U.S. Department of Transportation
Federal Highway Administration
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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
REPORT |
This report is an archived publication and may contain dated technical, contact, and link information |
|
Publication Number: FHWA-HRT-12-054 Date: December 2012 |
Publication Number:
FHWA-HRT-12-054
Date: December 2012 |
This section describes the methodologies used to select and obtain many of these measures. In many cases, the data structures described in this section are employed. (Note that table 6 identifies the measures examined in this study.)
Many FMSs are equipped with point-based and, in some cases, probe-based traffic detectors to perform normal traffic management functions. Since these detectors provide a basis for automatic data collection for performance evaluation purposes, the manual effort to obtain measures based on speed and travel time is minimal.
Many of the measures in table 6 involve the computation of travel time and delay. System delay is defined as is the sum of freeway mainline delay, freeway ramp delay, and intersection delay for all vehicles. System travel time has a similar relationship. Vehicle travel time and delay consider these quantities on an individual trip basis.
The relationships provided below describe the requirements for obtaining freeway mainline data.
5.1.1.1 Mainline Delay and Travel Time Evaluation for Point Detectors
Figure 6. Equation. Domain system travel time.
Where:
TT = System mainline travel time (vehicles
per hour).
DO = Domain ID.
N5 = 5-min evaluation period index
number.
T5 = 5-min period for mainline and ramps.
V = Roadway volume (vehicles per hour).
LE = Length of link, domain, or probe
sensing region (mi).
SD = Domain speed (mi/h).
In some systems, SD represents weighted speed.(9) Since speed and volume varies in
different lanes, weighted speed is the product of lane volume and lane speed
divided by the total volume.
Figure 7. Equation. Domain system delay.
Figure 8. Equation. Link system travel time.
Where:
L = Link ID.
Figure 9. Equation. Link system travel time for 15-min periods.
Where:
P = 15-min period index.
NF = 5-min index at
the beginning of the 15-min period.
Figure 10. Equation. Link system delay.
Where:
D = System mainline delay for measurement interval (vehicle hours).
Figure 11. Equation. Link system delay for 15-min periods.
Figure 12. Equation. Domain vehicle travel time.
Where:
VT = Vehicle travel time (hours).
Figure 13. Equation. Domain vehicle delay.
Where:
VD = Vehicle delay (hours).
SR = Reference
speed for delay (mi/h).
Figure 14. Equation. Link vehicle travel time.
Figure 15. Equation. Link vehicle travel time for each 15-min period.
Figure 16. Equation. Link vehicle delay.
Figure 17. Equation. Link vehicle delay for each 15-min period.
5.1.1.2 Mainline Delay and Travel Time Evaluation for Probe Detectors
Probe detectors provide the basis for developing link delay and link travel time. Because the boundaries of probe sensing regions may not directly correspond to link boundaries, a domain structure (see figure 4) or an equivalent relationship is required. The basic concept requires determining the speed in the set of domains included in the probe sensing region by dividing the region's length by the travel time measured by the probe vehicles, as shown in figure 18 and figure 19. SP represents the speed for all domains encompassed by the probe-sensing region and is used to compute domain and link vehicle travel time and delay in figure 12 through figure 17 at the 5-min level. It is also used for probe detection in place of SD in figure 6 and figure 12
.
Figure 18. Equation. Travel time as sensed by probe PR.
Figure 19. Equation. Probe-sensing region speed for region PR.
Where:
TP = Travel time as sensed by probe
vehicles (hours).
PR = Probe sensing region ID.
x = Number of vehicles in 5- or 15-min
probe vehicle sample.
SP = Probe sensing region speed (mi/h).
RRT = Reference
ramp travel time.
Probe detection technologies are discussed in section 6 of this report.
In order to develop system delay and system travel time measures, the volume variable required by figure 6 and figure 7 must be obtained. A source of link volume data, such as a point detector station, is required.
5.1.1.3 Entry Ramp Travel Time
Unlike the mainline, most ITSs do not provide an automatically based sensing methodology for obtaining entry ramp time and delay. Ramp data, if employed, are most conveniently accumulated on a 15-min basis when considering the ramp as a link.
5.1.1.4 Freeway System Travel Time and Delay
Freeway travel time and delay are the sum of mainline travel times and (optionally) ramp travel times and delays. Computation on a 15-min basis is convenient for further measure development.
Figure 20. Equation. Freeway system travel time.
Figure 21. Equation. Freeway system delay.
Where:
FT = Freeway system travel time.
RT = Entry ramp travel time (hours).
R = Ramp index.
RN = Total number of ramps.
FD = Freeway system
delay.
5.1.1.5 Private Vehicle Occupant System Delay
The basic measure is computed on a 15-min basis and link basis and aggregated annually on a system-wide basis, as shown in figure 22.
Figure 22. Equation. Private vehicle occupant system delay.
Where:
K1 = Average number of
travelers in a private passenger vehicle.
FP = Private passenger vehicle fraction
of traffic volume.
LPP = Traveler
system delay in private passenger vehicles (person hours).
5.1.1.6 Commercial Vehicle Occupant System Delay
The basic measure is computed on a 15-min basis and link basis and aggregated annually on a system-wide basis, as shown in figure 23.
Figure 23. Equation. Commercial vehicle occupant system delay.
Where:
K2 = Average number of occupants
in commercial vehicle.
FC = Commercial vehicle fraction of
traffic volume.
LPT = Occupant
delay in commercial vehicles (person hours).
5.1.1.7 Goods Inventory Delay
The basic measure is computed on a 15-min basis and link basis and aggregated annually on a system-wide basis, as shown in figure 24.
Figure 24. Equation. Goods inventory delay.
Where:
K3 = Average weight of load
in trucks carrying goods (tons).
FR = Traffic volume fraction of trucks
carrying loads, excluding deadheading trucks.
LPG = Goods delay
(ton hours).
5.1.2.1 Route Travel Time
Route travel time is commonly provided to the motorist by DMS on the freeway mainline as well as through Web sites. Designated routes are often provided for this purpose, and these routes are convenient to use for evaluation.(17)
Route travel time is the sum of route link travel times and may be computed as follows:
Figure 25. Equation. Route travel time.
Where:
RTT = Route travel time (hours).
RI = Link on start of selected route.
RO = Link on end of
selected route.
VT = Route link travel time (hours).
If a trip starts at 7 a.m., the travel time for the first link on the route (designated as RI) becomes VT. N5 for the first link in this case is 73 (12 5-min periods for the period from midnight until 7 a.m. plus the current evaluation period). It is designated as NSTART.
Recognizing that the links on the route might be covered during different time periods and consequently at different speeds, a laddered concept for computing route travel times was studied.(17) Route travel time is the sum of route link travel times and is computed for the appropriate time period for that link. The concept is described below.
If VT for a link is less than 5 min, then the travel time for the next link uses the same 5-min time period. If VT is greater than or equal to 5 min, then the travel time for the next link uses the subsequent 5-min time period. Higatani et al. indicate that this approach is more accurate than the summation of link travel times computed for a single time period.(18)
Figure 26 provides a flow chart that implements this concept.
Figure 26. Flowchart. Route travel times.
Similarly, freeway route delay (ROD) may be computed as follows:
Figure 27. Equation. Freeway route delay.
For evaluation purposes, route delay is most meaningful when used as an average value for a peak hour or peak period. To be statistically meaningful, a sufficiently large data sample (number of days for data collection) is required. For a peak hour evaluation, 12 data samples are generated per day. It may be expected during the course of 1 month that data will be available for a minimum of 15 days after eliminating weekends, holidays, and other days that may not be typical because of weather problems, special events, etc. Based on these values, the standard estimate of the mean value of route delay is approximately 7.5 percent.(19)
5.1.2.2 Route Travel Time Reliability
Travel time reliability measures the extent of this unexpected delay. A formal definition for travel time reliability is the consistency or dependability in travel times, as measured day-to-day and/or across different times of the day (20)
Travel time variability may be measured by comparing travel times for a specified route for a given time period (e.g., for a peak hour starting at 7 a.m). Shaw recommends a minimum data collection period of 4 weeks at 15-min intervals.(10) Coupling this criterion with the previous discussion of route travel time, if a "trip" is considered to be a calculation of three 5-min travel times for each 15-min period in a weekday peak hour, eliminating holidays and other non-representative days, a 1 month data collection cycle is a sufficiently representative time period.
The basis for travel time variability and the measures that are used to express it is the standard deviation of the travel time measurements. This is given by Martin and Wu as follows:(7)
Figure 28. Equation. Standard deviation of travel time measurements.
Where:
s = Estimate of travel time standard deviation.
Tj = Travel time of the ith trip on a specific route.
M = Mean travel time of a set of sample trips
for the period (e.g., 15 min).
n = Number of sample trips.
Commonly used measures of route travel time reliability are the completion of 90 or 95 percent of the trips within a given time. Statistical tables indicate that the relationship between the sample of travel times and the mean are as follows:
Measures that are commonly used include the following:(20)
Figure 29. Equation. Buffer time.
Figure 30. Equation. Planning time.
The relationship among these measures is shown in figure 31.(20)
Figure 31. Graph. Relationship of travel time reliability indices.
The basis for all of the reliability measures is route or point-to-point travel times. The following lists shows the four basic ways in which these travel times can be developed:(20)
Throughput may be evaluated as VMT for a link for the peak hour. For the evaluation process, for each 5 min of the peak hour, the lowest volume for each domain in the link (LV) is identified. Peak hour throughput (PHT) is provided in figure 32.
Figure 32 . Equation. Peek hour throughput.
Throughput may be considered a measure of system efficiency for a freeway link, particularly during the peak period. Gordon et al. suggest that plots of traveler miles versus traveler hours for various conditions may be useful for evaluating the general performance of ITS improvements.(21) This concept is shown in figure 33, where the solid curve represents improved system operation for all traffic conditions relative to the dashed curve. The slope of the line from the origin to a point on the curve represents speed for the link.
Figure 33. Graph. Link throughput.
The throughput measures originally shown in table 6 include the following:
Signalized surface streets experience discontinuous flow. As a result, speeds measured by point detectors (where available) do not provide information that may directly be used to develop link speeds and travel times. While technologies that make greater use of automatic data are emerging, current evaluations often feature a strong manual component. Section 6 of this report provides more information on these technologies.
The total delay experienced by a road user can be defined as the difference between the travel time actually experienced and the reference travel time that would result in the absence of traffic control, changes in speed due to geometric conditions, any incidents, and the interaction with any other road users. Control delay is defined as the portion of delay that is attributable to the control device (i.e., the signal, its assignment of right-of-way, and the timing used to transition right-of-way in a safe manner) plus the time decelerating to a queue, waiting in queue, and accelerating from a queue. For typical through movements at a signalized intersection, total delay and control delay are the same in the absence of any incidents.(22) Figure 34 shows control delay in a time-space context.(4)
Figure 34. Graph. Control delay.
Control delay for a lane group may be obtained by observations at the intersection or by measuring the time it takes for a vehicle to traverse a path. The relationship between travel time and control delay for a lane group is given by figure 35 as follows:(4)
Figure 35. Equation. The relationship between travel time and control delay.
Where:
LCD = Control delay for the intersection
lane group associated with a travel link for a 15-min time period.
RET(LI, LG) = Reference vehicle travel time
for the lane group for the travel link.
RLTT(LI, LG)
= Vehicle travel time for the lane group for the travel link.
Evaluation methodologies generally include either measuring control delay and computing vehicle travel time using the equation in figure 35 or measuring the link travel time and identifying the control delay using that equation.
Current evaluation methodologies primarily use intersection observations and/or measurements using floating vehicles to obtain the variables. Recent technology developments, as described in section 6 of this report, have resulted in a more efficient use of the manual labor required as well as automated techniques to obtain these data.
Chapter 31 of the Highway Capacity Manual provides worksheets to assist in recording manual queue observations and computing control delay from these observations.(4)
Table 10 provides an estimate of the number of runs required to achieve a 95 percent level of confidence.(23)
Table 10. Sample size requirements.
Average Range in Running Speed (mi/h) × R |
Minimum Number of Runs for Specified Permitted Error | ||||
---|---|---|---|---|---|
+1.0 mi/h | +2.0 mi/h | +3.0 mi/h | +4.0 mi/h | +5.0 mi/h | |
2.5 | 4 | 22 | 2 | 2 | 2 |
5.0 | 8 | 4 | 3 | 2 | 2 |
10.0 | 21 | 8 | 5 | 4 | 3 |
15.0 | 38 | 14 | 8 | 6 | 5 |
20.0 | 59 | 221 | 12 | 8 | 6 |
* Interpolation should be used when R is a value other than those shown in column 1.
Figure 36 provides the basis for evaluating individual vehicle travel time and control delay for a lane group at a signalized intersection approach as well as the measures derived from them.
5.1.4.1 Surface Street System Delay
Intersection delay for a 15-min period is provided in figure 36 as follows:
Figure 36. Equation. Intersection delay.
Where:
LI = Intersection ID.
LG = Traffic signal lane group.
T15 = 15 min for
intersection signals and surface streets.
System delay (SSSD) for a 15-min period is provided in figure 37 as follows:
Figure 37. Equation. System delay.
5.1.4.2 Surface Street Route Delay
Surface street route delay (SSRD) is provided in figure 38 as follows:
Figure 38. Equation. Surface street route delay.
5.1.4.3 Surface Street Route Travel Time
Surface street route travel time (RTT) is provided in figure 39 as follows:
Figure 39. Equation. Surface street route travel time.
5.1.4.4 Other Surface Street Delay Measures
By substituting SSSD for FD, figure 22 through figure 24 may be used to compute system delay for private vehicle occupants, commercial vehicle occupants, and goods inventory.
Agencies typically collect and classify crash data based on crash reports to identify trends and areas requiring improvement. Depending on the type of data collected, the database management systems used by these agencies have a great deal of flexibility in providing data at required locations for various functions.
Table 11 shows an example of statewide statistics for Washington State, and table 12 shows an example of a Washington State summary report of crashes by type.(24)
The methodologies developed under this study focus on developing the data for the safety measures identified in table 6 by location. The measures required for the benefit-cost evaluation approach described in this report are as follows:
Table 11. 2009 average collision rates by functional class in Washington-Northwest region (State routes only).
Type of Area |
Principal Arterial |
Minor Arterial |
Collector |
Interstate |
All Highways |
---|---|---|---|---|---|
Rural Areas |
|||||
VMT (millions) |
554.74 |
455.55 |
216.70 |
940.03 |
2,167.02 |
Miles of highway |
133.41 |
255.98 |
158.96 |
57.61 |
605.96 |
Total collisions |
587 |
518 |
394 |
494 |
1,993 |
Collision rate* |
1.06 |
1.14 |
1.82 |
0.53 |
0.92 |
PDO collisions |
378 |
292 |
249 |
347 |
1,266 |
PDO collision rate* |
0.68 |
0.64 |
1.15 |
0.37 |
0.58 |
Injury collisions |
205 |
219 |
143 |
145 |
712 |
Injury collision rate* |
0.37 |
0.48 |
0.66 |
0.15 |
0.33 |
Fatal collisions |
4 |
7 |
2 |
2 |
15 |
Fatal collision rate** |
0.72 |
1.54 |
0.92 |
0.21 |
0.69 |
Urban Areas |
|||||
VMT (millions) |
4,124.91 |
503.58 |
0.00 |
6,827.04 |
11,455.53 |
Miles of highway |
333.18 |
98.04 |
0.00 |
141.43 |
572.65 |
Total collisions |
9,032 |
1,501 |
0 |
9,266 |
19,799 |
Collision rate* |
2.19 |
2.98 |
0.00 |
1.36 |
1.73 |
PDO collisions |
5,981 |
943 |
0 |
6,351 |
13,275 |
PDO collision rate* |
1.45 |
1.87 |
0.00 |
0.93 |
1.16 |
Injury collisions |
3,034 |
551 |
0 |
2,898 |
6,483 |
Injury collision rate* |
0.74 |
1.09 |
0.00 |
0.42 |
0.57 |
Fatal collisions |
17 |
7 |
0 |
17 |
41 |
Fatal collision rate** |
0.41 |
1.39 |
0.00 |
0.25 |
0.36 |
All Areas |
|||||
VMT (millions) |
4,679.65 |
959.13 |
216.70 |
7,767.07 |
13,622.55 |
Miles of highway |
466.59 |
354.02 |
158.96 |
199.04 |
1,178.61 |
Total collisions |
9,619 |
2,019 |
394 |
9,760 |
21,792 |
Collision rate* |
2.06 |
2.11 |
1.82 |
1.26 |
1.60 |
PDO collisions |
6,359 |
1,235 |
249 |
6,698 |
14,541 |
PDO collision rate* |
1.36 |
1.29 |
1.15 |
0.86 |
1.07 |
Injury collisions |
3,239 |
770 |
143 |
3,043 |
7,195 |
Injury collision rate* |
0.69 |
0.80 |
0.66 |
0.39 |
0.53 |
Fatal collisions |
21 |
14 |
2 |
19 |
56 |
Fatal collision rate** |
0.45 |
1.46 |
0.92 |
0.24 |
0.41 |
* Indicates per 1 million VMT.
** Indicates per 100 million VMT.
Table 12. 2009 leading collision type for all collisions in Washington (State routes only).
First Collision Type | Eastern Region | North Central Region | Northwest Region | Olympic Region | South Central Region | Southwest Region | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Number | Percent | Number | Percent | Number | Percent | Number | Percent | Number | Percent | Number | Percent | |
Rear-end (all types) | 748 | 24 | 408 | 22 | 10,457 | 48 | 4,254 | 44 | 736 | 23 | 914 | 28 |
Hit fixed object | 691 | 22 | 485 | 26 | 3,276 | 15 | 1,824 | 19 | 898 | 28 | 969 | 29 |
Side-swipe (opposite or same direction) | 181 | 6 | 85 | 5 | 2,856 | 13 | 963 | 10 | 245 | 8 | 293 | 9 |
Entering at angle | 417 | 13 | 194 | 11 | 1,715 | 8 | 1,055 | 11 | 231 | 7 | 289 | 9 |
All other-same direction | 145 | 5 | 81 | 4 | 951 | 4 | 401 | 4 | 187 | 6 | 144 | 4 |
Overturn | 268 | 9 | 162 | 9 | 416 | 2 | 276 | 3 | 386 | 12 | 153 | 5 |
All other-opposite direction | 173 | 6 | 98 | 5 | 1,180 | 5 | 408 | 4 | 128 | 4 | 135 | 4 |
Vehicle strikes deer | 287 | 9 | 145 | 8 | 171 | 1 | 186 | 2 | 139 | 4 | 154 | 5 |
All other-non-collision | 31 | 1 | 44 | 2 | 133 | 1 | 79 | 1 | 91 | 3 | 47 | 1 |
Vehicle-pedestrian | 43 | 1 | 10 | 1 | 193 | 1 | 80 | 1 | 9 | 0 | 19 | 1 |
One parked one moving | 18 | 1 | 25 | 1 | 118 | 1 | 76 | 1 | 47 | 1 | 63 | 2 |
Hit non-fixed object | 14 | 0 | 31 | 2 | 57 | 0 | 32 | 0 | 43 | 1 | 40 | 1 |
Vehicle-pedalcyclist | 22 | 1 | 8 | 0 | 106 | 0 | 43 | 0 | 4 | 0 | 25 | 1 |
Head-on | 20 | 1 | 14 | 1 | 67 | 0 | 39 | 0 | 17 | 1 | 16 | 0 |
Vehicle strikes elk | 3 | 0 | 8 | 0 | 18 | 0 | 13 | 0 | 41 | 1 | 29 | 1 |
Domestic animal | 15 | 0 | 19 | 1 | 15 | 0 | 15 | 0 | 24 | 1 | 12 | 0 |
Parked position (one car entering/leaving) | 10 | 0 | 4 | 0 | 22 | 0 | 18 | 0 | 2 | 0 | 3 | 0 |
Alternatively, the components of the general category of crashes may be used for the benefit-cost analysis. These components include the following:
An example of data from the New York State Department of Transportation (NYSDOT) crash record database that was used for a benefit-cost analysis is shown in table 13 and table 14.(25) The tables show the data sorted by the specific freeway links required for the study.
Depending on the TMC's hours of operation and the crash classifications provided by FMS, TMC-generated data may be used to supplement crash record data.
Table 13. Crash rates for selected links in Rochester, NY, during the accident period from March 1, 2000, to February 28, 2002.
Roadway Link | Link Description | Total Accidents | Average AADT | Link Length (mi) | Accident Rate | Statewide Average Rate |
---|---|---|---|---|---|---|
NYS Route 104 | Goodman Street interchange | 120 | 68,200 | 0.80 | 2.68* | 2.26 |
Culver Road interchange | 72 | 73,000 | 0.80 | 1.50 | 2.26 | |
Route 590 interchange | 71 | 70,000 | 0.80 | 1.54 | 1.94 | |
Route 590 to Bay Road | 46 | 68,000 | 1.60 | 0.55 | 1.78 | |
Bay Road interchange | 32 | 62,000 | 0.80 | 0.79 | 2.26 | |
Bay Road to Five Mile Line Road | 12 | 57,000 | 1.25 | 0.21 | 1.09 | |
Five Mile Line Road to Route 250 | 88 | 45,000 | 2.86 | 0.91 | 1.47 | |
Phillips Road to Salt Road | 16 | 42,000 | 0.90 | 0.52 | 1.47 | |
Salt Road interchange | 8 | 33,000 | 0.40 | 0.66 | 1.47 | |
Route 104 Total | 465 | 64,257 | 10.21 | 0.96 | 1.94 | |
Interstate 490 | Route 390 interchange | 141 | 90,000 | 1.46 | 1.38 | 1.94 |
Mount Read interchange | 60 | 100,000 | 0.47 | 1.44 | 2.26 | |
Mount Read Boulevard to inner loop area | 229 | 92,000 | 1.46 | 2.19 | 2.26 | |
Inner loop area | 330 | 107,000 | 1.59 | 2.50* | 1.94 | |
Goodman Street interchange | 80 | 92,000 | 0.50 | 1.99 | 2.26 | |
Route 490 Total | 840 | 105,770 | 5.48 | 1.95* | 1.94 | |
NYS Route 590 | Browncroft Boulevard interchange | 29 | 90,000 | 0.40 | 0.88 | 2.26 |
Browncroft Boulevard to Empire Boulevard | 31 | 101,000 | 0.67 | 0.55 | 1.78 | |
Empire Boulevard interchange | 113 | 101,000 | 0.58 | 2.25 | 2.26 | |
Empire Boulevard to Route 104 | 55 | 98,000 | 0.85 | 0.81 | 1.78 | |
Route 104 interchange | 27 | 76,000 | 0.60 | 0.70 | 1.47 | |
Ridge Road interchange | 18 | 22,000 | 0.60 | 1.60* | 1.47 | |
Route 590 Total | 273 | 50,725 | 3.70 | 1.94 | 1.94 |
*Average accident rate is higher than the statewide average rate for similar facility types.
Table 14. Crash classification by link in Rochester, NY, during accident period from March 1, 2000, to February 28, 2002.
Roadway Link | Link Description | Severity | Total Accidents | |||||
---|---|---|---|---|---|---|---|---|
Fatality | Injury | PDO | ||||||
Total | Percent | Total | Percent | Total | Percent | |||
NYS Route 104 | Goodman Street interchange | 0 | 0.00 | 31 | 25.83 | 89 | 74.17 | 120 |
Culver Road interchange | 0 | 0.00 | 17 | 23.61 | 55 | 76.39 | 72 | |
Route 590 interchange | 0 | 0.00 | 11 | 15.49 | 60 | 84.51 | 71 | |
Route 590 to Bay Road | 0 | 0.00 | 13 | 28.26 | 33 | 71.74 | 46 | |
Bay Road interchange | 0 | 0.00 | 14 | 43.75 | 18 | 56.25 | 32 | |
Bay Road to Five Mile Line Road | 0 | 0.00 | 5 | 41.67 | 7 | 58.33 | 12 | |
Five Mile Line, Hard, Holt, and Route 250 interchanges | 0 | 1.14 | 26 | 29.55 | 61 | 69.32 | 88 | |
Phillips Road to Salt Road | 0 | 0.00 | 4 | 25.00 | 12 | 75.00 | 16 | |
Salt Road interchange | 0 | 0.00 | 4 | 50.00 | 4 | 50.00 | 6 | |
Route 104 total accidents and severity distribution | 1 | 0.22 | 125 | 26.88 | 339 | 72.90 | 465 | |
NYSDOT average severity distribution | N/A | 0.35 | N/A | 33.12 | N/A | 66.53 | N/A | |
Interstate 490 | Route 390 interchange | 0 | 0.00 | 38 | 25.95 | 103 | 73.05 | 141 |
Mount Read interchange | 0 | 0.00 | 18 | 30.00 | 42 | 70.00 | 60 | |
Mount Read Boulevard to inner loop area | 0 | 0.00 | 58 | 25.33 | 171 | 74.67 | 229 | |
Inner loop area | 0 | 0.30 | 84 | 25.45 | 245 | 74.24 | 330 | |
Goodman Street interchange | 0 | 0.00 | 19 | 23.75 | 61 | 76.25 | 80 | |
Route 490 total accidents and severity distribution | 1 | 0.12 | 217 | 25.83 | 622 | 74.05 | 840 | |
NYSDOT average severity distribution | N/A | 1.35 | N/A | 33.12 | N/A | 66.53 | N/A | |
NYS Route 590 | Browncroft Boulevard interchange | 0 | 0.00 | 5 | 17.24 | 24 | 82.76 | 29 |
Browncroft Boulevard to Empire Boulevard | 0 | 0.00 | 9 | 29.03 | 22 | 70.97 | 31 | |
Empire Boulevard interchange | 0 | 0.00 | 29 | 25.66 | 84 | 74.34 | 113 | |
Empire Boulevard to Route 104 | 0 | 0.00 | 18 | 32.73 | 37 | 67.27 | 55 | |
Route 104 interchange | 0 | 0.00 | 2 | 7.41 | 25 | 92.59 | 27 | |
Ridge Road interchange | 1 | 5.56 | 1 | 5.56 | 16 | 88.89 | 18 | |
Route 590 total accidents and severity distribution | 1 | 0.37 | 64 | 23.44 | 208 | 76.19 | 273 | |
NYSDOT average severity distribution | N/A | 0.35 | N/A | 33.12 | N/A | 66.53 | N/A |
N/A = Not applicable.
While freeway crash data are generally best organized by links for benefit-cost analyses and when trying to identify locations requiring increased attention, crash data on surface streets are most often classified by intersection location. Crash record databases may be used to organize and analyze data in particular systems for comparison to agency averages. One measure that is useful in making these comparisons is crashes per 1 million vehicles entering the intersection or freeway ramp. Table 15 is an example of average values provided by NYSDOT.(26)
Table 15. Average intersection accident rates for State highways by intersection type based on accident data from January 1, 2007, to December 31, 2008.
Intersection Type | All Types ACC/MEV | Wet Road ACC/MEV | Left Turn ACC/MEV | Rear End ACC/MEV | Over- Taking ACC/MEV | Right Angle ACC/MEV | Right Turn ACC/MEV | Head On ACC/MEV | Side-Swipe ACC/MEV |
Three-Legged Intersections | |||||||||
Signal all lanes | 0.22 | 0.04 | 0.02 | 0.06 | 0.01 | 0.03 | 0.01 | 0.01 | 0.01 |
Sign all lanes | 0.15 | 0.03 | 0.01 | 0.03 | 0 | 0.01 | 0 | 0 | 0 |
No control all lanes | 0.09 | 0.01 | 0.01 | 0.01 | 0 | 0.01 | 0 | 0 | 0 |
Four-Legged Intersections | |||||||||
Signal all lanes | 0.50 | 0.09 | 0.06 | 0.11 | 0.02 | 0.11 | 0.02 | 0.01 | 0.01 |
Sign all lanes | 0.31 | 0.06 | 0.02 | 0.04 | 0.01 | 0.08 | 0.01 | 0 | 0 |
No control all lanes | 0.12 | 0.02 | 0 | 0.01 | 0.01 | 0.02 | 0 | 0 | 0.01 |
On Ramp (All Control) | |||||||||
Merge with one lane | 0.07 | 0 | - | - | - | - | - | - | - |
Merge with two+ lanes | 0.04 | 0.01 | - | - | - | - | - | - | - |
Off Ramp (All Control) | |||||||||
Merge with one lane | 0.08 | 0.08 | - | - | - | - | - | - | - |
Merge with two+ lanes | 0.04 | 0.01 | - | - | - | - | - | - | - |
ACC/MEV = Accidents per million vehicles entering the intersection.
- Indicates accident information was not collected.
Note: NYSDOT stopped processing most non-reportable accidents beginning with 2002 accident data. Therefore, the rates are based primarily on just reportable accidents from NYSDOT.
Kar and Datta describe a complex weighting of PDO, injury, and fatality crash costs, as well as crash frequency to develop a safety performance index (SPI).(27) Their findings indicate that SPI may be used for planning resource allocations to reduce crashes.
5.2.1.1 Crash Causality
Some agencies maintain extensive databases for classification
of crashes by causality factors.
For example, WSDOT maintains a database that reports on the details of a number
of factors, including the following:(24)
Because ITS has different impacts on these factors and agencies collect and report crash causality data using different formats with varying levels of detail and different importance scales, researchers in this project have generally not developed specific measures to deal with these items. However, it is recognized that work zone crashes are important to most agencies, and TMC operations often significantly include management assistance for this issue. Therefore, measures are included in table 6 and table 7 for work zone crashes.
Table 16. WSDOT crash data for contributing circumstances.(24)
Driver Contributing Circumstances | Fata Collisions | Serious Injury Collisions | Minor Injury Collisions | PDO Collisions | All Collisions |
---|---|---|---|---|---|
Exceeding reasonable safe speed | 107 | 462 | 7,317 | 13,808 | 21,694 |
Did not grant right of way to vehicle | 24 | 255 | 5,754 | 14,311 | 20,344 |
Follow too close | 4 | 118 | 6,323 | 10,548 | 16,993 |
Other | 56 | 281 | 2,818 | 11,359 | 14,514 |
Inattention | 25 | 167 | 3,347 | 6,240 | 9,779 |
Under influence of alcohol | 184 | 386 | 2,464 | 3,459 | 6,493 |
Disregard stop and go light | 8 | 70 | 1,408 | 1,935 | 3,421 |
Improper turn | 2 | 16 | 560 | 2,662 | 3,240 |
Driver distractions outside vehicle | 2 | 35 | 961 | 1,645 | 2,643 |
Exceeding stated speed limit | 80 | 216 | 932 | 1,346 | 2,574 |
Operating defective equipment | 12 | 56 | 668 | 1,639 | 2,375 |
Improper backing | 0 | 6 | 127 | 2,159 | 2,292 |
Disregard stop sign-flashing red | 20 | 60 | 854 | 1,301 | 2,235 |
Over center line | 54 | 154 | 679 | 884 | 1,771 |
Apparently asleep | 10 | 70 | 665 | 907 | 1,652 |
Did not grant right of way to pedestrian/pedal cyclist | 16 | 136 | 1,286 | 40 | 1,478 |
Driver interacting with passengers, animals, or objects in the vehicle | 6 | 26 | 589 | 782 | 1,403 |
Other driver distractions inside vehicle | 1 | 22 | 481 | 717 | 1,221 |
Improper passing | 22 | 45 | 296 | 847 | 1,210 |
Unknown driver distraction | 1 | 8 | 299 | 594 | 902 |
Driver operating handheld telecommunication device | 4 | 19 | 313 | 470 | 806 |
Apparently ill | 8 | 43 | 413 | 342 | 806 |
Under influence of drugs | 11 | 53 | 332 | 399 | 795 |
Improper U-turn | 2 | 15 | 205 | 562 | 784 |
Driver adjusting audio or entertainment system | 0 | 6 | 160 | 252 | 418 |
Driver eating or drinking | 4 | 9 | 119 | 225 | 357 |
Apparently fatigued | 1 | 8 | 142 | 164 | 315 |
Improper parking location | 0 | 7 | 17 | 188 | 212 |
Driver operating other electronic device | 1 | 3 | 71 | 107 | 182 |
Disregard yield sign-flashing yellow | 0 | 1 | 51 | 114 | 166 |
Had taken medication | 0 | 5 | 75 | 79 | 159 |
Failing to signal | 0 | 1 | 47 | 111 | 159 |
Driver smoking | 0 | 4 | 47 | 84 | 135 |
Headlight violation | 1 | 4 | 32 | 49 | 86 |
Driver reading or writing | 0 | 0 | 32 | 50 | 82 |
Driver operating hands-free wireless telecommunication device | 0 | 1 | 17 | 47 | 65 |
Improper signal | 0 | 2 | 11 | 51 | 64 |
Disregard flagger-officer | 0 | 3 | 20 | 26 | 49 |
Driver grooming | 0 | 0 | 6 | 12 | 18 |
The Work Zone Safety Performance Measures Guidance Booklet suggests the safety measures shown in table 17.(28)
Table 17. Safety work zone performance measures.
Condition | Measure |
---|---|
Site crash rate during construction/site crash rate prior to construction < 1.0 | Excellent |
Site crash rate during construction/site crash rate prior to construction = 1.0 | Good |
Site crash rate during construction/site crash rate prior to construction < 1.2 | Fair |
Site crash rate during construction/site crash rate prior to construction < 1.3 | Poor |
Site crash rate during construction/site crash rate prior to construction > 1.3 | Very poor |
Figure 40. Graph. Five leading contributing circumstances in all collisions.
Figure 41. Graph. Five leading contributing circumstances in fatal collisions.
An overall measure for the TMC is the average of the annual evaluations of the work zones included in the TMC's management region.
5.2.1.2 Secondary Crashes
Secondary crashes result from an existing incident. Many of these crashes occur at the tail of queues that result from the incident. It has been estimated that 14 to 30 percent of crashes are secondary crashes.(29,30)
Secondary crashes are often not identified as such by many of the accident reporting and classification systems. Since the ITS techniques that support more rapid incident clearance and provide advance motorist warning of queues may substantially reduce secondary crashes, secondary crashes are an important measure for ITS performance. These data are best obtained by ensuring that secondary crashes are included as a crash classification parameter in FMS. An overall measure for the TMC is the annual sum of the secondary crashes included in the TMC's management region.
Congestion significantly increases fuel consumption rates per VMT. The fuel consumption rates in table 18 were computed using the Motor Vehicle Emission Simulator (MOVES) model.(31) The model employs a representative vehicle class mix. The speeds listed in the table are average speeds for the driving cycle for which the model is based. The domain speed may be used in conjunction with the table.
Table 18. Fuel consumption rates in gallons per VMT.
Speed Range | Year | |
---|---|---|
2011 | 2016 | |
10 mi/h > s | 0.175 | 0.167 |
20 mi/h > s ≥ 10 mi/h | 0.077 | 0.073 |
30 mi/h > s ≥ 20 mi/h | 0.059 | 0.056 |
40 mi/h > s ≥ 30 mi/h | 0.052 | 0.050 |
50 mi/h > s ≥ 40 mi/h | 0.050 | 0.048 |
60 mi/h > s ≥ 50 mi/h | 0.048 | 0.046 |
s > 60 mi/h | 0.049 | 0.046 |
Note: s represents the speed range.
Fuel consumption (FUF) in gallons for a domain for a 5-min period is computed as follows:
Figure 42. Equation. Fuel consumption.
Fuel consumption and changes in fuel consumption are often reported on an annual basis.
Because surface street travel is characterized by several factors at locations upstream of a queue at a controlled intersection and by delays at the intersection and because detailed observations are usually unavailable at locations away from the intersection, an appropriate measure of system performance is the fuel consumption resulting from control delay at traffic signals.
Federal Highway Administration (FHWA) data developed for this project provide the following conservative fuel consumption rates when intersections experience control delay:
Fuel consumption resulting from control delay for each lane group for a 15-min evaluation period is given by the following equation:
Figure 43. Equation. Fuel consumption due to control delay.
Where:
GA = Fuel consumption rate.
FUP = Fuel consumption for
intersections for a 15-min period (gallons).
N15 = 15-min evaluation
period index number.
Aggregation of these data to an annual period provides a meaningful measure for improvements to traffic control measures.
Appendix B discusses emissions models and how they apply to performance evaluation.
Travel time information is commonly made available to motorists through DMS and other information delivery methods. As a result, motorists are aware of variations in travel time throughout the day as well as day to day. This information is usually provided in terms of the time to reach a freeway exit from a specific DMS or from a prescribed freeway entry location. Route delay is essentially route travel time minus the travel time for a reference speed. For surface streets, it is provided by the equation in figure 38 . Freeway route delay is the sum of link delay for the links comprising the route (see figure 16).
5.5.2 Route Travel Time Reliability
Section 5.1.2.1 describes the methodology to compute freeway route travel time. Some agencies provide information on travel time reliability to motorists, often by means of electronic information delivery techniques. Section 5.1.2.2 discusses the various measures for freeway travel time reliability.
5.5.2.1 LOS
LOS is a commonly used measure for quality of service.(10) The characteristics for freeway LOS are summarized in table 19.(32)
Table 19. Freeway LOS characteristics.
LOS | Description |
A | Free flow with low volumes and high speeds. |
B | Reasonably free flow, but speeds beginning to be restricted by traffic conditions. |
C | In stable flow zone, but most drivers are restricted in the freedom to select their own speeds. |
D | Approaching unstable flow; drivers have little freedom to select their own speeds. |
E | Unstable flow; may be short stoppages. |
F | Unacceptable congestion, stop-and-go, and forced flow. |
While the American Association of State Highway and Transportation Officials publication, A Policy on Geometric Design of Highway and Streets, suggests a level C LOS for urban and suburban freeways, the decision is based on a number of factors for the local agency to consider.(33) Agencies may also consider the availability of transit alternatives in the selection of a design LOS.(34)
The recommended measure includes LOSs worse than level C as well as a grouping of levels A, B, and C. Table 20 defines LOS in terms of traffic density LOS.(4)
Table 20. LOS criteria for freeway facilities.
LOS | Density (passenger cars/mi/lane) |
---|---|
A | ≤ 11 |
B | 11-18 |
C | > 18-26 |
D | > 26-35 |
E | > 35-45 |
F | > 45 or any component of demand volume to capacity ratio > 1.00 |
Density (DD) may be computed from detector measurements as follows:
Figure 44. Equation. Density.
Commonly used LOS measures include the following:
Figure 45. Equation. 5-min weighted average link density.
Figure 46. Equation. Peak hour weighted average link density.
Table 21. LOS for signalized intersections.
LOS |
Description |
---|---|
A | Control delay ≤ 10 s/vehicle |
B | 20 s/vehicle ≥ control delay > 10 s |
C | 35 s/vehicle ≥ control delay > 20 s |
D | 55 s/vehicle ≥ control delay > 35 s |
E | 80 s/vehicle ≥ control delay > 55 s |
F | Control delay > 80 s/vehicle |
5.5.2.2 User Satisfaction
Commonly used measures include the following:
5.5.2.3 Equity
While most ITS functions and operations result in improvements in travel time for the entire system as well as for each motorist, there are functions and operations that may result in delay reduction or reduction in crashes for the entire system but may adversely affect some individual highway users. Examples include the following:
Measures for equity include the following:
Figure 47. Graph. Example of Lorenz curve for a metered freeway entrance ramp.
Figure 48. Equation. Gini coefficient.
A major benefit of using ITS to reduce delay is the ability it provides operations managers to reduce incident clearance time. Although this benefit is included in section 5.1, "Delay and Travel Time Measures," its importance to the evaluation of TMC operations may merit special attention.
Gordon describes the following simplistic model for the total system delay from the time an incident occurs until the queue clears:(21)
Figure 49. Equation. Total system delay.
Where:
DT = Total system delay from
the time an incident occurs until the queue clears.
q1 = Volume at incident
clearance (roadway capacity).
q2 =
Volume entering incident location (demand volume).
q3 = Volume when incident is
present (restricted capacity resulting from incident).
T = Time from start
of incident to incident clearance (capacity is restored).
Rewriting figure 49 as figure 50, Gordon shows that the ratio of change in delay as a result of reduced incident clearance time to incident clearance time is given by figure 51.(21)
Figure 50. Equation. Rewriting total system delay.
Figure 51. Equation. Relationship between change in delay and reduced incident clearance time.
Where:
T = Incident
clearance time.
K = Percentage of delay.
From figure 51, it is observed that a small percentage of reduction in the time needed to clear an incident results in twice the percentage of delay reduced.
Measures to consider include the recording of the time needed to clear an incident and the total delay resulting from the incident. A number of evaluation studies employed techniques to estimate delay and the reduction in delay by service patrols; however, these methodologies are not well suited to non-research-related evaluation efforts.(37,38)
Incident clearance time (T) data may be obtained by subtracting the recorded clock time from the time that the incident is detected from the time that it is cleared (moving lanes cleared). An average incident detection period should be added to obtain the value for T. These data, along with the classification of incidents, are usually collected at the TMC by the traffic management system's incident management screens. Prior to obtaining the average value for T over the evaluation period for each incident class, it is recommended that incidents exceeding 6 h are deleted from the average (or are limited to 6 h) because these long periods are often the result of conditions over which the TMC has little control or influence, such as weather, roadway damage, or special hazardous materials situations.
Motorist service patrols have proved popular with the public.(39,37) Measures for evaluation include the service patrol assists, quality of service, and rating by public, as outlined in the following sections.
5.5.4.1 Service Patrol Assists
Most agencies that operate service patrol agencies maintain and often publish records of the number of assists and the type of service provided for each response.
5.5.4.2 Quality of Service
The following measures may be used to evaluate the quality of service provided:
Service patrol vehicle operators generally fill out a report for each assist, such as that used by WSDOT (see figure 52 ).(37) The detailed information collected is useful for operations improvements.
5.5.4.3 Rating by Public
Feedback from the public is often obtained through surveys completed by motorists at the time service is provided. Figure 53 shows a survey form used by WSDOT. The public's rating on service is shown in figure 54.
Figure 52. Illustration. Washington service patrol assist form.
Figure 53. Illustration. WSDOT service patrol survey.
Figure 54. Graph. Public rating on WSDOT service patrol program.
5.5.5 Response to Weather Situations
ITS may provide motorist information and other information to police and highway maintenance agencies to assist in responding to weather situations that affect travelling conditions. These conditions include snow/ice, fog, high winds, and flooding.
These conditions may be detected by road weather information systems, fog detectors, and reports by service patrols, motorists, and police. A measure for this service is the average time in minutes from receipt of the alert to the time that information is provided to motorists and to other response services.
Providing information to motorists is a key function of freeway and corridor TMCs. Information may be provided via the following:
It is important for the information provided by the TMC to be complete and consistent for all information delivery techniques. The following classes of information may be considered:
The capability of the TMC to provide data that may be accessed by the delivery methods described above may be rated on a scale of 0 to 10 for each of the above classes.