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Traffic Adaptive Approach for Pedestrian and
Vehicular Signal Timing Plan During Peak-Hour
Faisal Mahmud1, Rifat Aras2, and Tanweer Rashid3

Abstract—Pedestrian signal timing becomes an important issue
where pedestrian volumes are relatively high and the intersection
capacity utilization becomes critical for vehicular movement. It is
obvious to give right-of-way for both pedestrians and vehicles.
An intersection signal timing plan sometimes requires special
analysis for allocation of green time to both pedestrians and
vehicles for special scenarios. The situation also varies according
to the time of the day as well as the day of the week. In this paper,
an adaptive approach for pedestrian and vehicular signal timing
is investigated. This approach allocates green time based on
queue length of the individual phases. The data collection area is
chosen near Hampton Boulevard and 49th Street, which makes
this work unique in the sense that the pedestrian and vehicle
traffic patterns in a university/college area is significantly
different than the patterns considered in previous works. A
modular approach for modeling intersections is also used for the
analysis of traffic adaptive signal timings. Peak-hour demand
scenario is considered for accuracy and perfection to negotiate
pedestrians and vehicles at the same time. The proposed model
and fixed time model are simulated in ARENA under realistic
conditions. The fixed timing model is used as a base for
comparing the results of the proposed system.
Index Terms— Signal timing, pedestrian, timing plan,
simulation.
I. INTRODUCTION
T
raffic signal control strategies can be divided into three
main categories: (a) Fixed Traffic Control Strategy, (b)
Traffic Responsive Traffic Control Strategy, and (c) Traffic
Adaptive Traffic Control Strategy. Fixed traffic control
strategy applies fixed phase times to intersections that are precomputed using the traffic pattern variation of the target
intersection over the years. The phase times can be precomputed according to a particular time of the day, for
example, for three different time periods in a day three
different pre-computed traffic control signals can be applied.
Manuscript received February 25, 2010.
1. Faisal Mahmud. Author is a graduate student and research assistant of
Civil and Environmental Engineering Department, Old Dominion University,
Norfolk, VA 23529 (email: [email protected], phone: 757-606-7288).
2. Rifat Aras. Author is a graduate student and research assistant of
Department of Modeling, Simulation and Visualization Engineering, Old
Dominion University, Norfolk, VA 23529 (email: [email protected], phone:
765-337-8094).
3. Tanweer Rashid. Author is a graduate student and research assistant of
Department of Modeling, Simulation and Visualization Engineering, Old
Dominion University, Norfolk, VA 23529 (email: [email protected],
phone:631-943-1054 ).
Fixed traffic control strategies are relatively weak in
answering demands of instantaneous changes in traffic
patterns and they need periodic maintenance that is costly and
cumbersome.
In contrast to fixed control strategy, traffic responsive and
traffic adaptive control strategies are better able to answer
changing traffic patterns over time. Traffic responsive strategy
accomplishes this task by optimizing the intersection phase
times according to the predefined traffic volume thresholds. In
other words, when the traffic flow volume in an intersection
exceeds some threshold, traffic responsive control strategy
responds to the particular traffic condition. Traffic adaptive
control strategy, on the other hand deals with this situation by
optimizing the phase times based on the real-time traffic
arrival patterns. With varying traffic patterns, best phase time
values are determined for the given real-time traffic conditions
constrained within the minimum and maximum green time
parameters.
In a signaling intersection, signal phases are generally
designed with consideration for both vehicular and pedestrian
traffic. In most cases, pedestrian signal timing is determined
based on pedestrians calling to cross the intersection. Though
traffic approaches are given priority in the case of fixed timing
or semi-actuated as well as fully-actuated systems, there is not
enough supportive evidence that pedestrian calls are
considered in the same way. In case of pedestrian signal
timing at a particular intersection, usually pedestrians will
push the call button to alert the signal controller so that it can
calculate the timing plan to give right-of-way to the
pedestrian. Therefore, if the pedestrians do not give the call,
then signal phase for the pedestrian is not calculated. As a
result, at an intersection near a university area where
pedestrian volume demands vary with the time of the day,
fixed signal timing plan is not very effective. There are
possibilities that an area like this will always face signal
violations committed by the pedestrians. In order to prevent
such violations, either fixed timing strategies or frequent
pedestrian calls can be used. But, this will decrease signal
timing utilization, resulting in longer vehicular queue length
and delays.
In this study, we try to establish an adaptive signal timing
plan to accommodate both the pedestrian and the vehicles in a
university area where demand of pedestrian and traffic is of a
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different nature. Our goal is to give right-of-way to both
pedestrians and vehicles by considering their demand in a
rational and effective way. By the word effective way, we
mean less delay time for both vehicles and pedestrians. A
modular approach for modeling intersections is used for the
analysis of traffic adaptive signal timings. This modular
approach allows us to study either an isolated intersection, or
any number of consecutive intersections.
II. LITERATURE REVIEW
In the work of Cai et al. (2009), approximate dynamic
programming (ADP) method is used to develop a real-time
traffic adaptive intersection control system. Approximation is
performed by replacing the true value function in dynamic
programming with a linear approximate function. ADP
method works on isolated intersections and the challenging
issues of extending this method to traffic network are stated to
be embedding the traffic and controller state of adjacent
intersections in the local objective function.
In the work of Fang and Elefteriadou (2006), a Dynamic
Programming based optimization strategy for real-time traffic
signal control at diamond interchanges consisting of 8 lane
groups is proposed. The DP problem is solved over a horizon
of 10 seconds, which is divided into 4 equal stages. For the
considered diamond interchanges, at a given stage, there are a
total of 5 possible signal phases. The 4 stages and 5 phases are
then used to form a decision network that is solved by forward
recursive DP. To find the optimal decision trajectory in the
decision network, a performance measurement index (PMI)
for each lane group is introduced as the objective function.
PMI is basically the sum of the average queue length on each
of eight lane groups and calculated over a horizon of 10s.
Fixed or variable weights for lane groups can also be
embedded in the PMI formulation. The proposed method is
compared to other traffic adaptive signal control schemes such
as PASSER III and TRANSYT-7F and found to be superior
on conditions that are at or above 2300 vehicles per hour.
Hatfield et al. (2006) observed two thousand eight hundred
and fifty-four pedestrians crossing at signal-controlled
intersections to compare attention to traffic for different
combinations of pedestrian and traffic signals to investigate
the misunderstanding of right-of-way rules. One of their
findings is that pedestrians should be reminded that they do
not have right-of-way when waiting to enter a zebra crossing,
so they should not assume that approaching traffic will stop.
Their study results suggest that at signal-controlled crossings
pedestrian right-of-way is erroneously thought to be
influenced by the pedestrian signal.
Kim et al. (2008) made use of artificial neural networks
(ANNs) to generate cycle lengths for optimal v/c ratio at
various saturation levels. Their research only dealt with
vehicular traffic. Their results indicated that the cycle lengths
from the ANN were not that much different from the ones
obtained using TRANSYT. However, their research suffered
from several limitations, namely, the use of hypothetical
simulation data in the place of field data. A number of ANN
models were trained using these data, and these ANN models
varied in terms of number of hidden layers, number of neurons
in each hidden layer, learning coefficient, coefficient of
momentum, and the number of input elements. Less error and
statistical testing criterion were used to select a ‘best’ ANN
model.
Teodorovic et al. (2006) proposed a hybrid solution to the
real-time traffic adaptive signal control problem. Forward
recursive dynamic programming and neural networks are used
in conjunction to provide a real-time near optimal solution to
the problem. In this work, the optimization problem for an
isolated four-legged intersection is considered. A varying set
of hypothetical vehicle arrival patterns are generated, and for
each pattern, the optimal green time for all the four approaches
are computed using dynamic programming. The performance
index used for the optimization consists of number of stopped
vehicles, total delay of all vehicles and corresponding weights
of these parameters. A neural network is trained by providing
these hypothetical patterns and corresponding optimal green
times as input-output pairs (four cumulative performance
indices, and the amount of green time elapsed for the greenapproach as inputs and green time extension as output). The
trained neural network is then used to decide on whether to
extend the current green approach or terminate it immediately.
Ishaque and Noland (2006) showed a system of determining
the pedestrian crossing type (single, double or staggered),
depending on the number of pedestrians with respect to car
users, and the Pedestrian Value of Time (Ped VoT) that is
being considered. In their research, Ishaque and Noland used
travel cost per person in four scenarios with high and low
vehicle and pedestrian flows. The work was conducted for Ped
VoT being 0, 1, 2, 3 and 4, and pedestrian crossing types
being single, double and staggered.
Jianguo et al. (2006) worked for pedestrian model in China.
Considering the high rate of signal non-compliance, they
classified pedestrians into two types: law-obeying ones and
opportunistic ones. Opportunistic ones decide whether to
violate traffic signal during red man, depending on the states
of some external factors (like policeman, vehicle flow and
other pedestrians’ behaviors). Questionnaires were used to
determine the proportions of these two types of pedestrians
under different circumstances. In addition, a time gap
distribution extracted from videotape were used to determine
the criterion for pedestrians to decide whether to walk or wait
when they conflict with vehicle flows. However, their
simulation results deviated from the data extracted from
videotape in some degree.
Eleonora et al. (2007) did a review and critical assessment
on pedestrian behavior on urban area focusing on two separate
yet complementary aspects: route choice and crossing
behavior. Firstly, an exhaustive review of the existing route
choice models for pedestrians was presented showing that the
existing models were mainly more stochastic and more
macroscopic than required and seldom incorporate the
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interactions between pedestrians and traffic. Secondly, the
existing models on pedestrians crossing behavior were
presented and assessed. It was shown that, although their
approach was usually detailed, deterministic and trafficoriented, they were mainly devoted to a local level behavior
and focused on only one type of all the potential determinants.
The results of this review revealed a lack of an overall and
detailed consideration of pedestrian behavior along an entire
trip in urban areas. Moreover, the need for an integrated
approach based on flexibility, disaggregation and more
determinism was identified.
B. AREA OF ANALYSIS
Hampton BLVD & 49TH ST
At large signalized intersections, a technique for
determining signal phase for pedestrians’ twice crossing
(PTC) is described by Chen et al. (2007). This technique is
achieved by first overlapping the signal phases of both
pedestrians and vehicles, and obtaining several permutations
of these overlaps, and then combining these permutations. The
research used only 3 phase and 4 phase signal intersections.
III. METHODOLOGY
Figure 2: The Case Study Area.
A. DATA COLLECTION
For this research work, actual field data are used from the
City of Norfolk. Surveys were conducted of different
pedestrian counts for different days of the week as well as
times of the days. Pedestrian crossing attitudes were also
observed for behavior analysis and logic consideration. The
vehicle and pedestrian counts were used to form a basis to
determine the static weights of parameters in the objective
function. Figure 1 shows the count of pedestrians as well as
vehicles in both the south bound and north bound lanes.
Figure 1: Number of vehicles with respect to pedestrians at the
intersection of Hampton Blvd and 49th St.
In Figure 2, our study and analysis area is shown. The
critical intersection considered is Hampton BLVD and 49th
street because of its location and geometry. In this
intersection, pedestrian demand is more and thus pedestrianvehicle conflict situations are greater. The reasons to consider
this as our base study area are as follows:
1. Academic buildings are located on both sides of
Hampton Blvd. Thus at certain periods of the day
(namely, before and after class hours), pedestrian
volumes are particularly high, and signal crossing
violations are subsequently high
2. Student dorm buildings are also located around this
area
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(1)
(2)
Where:
Figure 3: Proposed Modular Design.
Figure 3 shows a diagram of the proposed module used in
this analysis. The proposed modular intersection design
consists of a common eight-phase intersection that is coupled
with assigned queue structures for each lane. Queue structures
are employed to track the instantaneous number of vehicles in
a lane and in determining the remaining capacity of a lane at a
given time.
A "Green Point" (GP) function is proposed to determine the
optimal green timings in a given intersection network. The GP
function is a function of number of pedestrians waiting for
crossing the intersection (P1, P2), incoming flows from
adjacent intersection modules, queue lengths of lanes, and
next intersection module's remaining queue capacity.
For a given eight-phase intersection module, we can define
2-element tuples of phases to form signal groups. In other
words, the phase elements of such tuples can be green at a
given instant. For an eight-phase intersection, we can form 8
such tuples and associate them with appropriate pedestrian
signals as (φ2, φ6)~P2, (φ1, φ5)~P2, (φ1, φ2)~P2, (φ5,
φ6)~P2, (φ4, φ8)~P1, (φ3, φ4)~P1, (φ7, φ8)~P1, and (φ3,
φ7)~P1. At the signal controller core of the intersection
module, GP values are computed for each phase tuple at fixed
time intervals. The phase pair with the greatest GP value wins
and gets the green light at the start of next time interval given
that it does not conflict with another phase pair’s minimum
green time constraint. The general equation for GP value
computation is (1). An example calculation of GP value for
the phase tuple (φ2, φ6) is shown in (2).
 GP  “Green Point” value of the particular phase
tuple,
 n  The index of intersection in the network,
 ΦiQL  Number of vehicles waiting i.e. queue length at
phase i lane,
 ΦjQL  Number of vehicles waiting i.e. queue length at
phase j lane,
 P1, P2  Number of pedestrians waiting to cross over
the intersection,
 γ  Incoming flow from the previous intersection
module,
 β  Remaining capacity of the next intersection
module,
 wi  Static weights. These weights are estimated using
the number of pedestrians and vehicles.
When the performance of a single intersection is in
question, the equation (2) is a sufficient metric to optimize the
traffic signals adaptively. In a network of connected
intersection modules, additional parameters such as incoming
flow from the previous module and remaining queue capacity
of the next module are introduced. In this research, we are
considering only one intersection, and hence we use only one
module, and we developed equations similar to (2) for this
research.
IV. ANALYSIS APPROACH
A. SIMULATION DESIGN
The simulation in ARENA was run for multiple
replications. Figures 4 and 5 shows the ARENA
implementation for our analysis of vehicles and pedestrians.
Figure 4 shows the implementation of vehicle and pedestrian
arrivals, while figure 5 shows the implementation of the
adaptive signal controlling mechanism.
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Table 1: Waiting Times in Queues (all values are in seconds)
Queue
North bound
North left bound
South bound
South left bound
East bound
East left bound
West bound
West left bound
Pedestrian North to South
Pedestrian East to West
Fixed Time System
(base)
8.43
18.80
8.47
18.81
14.86
23.13
14.91
23.18
3.87
11.38
Adaptive System
4.51
13.44
4.86
19.65
19.29
26.92
16.96
21.85
1.78
7.01
Table 2: Average Number of Vehicle and Pedestrians in Queues
Figure 4: Implementation of Vehicle and Pedestrian Arrivals.
Queue
North bound
North left bound
South bound
South left bound
East bound
East left bound
West bound
West left bound
Pedestrian North to South
Pedestrian East to West
Figure 5: Implementation of the Adaptive Signaling Mechanism.
A simulation using fixed signal timings was also conducted.
The vehicle and pedestrian arrivals were kept the same as in
the adaptive system, while a new signal controlling
mechanism as designed to account for fixed signal times. This
mechanism is shown in Figure 6.
Fixed Time System
(base)
8.38
4.69
8.47
6.27
2.97
2.90
3.72
2.90
0.77
3.81
Adaptive System
4.50
3.34
4.87
6.51
3.86
3.35
4.23
2.73
0.35
2.34
It can be seen from Tables 1 and 2 that the adaptive traffic
signaling approach results in better average queue wait time
and queue length than the fixed time signaling approach. For
the adaptive approach, only the east bound, east left bound,
south left bound and west bound queues shows larger average
queue wait times and queue lengths. This is because during the
simulations, the weights for these four phase queues were
assigned smaller values than the other phase queues.
VI. CONCLUSION AND RECOMMENDATION
Figure 6: Implementation of the Fixed Signal Timing Mechanism.
V. RESULTS
Simulations were run for both the adaptive system and the
fixed time system. The vehicle and pedestrian average waiting
times are summarized in Table 1. Table 2 shows the average
number of vehicles and pedestrians waiting in queues.
This study was to find out an alternative way of how we can
deal with special signaling priority for both pedestrians and
vehicles in a university crossing area without arising conflicts.
We can say that our study is unique and practical as it tried to
find out an alternative way of giving right-of-way by adjusting
demand factors as well as modular design approach.
This study is limited in the sense that we considered only
one intersection. Future research on this area can be done for
multiple intersections. The modular approach developed for
this research is a much generalized model, and can be applied
to any intersection or groups of intersections.
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