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driver satisfaction on urban streets. Drivers drove with two re- in Exhibit 29. The proposed MOEs reflect the input provided
searchers in their vehicle and talked aloud about the factors by the participants in the study, but combine like QOS fac-
that made them feel satisfied or dissatisfied with the drive they tors into, for the most part, measurable performance meas-
were experiencing in real time. The study was conducted in ures. For example, participants in the study often commented
four locations and one pilot study location. The locations con- negatively when they were forced to slow down or stop be-
sisted of two small urban areas (Tallahassee, Florida, and cause of poor arterial design that did not provide for bus pull-
Sacramento, California) and two large urban areas (Chicago, outs, turning facilities, on-street parking maneuvers, and
Illinois, and Atlanta, Georgia). In each location, routes re- poor access management that created many merge/diverge
quiring approximately 30 to 40 minutes of drive time were situations. The authors of this study have concluded that the
selected. Each of the routes incorporated characteristics in- MOE number of stops best represents the views of the partic-
cluded in Exhibit 26, taken from the HCM 2000. In small ipants in this study.
urban areas, the focus was on suburban and intermediate
characteristics; in large urban areas, the focus was on inter-
Intersection LOS Research
mediate and urban characteristics. Twenty-two participants
were in the four study locations; their characteristics are Sutaria and Haynes [15] focused on determining the
described in Exhibit 27. different levels of service at signalized intersections. The re-
The findings from this study resulted in 42 Quality of Ser- searchers investigated 30 signalized, isolated, fixed-time
vice (QOS) factors for urban streets that can be categorized intersections in the Dallas-Fort Worth area and determined
into several investment areas. Exhibit 28 contains the identi- that only 1 intersection experienced the full range of LOS cat-
fied factors according to driver transcripts and completed egories (then based on Load Factor defined as the ratio of the
surveys. total number of green signal intervals fully utilized by traffic
The researchers further refined the identified QOS factors during the peak hour to the total number of green intervals).
into nine proposed measures of effectiveness (MOEs) shown The intersection of Lemmon and Oaklawn Avenues in Dallas
Exhibit 26. Route Characteristics.
Route (Design) Category
Criterion
Suburban Intermediate Urban
Driveway/access
Low density Moderate density High density
density
Multilane divided; Multilane divided or Undivided one-way,
Arterial type undivided or two-lane undivided; one-way two-way, two or more
with shoulders two-lane lanes
Parking No Some Significant
Separate left-turn
Yes Usually Some
lanes
Signals/mile 1-5 4-10 6-12
Speed limit 40-45 mph 30-40 mph 25-35 mph
Pedestrian activity Little Some Usually
Roadside Low to medium Medium to
High density
development density moderate density
Exhibit 27. Participant Characteristics.
Number of
Field Site Ages Sex
Participants
Northern Virginia 4 2 20 - 30 year olds 2 women
(Pilot location) 2 35 - 50 year olds 2 men
Chicago 5 2 20 - 30 year olds 3 women
3 35 - 50 year olds 2 men
0 60 - 75 year olds
Tallahassee 5 1 20 - 30 year old 3 women
2 35 - 50 year olds 2 men
2 60 - 75 year olds
Atlanta 6 0 20 - 30 year olds 3 women
3 35 - 50 year olds 3 men
3 60 - 75 year olds
Sacramento 6 1 20 - 30 year old 4 women
3 35 - 50 year olds 2 men
2 60 - 75 year olds
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Exhibit 28. Driver-Identified QOS Factors For Urban Streets.
Investment Area QOS Factor
Cross-Section Roadway Lane width Parking
Design Pedestrian/bicyclist facilities Lane drop/add
# of lanes/roadway width Access management
Bus pull-outs Medians
Turning lanes/bays Two-way center left turn lane
Arterial Operations Number of traffic signals Travel time
Presence of large vehicles Traffic flow
Volume/congestion Speed
Intersection Operations Signal failure/inefficient signal Timing of signals
timing Traffic progression
Turning
Signs and Markings Quality of pavement markings Lane guidance--pavement
Advance signing markings
Lane guidance--signs Sign legibility/visibility
Too many signs Sign presence/usefulness
Maintenance Pavement quality Overgrown foliage
Aesthetics Presence of trees Cleanliness
Medians with trees Roadside development
Visual clutter
Other Road Users Illegal maneuvers Aggressive drivers
Careless/inattentive driving Pedestrian behavior
Driver courtesy Improper/careless lane use
Use of turn signals Blocking intersection
Other Intelligent transportation systems Roadway lighting
Planning
Exhibit 29. Proposed MOEs For Urban Streets.
MOEs QOS Factors
Number of stops Turning lanes/bays
Bus pull-out areas
On-street parking
Two-way center left-turn lane
Access management
Lane drop/add
Urban street capacity Heavy vehicles
Lane width
Number of lanes/roadway width
Intersection efficiency Signal timing (cycle length/cycle split)
Provision for turning vehicles
Urban street efficiency Progression
Number of traffic signals
Travel time
Travel speed
Traffic volume Volume/congestion
Traffic flow
Speed
Travel time
Positive guidance Quality of pavement markings
Sign legibility/visibility
Sign presence/usefulness
Lane guidance--signs
Lane guidance--pavement markings
Advance signing
Too many signs (clutter/distracting)
Visual clutter
Pavement quality Pavement quality
Perceived safety Presence of medians
Lane width
Pedestrian/bicycle facilities
Access management
Area type Roadside development
Cleanliness
Trees
Visual clutter
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was filmed using 16mm cameras for several hours to gather Exhibit 30. Sutaria
several film clips ranging from A to E Level of Service. For the and Haynes Scale A
study, 14 film clips, ranging from 42-193 seconds, were --Point Rating.
shown to the participants. The film clips were broken into Rating Description
two groups: microviews that showed the traffic situation from 5 = Excellent
the view of an individual driver seated in an automobile and 4 = Very Good
3 = Good
macroviews that showed the overall traffic situation on a
2 = Fair
given approach from high above. Seven clips, ranging from 1 = Poor
LOS A to LOS E, in each group were shown to participants. 0 = Very Poor
There were 310 participants in the study. The participants
were given a questionnaire about their perceptions of signal- between Average Intersection Delay (AID), Load Factor (LF),
ized intersections before viewing the films collected in the field. and volume-to-capacity ratio (v/c) to perceived or rated level
The participants were asked to indicate, in order of impor- of service. The researchers went on to make three recom-
tance, the factors that affect their perceived views of quality of mendations:
flow at signalized intersections. They were given five factors to
rank: delay, number of stops, traffic congestion, number of · AID should be used to predict level of service.
trucks/buses, and difficulty in lane changing. It does not appear · Similar studies should be conducted on signalized inter-
that definitions of the factors were provided to the participants. sections without full actuation.
Before viewing the films, the participants ranked the fac- · Simultaneous filming and field studies should be con-
tors as follows: ducted to allow for accurate measurement of traffic engi-
neering measures captured on film.
1. Delay,
2. Number of stops, Based on the findings of this single research study, the
3. Traffic congestion, Highway Capacity and Quality of Service Committee over-
4. Difficulty in lane changing, and hauled the 1985 HCM to represent level of service at signal-
5. Number of trucks/buses. ized intersections by AID versus LF.
The authors state, "Field studies and the attitude survey
After viewing the films, the rankings changed slightly as provided data for the development of two psychophysical
follows: models. Statistical analysis indicated that average individual
delay correlated better with level of service rating than with
1. Delay, measured load factor and encompassed all levels of service.
2. Traffic congestion, Of all parameters affecting levels of service, load factor was
3. Number of stops, rated highest by road users."
4. Difficulty in changing lanes, and Ha, Ha, and Berg [16] developed models for predicting the
5. Number of trucks/buses. number of conflict opportunities (potential conflicts) at an
intersection as a function of signal timing, intersection geom-
After viewing each of the 14 film clips, the participants were etry, and turn volumes. Based on a review of previous inves-
also asked to score the service quality of the various film seg- tigations, they limited their analysis to left-turn and rear-end
ments on two different opinion scales: a 6-point scale (Scale A) accident analyses. The "total hazard" at an intersection is the
and one of five descriptions (Scale B) (See Exhibits 30 and 31.) sum of the likely number of rear-end and left-turn accidents
Based on input gathered from this study, the researchers multiplied by their severity. The total hazard is converted to a
developed a nomograph that depicted the relationship hazard index by dividing by the number of vehicles. The
Exhibit 31. Sutaria and Haynes Scale B Descriptive Rating.
Description Rating of Quality of Service
I would describe the traffic situation presented in this film segment as a condition of:
Free flow or as "free flowing" as can be expected if there is a traffic signal at the intersection
under study. OR
Tolerable delay, and nearly as good as could be expected at a signalized intersection. OR
Considerable delay but typical of a lot of ordinary signalized intersections during busy times. OR
Unacceptable delay and typical of only the busiest signalized intersections during the rush hour.
OR
Intolerable delay and typical only of the worst few signalized intersections I have seen.
