Real-Time Dynamic Traffic Assignment and
Path-Based Signal Coordination:
Application to Network Traffic Management
Khaled F. Abdelghany
Kfaissal@mail.utexas.edu
Didier M. Valdes
Dvaldes@mail.utexas.edu
Akmal S. Abdelfatah
Akmal@mail.utexas.edu
Hani S. Mahmassani
Masmah@mail.utexas.edu
Department of Civil Engineering
ECJ 6.2
The University of Texas at Austin
Austin, Texas 78712
Phone: (512) 475-6361
Fax: (512) 475-8744
July 1998
Prepared for presentation at the 78th Annual Meeting of the Transportation Research Board,
January 1999, Washington D.C. and publication in Transportation Research Board
Abdelgany, Valdes, Abdelfatah and Mahmassani
2
REAL-TIME DYNAMIC TRAFFIC ASSIGNMENT AND
PATH-BASED SIGNAL COORDINATION:
APPLICATION TO NETWORK TRAFFIC MANAGEMENT
INTRODUCTION
Real-Time Dynamic Traffic Assignment (RT-DTA) is a core methodology for the
operation of Intelligent Transportation Systems (ITS) technology in the management of
traffic networks to alleviate both recurrent and non-recurrent congestion. Used in
conjunction with incident detection capabilities, the RT-DTA system is intended to predict
the traffic flow pattern, and provide decision support capabilities in the generation of
information to users (for route diversion selection) and the evaluation of alternative traffic
controls. Efficient use of the available capacity in the highway corridor network through
integrated operation of the freeway and the surrounding surface streets is a major objective
of incident management schemes. In response to real-time information and route diversion
advice via Variable Message Signs (VMS) upstream of the incident as well as various onboard devices, freeway users may divert to the surface street network. To absorb this
diverted traffic, the capacity of the surface street network should be optimized by
modifying the traffic signal control to serve the new flow pattern.
Prototypical ATMS schemes envision signal coordination along the freeway
frontage roads (if available) or along parallel arterials to provide a through band of
progression for through traffic along these facilities. However, diversion traffic may not
necessarily follow a through path along these major parallel facilities. Instead, diversion
paths may involve turning movements at key junctions. Incident management, and
coordinated network control can be considerably more effective if they recognized these
flow patterns and favored dominant movements through the network, e.g. by providing
coordination to achieve a through band for particular paths followed by these dominant
streams. We refer to such an approach as path-based signal coordination.
One of the difficulties of providing such path-based coordination is to identify the
particular path along which coordination should be provided, especially in dynamic
environments such as non-recurrent congestion spots. Recent development in dynamic
assignment, especially the RT-DTA capabilities mentioned earlier, make it possible to
extract such paths from the solution, i.e. from the predicted flow patterns in the network.
The objectives of this paper are two-fold: (1) to illustrate the benefits of
coordinated network operation under incident conditions, with particular emphasis on pathbased signal coordination; and (2) to describe procedures for identifying paths along which
coordination would be beneficial, given RT-DTA capabilities, and for implementing such
coordination schemes.
Although traffic signal coordination is a topic that has been extensively covered in
the literature, it does not appear that path-based coordination has been described and tested.
Nevertheless, its essence lies in some of the early seminal work of Gazis [1, 2] on
Abdelgany, Valdes, Abdelfatah and Mahmassani
3
signalized network control, which discussed conceptually the potential of combining
routing with signal control for efficient use of a traffic network. One may view this
approach as a natural step from straightforward arterial progression towards the more
complex problem of network coordination in the case where dominant flow patterns may
be identified.
This paper first describes the RT-DTA system used in this paper. It also introduces
the notion of path-based coordination along paths that combine consecutive through and
turning movements. A set of numerical experiments on an actual network are designed to
evaluate the network performance under path-based coordination compared to the donothing base case and to arterial-based coordination, for both pretimed and vehicle actuated
signal control. The experiments also illustrate the role of real-time information through
VMS in incident network management, and the decision-support function of the RT-DTA
in this process.
BACKGROUND AND GENERAL APPROACH
The real-time dynamic traffic assignment (RT-DTA) capabilities illustrated in this
paper are provided by the DYNASMART-X simulation-assignment system. Its structure,
depicted in Figure 1, includes the following modules: O/D estimation, O/D prediction, realtime network state simulation, consistency checking, updating and resetting functions, and
network state prediction. These modules are integrated through a flexible distributed
architecture, using CORBA (Common Object Request Broker Architecture) standards, for
real-time operation in a rolling horizon framework with multiple asynchronous horizons
for the various modules. Activation of these modules may be time-based and/or eventbased. Time-based activation takes place according to the module-specific cycles, while
event-based activation depends on the occurrence of particular situations (e.g. pre-defined
threshold values for certain network state variables). The OD estimation and prediction
module is first activated to predict the time dependent OD pattern for the next (OD
prediction) stage using OD historical information and input from the surveillance system
and/or vehicle probes. Simultaneously, the real time simulator supported by a series of
consistency checking and updating functions, estimates current network traffic conditions.
The supporting consistency checking functions compare measured values of selected state
variables in the actual system to the corresponding values in the simulator, and update the
internal representation within the simulator to ensure consistency with actual conditions. At
the start of each state prediction stage, the predictor reads the current network conditions
from the real time simulator and uses the predicted time-varying O/D values to predict
network conditions over the next stage. New predictions may be computed in real time
when a major disruption, such as an incident, is detected. To further support incident
management decisions, as the current control plan may no longer be suitable for the new
flow patterns, several control plans may be suggested and evaluated and the selected plan is
directly forwarded to the signal controllers in the field. An overview of the principal
components of the RT-DTA system and more details can be found in Mahmassani et al. [3,
4].
Abdelgany, Valdes, Abdelfatah and Mahmassani
4
The interaction between signal settings and the associated flow patterns complicates
the optimal design of the signal settings. The degree of this interaction depends on the
availability of information to users. The RT-DTA is capable of predicting the future flows
and associated network performance that may result under proposed new signal settings.
However, if these signals were optimized for the previously prevailing flow pattern, they
may no longer be optimal for the new flow pattern when information is readily available to
users. Several authors have sought procedures to determine signal settings that are
consistent with a long run (typical time-invariant) equilibrium flow pattern in the network.
Allsop and Charlesworth [5] presented an iterative optimization reassignment procedure
which iteratively sets the signal timing at each intersection to minimize delay, holding flow
constant. Flows are then adjusted to new user equilibrium and the process is repeated until
flows are at equilibrium and the signal timing is optimal. Smith [6, 7] pointed out that this
method does not guarantee convergence, not even to a local optimum. Sheffi and Powell
[8] reformulated the problem to find signal timings that minimize the total travel time spent
in the network, subject to the constraint that the flow pattern satisfies static user
equilibrium conditions. Abdelfatah and Mahmassani [9] have presented an approach to
solve for a time-varying system optimal path assignment jointly with optimal signal
settings. However, the approach assumes a priori knowledge of OD trips over a long
horizon, and is computationally demanding, limiting its use to off-line applications.
For real-time incident management purposes, longer-term interactions are not of
concern, and the information provided through the VMS gives rise to dominant flows and
movements through the network. The approach followed here therefore relies on efficient
heuristic procedures aimed at producing good, though not necessarily optimal, settings and
signal coordination schemes. These procedures are patterned after current practice for
obtaining traffic signal coordination, with the main difference being in the application to
paths obtained from the RT-DTA instead of simply following through movements on
arterials.
The effectiveness of this approach depends on the appropriate determination of the
dominant paths along which coordination can be provided. The dominant paths are defined
as those paths that are used by the largest number of travelers in the network over the
period of interest. These paths may consist of a combination of straight portions and
turning movements. As mentioned earlier, DYNASMART-X provides prediction
capabilities for each stage in the operation horizon. This prediction includes path
trajectories between the origin and the destination for all the vehicles in the network;
however, a RT-DTA that provides aggregated time-varying path flows between origins and
destinations would be sufficient. The number of vehicles that use each path over the
duration of interest provides the basis for ranking paths in order to determine the dominant
ones.
The experiments in this paper are designed to compare the network performance
under different coordination schemes for pretimed and vehicle-actuated signal control. For
pretimed signal control, the procedure developed by Abdelfatah and Mahmassani [9] is
first used to calculate the traffic signal settings for the isolated intersections using the
Abdelgany, Valdes, Abdelfatah and Mahmassani
5
predicted traffic flows. In their procedure, the cycle length and the green splits for each
intersection were calculated using Webester’s formula [10]. The average traffic volumes
were obtained by aggregating the time varying flows that result from DYNASMART-X
prediction. The cycle length of the whole coordinated corridor (path) is selected to be the
maximum cycle length among all the intersections along this corridor. Following Newell’s
suggestion [11], for any intersection that has an optimal cycle length close to half the path’s
cycle length, the cycle length is set to be half of the common cycle length. The average link
travel times associated with the predicted flows along the coordinated corridors (paths) are
taken as the signal offset values for the coordinated signals. For the vehicle actuated
control case, the continuous green band is obtained by increasing the maximum green for
the coordinated phases along the corridor. Sensitivity analysis is used to select the best
practical maximum green value; overall network performance is compared for extensions
of 10 sec, 20 sec and 30 sec for this purpose. The same extension value is used for all the
intersections along the coordinated corridor. In both cases, and for comparative purposes,
the effect of vehicle reassignment is controlled in these experiments by fixing the vehicle
paths before and after changing the signal design.
EXPERIMENTAL DESIGN
Figure 2 depicts the test network used in the current study which represents the
south central corridor in the Fort Worth area. The network consists of a freeway (I-35W)
surrounded by a street network with a total of 178 nodes and 441 links. The demand
pattern is set to represent a peak period flow in which about 17,000 vehicles are generated
over 35 minutes and simulated for 100 minutes. For the signalized intersections (61
intersections), the experiments are designed to simulate two different methods of control:
pretimed fixed signal control and vehicle actuated signal control. In both cases, the
unsignalized intersections are set as follows: no control (62 intersections), yield sign
control (24 intersections) and stop sign control (31 intersections). A series of variable
message signs were placed along the freeway at a sufficient distance upstream of the exit
ramps. These signs are capable of carrying messages to divert the traffic to the surface
street network in case of a downstream incident.
Four experimental factors are considered in the design of these experiments. Two
of these factors pertain to signal control: (1) pretimed vs. vehicle-actuated control, and (2)
coordination scheme, for which three different levels are considered: do-nothing base case
(with no coordination), arterial based coordination and path-based coordination. The third
experimental factor also consist of two levels: (1) no incident vs. (2) presence of incident
along the freeway. Specifically, as shown in Figure 2, the incident considered has 75%
severity (i.e. it blocks 75% of available capacity), starts at minute 20 and ends by minute
40 of the simulation time on link 41-37 of the freeway.
The fourth experimental factor consists of the provision of real-time information
through VMS; two levels are considered: (1) no VMS, with only historical information
regarding the best path at the start of the trip available to drivers; vs. (2) VMS activated to
provide drivers with information upstream of the incident. The second level of this factor is
Abdelgany, Valdes, Abdelfatah and Mahmassani
6
conditioned upon the presence of an incident; hence it is defined only in conjunction with
level two of the incident presence experimental factor. The two variable message signs
upstream the incident are located on the freeway to switch the vehicles to the street
network using the two ramps 52-51 and 48-47. In all these experiments, users are assumed
to have only historical information about the best current path at the start of their trips.
They are also assumed to fully comply with the information supplied by the VMS’s
(though this assumption can be readily relaxed to consider various compliance ratios, with
no loss of generality).
For ease of presentation, three scenarios are defined. The first scenario does not
involve an incident along the freeway, and hence the VMS are not activated for diversion.
The second and the third scenarios correspond to the above incident along the freeway link.
In the third scenario, two variable message signs are activated upstream of the incident to
divert the traffic to the surface street network, while no VMS is activated in the second
scenario.
For arterial-based coordination, the arterials selected for consideration arise
naturally from the network configuration. Figure 3 shows the three coordinated arterials
used in these experiments, namely the frontage road and one arterial on each side of the
freeway. This set of arterials is fixed for all the arterial-based coordination experiments.
For path-based coordination, a set of paths with nearly the same number of intersections (as
those along the selected arterials) is obtained by investigating the traffic flow pattern
obtained from the prediction module as described in the previous section. The number of
signalized intersections along the coordinated paths and the coordinated arterials is taken to
be nearly the same to avoid the advantage of coordinating more intersections along one of
the two schemes while comparing their performance. The paths to be coordinated are not
necessarily fixed in all the experiments, again, to reflect the flow distribution in the
network associated with the method of control (e.g. pretimed or vehicle actuated).
EXPERIMENT RESULTS
Table 1 summarizes the results of the experiments conducted under pretimed signal
control. The table shows the network performance under the three different coordination
schemes: do-nothing, arterial-based coordination, path-based coordination. The results are
given for the three different scenarios described earlier: (1) no incident, (2) incident, (3)
incident with VMS. In scenarios 1 and 2, no obvious dominant paths are found. For this
reason, the flow pattern obtained from the scenario 3 in which the VMS's are activated is
used to determine the dominant paths for the other two scenarios, as shown in Figure 4.
Several results are illustrated in Table 1. Under the different scenarios, the pathbased coordination improved the overall network performance in terms of the average
overall travel time and the average stopped time, with an estimated improvement of
11.36% in the average travel time and 13.68% in the average stopped time under the noincident scenario. Under the same scenario, the arterial-based coordination scheme actually
results in an increase in the average travel time by 5.42%, and in the average stopped time
Abdelgany, Valdes, Abdelfatah and Mahmassani
7
by 7.7% compared to the no-coordination base case. Although the arterial-based
coordination may reduce the travel time and the number of stops for the traffic moving
along the coordinated arterials, as shown in many previous studies, the results of the
present experiments suggest that arterial-based coordination may have an adverse effect at
the network level. Because it explicitly considers the flow patterns in the network, pathbased coordination outperforms the arterial-based coordination, which can be viewed as a
subset of the possible path-based solutions. Coordination along an arterial may reduce the
delay along that arterial only, while at the same time increasing the delay for the
intersecting arterials which may also have high traffic flows. In addition, path-based
coordination allows the flexibility of coordinating segments of the arterials that experience
the highest traffic flows. It can be noted that, under the scenarios in which the VMS's were
not activated, the average travel distance is constant. This ensures that all drivers use their
historical information about the best path and use these paths regardless of the incident.
When the VMS's are activated, a change in the average travel distance is observed.
Relative to the other schemes, a very slight increase in the average travel distance is noted
under the path-based coordination scheme. Clearly, this increase is not meaningful
compared to the associated reduction in the average travel time and the average stopped
time.
In addition, the results in Table 1 show the adverse effect of the incident on the
network performance, reflected in increases of about 4 to 5 minutes in the average travel
time and the average stopped time. The variable message signs redistributed the traffic in
the surface street network. The coordination schemes are intended to increase the capacity
of the street network to accommodate this diverted traffic. Of course, the better the design
of the coordination scheme is, the greater the capacity of the network, and the less the
effect of the incident on overall network performance will be. The results in Table 1 reveal
that the average travel time (26.48 min) and the average stopped time (17.51 min) for the
path-based coordination scheme under scenario three (which activates the VMS's) are even
(slighter) better than the average travel time (26.71 min) and the average stopped time
(17.99 min) under the no-incident arterial-based coordination scenario.
The results of a similar set of experiments conducted under vehicle-actuated signal
control are summarized in Table 2. Similarly to the fixed control case, the third scenario is
used to determine the dominant paths for the other two scenarios. However, these paths are
different from those of the previous set, because of the change in signal control from
pretimed to vehicle-actuated; the new set of dominant paths are shown in Figure 5. As
described earlier, sensitivity analysis is used to determine the best practical maximum
green value. The results for 10 sec, 20 sec and 30 sec green extensions are given in Table 2.
This extension was added to the maximum green used in the do nothing scenario to allow
the natural emergence of a continuous green band for the traffic along the coordinated
corridor.
One can first note the reduction in the average travel time and the average stopped
time under the vehicle actuation scheme compared to the pretimed fixed control. An
impressive average reduction of about 25% in the travel time and the stopped time
Abdelgany, Valdes, Abdelfatah and Mahmassani
8
underscores the importance of traffic responsive method of control in improving overall
network performance. The path-based coordination scheme in the traffic responsive control
environment appears to exhibit similar relative performance as in the case of pretimed
signal control. Under the no-incident scenario, and for green extension of 10 seconds,
arterial-based coordination improves the average travel time and the average stopped time
by 2.40% and 4.93%, respectively. This improvement is nearly doubled under the pathbased coordination to 5.54% for the average travel time, and 8.11% for the average stopped
time. Under the incident scenarios, arterial-based coordination again failed to significantly
improve overall network performance. The adverse effect in some cases is notable. An
increase of about 5.42% in the average travel time and 6.58% in the average stopped time
were estimated when arterial-based coordination was used with the incident scenario.
Under the same scenario, the path-based coordination actually improved the average travel
time and the average stopped time by 3.41% and 4.06%, respectively. Under vehicleactuated control, as in the pretimed control case, the change in the average travel distance
associated with the activated VMS's is negligible relative to the associated improvement in
the average travel time and the average stopped time.
CONCLUSIONS AND SUGGESTIONS FOR FURTHER RESEARCH
This paper introduces and illustrates the notion of path-based coordination in
transportation networks as one example of integrating signal control with network traffic
assignment, which is one of the benefits of using RT-DTA systems for incident
management. The use of a RT-DTA system such as DYNASMART-X allows the
prediction of the traffic flow pattern and identification of the dominant paths in the
network. The signalized intersections along these paths, which may consist of
combinations of straight sections and turning movements are coordinated, so as to increase
the capacity of the freeway and the surface street system to efficiently absorb diverted
traffic from the freeway. The path-based coordination scheme is shown to outperform
arterial-based in the cases of both pretimed fixed control and vehicle-actuated signal
control. A significant reduction in the network average travel time under the path-based
coordination scheme is observed compared to the other schemes. This reduction illustrates
the potential of leveraging information about path flow patterns in the network as predicted
by a real time dynamic traffic assignment capabilities, to increase the effectiveness of the
traffic signal control in the network, which is particularly meaningful under incident
conditions.
The work in this paper shows a simple example of an efficient integration between
the freeway and surrounding street network to alleviate the effect of severe incident
congestion. Of course, the results in this paper should be viewed only as illustrative of the
potential of RT-DTA systems in designing path-based coordination. One evident limitation
of path-based coordination is the presence of conflicting dominant flows, or the lack of
dominant flows. However, the realism of these experiments is enhanced by the
consideration of an actual network with corresponding OD flow patterns estimated from
actual data. Similar results have also been obtained in other test networks. This example
can be extended to include other control measures such as ramp metering as well as the
Abdelgany, Valdes, Abdelfatah and Mahmassani
9
advanced information dissemination strategies. In addition, the environmental impact of
diverting freeway traffic to the surface street network might be considered in assessing
such incident management schemes.
ACKNOWLEDGEMENTS
The work presented in this paper is based on research partially supported by the U.S.
Federal Highway Administration and the Texas department of Transportation. The work
has also benefited of the cumulative development effort of many former and current
students. The Authors are particularly grateful to Yasser E. Hawas and Yi-Chang Chiu for
their considerable effort in the development of the Real-Time Dynamic Traffic Assignment
System (DYNASMART-X) which used to conduct the experiments presented in this paper,
as well as their contribution to the overall research effort. The Authors have also benefited
from the insightful comments of Dr. Henry Lieu of FHWA and Drs. Shaw-Pin Miaou and
Michael Summers of the Oak Ridge National Laboratory. Of course, the authors are solely
responsible for the findings and views expressed in this paper.
REFERENCES
1. Gazis, D.C., Traffic Science. John Wiley and Sons., Inc., New York, 1974.
2. Gazis, D.C. and Potts, R. B. Route Control at Critical Intersections. Proceedings of the
Australian Road Research Board 3, part 1, 1966, pp. 354-363.
3. Hawas, Y., Mahmassani, H.S., Taylor, R., Ziliaskopoulos, A., Ghang, G-L., and Peeta,
S., Development of DYNASMART-X Software for Real-Time Dynamic Traffic
Assignment, Technical Report ST067-85-Task E, Center for Transportation Research,
The University of Texas at Austin, 1997.
4. Hani S. Mahmassani and Yaser E. Hawas. A Hierarchical Distributed Computational
Architecture for A Centralized Real-time Dynamic Traffic assignment. Proceeding of
TRISTAN III Conference, San Juan, Puerto Rico, June 1998.
5. Allsop, R. E. & Charlesworth, J. A., Traffic in Signal Timings Including Different
Routings. Traffic Engineering and Control, Vol.18, No 5, 1977, pp. 262-264.
6. Smith, M. J., Properties of Traffic Control Policy Which Ensure the Existence of a
Traffic Equilibrium consistent with the policy. Transportation Research, Vol. 15B, No.
6, 1981, pp. 453-462.
7. Smith, M. J. & Ghali M., The Dynamics of Traffic Assignment and Control: A
Theoretical Study. Transportation Research, Vol. 24B, No. 6 , 1990, pp. 409-422.
8. Sheffi, Y. & Powell, W. B., Optimal Signal Settings over Transportation Networks.
Journal of Transportation Engineering of ASCE, Vol. 109, No. 6., 1983, pp. 824-839.
9. Abdelfatah, A.S., Mahmassani, H.S., System Optimal Time-Dependent Path Assignment
and Signal Timing in Traffic Networks. Paper presented at the 77th annual meeting of
the TRB.
10. F. V. Webster and B. M. Cobbe, Traffic Signals, Road Research Labratory, Technical
Paper No. 56, Her Majesty’s stationary office, London, 1956.
11. Gordon F. Newell. Theory of Highway Traffic Signals. Institute of Transportation
Studies-University of California at Berkeley, Course Notes 1989.
Abdelgany, Valdes, Abdelfatah and Mahmassani
10
LIST OF TABLES
TABLE 1: RESULTS OF THE PRETIMED SIGNAL CONTROL EXPERIMENTS.
TABLE 2: RESULTS OF THE VEHICLE ACTUATED SIGNAL CONTROL
EXPERIMENTS.
Abdelgany, Valdes, Abdelfatah and Mahmassani
11
LIST OF FIGURES
FIGURE 1: STRUCTURE OF THE DYNASMART-X REAL-TIME DYNAMIC TRAFFIC
ASSIGNMENT
FIGURE 2: THE TEST NETWORK.
FIGURE 3: THE COORDINATED ARTERIALS.
FIGURE 4: THE COORDINATED PATHS FOR THE PRETIMED FIXED CONTROL
CASE.
FIGURE 5: THE COORDINATED PATHS FOR THE VEHICLE ACTUATION
CONTROL CASE.
Abdelgany, Valdes, Abdelfatah and Mahmassani
12
O/D Estimation &
Prediction
Surveillance
Data
Consistency
Checking
Control Strategy
DYNASMART
SIMULATOR
Signal Plan
Evaluation of
ATMS/ATIS
Future Network
State Prediction
Routing
Information
Figure 1: Structure of the DYNASMART-X Real-Time Dynamic Traffic Assignment
System.
Abdelgany, Valdes, Abdelfatah and Mahmassani
13
100
156
116
118
67
130
81
1
2
19
68
131
82
121
69
80
83
70
3
122
4
85
89
97
151
103
91
144
96
104
26
5
6
27
29
65
66
140
145
7
8
92
146
152
99
31
33
141
147
153
155
90
176
30
32
34
35
37
36
86
102
24
28
71
159
123
72
124
73
40
125
74
11
158
160
161
25
84
132
101
22
23
120
157
98
21
20
119
139
39
105
106
163
164
107
38
10
9
162
165
108
93
177
178
142
148
154
109
94
149
95
110
166
41
167
42
126
75
175
76
127
77
133
12
43
44
45
47
46
13
134
48 14
168
143
150
49
51
52
50
135
137
111
53
54
128
78
87
88
15
16
171
113
114
112
55
169
56
136
129
79
138
59
57
17
172
58
60
61
18
63
62
173
174
64
115
117
Figure 2: The test network.
170
Abdelgany, Valdes, Abdelfatah and Mahmassani
14
100
156
116
118
67
130
81
1
2
19
68
131
82
121
69
80
83
70
3
122
4
85
102
89
97
151
103
91
144
96
104
24
26
5
6
27
29
65
66
140
145
7
8
92
146
152
99
31
33
141
147
153
155
90
176
28
71
159
30
32
34
35
37
36
72
86
124
73
40
125
74
11
160
161
106
39
163
164
38
10
9
162
105
107
123
158
25
84
132
101
22
23
120
157
98
21
20
119
139
165
108
93
177
178
142
148
154
109
94
149
95
110
166
41
167
42
126
75
175
76
127
77
133
12
43
44
45
47
46
13
134
48 14
168
143
150
49
51
52
50
135
137
111
53
54
128
78
87
88
15
16
171
113
114
112
55
169
56
136
129
79
138
59
57
17
172
58
60
61
18
63
62
173
174
64
115
117
Figure 3: The coordinated arterials.
170
Abdelgany, Valdes, Abdelfatah and Mahmassani
15
100
156
116
118
67
130
81
1
2
19
119
68
131
82
121
69
80
83
70
20
22
3
4
122
89
85
159
102
24
26
5
6
151
103
90
91
144
96
104
27
29
65
66
140
145
7
8
92
146
152
99
31
33
141
147
153
155
176
123
72
86
30
32
34
35
37
73
161
162
106
163
164
38
10
9
39
124
160
105
107
36
158
25
28
71
101
97
84
132
157
98
21
23
120
139
165
108
93
177
178
142
148
154
109
94
149
95
110
166
41
167
40
42
125
126
175
127
11
74
75
133
12
43
44
45
76
46
77
13
134
47
48 14
168
143
150
49
51
52
50
135
137
111
53
54
128
78
87
88
15
16
171
113
114
112
55
169
56
136
129
79
138
59
57
17
172
58
60
61
63
62
170
18
173
174
64
115
117
Figure 4: The coordinated paths for the pretimed fixed control case.
Abdelgany, Valdes, Abdelfatah and Mahmassani
16
100
156
116
118
67
130
81
1
2
19
119
68
131
82
121
69
80
83
70
20
22
3
4
122
89
85
159
102
24
26
5
6
151
103
90
91
144
96
104
27
29
65
66
140
145
7
8
92
146
152
99
31
33
141
147
153
155
176
123
72
124
73
86
160
161
30
32
34
35
37
106
39
163
164
38
10
9
162
105
107
36
158
25
28
71
101
97
84
132
157
98
21
23
120
139
165
108
93
177
178
142
148
154
109
94
149
95
110
166
41
167
40
42
125
126
11
74
75
175
76
127
77
133
12
43
44
45
47
46
13
134
48 14
168
143
150
49
51
52
50
135
137
111
53
54
128
78
87
88
15
16
171
113
114
112
55
169
56
136
129
79
138
59
57
17
172
58
60
61
18
63
62
170
173
174
64
115
117
Figure 5: The coordinated paths for the vehicle actuation control case.
Abdelgany, Valdes, Abdelfatah and Mahmassani
17
Table 1: Results of the Pretimed Signal Control Experiments.
Case
Scenario
Coordination
Scheme
No
Incident
No-Coord.
Arterial.
Coord.
Path Coord
No-Coord.
Arterial
Coord.
Path Coord
No-Coord.
Arterial
Coord.
Path Coord
Incident
Incident +
VMS
Average
Overall
Travel
Time
(Min)
25.34
26.71
%
Improvement
-5.42%
22.46
29.37
29.90
11.36%
27.92
27.06
26.72
4.94%
26.48
Average
Stopped
Time
(Min)
16.71
17.99
%
Improvement
-7.7%
Average
Trip
Distance
(Miles)
4.09
4.09
14.42
20.42
20.84
13.68%
6.10%
1.36%
19.18
18.27
17.91
1.97%
4.09
4.11
4.11
2.14%
17.51
4.15%
4.13
-1.81%
-2.07%
4.09
4.09
4.09
Abdelgany, Valdes, Abdelfatah and Mahmassani
18
Table 2: Results of the Vehicle Actuated Signal Control Experiments.
Case
Scenario
Coordination
Scheme
No Incident
No-Coord.
Arterial Coord. +10
+20
+30
Path Coord.
+10
+20
+30
No-Coord.
Arterial Coord. +10
+20
+30
Path Coord.
+10
+20
+30
No-Coord.
Arterial Coord. +10
+20
+30
Path Coord.
+10
+20
+30
Incident
Incident +
VMS
Average
Overall
Travel
Time
(Min)
20.22
19.74
20.09
19.81
19.10
20.08
20.24
24.91
26.26
25.14
24.83
24.06
24.33
24.29
22.61
22.66
22.83
22.94
22.59
21.29
21.72
%
Improvement
2.40%
0.64%
2.01%
5.54%
0.68%
-0.08%
-5.42%
-0.90%
0.33%
3.41%
2.33%
2.51%
-0.22%
-0.98%
-1.46%
0.08%
5.83%
3.92%
Average
Stopped
Time
(Min)
12.31
11.70
11.99
11.74
11.31
12.20
12.32
16.13
17.19
16.27
15.96
15.48
15.74
15.64
13.99
13.94
14.16
14.26
13.99
12.95
13.25
%
Improvement
4.99%
2.53%
4.63%
8.11%
0.083%
-0.14%
-6.58%
-0.84%
1.07%
4.06%
2.45%
3.04%
0.35%
-1.23%
-1.95%
-0.04%
7.37%
5.27%
Average
Trip
Distance
(Miles)
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.09
4.08
4.11
4.07
4.11
4.11
4.07
4.09