effect of side friction parameter on urban road traffic
TRANSCRIPT
Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
314
Effect of Side Friction Parameter on Urban Road Traffic: Under Mixed
Traffic Scenario
Ritvik CHAUHANa, Ashish DHAMANIYAb, Shriniwas ARKATKARc, Prasanta K. SAHUd,
Durgesh VIKRAMe
aResearch Scholar, Department of Civil Engineering, Sardar Vallabhbhai National Institute
of Technology, Surat, 395007, India; E-mail: [email protected]
bAssociate Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute
of Technology, Surat, India
Email b: [email protected]
Email c: [email protected]
dAssistant Professor, Department of Civil Engineering, Birla Institute of Technology and
Science - Pilani, Hyderabad, India; Email: [email protected]
e Assistant Professor, Department of Civil Engineering, Birla Institute of Technology and
Science - Pilani, Pilani, India; Email: [email protected]
Abstract: US Highway Capacity Manual and Indian Highway Capacity Manual define road
capacity as the maximum sustainable flow rate at which vehicles or persons can reasonably be
expected to traverse a point under the given roadway, geometric, traffic, environmental, and
control conditions. The capacity of the roadway system often gets reduced by the roadside
activities called side friction. In India, side friction prevails predominantly in urban areas due
to a significant rise in population and economic activities. These activities restrain the urban
road system to serve at full operating capacity. In this study, the impact of a side friction
parameter (on-street parking, and non-motorized vehicles) on the performance of an urban
road has been measured. Secondarily, the product limit method of capacity estimation has
been implemented to overcome the randomness observed in capacity value. A
microsimulation model is developed to validate the results.
Keywords: On-street parking; Capacity estimation; Mixed traffic condition; Product limit
method; Fundamental diagram
1. INTRODUCTION
1.1 General
Surplus amount of economic activities are observed in the urban areas of all the
communities of the world. Road transportation and Infrastructure acts as a backbone for all
the activities. In terms of logistics, more emphasis is given towards the development of road
infrastructure and network as it plays a major role in the economic growth and development
of the communities. Despite the observed increase in the road infrastructure, it has failed to
cope with the traffic growth rate and transportation demand. Unavailability of space, as most
of the urban areas are already built-up, makes it difficult to carry on any significant
expansion. In addition, it might not be economical to increase the road space in the instance,
though it might be required in the future, following the traffic growth. Even so, the primary
goal should be the optimization of the road network, to increase its efficiency in terms of
traffic movement instead of expansion. Studies conducted in developing countries have stated
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that significant amount of activity is present alongside and on the roads, which affects the
operation on the carriageway(Bang, 1995; Chiguma and Bang, 2006; Marler et.al. 1994). This
interference to the smooth flow of traffic is “side friction”. US Highway Capacity Manual
(HCM 2010) and Indian Highway capacity manual (Indo-HCM 2017)specifies various
factors, including roadside activities that reduce capacity and affect speed-flow relationships.
Karl Bang (1995) defined side friction as a composite variable describing the degree of
interaction between the traffic flow and activities along the side(s) and sometimes across or
within the travelled way. Activities likely to disrupt traffic flow include blockage of travel
way (public transport vehicles, pedestrian crossing or movement, non-motorized or slow
moving vehicles), Roadside parking and un-parking activities, vehicles leaving or entering
roadside premises, roadside vendors, etc. Studying these factors and analyzing their effect on
the operational performance of the road network of the urban areas can be termed as studying
the effect of side friction on urban roads in broader terms.
Capacity is defined as the maximum sustainable flow rate at which vehicles can be
expected to traverse a point or uniform section of a lane or roadway during a given time
period under prevailing roadway, traffic and control conditions (Indian Road Congress
106:1990; HCM 2010).Some studies have stated that the maximum flow value may not
necessarily indicate or coincide with the transition of traffic from uncongested state to
congested state(Asgharzadeh and Kondyli, 2018; Brilon et.al. 2005; Lorenz and Elefteriadou,
2001; Minderhoud et.al. 1997). To overcome such randomness in capacity Breakdown
probability models have been developed. Following the studies related to comparison and
applicability of capacity estimation methods (Asgharzadeh and Kondyli, 2018; Ben-Edigbe et
al. 2013; Li and Laurence, 2015; Minderhoud et.al. 1997; Suresh and Umadevi, 2014), two
methods, namely macroscopic fundamental diagram method and Product Limit Method were
found to be the best and reliable.
1.2 Problem Statement
In India, the number of side friction parameters are too many and their effect on the
traffic flow is substantial. Similar situation is also observed in countries with similar traffic
and demographic characteristics. Even if we follow, the methodologies prescribed in the US
HCM (2010) there are factors, which have not been considered. Factors like on-street trading,
nonmotorized vehicles, on-street parking’s, irregular stopping of public transport vehicles for
boarding and alighting of passengers, pedestrian movement, un-designated crossing, etc. All
these activities are highly observed to be plying on the Indian roads. But among all the
parameters on-street parking is found to be the most dominant(Lim et.al. 2012; Peprah et.al.
2014; Yousif, 1999). The traffic is heavily interruptedbut this, and hence reduction in the
performance of the road infrastructure is observed. Slow moving vehicles (non-motorized
vehicle class) can also prove to be a form of side friction as these vehicles move at
comparatively slower speeds and if present in higher concentration, may block some part of
lanes in the carriageway. The main aim of the study is to find the impact of on-street parking,
non-motorized trafficand Motorized two-wheeler concentration in traffic stream on
performance and capacity of the Urban Road link. Secondarily this study also aims to test the
stochastic approach of capacity estimation using the product-limit method.
2. LITERATURE REVIEW
Some of the initial work concerning side friction was laid down by Karl. L. Bang
(1995). He suggested that importance should be given to corporate the effect of side friction
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explicitly into procedures for calculation of speed and capacity. The factors that could be
considered as side friction parameters were properly described and summarized in the study.
Furthermore, side friction is also classified based upon the level of impact it had on the traffic.
The analysis done in the study is based on the HCM model and the author tried to determine
the adjustment factor for the free flow speed calculation. Reduction factors related to
carriageway width reduction and shoulder width reduction have been determined for different
road types and class of side friction. Chiguma and Bang(2006)analyzed side friction impacts
on the urban road links based on a case study for Dar-es-salaam a city in Tanzania, they
studied the effect of side friction on the microscopic and macroscopic level. The effects on
speed and capacity are aggregated for the macroscopic analysis whereas each side friction
parameter is studied on an individual basis for the microscopic analysis. The impact is
represented as a reduction in average speed caused by each individual side friction factor on
the corresponding type of road.
Capacity is defined precisely in the Highway capacity manual, buta misunderstanding in
the interpretation of the derived value is prone to occur. According to the study “Assessment
of Roadway Capacity Estimation method”(Minderhoud et.al. 1997), this problem is caused
due to the presence of different types, approaches, and procedures for capacity estimation. To
simplify future proceedings, the authors have classified various capacity estimation methods
into the direct empirical and indirect empirical method. An overview of various methods
containing the most important of their characteristics is presented in a tabulated manner. The
characteristics may serve as criteria for adoption of the method suitable for the need, analysis,
and data availability. Based on the above findings Product limit method for capacity
estimation is utilized in this study. Developed recently, the method is useful in tackling the
randomness in capacity. The product-limit method is based on the survival analysis method
by Kaplan and Meier. The method is used to find the probability of breakdown or survival of
traffic condition corresponding to the flow value. Though other studies (Li and Laurence,
2015; Shao, 2011) have been conducted, precise description defining breakdown or
breakdown probability value has not been specified. (Asgharzadeh and Kondyli, 2018; Shao,
2011), studied the product limit method and compared the results achieved with the simulated
results validating the use of the method.
Simulation is recognized as a versatile tool for traffic flow modelling by various
researchers. But modelling the heterogeneous traffic proves to be rather challenging than
modelling of homogeneous traffic as several factors like driver aggressiveness, lane
discipline, driver behaviour for a different class of vehicles and varying traffic mix causes
challenges in the calibration of the model to the field like condition. Studies have provided
efficient steps and parameter measures, methodology and to calibrate and simulate mix traffic
conditions (Ashish Dhamaniya and Chandra, 2016; Bains et.al. 2012; Siddharth and
Ramadurai, 2013). Although several studies have been done for calibration of Indian Traffic,
unfortunately, the studies and values presented in them cannot be used to full extent. Studies
are useful, to set some general benchmark of calibration values for different traffic conditions,
road infrastructures, systems or networks. This will help in achieving faster calibration of the
models. Heterogeneous traffic composition, irregular geometry, loose lane discipline make it
difficult to analyze the traffic and arrive at precise results. Micro-simulation tools offer vivid
flexibility in studying the behaviour of such traffic condition. Being widely used in
transportation operations and management, researchers say that analysis is safer, less
expensive and much faster than field implication and testing (Daamen et al. 2014). It
effectively analyses and evaluates proposed improvements and alternatives. Moreover, any
variation in geometric or traffic conditions can be analyzedeasily. Traffic model in VISSIM
needs to be calibrated, to efficiently represent the field condition. In addition, for the
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calibration, the required parameters to model and simulate the field conditions must be known
in advance.
3. STUDY AREA AND DATA COLLECTION
3.1 Site Selection
A preliminary survey was conducted in the city of Jaipur from the state of Rajasthan in
India. Possible locations were scouted after inquiring about the traffic condition with local
residents and traffic authorities stationed in various parts of the city. Following
comprehensive visits to the sites during different times of the day, observing traffic,
geometric and surface condition of the roads the study stretch was finalized. It is desirable
that the study section meets the following characteristics to provide ease in classification and
analysis of the side friction parameter. Only a single type of side friction parameter (SFP) is
present on the site. There is no disturbance to the site from either merging/diverging traffic or
any other different type of side friction parameter. The SFP exists for a considerable distance
and prolonged duration. The traffic condition in the congested state is observed on the site to
help determine the capacity.
Figure 1a
Figure 1b:
Figure 1 Site Details. (1a: Satellite view showing on-street parking at the site. 1b: Base
Geometry of the site when side friction is absent)
(Figure 1 a, b) The road section selected is a sub-arterial road part of MI Road in Jaipur
near Collectorate Circle and adjacent to the District Court. Road section is a two-way divided
road with a median, restricted access and located in a straight flat terrain in urban areas where
mobility is of primary importance. Raised kerb path exists on both the side of the carriageway
for pedestrian movement. Road width is constant; the surface is paved and in good condition,
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lane markings are present and clearly visible. The research is limited to studying a specific
factor on a single road stretch. In the presentscenario, on-street parking is considered.
Average carriageway width without side friction is 8.4m, whereas when side friction exists
the average carriageway width is reduced to 6.4m.
3.2 Data Collection and Extraction
Video-graphic data is collected on a bright clear sunny day. The data is collected for
nearly five hours on three working weekdays. The time for data collection is decided such that
it captures data for the time duration when side friction parameter prevails and when it
diminishes completely. During this period the traffic’s, transition from uncongested to
congested state when moving from far upstream section to SFP affected area is observed.
Geometric data required in model creation for simulation is obtained manually from field
measurement.
The recorded video was replayed on a large screen in the laboratory. The observed
heterogeneous traffic is classified into seven classes. The non-motorized vehicle, two-
wheelers, three-wheelers, small cars, big cars, Bus/Truck, commercial vehicles. Traffic
composition is shown in Figure 2. Classified volume count and its corresponding average
traffic speed for every 5-min interval are extracted. Volume counts for every vehicle class are
converted to PCU using their passenger car equivalents calculated using the dynamic PCU
estimation method(Chandra et al. 1997). After addition, total PCU count for every 5-min
interval is converted to its equivalent hourly volume. Space mean speed is calculated for each
corresponding 5-min interval. Figure 3 shows the detailed speed volume plot.
Figure 2. Traffic composition
1.43%3.2%
1.18%
4.92%
26.47%8.75%
54.05% Two-Wheelers
Auto Rickshaws
Passenger Cars
SUV's
Commercial Vehicles
Bus/Heavy
Non-Motorized
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Figure 3.Speed Flow Data
4. METHODOLOGY
4.1 Effect of On-Street Parking
As the preliminary objective is to determine the capacity of the selected road section
while the side friction parameter prevails and when its effect is negligible or absent, the
analysis is performed for both the conditions using the methods of Product limit and
Simulation. The collected data is separated into two parts for each day. Division of data was
done such that the time duration when on-street parking is present is considered as data when
side friction prevails. While the time duration when the presence of on-street parking
11:50 - 11:55
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peed
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diminishes, such that no vehicle is affected during its traversing through the section is
considered as the data when side friction is absent. We would like to specify that the time of
this division is not constant on each day. The division is done completely based on
observations made from the video data.
4.1.1 Product-limit method
Based on the Survival Analysis by Kaplan and Meir is one of the methods to develop
the breakdown probability models.Maximum number for volume or PCU count may not
necessarily represent the transition of traffic state from congested to the non-congested
condition. Hence, various researchers have tried to identify as to what value of capacity
should be used. Volume at transition point or the maximum volume before breakdown.
Therefore, it is necessary if we are to pursue capacity value. For breakdown to occur a section
with bottleneck where frequent congestions are observed is to be selected. Also, the transition
from uncongested to the congested state would signify that the capacity is reached. However,
it may not define the carrying capacity of the upstream section, since the congestion is due to
the spill-back and queuing caused due to bottleneck. The bottleneck characterizes the capacity
of the downstream section. However, for this study, we are precisely looking for such kind of
section, where congestion is due to the bottleneck created by side friction parameter (on-street
parking)
Table 1 Comparison of lifetime survival with capacity survival
Characteristics Lifetime analysis Capacity analysis
Parameter Time t Traffic volume q
Failure event Death at time t Breakdown at volume q
Lifetime variable Lifetime T Capacity c
Censoring Lifetime T is longer than the Duration
of the experiment
Capacity c is greater than
Traffic demand
Survival function S(t) = 1 – F(t) Sc(q) = 1 – Fc(q)
Capacity has been defined as a random variable due to traffic breakdown occurring at various
flow rate levels [5]. Capacity probability function Fc(q) can be expressed as:
Fc(q) = P(C<q) (1)
Where,
q:maximum flow rate
C: Capacity of the road section.
So when the flow rate reaches the capacity value (i.e. Q=C) breakdown will occur. Therefore,
Fc(q) will denote the probability at the corresponding flow rate q, the breakdown will occur.
Contrarily survival probability function is given as Sc(q), which can be expressed as
Sc(q) = P(C>q) (2)
Therefore, as long as flow rate does not reach capacity value the traffic will move in
uncongested state and the breakdown does not occur. So,
Sc(q) = 1- Fc(q) (3)
Thus, the estimator of product limit for the survival function is given as
𝑆(𝑞) = ∏Ki−di
Ki𝑖:𝑞𝑖≤𝑞 , i∈ 𝐵𝑖 (4)
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Where,
(Ki): No of intervals with traffic volumeqi ≤ q;
(Di)no of intervals with traffic volume qi > 𝑞 ;
(B) Breakdown Interval sets {B1, B2, B3….}
As described earlier analysis for conditions when side friction prevails and it is absent,
are done separately. As the procedure goes the first part is to define and identify the
breakdowns from the data. The breakdown is defined as the transition of operations from non-
congested to congested conditions. Kondyli and Brilon(2011)concluded that speed as a
measure can be used to identify the Breakdown. Lorenz and Elefteriadou (2001)defined
breakdown as occurring when the average speed of all lanes drops below a specified threshold
for a period of at least 5 minutes. Brilon et.al.( 2005)defined breakdown as a reduction of
average below a threshold of 70 Km/h for one particular 5-min interval can be defined as a
breakdown. Elefteriadou and Lertworawanich(2002) during their study on-ramp merging
proposed that a breakdown is defined when the speed decrease between two consecutive time
intervals exceeds a pre-specified threshold, and this lower speed is sustained for a predefined
length of time. Another study on freeways in Germany (Brilon et.al. 2005)defined breakdown
as occurring when the average speed of all lanes drops below 90 km/ h for a period of at least
5 min. Hence, following such findings we have specified two conditions to define and identify
a breakdown. Firstly, the average speed of all lanes drops for a period of at least 5 min. before
traffic recovers to its original condition prior to speed reduction (Table 2). The second
condition is thatthe low-speed condition is below a specified threshold and is sustained for at
least 5-min. Threshold speed is considered as the maximum of post-breakdown speeds
observed among all the identified probable breakdowns that meet the first condition.
Table 2. Probable Breakdowns satisfying condition 1
Time Pre-Breakdown
Speed
Post Breakdown
speed
Speed
Difference
Duration of
Congested Traffic
1 (D2) 11:05-11:10 23 9 14 35 min.
2 (D2) 13:25-13:30 24 9 15 10 min.
3 (D2) 12:55-13:00 25 16 9 20 min.
4 (D2) 10:45-11:00 23 14 8 15 min.
5 (D3) 09:25-09:30 21 15 6 15 min.
6 (D3) 11:25-11:40 21 9 12 30 min.
7 (D3) 11:10-11:15 16 10 6 10 min.
8 (D3) 09:55-10:15 16 8 8 45 min.
9 (D3) 12:05-12:10 26 9 17 10 min.
10 (D3) 12:25-12:30 19 11 8 25 min.
The absence of specific literature that defines threshold speed value for a breakdown on
urban roads, makes the task difficult. In this case, considering 15 percentile speed does not
reflect the actual conditions observed during the breakdown. As the speeds observed
throughout the peak period when side friction exists are consistently low. But considering all
the initially identified breakdown for the calculation will not justify the process as the
magnitude of speed difference in pre-breakdown and post-breakdown speed might not be
significant. Hence for this study, the threshold speed value is considered as a maximum of the
post-breakdown speeds observed among all the identified probable breakdown conditions
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(i.e., 16 Km/Hr.). With this, we can eliminate the probable breakdowns whose effect on the
traffic stream may be insignificant. After shortlisting of actual breakdowns (Figure 4), the
calculation to find corresponding breakdown probability is carried out. Similarly, breakdowns
were identified for the duration when side friction is absent and the calculations are
performed. Figure 4 shows the Breakdown probability against the flow. It can be seen that the
probability goes on increasing at the maximum flow observed during the time period when
side friction exists and when it is absent. This can be attributed to the breakdown observed at
the maximum flow values. Following the methodology, if we substitute corresponding values
in Eq.4 to calculate survival probability. For the condition when side friction exists, survival
probability obtained will be zero. And thereafter using Eq. 1 we get the probability of
breakdown equal to 1 or the 0.7 as in the case when side friction is absent
Figure 2. Final Breakdown Identified after assigning Threshold Value
10:45 - 10:50
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(a) (b)
Figure 3Breakdown probability Flow; (a) Side friction present; (b) Side friction absent
4.1.2 Simulation
VISSIM is used for simulating the traffic conditions. As for model creation, Geometric
data is collected manually. An aerial view screenshot of the study site is acquired from
Google maps and used as a background image in VISSIM. The image is set to proper scale.
The background image is used for reference purpose and does not affect the geometry of the
modelled created using the field data. Point to note is that model creation, calibration and
validation are done using the data when side friction is absent. Sufficient Buffer length is
provided at the upstream of study location for the model to prime and achieves better results
from the model. Traffic composition observed over the duration of 3 days is averaged and
used as input for vehicle composition. Desired speed distribution curves for each vehicle class
are generated from the speed data extracted. Link behaviour for the road section and driver
behaviour for each vehicle class is defined. Calibration of driver behaviour parameters is done
by trial and error basis. The model is considered to be calibrated when for a given volume; the
traffic composition, average speed of each vehicle class and flow values are found similar to
the corresponding values observed in the field. The time period for which each simulation
shall run is set to 4300 seconds, and time duration of for which the data measurement point
shall start and stop recording the data is set from 600 sec. to 4200 sec. (1 hr.) This is done to
allow the simulation of a priming period of 600 sec. (10 mins.) to reach field like conditions
After the calibration of the model, the side friction effect of capacity is analysed. Side
friction is introduced by reducing carriageway width at a set of regular intervals. Beginning
from the actual field conditions when no on-street parking is present and uniform carriageway
width is available throughout the stretch. In such a case, road width reduction is said to be
0%. Variation in average traffic speed and flow values are observed by increasing the input
vehicle volume at a regular interval of 100 vehicles. Results of vehicles passing the data
collection point during the simulation are converted to their passenger car equivalent and
added to define the per hour flow for the given conditions. After one such cycle, carriageway
width is reduced by 10% and a similar procedure is followed for every cycle until 30% of
road width reduction. It should be noted that after starting of simulation, the run is allowed to
prime for 10 minutes before beginning the data collection at data collection point defined in
the model.
Simulations were performed at increasing input volumes while varying the carriageway
width in a pre-specified and cyclic order described earlier (Figure 6).
050
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Figure 4 Geometric Model for parking condition in VISSIM
4.2 Effect of Non-MotorizedVehicles
Although, the concentration of non-motorized traffic in urban areas is less, in some
cases, it has been observed to be more than 5%. Moreover, with the idea of sustainable
transport development taking root, studying the effect of increasing non-motorized transport
share in the traffic is warranted. Some studies have already been conducted in these
aspects(RAHMAN et.al. 2014; Shi and Li, 2008). Although studies have gone as to estimate
results for up to 95% (Rahman et al. 2003) of non-motorized vehicles in traffic composition,
for the present study a maximum compositional share of 10% for NMV’s has been
considered. Since such conditions and data is scarcely observed in urban areas, moreover on
urban arterial roads, a simulation-based approach is used to measure the effect of NMV on the
carriageway capacity. To tackle this hurdle microsimulation approach is used. Simulation
model used to measure the effect of on-street capacity was used, with the base condition, i.e.
actual carriageway width was used without any reduction. Traffic composition was changed
for non-motorized vehicles and standard cars accordingly.
It is found that the traffic composition also affects the capacity of the
carriageway(Chandra et al. 2016). Hence, the composition of the dominant vehicle class of
2W was varied accordingly. The 2Wcomposition was changed and respective alterations were
made to passenger car composition as it is the standard measure for volume. But, this change
was made only for side friction affected section. Means, the original microsimulation model
with reduced carriageway width was considered. Capacity for all cars condition was
determined and subsequently, the 2W composition was increased from 0% to 50%.
5 RESULTS
5.1 Effect of On-street parking
From the results obtained using Product limit method and simulation, it is evident that
side friction results in a reduction of operational performance of urban roads. 10% Breakdown
probability (90% Survivable Probability) (Brilon et.al. 2005; Kondyli and Brilon, 2011; Li
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and Laurence, 2015)is considered as a threshold value for capacity estimation. Following the
field condition, it is observed that 21% carriageway width reduction exists. Table 3 and
Figure 7 shows theresults from PLM and simulation are compared.
(a) (b)
Figure 7: Simulation Results for decreasing carriageway width. (a) Flow variation with respect to
increasing influx volume. (b) Speed Flow relation
Table 3: Result of MFD, PLM and Simulation
On-Street
parking PLM Method Simulation % Reduction
Capacity Present 4200 PCU/Hr. 4159 PCU/Hr. 11.56%
Absent 4749PCU/Hr. 4708PCU/Hr. 11.66%
Average
Speed
Present 14 Km/Hr. 13 Km/Hr. 46.15%
Absent 26 Km/Hr. 24 Km/Hr. 45.83%
5.2 Effect of Non-Motorized Vehicles
1000 1500 2000 2500 3000 3500 4000 4500 5000
20
25
30
35
40 0% NMV
1.72% NMV
5% NMV
10% NMV
Avera
ge S
peed (
Km
/Hr.
)
Flow (PCU/hr)
Figure 8 Speed Flow plot of simulated results for varying Non-motorized vehicles composition
Generally non-motorized vehicles are found to traverse along the extreme end from the
carriageway median and hence do not usually block the traffic on the carriageway, but when
3000 3500 4000 4500 5000 5500 60003000
3200
3400
3600
3800
4000
4200
4400
4600
4800
5000
Flo
w (
pcu
/h)
Input flow value (vehicles)
0% Width Reduction
10% Width Reduction
20% Width Reduction
30% Width Reduction
0% Width Reduction
10% Width Reduction
20% Width Reduction
30% Width Reduction
3000 3250 3500 3750 4000 4250 4500 4750 50000
5
10
15
20
25
30
35
Av
g.
Sp
eed
(K
mp
h)
Flow (pcu/h)
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the composition increases, it might happen that an NMV to be traversing along the middle of
the lane or right end (Left-Hand Traffic) and end up creating a temporary moving bottleneck.
Simulation results have been specified in figure 8 and table 4.
Table 4 Results for varying NMV composition
Non-Motorized Vehicles Simulated Capacity Average Speed
0% 4855 PCU/Hr. 28 Km/Hr.
1.72% (Field Observed) 4708 PCU/Hr. 24Km/Hr.
5% 4489 PCU/Hr. 21 Km/Hr.
10% 4242 PCU/Hr. 21Km/Hr.
5.3 Effect of Change in Traffic Composition
0 1000 2000 3000 4000 5000
15
20
25
30
35
40
All Cars
0% 2W
10% 2W
20% 2W
30% 2W
40% 2W
50% 2W
Av
era
ge S
peed
(K
m/H
r.)
Flow (PCU/Hr.)
Figure 9 Speed flow plot for varying 2W composition in the presenceof on-street parking
Multiple simulationruns with increasing traffic volume for respective traffic composition were
carried out. Due to higher manoeuvrability and less space, it was expected that the capacity of
the carriageway should also increase following the findings by (Ashish Dhamaniya and
Chandra 2016; Chandra et al. 2016; Meena et al. 2018), but the results are found to be
conflicting.
Table 5 Results for varying 2W composition in the presence of on-street parking
2W Composition Simulated Capacity Average Speed
0% 4619 PCU/Hr. 25 Km/Hr.
10% 4532 PCU/Hr. 24 Km/Hr.
20% 4544 PCU/Hr. 24 Km/Hr.
30% 4244 PCU/Hr. 25 Km/Hr.
40% 4185 PCU/Hr. 24 Km/Hr.
50% 3979 PCU/Hr. 25 Km/Hr.
All Cars Scenario 4559 PCU/Hr. 30 Km/Hr.
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Following the trends from results, when compared for all the compositions, increasing 2W
traffic composition shows a pattern towards an increase in capacity of the carriageway but
until a certain point and then goes on the reverse with increasing traffic volume at the same
composition as seen in figure 9. The final results are specified in table 5.
6 SUMMARY AND CONCLUSION
From the results, it is evident that the fringe conditions on the carriageway, affect the
performance of the road and results in increased discomfort to the travellers. Therefore, the
study analyzing their effect is much needed to help policymakers in proper decision making in
terms of design, operation, and enforcement to effectively manage the traffic plying on the
road network and mitigate the effect of side friction. Capacity estimation methods generally
used, provide a deterministic and discrete value, but they fail to consider the stochastic nature
of traffic. With the application of Product Limit Method, we have attempted to take into
account the random nature of capacity value. Instead of providing a specific value, the method
provides a distribution curve, for the researcher or planner to consider the value as per his
requirements or the intended purpose of the analysis. But, the implementation of the method
has some challenges, primarily what is perceived to be a breakdown scenario during the
observation and the breakdown probability value that should be considered to represent the
capacity of the selected road section.
a) The breakdown has been defined differently by various authors. However, it can be said
that it will largely depend on the location of the study. The speed threshold defining the
breakdown at freeways may be much higher than the threshold defining breakdowns in
urban roads.
b) Based on the Breakdown probability considered, the capacity value may also vary. Higher
breakdown probability might represent higher capacity value.
So, either a systematic and well-defined set of the condition should be made to identify
and define breakdowns. Alternatively, a questionnaire survey can be carried out at the site to
consider the traveller's perception of breakdown [8]. For the breakdown probability, it may
vary as per the demand of the study. Higher breakdown probability may be considered for
traffic management purposes, while lower breakdown probability may be applied for
designing purposes considering the future growth of the traffic. Again, the analysis and result
will vary based on the purpose of the study. As the breakdown volume considered may be
different for designing or management purposes.
Coming to the effect of side friction parameters, it was found that the presence of on-
street parking resulted in around 12% reduction in capacity of the carriageway causing around
an average delay of 60 secs. in travel times.A 13% decrease in operational capacity was
observed for a traffic composition with 10% NMV. Traffic composition always affects the
capacity of the carriageway. When studied for side friction affected area (reduced carriageway
width due to on-street parking), it was found that the dominant vehicle class of 2W caused a
reduction in the capacity. This is in contrast to the prediction and results by (Chandra, 2004;
Meena et.al.. 2018) where, contrarily to base capacity with all cars condition, increase in 2W
vehicle composition causes increase in capacity. Following the results in figure 9 the theory
was found to be valid till a certain breakpoint(around 2900PCU/Hr. in this study) but with
increasing traffic flow, the trend reverses and any increase in 2W vehicle composition in
traffic reduces the volume carrying capacity of the carriageway. This might be attributed to
the higher manoeuvring tendancyof 2W vehicle class and smaller lateral gaps causing
increase in conflicts at the bottleneck (onset of on-street parking as shown in figure 6). Till
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the breakpoint it can be said that the 2W traverse around the carriageway with higher
manoeuvrability and better Level of service conditions causing faster dispersing of traffic. But
after the breakpoint it can be said that the absence of lane following, the higher manoeuvring
tendancy of 2W vehicle class, smaller lateral gaps and tendency to prioritize self through
movement, results in increase of conflicts and interruption to other vehicles thus increasing
congestion and delay, subsequently resulting in a decreased capacity. It should also be noted
that this condition was observed in simulation based side-friction affected scenario, which is
the existence of bottleneck (reduced carriageway width due to on-street parking) might be the
key reason for such results. In absence of such situation (i.e. base condition) the results by
(Ashish Dhamaniya and Chandra, 2016; Chandra et.al.. 2016; Meena et.al.. 2018)should hold
true, this can be verified by further comparison of the two scenarios with varying 2W
compositional share in traffic.
This study presents the effect of three parameters namely on-street parking, non-
motorized traffic and increasing two-wheeler composition in side-friction affected area on the
performance of the carriageway. Though presented individually, it should be noted that the
on-field conditions might be different. Generally, a single side friction parameter may not
exist on the field but another additional SFP also is affecting the traffic performance. So the
decrease in capacity might be more than specified results. In any degree, side friction hinders
smooth and efficient operation of traffic on the carriageways. The notion of proper design of
facility, education to use the existing facility and enforcement of for wrongful usage should
be cyclically self-evolving, leading towards better and safer design and operation of a traffic
infrastructure. Vehicular traffic oriented deigns often lead towards neglecting of other road
users and hence resulting in rise of side friction parameters, which as this study states,
deteriorates the performance of traffic infrastructure. With this study the authors aim to
emphasis on the negative effect of side friction parameter on the urban traffic infrastructure so
as to help designers and policymakers towards efficient and safer traffic infrastructure design
and operation.
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