .1 2 3 California Total - 1 1 3 1 3 2 2 1 12 The traffic and geometric characteristics of each site are listed in Table B-3. In general, the study sites satisfied almost all ofthe geometric and traffic demand criteria previously described. In a few instances, the distance to the adjacent Intersection exceeded the desired 275-meter maximum distance; however, the traffic demands at these sites were sufficiently high as to precipitate the extensive queuing considered desirable for study purposes. A capacity study was conducted at each of the twelve study sites. In addition, weaving studies were conducted at six of the sites. All total, eighteen studies were conducted in eight cities and five states. The traffic signal characteristics of each study site are listed in Table B-4. It is interesting to note that a few of the interchanges are not coordinated with the adjacent intersection. The reason for this lack of coordination is different in each case. The Peona Road site is not coordinated because it is currently standard practice in Arizona for the state DOT to operate the interchanges and the city to operate the adjacent intersections. The Towneast Boulevard site is not coordinated due to a lack of funds for coordination hardware. The Stevenson Boulevard site is not coordinated because the existing coordination hardware failed in service and resources were not available to replace it. B-8 .
Table B-3. Traffic and geometric character~sffcs of the shady sites Ramp to Ramp to Interchange Arterial City, Arterial Arterial Ram p Intersection Speed Type State AADT Thru Distance Distance Limit l ~ | ~ Lanes ~ (meters)I ~ (meters~l ~ (l =~ Compressed |MetcalfAve |Overland Perk, | 58,600 | 6 | 200 | 204 | 72 || Diamond 1110~ to I-435 IKansac l l l I I 1 |75th Street 1 Overland Park, | 32,000 1 4 174 1 155 1 56 |I-35toFronta ;e |Kansas l l l I I 4 |Maple Street 1 Omaha, 1 34,200 1 4 268 1 198 1 72 102nd to I-680 Nebraska Fight Urban |Peoria Road 1 Phoenix, | 34,400 | 6 107 | 276 | 64 || diamond |25~Ave. to I 17 1 Arizona I I l l I 41 |MathildaAv |Sunnyvale, 1 34,540 1 6 1 87 1 110 1 72 11 | SR-237 to Rot ~| California l l l I I :1 Fexas |Arapaho Roa |Richardson, | 39,000 | 6 | 99 | 265 | 64 || Diamond US75 to Greenville Texas .. . |Towneast Blv 1 Mesquite, 1 35,000 1 6 137 1 223 1 56 I Emponum to ] -635 I Texas l l l l l arclo AB |60th Street 1 Omaha, 1 31,800 1 4 259 1 216 1 64 11 2 quad) |I-80to Grover INebraska l l l I I ~1 _ _ Parclo B Somersville Rd Antioch, 39,700 4 265 119 56 2 quad) | Delta Fair to S Rat | California l l l I I 41 Istevenson Bh d 1 Newark, | 55,600 | 4 264 | 157 | 56 || |Balentine to I- ;80 |California l l l I I 4 . ingle Point |7th Street |Phoenix, 1 42,000 1 6 1 78 1 331 1 56 1 Jrban |I-10toMcDo,rell IArizona l l l I I 1 Diamond (SPUI) Indian School Rd Phoenix, 54,500 6 91 316 56 16th St. to SR-51 Arizona Notes: 1 Distance measured from stop line to stop line in the same d~rection, except at SPUI's. At SPUI's, ~e "same direction" concept is also applied but the opposing direction through stop line is used as the reference point at the second ramp terminal (since the Trough stop line at the second ramp terminal does not exist at the SPUI). B-9
Table B-4. Signal charactenstics of the study sites . , Interchange | Adjacent Intersection Interchange Arterial Signal Control Signal Control Type _ Arterial No. of Control Cycle Control Coordi Lett-Turn control- Type ~Length2 Type ~nation3 Protection lers (see) Compressed Metcalf Ave Protected 2 SA 126,1 05 SA C D~amond 75~ S~eet Protected 1 SA 82, 90 SA C ~ ~ Maple Street TProtlPenn ~1 T SA ~ 90T SA ~ ~ ight Urban TPeoria Road ~Prot./Pe~m. ~ 1 ~ FA ~166T SA ~ . amonc . _ Ma~ildla Ave Protected 2 SA100 SA C 1 Texas Arapaho Road Protected 1 SA90,120, 128 SA C Diamond Tou neast Blvd Prot./Pem~. 1 FA1 1 8 FA N Parclo AB 60~ S~eet Prot./Pe~m. 2 SA90 SA C (2 quad) 1 Parclo B Somersville Rd Protected 2 SA110 SA C (2 quad) Stevenson Blvd Protected 2 SA110, 100 FA N Single Point 7~ StreetProtected 1 SA 90 SA C Urban diamond | Indian School~d |Protected | 1 | SA | 8, 90, 102 | SA | ( ~. Notes: 1 - FA = fillly actuated, SA = semi-actuated. 2 - Values listed are ~ose observed dunng ~e study periods. Underline values are averages. 3 - C = coordinated wi~ interchange s~al~s), N = not coordinated wi~ interchange si~al~s) B.3 DATA COLLECTION B.3.1 Approach Cycle Length2 (see) 26, 105 82, 90 90 90 100 90,120, 128 3 90 0 24 90 78,90, 102 The data collection equ~pment used to collect the field data included v~deo cameras and computer-monitored tape switch sensors placed in the traf Eic lanes. As described in a preceding section, the equ~pment deployment followed one of two study types (i.e., a capacity or weaving study). Data collected dur~ng these studies is descr~bed in this section. All data were collected dunng weekday, daytime penods between the hours of 7:00 am and 7:00 pm. The study penod generally included the hours of peak traffic demand at ~e study site. Data were not collected dunng ~nclement weather nor dunng unusual traffic conditions (e.g., a traffic accident). B-10
B.3.2 Capacity and Lane Utilization Data During each ofthe capacity studies, the equipment was deployed in a manner consistent with that shown in Figure B-] . At some sites, the location of power lines, fences, or private property required slight deviations from Me desired camera position. Typical camera positions are shown in Figure B-3 for one study site. The corresponding fields of view obtained wad We video cameras are shown in Figure B-4. Data collected dunng the capacity studies were used to calibrate the capacity and lane utilization models. Data for the capacity mode} were collected using bow the computer-mon~tored tape switches arid the video recorders at each of the twelve Interchange study sites. In general, three traffic movements were monitored during each study. These movements included an interchange off-ramp or arterial left-turn, an interchange arsenal through, and either the second interchange through movement (for the downstream case) or the upstream intersection through movement (for Me upstream case). The tape switches were used to record traffic flow behavior in two traffic lanes for each movement monitored. A speed trap consisting of two parallel tape switches was typically located in the inside-most lane. This trap provided information on vehicle headway, speed, acceleration, and wheelbase. A single tape switch was located in the adjacent lane. This single tape switch provided additional headway information. The computer monitoring the tape switches was also connected to Me signal controller (using photo-cell sensors) and used to monitor the signal indication status. The video recorders were positioned to provide a visual record of traffic crossing the taDe switches as well as a view of the downstream street segment. 4, - -- ~ Data for the large utilization mode} were collected using two video cameras and recorders at each ofthe twelve interchange study sites. All traffic movements entenug Me study boundary were tracked as Hey traveled Trough the study area. The data collected Included Me approach lane used at each intersection or terminal and the travel time through the system. B-~]
):J1( 1. Frontage Rd. ~ us (n cn Len or) 1 - 1 ~ \1 ' 'f. To ~ ~ ~ . ~ Wib :' 1 N: ! ~ . ~ Camera 2 a) nit a' Camera 1 Figure B-3. Typical data collection equipment locations for a capacity study. B-12
a) Camera l field of view. b) Camera Afield of view. Figure B-4. Fields of view for the capacity study camera locations shown in Figure B-3. B-13
B.3.3 Queue Length Data The queue length model database was assembled using the data gathered during both the weaving arid capacity studies. Data collection focused on the traffic lane length occupied by a queued vehicle. The video cameras were used to record the location of the first and last vehicles in queue at Me start of the signal phase. Queued driver reaction time data were also collected for this database. The reaction time ofthe first driver in queue was measured relative to the start ofthe green indication. The reaction time of the second and subsequent Divers in queue was measured relative to the start of motion of the vehicle ahead in queue. B.3.4 Weav~ng Data Dunng each ofthe weaving studies, the equipment was deployed In a mariner consistent wad Mat shown in Figure B-2. At some sites, the location of power lines, fences, or private property required slight deviations from the desired camera position. Typical camera positions are shown In Figure B-S for one study site. The corresponding fields of view obtained wad the video cameras are shown In Figure B-6. Weaving data were collected at six of the twelve Interchange study sites. In general, the video cameras were deployed to capture the behavior of off-ramp right-turn vehicles as they entered the arterial, weaved across through traffic, and Den turned left at the downstream intersection. The interaction of these weaving vehicles with other through traffic was also monitored during each study. Specific types of data included the running hme of the weaving and non-weav~ng vehicles and the time each spent In queue at Me downstream signalized intersection. The travel distance corresponding to Me running time was also recorded to facilitate the computation of runt leg speed. B.4 DATA REDUCTION B.4.1 Approach The data reduction effort proceeded on a model-by-mode] basis. Initially, efforts were focused on developing Me weaving mode! database. This effort was followed by the reduction of data for We queue length, capacity, arid lane utilization models. For all models, the data reduction procedures were defined, documented, arid tested on a sample portion of Me collected data prior to their fi~-scale implementation. An overview of these data reduction procedures are provided for each mode] In the next four sections. B.4.2 Capacity Mode! Database Traffic data collected by the video recorders and Me computer-monitored tape switches were used to create a database of traffic characteristics and performance measures for use in calibrating the capacibr model. The video recorders provided information about downstream queueing conditions. The tape switch data were the primary source of traffic characteristics (i.e., headway, speed, and acceleration) at Me upstream intersection. The phase start and end tunes were also collected by the same computer that monitored Me tape switches. B-14
Camera 2 , McDowell Road o . _ x . _ a) o Q to - a) - UO ~ ~ . _q _ Lyowood Street ~ 1 t~ 3 4, 11 lll an 11 111 ~ 11 \ ll\ - ~ : ill Street ''1'' I I ~ rlTI N ~ No Scale to Street iCamera 1 1- 1 0 SPU! . ~ Figure B-5. Typical data collection equipment locations for a weaving study. B-15
a) Camera l field of view. b) Camera 2field of view. Figure B-6. Fields of view for the weaving study camera locations shown in Figure B-S. B-16
Data reduction for this database was the most complicated and tune-consuming of all four databases. The complication stemmed from the need to combine the video and tape switch data into one large database. Special computer programs had to be developed to sort, synchronize, and merge the two data files. The tape switch and video data were first processed individually for common hour intervals. Then, they were combined using the start of the upstream signal phase as a means of synchronizing the data files. Dunug this process, the number of vehicles on the downstream segment at the start of green was combined with segment length to obtain the distance to the downstream back of queue and the average density on the downstream segment. The combined database is described as a "vehicle-specific" database because each line entry represents the discharge characteristics of one vehicle In a given traffic lane. It can also be described as a "phase-specific" database because appropriate phase statistics (e.g., phase length, cycle length, etc.) are attached to each vehicle entry within it. It should be noted that multi-axle vehicles (i.e., vehicles with more than two axles) were excluded from the database, thus, the database represents the discharge characteristics of passenger car vehicles and a few single-unit trucks. The format of the combined database is shown In Table B-5. The sample data shown represent the discharge characteristics of one filly-utilized signal phase. The phase started at 16:09:10.59 (about 10 minutes after 4:00 p.m.) and had a duration of 51.8 seconds. This was the fifth phase/cycle observed in We Bird study period for a through movement at the signalized intersection upstream of the interchange at Metcalf Avenue and I-435 in Overland Park, Kansas. Nineteen queued vehicles were served during the phase, their departure times and headways are shown in Field/columns M and N. respectively. Closer exarrunation of the sample data In Table B-5 Indicates that, at the start of green, there were no vehicles In the first 30-meter zone downstream of the signal, one vehicle in the second 30- meter zone, etc. At the start of the phase, the density of the downstream link was relatively high at 78.2 v/km and, as a result, the first ~ ~ vehicles to discharge into Me downstream segment filled it and caused spillback into the intersection. The effect of this spillback can be seen on the speed and acceleration of the discharging vehicles (i.e., Field/columns P and Q. respectively). After reaching a speed of 8.4 m/s, the discharging queue begins to slow down as the wave of spillback approaches it. When the wave arrives, it results In longer discharge headways as the affected vehicles begin to "crawl" over the stop line in a highly-congested state. Finally, after Me wave passes, the density steadily decreases (due to the downstream green, see Field/column S) until the discharging downstream queue dissipates and the spillback is eliminated. At which time, the subject queue returns to more typical discharge speeds and headways. The assembled capacity database includes the discharge characteristics of more Man 51,000 queued vehicles. These vehicles were observed at twelve sites for 33 traffic movements in 63 ms~umented lanes. All of the sites had extensive traffic queues during some or all of Me six-hour study period and at least eight sites had some degree of queue spillback. There were more Dart 3,800 signal cycles observed during the study periods. B-17
Table B-5. Capacity mode! database - format Sample Data field: A _ B C D _ E F _ G H I _ ~ _ K _ L M_ N_ O_ P_ Q R S T U_ V_ K1 S 3 62 5 16 9 10.59 51.8 19 19 1 4.332 4.332 2.8 5.80 1.63 0 1 2 2 4 4 3 x x x x G 12 78.2 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 2 5.926 1.594 2.8 7.53 0.81 0 1 2 2 4 4 3 x x x x G 12 72.5 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 3 8.768 2.843 2.9 7.03 0.99 0 1 2 2 4 4 3 x x x x G 12 73.5 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 4 10.194 1.425 2.7 8.40 0.95 0 1 2 2 4 4 3 x x x x G 12 71.5 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 5 11.654 1.461 2.6 8.31 0.05 0 1 2 2 4 4 3 x x x x G 12 72.9 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 6 12.850 1.196 2.8 8.28 -0.13 0 1 2 2 4 4 3 x x x x G 12 74.2 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 7 14.085 1.235 2.7 7.78 -0.75 0 1 2 2 4 4 3 x x x x G 12 76.2 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 8 16.486 2.401 2.6 6.66 -0.01 0 1 2 2 4 4 3 x x x x G 12 78.0 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 9 18.328 1.842 2.7 7.13 -1.26 0 1 2 2 4 4 3 x x x x G 12 77.0 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 10 20.803 2.475 2.7 7.50 -1.18 0 1 2 2 4 4 3 x x x x G 12 77.4 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 11 23.695 2.892 2.6 5.17 -1.60 0 1 2 2 4 4 3 x x x x G 12 76.2 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 12 31.333 7.638 2.7 1.19 2.51 0 1 2 2 4 4 3 x x x x G 12 74.1 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 13 33.776 2.442 2.8 6.09 1.73 0 1 2 2 4 4 3 x x x x G 12 60.3 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 14 35.207 1.431 2.8 7.50 1.47 0 1 2 2 4 4 3 x x x x G 12 59.2 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 15 38.657 3.450 2.7 10.81 0.15 0 1 2 2 4 4 3 x x x x G 12 60.6 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 16 41.163 2.506 2.8 11.29 0.30 0 1 2 2 4 4 3 x x x x G 12 57.0 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 17 43.250 2.087 2.7 10.27 0.87 0 1 2 2 4 4 3 x x x x G 12 55.9 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 18 44.350 1.100 2.7 10.84 0.82 0 1 2 2 4 4 3 x x x x G 12 55.6 205 K1 S 3 62 5 16 9 10.59 51.8 19 19 19 47.462 3.113 3.5 10.48 -1.45 0 1 2 2 4 4 3 x x x x G 12 57.8 205 . Field Description A | Site Co e - indicates city and interchange location B | Junctio Type - S=Intersection, C=Interchange C | Time ol Day Code - I=first two-hour study period, 2=second..., etc. D Movement Code - indicates movement type (i.e., left or through) and lane number E Cycle Count - signal cycle number since the start of the study period _ F,G,H Start of Phase - start of the subject phase green ~| Phase ~ Iration - duration ofthe phase green and yellow indications J | Numbe in Queue - number of queued vehicles served during initial portion of phase K | Numbe in Phase - number of vehicles served during phase (No. in queue & arrivals after queue) L Vehicle Count - number of queued vehicles served since start of phase M Discharge Time - time back axle of vehicle crosses stop line/tape switch relative to start of phase N Discharge Headway -headway between subject vehicle back axle and preceding ~rehicle back axle O Wheelbase - subject vehicle wheelbase P Discharge Speed - subject vehicle speed when crossing the stop line/tape switch Q Acceleration/Deceleration - subject vehicle acceleration when crossing the stop line/tape switch R Zone Counts - number of vehicles in each 30-meter zone in downstream lane (left to right - up to downstream) at the start of subject phase S | Downst ~am Indication -status of downstream through signal indication (G=green/yellow, R=red) T | Spillbac i Number - queue position experiencing spillback while crossing stop line/tape switch U Downstream Density - density in the downstream lane at the time of subject vehicle discharge V | Segmen Leng~b - length of the downstream street segment (stop line to stop line) , ' Notes: 1 - MT (Military Time): Field F = hours, Field G = minutes, Field H = seconds. B-18 Units na na na na na MT sec na na na sec sec meters mls mls2 na na na v~ meters
B.4.3 Lane Uff~zation Mode' Database Traffic events recorded on video tape during the capacity studies were used to create a database of traffic characteristics arid performance measures necessary for calibrating the large utilization model. The specific data collected included the large volume per cycle, the number of approach traffic lanes, the distribution of traffic volumes to downstream turns, arid the type of nterchar~ge. The lane utilization analysis focussed on Me large use of through traffic movements served by multi-lar~e large groups. Data reduction required the use of two video cameras to track vehicles through the study boundaries. The two cameras were synchronized in time and the recorded unages played back simultaneously. This synchronization facilitated the tracking of each vehicle from entry point to exit point along the arterial. Dunng video playback Me ent~y/ex~t locations and times were recorded for each tracked vehicle. The time required for tracking each vehicle was somewhat lengthy and required the use of a sampling technique. Specifically, it was determined that only vehicles entering the study boundaries during Tree five-minute periods would be tracked. These five-minute penods were allocated to each of the three highest traffic hours studied. This approach typically yielded data for 250 or more vehicles for each entry movement at each ofthe twelve sites. The assembled lane utilization model database includes the entry and exit time and location for 8,198 vehicles observed at twelve sites for 32 traffic movements. Of these vehicles, about 65 percent (or 5,292) were recorded as being a through movement on at least one of the studied intersection approaches. B.4.4 Queue Length Mode' Database The data reduction procedure for the queue length database required a camera view of the front and back of Me through movement traffic queue on an intersection approach. The front view was used to measure the distance-to-stop-line and starting-reaction tune of the first queued driver. The back view was used to measure the same statistics for the last queued vehicle. When the last queued vehicle extended beyond the field of view or was too distant to precisely ascertain, a vehicle in a lower numbered queue position, nearer to the camera, was used instead. This technique of selecting a vehicle nearer to the camera maximized the precision of the queue length and reaction time measurements. All distance measurements were made at the start of the phase. Queues with trucks or motorcycles were not considered. Left-turn queues were not studied. The assembled queue length database contains queue length and reaction time measurements for 122 Brst-in-queue passenger cars and 1,053 last-~n-queue passenger cars. This data was obtained at eight of Me twelve study sites. Studies were not conducted at four sites because of the lack of an adequate view of the traffic queue. B.4.5 Weaving Mode! Data reduction required the use of bow camera views to track vehicles through the weaving section. The weaving maneuver examined ~ this study was the off-ramp-right-turn-to-downs~eam B-19
ntersection-left-turn. The two camera recordings were synchronized in time and played back simultaneously to obtain Me travel time and stopping location of weaving and non-weav~ng vehicles. During video playback, vehicles entering the study section were tracked as they proceeded Tom the upstream ramp terminal to the adjacent, downstream intersection. This tracking required recording the manner in which the vehicles entered (i.e., off-ramp right-turn or arterial through movement) and their arrival time to various points downstream (i.e., back of queue, stop line). A sampling technique was used to select the tracked vehicles as the lengthy tracking tune for each ~ ~ . ~ ~ ~ ~ ~ . . . . ~ ~ . ~ . ~ ~ ~ vehicle precluded the collection of a 1 (J()-percent sample. 1 his technique speckled that three through vehicles and three on-ramp nght-turn vehicles be picked randomly at five-m~nute intervals for the purpose of tracking. This pattern was followed during We I.S-hour period that bracketed the peak hour at each site. After this initial sampling effort, it was determined that additional data were needed for the weaving category. Thus, a second review of the videotape was conducted and data for an additional 332 weaving vehicles were added to the database. The composition of We weaving mode] database is shown ~ Table B-6. As this table indicates, We database contains entry dines for 17,939 vehicles. Of these vehicles, 980 vehicles were hacked Tough We study segment. About one-half of We hacked vehicles (i.e., 421 of 980) were observed to complete a weaving marleuver. Table B-6. Weaving mode' database - sample size Attribute or l Study Site | Statistic | Metcalf | 75th Stre' t | Arapaho | Towneast | 60th | Avenue Road Blvd. Street Interchange Type Compressed CompressedTexasTexas Parclo AB Diamond Diamond Diamond Diamond Total Entries3,457 2,761 3,272 3,389 2,007 Artenal Enhies ~2,722 ~1,671 ~2,195 ~2,568 ~1,432 Ramp Envies735 1083 1,077 821 575 Envies Sampled162 159 146 159 192 Weaves in Sample ~68 ~5' 1 48 1 67 1 109 L - - · --- ~ , . B.S SUMMARY STATISTICS B.5.1 Approach 7th Street SPUI 3,053 2,093 960 162 Total 17,939 12,688 5,251 980 1 71 421 The model calibration activity began with a review of relevant summary statistics for each of Me data bases. Specifically, this review included Me computation of average values for selected traffic characteristics and performance measures (as described in Table Bob. These averages are categorized by site, interchange type, and traffic movement where appropriate. A more detailed B-20
examination of the database was conducted during the model calibration activities; the findings from this examination are described in Appendix C. B.5.2 Capacity Mode! Database Summary As indicated in a previous section, the capacity model datable contair q ~ ~ v~riPh~ of traffic characteristics and signal timing data. Specifically, the database contains information on signal cycle length, phase duration, the frequency and extent of drivers entering the intersection after the yellow indication, and the discharge headway of each queued passenger car. This last characteristic was used to compute average saturation flow rate and start-up lost tune. Driver use of the yellow indication was used to estimate the lost time at the end of the signal phase. ~^~- ~ ~J In general' cycle lengths ranged Dom 78 to 166 seconds among the twelve study sites. The sites had either sem~-actuated or fill-actuated signal control. The cycle length was found to vary by +10 to +20 seconds at any one site. The sites with larger variations in cycle length are characterized as having semi-actuated control, time-of-day signal timing plan selection, and frequent pearl implementation. The transition periods between signal timing Claris at these sites tended to produce a few, extremely long cycle lengths that led to the high variability in cycle length that was observed at the study sites. The average minimum discharge headway and corresponding saturation flow rate are summarized ~ Table B-7 for He two junction and movement types studied. The mourn discharge headway H is computed as the average of all headways observed for the fifth and higher queue positions. The reciprocal of H. with proper unit conversion, is the saturation flow rate s (i.e., s = 3600/H, vphgpl). This technique for computing the saturation flow rate is consistent with the procedure described in the ~ 994 Highway Capacity Manual (1, Chapter 99. In general, the saturation flow rate is very similar among the Interchanges and intersections studied. An examination ofthe saturation flow rates categorized by interchange type indicated that there were no distinct differences In flow rate among types. On the other hand, the data in Table B-7 indicate that the left-turn movements may be discharging more efficiently than the through movements at He study sites, however, the difference is relatively small. The database was segregated into "with" and "without" spillback categories. The "with" spillback category relates to the headways observed during signal phases that experienced queue spillback from the downstream intersection. The headways included in this category represent only those vehicles able to discharge before the onset of spilIback. Vehicles that discharge prior to spillback were found to have low saturation flow rates. They had lithe incentive to discharge at higher rates because they were essentially discharging into the back of He downstream queue. The average start-up lost time is shown in Table B-8 for the two junction and movement types studied. This characteristic was computed as the sum of the portion of the headway for each ofthe first four queued passenger cars Hat was in excess ofthe rniliirnllm discharge headway. While He magnitude of the start-up lost time is largely dependent on the minimum discharge headway, it is also dependent on the distance between the first queued vehicle and the reference line used to B-21
demarcate entry to Me ~ntersechon (typically this is We stop lined. To maze Me influence of this latter factor, Me reference line location was detennined for each study site. Specifically, Me reference Ime was established as Spout I.0 to 1.5 meters downstream of Me Font of Me first queued vehicle. Typically, this technique resulted in the reference line being located near Me stop line. Table B-7. Capacity mode! database - summary saturation flow statistics Ir . - ~ - - ~ - ~ Junction Type interchange Intersection Movement Types Left-Turn Through Leh-Turn Through Average: Min. Discharge Headway2 : Without Spillback (sec/veh) No. _ 5,285 9,292 42 5,971 20,590 l Avg. l 1.84 1.87 1.83 . 1.88 1.86 l Std Dev . 0.45 0.52 0.40- _ 0.52 _ 0.47 Sat. Flow Rates (pcphgpl) 1,957 1,925 1~967 1,915 1,935 Min. Discharge Headway4 With Spillback (sec/veh) . No. | Avg. . -- 1 - _ 408 2.17 _ 20 2.22 _ _ 786 2.16 _ 1,214 ~ ~2.18 Std Dev 0.95 0.44 0.94 0.78 Sat. Flow Rate3 (pcphgpl) 1~659 1~622 1~667 1 651 ~e. Notes: 1 - Le0-turn movements Dom bow the off-ramp and He arsenal. Through movements along He arsenal. 2 - Based on the average headway of the fit through last queued passenger car. 3 - Computed as 3,600 divided by the minimum discharge headway. 4 - Based on tibe average headway of He fit through last queued passenger car able to discharge prior to queue Spillback Dom He downstream intersection. "--" no data available. Junction Type ~ eft-T~n' Intersection Left-Turn Table By-. Capacity mode! database - summary start-up lost time statistics 1. . Movement Types Start-Up Lost Timer Without Spillback (see) Start-Up Lost Time3 With Spillback (see) No. Std Dev Average 2 65 4.40 1.04 1 08 1.07 1 07 1.07 No. 108 166 1.69 3.04 1.87 2.20 Std Dev l 1.58 0.98 1.85 1.47 . - Notes: 1 - Leflc-turn movements from bow the off-ramp and He arterial. Through movements along He arsenal. 2 - Based on He average headway of He fifth Trough last queued passenger car. 3 - Based on He average headway of He fit Trough last queued passenger car able to discharge prior to queue Spillback Dom He do~ms~eam intersection. "--" no data available. As with the headway and saturation flow rate fatal the start-up lost times shown In Table B-8 were categorized as being "with" arid "without" spiliback. In general, start-up lost time in Me B-22
"without" spillback category tends to be higher as a consequence of the extra time lost by the discharging traffic queue as it accelerates to the higher speeds associated with the higher saturation flow rates. Typical values for the "without" category range from 2.46 to 2.80 seconds (excluding the data in the "~ntersection/left-turn" category) whereas values for the "with" category range from 1.69 to 1.87 seconds. The difference between these two ranges suggests that the more normal, "without" spillback condition is associated with about 1.0 seconds more start-up lost time than the "with'' spillback condition. The data in Table B-8 also suggest that the start-up lost time may be higher for a left-turn movement than a through movement. Table B-9 summarizes Me extent of driver use of We charge interval (i.e., "green extension") and the implications of this use on lost time at the end of the phase. The green extension reported In this table represents Me average time after the yellow indication was presented that the last vehicle entered Me intersection. Subtraction ofthis time from the yellow-plus-red-clearance interval yields Me lost time at the end of the phase. Table B-9. Capacity mode' database - summary clearance lost time statistics l Junction ~ Movement | ~ reen extension (see) | Clearance Lost Time ksec) ~ Type ~ Typel ~i;; ~ Average ~ SldDev ~Vo. ~ Average ~ ? I ~ t e r c h a ~ g e L e ~ c - T u ~4 9 1 2 . 8 2 1 . 4 8 4 9 1 2 . 7 7 Through 675 2.65 1.46 675 2.60 ntersection ~Left-Tu~n I 'S T 2.45 1 1.52 1 58 1 2.55 Through 369 2.54 1.31 369 3.1 1 Average: 1 1,593 1 2.67 1 1.44 1 ,593 1 2.77 Std Dev l.9g 2.15 .52 1 1.43 1.94 Notes: 1 - LeR-turn movements Mom bow Me off-ramp and Me arsenal. Through movements along Me arterial. The frequency of green extension was also examined using the capacity database. In general, it was fourld that drivers entered the intersection after the yellow was presented in about one-half of the phases studied. Although the average amount of green extension is relatively constant at about 2.67 seconds across the twelve study sites, the frequency of green extension was fourld to vary with geographic region and with cycle length. Specifically, drivers in some regions of Me country were found to be more frequent users than in others. This finding may relate to local traffic laws regarding use of the yellow interval and to the level of enforcement of these laws. Drivers were also found to be more likely to enter Me intersection when the cycle length was long. Presumably, these drivers were willing to accept the risks associated with entry on yellow in order to avoid the lengthy delays associated with long cycle lengths. The data in Table B-9 indicate Mat the average clearance lost time is about 2.77 seconds. This value is within the range of I.2 to 2.8 seconds recognized by the 1994 Highway Capacity Manual (1, Chapter 2), although it is near the upper limit. This trend is likely due to Me longer change intervals used at some of the interchanges and intersections studied. The charge intervals B-23
at Me study locations are likely to be longer than those at more typical intersections because of the larger conflict area and the higher speeds associated with intersections near or at interchanges. B.5.3 Lane Utilization Mode' Database Summary The analysis of the lane utilization mode! database focused on the computation of lane utilization factors for each of Me approaches studied. The utilization factor was computed for each of Me multi-lane approaches using the following equation: v' N U man ~ vi' where: U= lane utilization factor for We lane group; v ',~ = maximum demand flow rate in any of N lanes, vpcpl, v, = demand flow rate in lane i, i = I, 2, ... N. vpcpl; and N= number of lanes in the lane group. (Ball) The variables for this equation were computed as follows: demand flow rate for lane i was computed as the count of vehicles In Mat lane for a given cycle. The largest of these lane counts was recorded as the maximum demand flow rate for the same cycle. These counts were taken for each signal cycle that occulted during a 5-minute period and used to compute average values of vi ' and v ',~ for Mat 5-minute period. The lane coinciding with Me maximum demand flow rate was often found to vary each cycle. The lane utilization factors computed for the through movement lane groups at the twelve study sites are shown in Table B-IO. As the data in this table indicate, the factors range from 1.12 to 1.72, with a strong sensitivity to the number of I=es in Me lane group. Specifically, Me factor is lowest for the two-lane lane group and increases web increasing number of lanes. This trend suggests Mat traffic volumes become more unbalanced as the number of available lanes increases. Table B-IO. Lane utilization mode! database - summary staffstics1 l Number of Lanes in the Lane Group Movement Type 2 Lanes 3 Lanes 4 Lanes 5 Lanes No. ~ Avg ~ Std Dev T 3 o. | Avg. | S1 d Dev7 No. ~ Avg. T Sty Dev T r 0. fAvg. ,eft-Turn ~14 ~ I.1 1 ~ 0.~8 ~ 2 ~ I.28~ 0.~ --I -. ~ ~ ~ . , ~ tough T 651 1.1!] 009T 26t 126t 0.21] ~1.3 ~0.084 2T 1.72, ~ Std Dev 0.47 Notes: 1 - Observations represent five-minute averaging intervals. "--" no data available. B-24
The factors for the two and three-lane lane groups were compared with the values recommended in the 1994 Highway Capacity Manual (1, Chapter 9J for application at isolated intersections. These recommended values (i.e., 1.05 for two lanes and 1.10 for three lanes) confirm the trend noted above regarding larger values for lane groups with more lanes. On the other hand, the recommended values tend to be much lower than the values reported in Table B-10. This trend suggests that lane utilization in interchange areas tends to be more unbalanced than at isolated intersections. This result was anticipated because of the significant fuming activity in interchange areas and the resultant need for drivers to preposition themselves in the left-most (or r~ght-most) lane on the street segment prior to the segment Tom which the turn wall be made. B.5.4 Queue Length Mode! Database Summaty The analysis of the queue length mode] database focussed on We computation of the average lane length occupied by a queued passenger car and the average queued driver starting reaction time. These average values are listed In Table B-! 1 . Table By-. Queue length model database - summary statistics First Queued Passenger Car Subsequent Queued Passenger Car | Variable ~ Average ~ Min. ~ Max. Average ~ Min. - Lane length (storage), meters/car 5.0 1.6 7.5 7.0 6.8 , reaction time, seconds ~1.52 ~1.17 ~1.73 1.06 ~0.9~_ Max. 7.5 1.19 As the statistics in Table B-1 1 indicate, the average lane length occupied by Me first queued passenger car was found to be 5.0 meters. This length was found to vary from 1.6 to 7.5 meters among the study sites. Further examination of this variability indicates that it correlates with differences in the location of the stop line relative to the travel path of Me nearest conflicting traffic movement. Sites wad low average lane lengths for the first queue position tended to have stop lines that were more distant from the intersection conflict area. As a result, drivers at these sites would frequently stop downstream ofthe stop line, nearer to the conflict area, presumably to maze Bed travel time to the intersection once the green indication is presented. The average lane length occupied by the second and subsequent queued vehicles was found to vary from 6.S to 7.5 meters among the study sites. The overall average was fourld to be 7.0 meters per passenger car. The overall average of 7.0 meters/car is slightly less than that reported In the literature. Specifically, Messer and Farnbro (2) studied Me through movements at two intersections and found averages of 7.3 and 7.7 meters/car. In an earlier study, Herman et al 63J examined Me lane length occupied by queued vehicles using a test track facility and several full-size 1970 Chevrolet sedans. They reported finding an average lane length of 7.9 meters/car. As both of these studies were B-25
conducted in the 1 970's, it is likely that the shorter storage length found in this research is due to reductions in average vehicle length during the last 20 years. The average reaction time for the firsts queue drivers was found to vary from 1 .17 to 1 .73 seconds among the study sites. The overall average reaction time was fourth to be ~ .52 seconds. In contrast, the average reaction time for the subsequent queued drivers was found to be ~ .06 seconds, with a range of 0.96 to 1. 1 9 seconds among sites. This trend was expected because the first driver has more of a Surprise situation (i.e.' the signal indication changing from red to green) than the subsequent queued drivers who can look ahead, see that the indication is green, and anticipate their tune of departure. As a result, the first drivers require slightly more reaction time than subsequent queued drivers. The average reaction times floured in this research are generally consistent with hose reported in We literature. Specifically, the study by Messer and Fambro 629 found that the first queued driver required about 2.0 seconds of reaction time and that subsequent queued drivers required about ~ .0 seconds. An earlier study by George and Heroy 649 at five intersections found that the first queue driver required about I.8 seconds and that subsequent queued drivers required about I.3 seconds. B.5.5 Weaving Mode! Database Summary The analysis of the weaving mode] database focussed on the volume and speed of the weaving and non-weav~ng traffic streams. These data were collected because it was hypothesized that Me volume ofthe two conflicting streams would affect Weir individual running speeds through Me weaving section. It was theorized that these speeds would decrease with Increasing volume. The weaving movement that is the subject of this analysis is the off-ramp r~ght-turn movement that weaves across the arterial to make a left-tu~n at the downstream signalmen Intersection. The average volumes and speeds through the weaving section for the six study sites are listed in Table B-12. Table B-12. Weaving mode! database - summary statistics Statistic Variable | No. | Average | Minimum | Maximum | Volume otal arterial volume entering weaving section, vph ~6 ~1,409 ~954 I=. v~ mends weaving section, vphpl |6 | 575 | 465 | Veavingvolume(off-r~Tnp right todownstrea~nleft),vph |6 | 151 | 100 | speed | Arterial vein. spot speed at entry to weaving section, m/s |324 | 14.1 | 10.3 Artenal vein. running speed Trough weaving section, m/s 324 10.6 8.3 | ~rterialveh.speed reduction due to weaving activity,rn/s |324 | 3.4 | 1.4 | Weaving vein. running speed Cough weaving section, m/s 421 8.0 6.6 , ,813 8s6 230 9.3 12.1 7.2 10 3 B-26
As the volumes in Table B-12 indicate, the six study sites have relatively high weaving volumes. On average, the weaving vehicles accounted for about one-half of the off-ramp right-turn volume at any one site. The arterial lane volumes were also relatively high such that weaving opportunities were limited during a significant portion of the signal cycle. It should be noted that the off-ramp right-turn movement at three of the sites was signalized (with right-turn on red allowed), the over three were yield-controlled. Two types of speed statistic were reported for the arterial vehicles. One statistic is the spot speed of the arterial vehicles at a point just upstream of the off-ramp. The second statistic is the running speed of the same arterial vehicles. This speed related the distance traveled through the weaving section to the corresponding travel time. The distance and time were measured from the point of entry to the weaving section to the downstream intersection stop line or to the first point of joining the stopped queue associated with the downstream signal, whichever was reached first. The running speed measured in this manner was reasoned to be the better measure of weaving vehicle Impact because it excluded the effect of downstream signal delay on the speed estunate. The difference between the arterial spot speed and Me running speed is an indicator of a speed reduction in the weaving area due to weaving activity. The average speed reduction at the study sites was 3.4 m/s. This statistic is more useful than the spot or running speeds alone because it eliminates the effect of differing speed limits among the sites. A preliminary examination of this speed difference indicates a strong correlation between it and the total arterial and weaving volumes. Increases In either volume level tended to Increase the speed reduction. We average weaving vehicle speed is also shown In Table B-12. The weaving vehicle speed tends to be lower than that of the arterial vehicles because the weaving vehicle enters the weaving section at a relatively slow speed due to the ramp control (i.e., signal or yield sign). Some preliminary analysis of this speed indicates that it decreases with increasing arterial lane volume. B.6 APPENDIX B REFERENCES 1. TAB Special Report 209: Highway Capacity Manual, 3rd ed. TRB, National Research Council, Washington,D.C. (1994~. 2. Messer, C.J., and Fambro, D.B. "Effects of Signal Phasing and Length of Left-Turn Bay on Capacity. In Transportation Research Record 644, TRIP National Research Council, Washington, D.C. (1977) pp. 95-101. 3. Herman, R., Larn, T., and Rothery, R.W. "The Starting Characteristics of Automobile Platoons." Proc., 5th International Symposium on the Theory of Traffic Flow and Transportation, American Elsevier Publishing Co., New York (1971) pp. 1-17. 4. George, E.T., and Heroy, F.M. "Starting Response of Traffic at Signalized Intersections. - Traffic Engineering, Institute of Transportation Engineers, Washington, D.C. (July 1966) pp. 39-43. B-27
