free speeds

CRITICAL EVALUATION OF ROADWAY CAPACITY OF MULTI-LANE HIGH SPEED CORRIDORS UNDER 235HETEROGENEOUS TRAFFIC CONDITIONS THROUGH TRADITIONAL AND MICROSCOPIC SIMULATION MODELS

Journal of the Indian Roads Congress, October-December 2010

Paper No. 566

CRITICAL EVALUATION OF ROADWAY CAPACITY OF MULTI-LANEHIGH SPEED CORRIDORS UNDER HETEROGENEOUS TRAFFIC

CONDITIONS THROUGH TRADITIONAL AND MICROSCOPICSIMULATION MODELS

S. VELMURUGAN*, ERRAMPALLI MADHU**, K. RAVINDER***, K. SITARAMANJANEYULU****& S. GANGOPADHYAY*****

ABSTRACT

The major factors affecting the Road User Costs (RUC) are the speed coupled with traffic flow characteristics at which vehicles operate onroads, which in turn determines fuel consumption and other cost components per unit distance traveled. Considering this, the Governmentof India has been involved in roadway capacity augmentation by building multi-lane divided carriageways to link major cities through theimplementation of various projects, like, Golden Quadrilateral, North-South, East-West and Expressway Corridors during the last decade.These radical changes in road network coupled with radical advancements in vehicle technology have resulted in huge variations inspeed - flow characteristics, which necessitated the evolution of exclusive speed-flow equations and roadway capacity for multi-lanehighways. Accordingly, an attempt has been made in this Paper to explicitly study the speed - flow characteristics on varying types ofmulti-lane highways encompassing four-lane, six-lane and eight-lane divided carriageways in plain terrain. From the collected data, freespeed profiles and speed - flow equations for different vehicle types for varying widths of multi-lane highways in the country has beendeveloped based on traditional and microscopic simulation models and subsequently roadway capacity has been estimated. Further, the lanechange behavior of different vehicle types has been extensively studied and its impact on roadway capacity has been estimated on multi-lanehighways. Finally, the Design Service Volume for varying types of divided carriageways including four-lane, six-lane and eight-lane has beenevolved with reasonable degree of authenticity under the prevailing heterogeneous traffic conditions on multi-lane highways in India.

1 PREAMBLE

The sustained economic growth in India in recent yearshas brought opportunities and challenges to the planningand management of the Indian transportation system. Likein other developing countries, the transportation systemin India is characterized by limited roadway infrastructureand the lack of operation and management experience.Among the most critical issues in highway planning andmanagement is to determine the roadway capacities ofany highway. As such, India has one of the largest roadnetworks in the world hovering around 3.5 million km atpresent. For the purpose of management and

administration, roads in India are divided into fivecategories namely, National Highways (NH), StateHighways (SH), Major District Roads (MDR), OtherDistrict Roads (ODR) and Village Roads (VR). NHs areintended to facilitate medium and long distanceinter-city passenger and freight traffic across the countryand they are also serve as main arterial roads which runthrough length and breadth of the country connecting seaports, state capitals, major industrial and tourist centers.Though the NHs constitutes less than 2 per cent of thetotal road network, but carries 40 per cent of the totalroad traffic. The road infrastructure and available

Key Words: Free Speed, Speed-Flow Equations, Roadway Capacity, Lane Change Behaviour, High Speed Corridors;

Central Road Research Institute (CRRI) Mathura Road, CRRI (P.O.)New Delhi - 110020, IndiaE-mail: [email protected]; [email protected]

*

**

***

****

*****

Contact Author and Scientist, TE & TPA

Scientist, TE & TPA

Scientist, TE & TPA

Scientist, PED

Director

}


236 VELMURUGAN, MADHU, RAVINDER, SITARAMANJANEYULU & GANGOPADHYAY ON

transport services in the country are highly inadequatefor achieving faster movement of passenger and goodsmovement in comparison with the situation in thedeveloped world. Many sections of the highways are inneed of capacity augmentation, pavement strengthening,rehabilitation of bridges, improvement of riding quality,provision of traffic safety measures, etc. Eventually ithas been realized by the Government of India that theabove mentioned gross inadequacies in the NH networkcoupled with congestion caused by heterogeneity in trafficmix is contributing to huge economic losses in terms ofhigh Road User Cost (RUC), which is also contributingto high rate of road accidents. Realising the presentshortcomings in the transport sector, the Government ofIndia has initiated massive construction programmes ofhighways linking major cities/activity centres. This hasled to gradual growth in the quantum of NH network,which was around 22,255 km in 1951 has risen to 70,548km as of March 2009 (Velmurugan et. al., 2009).

Considering the above mandate of the Government, itwas felt essential to quantify the investment made on themultilane highways by developing RUC models exclusiveto multi-lane highways. Eventually, a study has beenundertaken by the Central Road Research Institute(CRRI), New Delhi under the aegis of PlanningCommission, Government of India in 2008 focusing onevolving free speed and speed - flow relationships andfinally develop RUC models for high speed corridors. Theterm 'High Speed Corridors' used in this study impliesfour-lane, six-lane and eight-lane divided inter-cityhighways. The automobile industry and road designstandards in India have also undergone tremendouschanges in the recent decade. Therefore, it wasconsidered necessary to take a look at the changing trendsof prevailing speed - flow characteristics considering theemerging high speed corridors of India. In 1982,speed - flow studies were carried out as a part of RoadUser Cost Study (RUCS) and they are subsequentlyupdated in the years 1992 and 2001 for varyingcarriageway widths (CRRI, 1982, Kadiyali, 1992 andCRRI, 2001). From here onwards, these studies will bereferred in this Paper as RUCS-1982, URUCS-1992

and URUCS-2001, respectively. Basically, the speed -flow relationships developed in the above studiesencompassed only up to four lane divided carriagewaysas the multi-lane highways beyond such carriagewaywidths were not existent at that time in India.

In this Paper, an attempt has been made for the first timeto explicitly study the free speed profiles andspeed - flow characteristics on varying types ofmulti-lane highways covering four-lane, six-lane andeight-lane divided carriageways in plain terrain. In orderto assess these characteristics, Time Mean Speed (TMS)under free flow conditions and Space Mean Speed (SMS)coupled with traffic flow data was extensively collectedspread over different regions of India. From the collecteddata, free speed profiles of different vehicle types onhigh speed corridors and speed - flow equations havebeen developed based on traditional and microscopicsimulation models. Subsequently, capacity norms for suchhigh speed corridors were also evolved. The impact oftypical Indian driving behavior i.e. how the lane changebehavior affects roadway capacity on multi-lane highwayshas been assessed through microscopic simulationapproach. Finally, based on the results derived in this study,the Design Service Volume (DSV) for varying types ofmulti-lane highways including four-lane, six-lane andeight-lane divided carriageways has been evolved withreasonable degree of authenticity under the prevailingheterogeneous traffic conditions. The outcome from thisstudy is expected to form an important input for developingRUC models exclusively for varying type of multi-lanehighways. This Paper has been structured as given below.

In the next section, an exhaustive overview ofspeed - flow studies carried out in India and elsewherefor multi-lane highways is summarized. The details inrespect of free speed and speed-flow data collected atvarious road sections are discussed in Section 3 coupledwith the description of data collection methodology. Theresults derived from free speed analysis are presented inSection 4, whereas speed - flow equations developed fordifferent vehicle types covering varying carriagewaywidths on multi-lane highways based on traditional andmicroscopic simulation models is dealt in Section 5. The



procedure adopted for evolving the roadway capacitynorms for varying carriageway widths on multi-lanehighways is also discussed in the same section. The impactof lane change behavior on roadway capacity is presentedin detail in Section 6. Finally, the conclusions emergingfrom this study are discussed in Section 7.

2 CAPACITY STUDIES ON MULTI-LANEHIGHWAYS

This section focuses on the studies accomplished formulti-lane highways only. As such, the determination ofhighway capacity is one of the most important applicationsof any traffic theory (Kerner, 2004). Some previoustheories and empirical researches focused on theinterrelationships among the influence of capacity, trafficfeatures, geometric elements, environmental conditionsand temporal weather factors on interrupted multi-lanehighways (see for example, Hoban, 1987; Iwasaki,1991; Ibrahim and Hall, 1994 and Shankar andMannering, 1998). Many years of research has led to thedevelopment of theories and methodologies in roadwaycapacity analysis in the developed countries. For example,the Highway Capacity Manual (HCM) developed in theUnited States of America describes roadway capacityunder ideal conditions and then estimates practicalcapacities under prevailing conditions in the field.US-HCM 2000 (TRB, 2000) suggested that a maximumflow rate that can be achieved on a multilane highway is2200 PCU/hour/lane. The Danish method is also amodification of U.S. HCM to suit Danish conditions. Theadjustment factors in the Danish method cause a steepercapacity reduction than in US-HCM 2000 as theconditions become less ideal and therefore, the capacityunder ideal conditions on a four-lane highway is 2300PCU/hour/lane on Denmark highways (Nielsen andJorgensen, 2008). Similarly, in Finland and Norway too,US-HCM 2000 (TRB, 2000) was followed with minormodifications to suit the local conditions and the roadwaycapacities obtained by the Finnish and Norwegian methodsfor multi-lane highways is 2000 PCU/hour/lane Thestructure of the Swedish method is similar to theUS-HCM (1995) and it uses the 1995 HCM adjustmentfactors for the roadway width, whereas other adjustments

factors are mostly omitted. Consequently, the Swedishmethod yielded higher capacity estimates and theestimated capacity of four-lane divided highways was4200 PCUs/hour per direction (Luttinen and Innamaa,2000). The Australian method for analysis of capacitywas basically same as that of US-HCM method with thebasic difference being additional modification has beensuggested for specific problems. Under ideal conditions,the average minimum headway of 1.8 seconds wasconsidered and maximum flow of 2000 vehicles per hourper lane was assumed. The succeeding paragraph focuseson the roadway capacity evolved in Asian countries likeIndonesia and China for multi-lane highways whereinlargely heterogeneous traffic conditions as experiencedon Indian highways is witnessed.

Bang et. al. (1997) in their study for establishing IndonesiaHCM mentioned that travel speed (synonymous withjourney speed) as the main measure of performance ofroad segments, since it is easy to understand and tomeasure, and is an essential input to road user costs ineconomic analysis. Travel speed is defined in this manualas the space mean speed of light vehicles (LV) over theroad segment as given below:

V = L

TT...(1)

where, V = space mean speed (km/h) ofLight Vehicles (LVs)

L = length of segment (km)

TT = mean travel time of LVs over thesegment (in hours)

Using this analogy, the capacity of multi-lane highwayshas been estimated as 2300 LVs/hour/lane for Indonesianmulti-lane highways. In the case of Chinese conditions,based on the field data collected, VTI highway simulationmodel was calibrated and validated and this model wasused for the determination of Passenger Car Equivalents(PCE) and speed-flow relationships for different terraintypes in parallel with multiple regression analysis ofempirical speed-flow data. The results showed that thefree-flow speeds of vehicles were substantially low and



that the roadway capacity was also marginally lower(2100 PCEs per lane on four-lane divided carriageways)under Chinese conditions as compared with the valuesobtained for Indonesian multi-lane highways. Further, Yangand Zhang (2005) have established based on theirextensive field survey of traffic flow on multi-lanehighways in Beijing and subsequent empirical modeldevelopment that the average roadway capacity per hourper lane on four-lane, six-lane and eight-lane dividedcarriageways is 2104, 1973 and 1848 PCUs, respectively.This is unlike HCM results obtained for many developedcountries which prescribe that average capacity per laneon different highways is equal as they assume thathighway capacity is constantly proportional to the numberof lanes on multi-lane divided carriageways.

Based on the review of above studies in both developedand developing countries it is obvious that the roadwaydesign and traffic control practices are mostly country orregion specific and hence cannot be simply transferredto any country for direct applications. In this context, it isbe noted that the roadway capacity and the conditionsfor adjustment are vastly different on Indian roadwaysas the local roadway design (i.e. lane width, curves andgrades), vehicle size and more importantly, traffic mixand behaviour of a driver especially lane changing andlane discipline phenomenon are entirely different. Further,since there is not a systematic approach to this problem,coupled by a lack of fundamental data, the adjustmentfactors from say, the US HCM 2000 (TRB, 2000)cannot be easily revised and applied to Indian highways.This is because adherence to lane discipline characterizeshomogeneous traffic in the developed nations whereasvery loose lane discipline describes heterogeneous trafficwhich is very much an integral part of all roadways inIndia including multi-lane highways. This is due to fastmoving vehicles cars, goods vehicles, motorized twowheelers sharing the same road space with bicycles, farmtractors, tractor trailers and other types of slow movingvehicles (like cycle rickshaw, animal drawn vehicles, etc.)on the Indian traffic scene accounting for varyingproportion on multi-lane highways depending on itsgeographical location.

Ironically, most of the models discussed above developedfor homogeneous condition are not applicable for theheterogeneous traffic prevalent on Indian roads.Eventually, the first major research effort in India in thisdirection was done as part of the RUCS-1982 and thiswas followed by URUCS-1992 and URUCS-2001.IRC-64 (1990) suggested the tentative DSV of 40,000PCUs for the four-lane divided carriageway in plainterrain, which is significantly lesser than the values evolvedin most of the developed and developing countries andtherefore the need was felt for revisiting the DSV valuesevolved under IRC-64. Consequently, many researchstudies (Kadiyali, et. al., 1991, Tiwari, et. al., 2000,Velmurugan et. al., 2002, 4. Chandra S. and Kumar U.,2003, Reddy, et. al., 2003, Chandra, 2004, Errampalli,et. al., 2004, Velmurugan, et. al., 2004, Dey, 2007,Errampalli, et. al., 2009, Velmurugan et. al., 2009) aimedat assessing the roadway capacity for varyingcarriageway widths including single lane, intermediatelane, two-lane bi-directional and four-lane dividedcarriageway widths covering different terrains have beencarried out during the last two decades. URUCS-2001recommended tentative roadway capacity of 70,000-90,000 PCUs/day for a four-lane divided carriageway inplain terrain (1750-2250 PCU/hr/lane considering 10 percent peak hour flow). Chandra and Kumar (2003) studiedthe effect of roadway width on capacity under differentvolume capacity ratios and varying proportions ofvehicles. Shukla (2008) studied the mixed traffic flowbehavior on four-lane divided highway for varyingconditions of traffic volume and shoulder and developeda simulation model for the observed traffic flow to estimateroadway capacity under these conditions. To understandthe traffic flow behavior on four-lane divided highwaysunder mixed traffic condition, the arrival pattern ofvehicles, speed characteristics, lateral placement ofvehicles and overtaking behavior was analyzed. Shukla(2008) further reported that the roadway capacity of four-lane divided carriageways as 4770 vehicles/hour (vph) ineach direction is estimated for 'all cars' situation.

This exhaustive look at the literature indicates that nosubstantial work has been carried out for establishing the



roadway capacity and DSV for varying carriagewaywidths on multi-lane highways covering four-lane,six-lane and eight-lane divided carriageways for theheterogeneous traffic mix prevalent on Indian highwayswith reasonable degree of confidence and hence thisresearch endeavor can be termed as a significant attemptin this direction. The following section describes themethodology adopted for accomplishing this study.

3 STUDY METHDOLOGY

As mentioned in Section 1, the primary objectives of thepresent study are to assess the free speeds of differentvehicle types on multi-lane high speed corridors anddevelop a realistic speed - flow equations for estimatingthe roadway capacity. From these results, DSV valuesare proposed to determine for varying types ofmulti-lane high speed corridors. In order to achieve theabove envisaged objectives, separate methodologies areadopted for deriving free speed profiles and speed - flowrelations for varying carriageway widths. To accomplishthe above stated objectives, the following studies wereconducted:

a) Free Speed studies

b) Speed - Flow studies

3.1 Free Speed Studies

In the present study, free speed data was collected ondifferent NHs and Expressways spread across the lengthand breadth of the country using Pro-LaserInstrumentation System (Laser Speed MeasurementGun) and trap length method at 21 selected road sectionscovering varying carriageway widths on multi-lanehighways. As the selected test sections are divided roadsegments possessing varying horizontal curvature andtraffic conditions in the two directions of the roadway,the above traffic data collection was carried out at eachof the locations on both directions of travel as thougheach direction was a separate one-way road and hencethe total number of study sections amounts to 42. The listof test sections considered is given in Table 1. All thesestudy sections possess good riding quality with roughness

ranging around 2500 mm/km. The test sections have beenchosen as far as possible away from the urban influenceso that free flow conditions can be experienced. As canbe inferred from Table 1, the number of six-lane dividedcarriageway road sections considered is only 3, whereaseight lane divided carriageway considered is only 1. Thismay be attributed to the relatively lesser number of roadsections presently available in these categories withouthaving the urban influence as compared to the four lanedivided carriageways on inter-city corridors. As the testsections include four-lane, six-lane and eight-lanecarriageways, separate analysis has been carried out forthese carriageways. Further, it can also be noted fromTable 1 that some of the selected test sections are lyingin curved sections. These sections are specificallyselected to incorporate the impact of horizontal curvatureon the vehicular speeds and determine the generalizedfree speed characteristics on these highways. The freespeed data was collected by classifying the vehicles intofollowing categories namely Cars (which is furthersub-classified into two categories namely SmallCars < 1400 cc engine capacity and Big Cars > 1400 ccengine capacity), Two Wheelers (TWs), Auto Rickshaws(Autos), Buses, Light Commercial Vehicles (LCVs),Two-axle Heavy Commercial Vehicles (HCVs) andMulti-axle Heavy Commercial Vehicles (MAVs).

3.2 Methodology for Free Speed Analysis

The observed free speeds of different vehicle types wereclassified into suitable intervals generally of 5 kilometersper hour (kmph) to determine the frequency distributionof vehicles as per speed. The mean speed and standarddeviation (SD) values were calculated from the frequencydistributions. Further, these data were fitted to normaldistribution using mean and SD of vehicle speeds. Fromthese distributions, important parameters namely 15th

Percentile Speed (V15

), 50th Percentile Speed (V50

), 85th

Percentile Speed (V85

), 95th Percentile Speed (V95

) andSpread Ratio (SR) were calculated to check the validityof the data. V

15 is used to determine the lower speed

limit whereas V85

is used for upper speed limits and V95

is



used as a design speed for geometric design of highways.The SR is used to explain normality of the observed dataand it is defined as,

SR = 85 50

50 15

−−

V V

V V...(2)

The estimated frequency curve will be truly normal whenSR is unity. It will tend to deviate from the normaldistribution as SR deviates from the unity. As can be seenfrom the fitted normal distributions, the speed data followthe normal curve only when SR is ranging between 0.69and 1.35 (Dey, et. al., 2006).

Table 1 Selected Test Sections for Free Speed and Speed - Flow Studies

S. No. National Highway Location Direction Number Type of Section(NH) / Expressway (Chainage) of Lanes

1 Km 98 Delhi - Mathura Four Straight

2 Km 98 Mathura - Delhi Four Straight

3 Km 629 Durgapur - Kolkata Four Curved

4 Km 629 Kolkata - Durgapur Four Curved

5 Km 643 Durgapur - Kolkata Four Straight

6 Km 643 Kolkata - Durgapur Four Straight

7 Km 1242 Bangalore - Chennai Four Straight

8 Km 1242 Chennai - Bangalore Four Straight

9 Km 1501 Chennai - Kolkata Four Curved

10 Km 1501 Kolkata - Chennai Four Curved

11 Km 44 Kharagpur - Kolkata Four Curved

12 Km 44 Kolkata - Kharagpur Four Curved

13 Km 47 Kharagpur - Kolkata Four Straight

14 Km 47 Kolkata - Kharagpur Four Straight

15 Km 29 Chenglepat - Chennai Four Straight

16 Km 29 Chennai - Chenglepat Four Straight

17 Km 58 Chennai - Villupuram Four Curved

18 Km 58 Villupuram - Chennai Four Curved

19 Km 98 Chennai - Villupuram Four Straight

20 Km 98 Villupuram - Chennai Four Straight

21 Km 5 Vijayawada - Kolkata Four Straight

22 Km 5 Kolkata - Vijayawada Four Straight

23 Km 1069 Vijayawada - Guntur Four Straight

24 Km 1069 Guntur - Vijayawada Four Straight

25 Km 15 Hyderabad - Warangal Four Straight

26 Km 15 Warangal - Hyderabad Four Straight

27 Km 25 Hyderabad - Bangalore Four Straight

28 Km 25 Bangalore - Hyderabad Four Straight

29 Km 462 Hyderabad - Nagpur Four Straight

30 Km 462 Nagpur - Hyderabad Four Straight

NH-2

NH-2

NH-2

NH-4

NH-5

NH-6

NH-6

NH-45

NH-45

NH-45

NH-5

NH-5

NH-202

NH-7

NH-7

(Table 1 Contd...)



S. No. National Highway Location Direction Number Type of Section(NH) / Expressway (Chainage) of Lanes

3.3 Speed - Flow Studies

The speed - flow studies were conducted along with freespeed studies at the test sections mentioned in Table 1.In the case of speed - flow studies, Registration PlateSurvey was conducted for determination of journey speedsand simultaneously Classified Traffic Volume Counts wereconducted to estimate flow by synchronizing start timeof the traffic volume with that of the Registration Platesurvey. Traffic volume counts and Registration PlateSurvey were conducted for 8 hrs on a typical normalworking day by following the vehicle classification adoptedin the free speed studies. Space Mean Speed (SMS) datawas extracted out of the Registration Plate survey. Thisis a conventional procedure used for determining thespeeds by recording the vehicle registration number, entryand exit time of vehicles on a defined trap length with thehelp of two synchronized stop clocks. The trap lengthsof the road stretches selected for the mean speedmeasurements ranged between 400 m to 900 m. By notingthe registration number and time of arrival and departureof the vehicles at the entry and exit points, the travel timeover the selected trap length was determined and therebythe travel speed was derived. Based on the collectedspeed and flow data, speed - flow relationships areproposed to develop for different vehicle types forfour-lane, six-lane and eight-lane divided carriagewaysseparately.

3.4 Methodology for Speed - Flow Equations andRoadway Capacity

In the present study, the traffic flow data was analyzedby typically dividing the traffic volume into two segmentscorresponding to congested and uncongested trafficconditions as shown in Fig. 1 (Yao, et. al., 2009). Thetwo segments encompass the following:

(i) Uncongested (Upper Part):Traffic related toUncongested and Queue Discharge states

(ii) Congested (Lower Part): Traffic related toQueuing state (Stop and Go)

31 Km 462 Hyderabad - Mumbai Four Straight

32 Km 499 Mumbai - Hyderabad Four Straight

33 Km 30 Hyderabad-Vijayawada Four Straight

34 Km 30 Vijayawada-Hyderabad Four Straight

35 Greater Noida Near Lotus Delhi - Noida Six Straight

36 Expressway Valley School Noida - Delhi Six Straight

37 Greater Noida Near Panchsheel Delhi - Greater Noida Six Curved

38 Expressway Bal College Greater Noida - Delhi Six Curved

39 Km 38 Delhi - Sonepat Six Straight

40 Km 38 Sonepat - Delhi Six Straight

41 Delhi-Gurgaon Near IFFCO Delhi - Gurgaon Eight Straight

42 Expressway Chowk Gurgaon - Delhi Eight Straight

(Table 1 Contd...)

NH-9

NH-9

NH-1

Fig. 1 Uncongested and Congested Parts of Speed-FlowCurve



on the formulations given in Table 2. These formulationsare adopted for developing speed-flow equations andselected based on their statistical validity. Using thisprocedure, the speed - flow equations for different vehicletypes on varying carriageway widths on multi-lanehighways namely four-lane, six-lane and eight-lane dividedcarriageways have been developed. In this study, theroadway capacity is considered as the intersecting pointof best speed - flow models developed for the upper andlower part as shown in Fig. 2.

It is proposed to analyze these two parts separately anddetermine the speed - flow relationships separately inthe present study. For upper part (Uncongested) of thecurve, different models including linear, exponential,polynomial, logarithmic, power, Akcelik and Bureau ofPublic Roads (BPR) have been attempted whereas forthe lower part of the curve, linear, polynomial, logarithmic,power and exponential models were tried to be fitted andthe model exhibiting best fit with the field data wasadopted. The forms of these models are selected based

Table 2 Functional Form of Candidate Models for Speed-Flow Curves

Name of the Functional CommentsEquation Form

Linear v = α - x + β Not always advisable; Reaches zero speedat high F/Fcap

Logarithmic v = - α ln x + β Not always advisable; Has no value at x = 0

(the logarithm of "x" approaches negative infinity).

Exponential v = α vf exp(- β x) Has all the required traits for equilibrium assignment

Power v = α /x β Not always advisable; It goes to infinity atF/F

cap at x = 0.

Polynomial v = -α x2 - β x + γ Not always advisable; It reaches zero speedat high F/F

cap

Bureau of Public v = vf/(1+ α (x) β ) Has all the required traits for equilibrium assignment

Roads (BPR)

Akcelik V = L/[L / vf + 0.25{(x - 1) Has all the required traits for equilibrium assignment.

+ SQRT{(x - 1)2 + α x}}]

Note v = Speed; α , β and γ = Global Parameters for Equation; x = F/Fcap

ratio; vf = Free - Flow Speed;

F = Flow; Fcap

= Capacity Flow; L = Link Length;

Fig. 2 Capacity Estimation from Speed-Flow CurvesFlow (PCU/hr)

3.5 Design Service Volume (DSV)

Design Service Volume (DSV) is defined as the maximumhourly volume at which vehicles can reasonably beexpected to traverse a point or uniform section of a laneor roadway during a given time period under the prevailingroadway, traffic and control conditions while maintaininga designated Level of Service (LOS). From the view pointof smooth traffic flow, it is not advisable to design thewidth of carriageway (or for determining the number oflanes) for a traffic volume equal to its capacity which isavailable at LOS-E. At this level, the speeds are low(typically half the free speed) and freedom to maneuver



travelling below a specified speed range was ignored asthey have outliers based on the scatter plot of the data.Hence, the speed data considered for Two Wheelers,Auto Rickshaws, Buses, Cars, LCVs/Two Axle HeavyCommercial Vehicles and Multi-Axle heavy CommercialVehicles are more than 65 kmph, 50 kmph, 60 kmph,80 kmph, 60 kmph and 55 kmph, respectively.

From Table 3, 4 and 5, it can be observed that the normaldistribution curve described the speed distributionssatisfactorily in most of the vehicle types, since the SRvalue is ranging around 1.0 (from 0.950 to 1.157)demonstrating that SR is well within the limits. A criticalevaluation of the free speed studies on four-lane, six-lane and eight-lane divided carriageway reveals thefollowing:

a) The free speed of both small and big cars is muchhigher when compared with other vehicle typesimplying the rapid advancements in carmanufacturing technologies and superiority ofthese engines.

b) The mean free speed of HCVs and LCVs aremore or less same.

c) The mean free speed of TW is marginally higherthan that of LCVs and Buses.

A summary of the free speeds in four-lane, six-lane andeight-lane divided carriageway is presented in Fig. 4. Thegrowing speeds of different vehicle types can be easilyunderstood from Fig. 4 and the following inferences havebeen drawn:

a) Generally, the mean free speeds of differentvehicle types on eight-lane are higher whencompared to four-lane and six-lane dividedcarriageways.

b) Free speeds of two wheelers and cars marginallyincreased from four-lane to six-lane while theincrease is somewhat significant from six-laneto eight-lane. This can be attributed to theachievement of their desired speeds on four-lanedivided carriageway itself; hence, there isinsignificant improvement of speeds on six-lanecarriageway. However, the addition of one morelane on eight-lane divided carriageway offeringhigher freedom for vehicular movements mighthave aided in attaining substantial increase indesired speeds and thus resulting in enhancedfree speeds.

within the traffic stream is extremely restricted. Besides,at this level of service, even a small increase in volumewould lead to forced flow situation and breakdowns withinthe traffic stream. Even the flow conditions at LOS-Cand LOS-D involve significant vehicle interaction leadingto lower level of comfort and convenience. In contrast,LOS-B represents a stable flow zone which affordsreasonable freedom to drivers in terms of speed selectionand maneuvers within the traffic stream. Under normalcircumstances, therefore, the use of LOS-B is considereddesirable for the design of rural highways. At this level,volume of traffic will be around 0.5 times the roadwaycapacity and this is taken as the DSV for the purpose ofdetermining the carriageway width.It is recommendedthat for major arterial routes, LOS-B should be adoptedfor design purposes. On other roads under exceptionalcircumstances, LOS-C could also be adopted for design.Under these conditions, traffic will experience congestionand inconvenience during some of the peak hours, whichmay be acceptable. This planning decision should be takenin each case specially after carefully considering factors,like, suburban conditions, economic feasibility, etc. ForLOS-C, DSV can be taken as 40 percent higher thanthose for LOS B.

4 FREE SPEED ANALYSIS

The analysis of collected free speed data was carriedout as per the methodology explained in Section 3. Asmentioned in Section 3.1, the data collected for all the 42test sections have been utilized. These sections includefour-lane, six-lane and eight-lane divided carriageway,but separate analysis has been carried out for thesecarriageways. The observed free speed data was fittedthrough normal distribution and relevant parametersnamely average speed, standard deviation, percentilespeeds and SR were estimated. The typical normaldistribution and cumulative distribution curves for freespeeds are given in Fig. 3.

From the normal distribution curves, free speeds ofvehicles on various selected sections of the multi-lanehigh speed corridors are estimated and presented in Table3, 4 and 5 for four-lane, six-lane and eight-lane dividedcarriageways respectively. These tables also present thevarious percentile speeds and the spread ratio of differenttype of vehicles. Since the free speed analysis mainlyfocuses on free-flow conditions, the vehicles travellingwith higher speeds are considered while arriving at theaverage free speeds. For this purpose, the vehicle



Table 3 Free Speed Statistics of Different Vehicles on Four-Lane Divided Carriageways

Vehicle Sample Avg. V15 * V50 * V85 * V95 * Max. SD* SRType Size Speed * Speed*

TW 1191 74.2 63.5 71.7 80.1 85.0 120 7.8 1.027

Auto 753 54.2 46.3 51.6 56.9 60.5 79 5.0 0.995

Small Car 2688 92.4 80.7 89.9 100.6 106.7 161 10.1 1.157

Big Car 4137 93.0 79.7 90.1 100.4 106.3 149 9.8 1.000

Bus 2138 71.1 59.8 68.6 77.1 82.1 108 8.2 0.961

LCV 1614 68.6 58.5 66.1 73.9 78.1 113 7.3 1.019

HCV 504 68.5 58.0 65.9 74.2 78.9 103 7.7 1.038

MAV 1924 64.0 58.2 64.6 71.2 75.2 97 6.1 1.019

*kmph

Fig. 3 Typical Distribution of Free Speed on Four-Lane Divided Carriageways

(NH-45 at Km 98)



Table 4 Free Speed Statistics of Different Vehicles on Six-Lane Divided Carriageways


TW 723 75.0 59.3 72.5 85.7 92.5 109 12.6 1.000

Auto 95 56.3 44.8 54.1 63.8 68.6 88 8.7 1.043

Small Car 749 93.1 80.2 90.6 100.9 106.8 136 9.8 0.998

Big Car 1132 95.5 82.1 93.0 104.0 110.6 135 10.5 1.014

Bus 283 74.4 65.1 71.8 78.4 82.1 102 6.2 0.997

LCV 93 73.6 62.9 71.0 79.5 84.4 105 7.9 1.041

HCV 83 70.7 58.6 68.2 78.3 84.4 101 9.7 1.049

MAV 109 70.5 59.0 68.0 76.8 81.9 100 8.5 0.980

*kmph

Table 5 Free Speed Statistics of Different Vehicles on Eight-Lane Divided Carriageways


TW 343 77.5 63.7 75.0 86.3 92.5 90.0 10.8 1.000

Auto 11 56.7 48.8 53.9 58.8 61.7 64.0 4.5 0.950

Small Car 165 98.0 83.2 95.5 107.5 115.2 97.0 11.8 0.980

Big Car 180 101.4 84.7 98.9 113.7 122.5 104.0 14.2 1.050

Bus 246 75.7 64.9 73.2 81.3 86.2 72.0 7.7 0.980

LCV 127 74.1 61.9 71.6 107.5 86.9 74.0 9.3 1.020

HCV 24 73.2 64.1 70.7 77.0 81.2 56.0 6.1 0.970

MAV 13 72.0 62.7 69.6 76.6 81.1 57.0 6.7 1.030

*kmph

c) Free speeds of heavy vehicles and autossignificantly increased from four-lane to six-lanewhile marginally increased from six-lane toeight-lane. This can be attributed to the abovevehicle types not able to attain the desired speedlevels on four-lanes whereas on six-lane dividedcarriageway the presence of additional lane ishelping in achieving significant increase in freespeed from four-lane to six-lane. However, thereis insignificant improvement in free speeds oneight-lane as compared to six lane carriageways.As auto and heavy vehicles has achieved theirdesired speed levels on six-lane itself, there is noimpact of eight-lane divided carriageway thoughit offers higher LOS for vehicle movements

Fig. 4 Comparison of Average Free Speeds on Four-Lane,Six-Lane and Eight-Lane Divided Carriageways forDifferent Vehicle Types



compared to six and four lane dividedcarriageways.

5 SPEED - FLOW ANALYSIS

5.1 Traditional Model

5.1.1 Development of Speed - Flow Equations

As mentioned in Section 3.3, the traffic data in respectof journey speed, free speed and flow were collected at

the test sections given in Table 1 through registration platemethod, Laser Gun and classified volume count surveysthrough manual means was collected for a period of8 - 12 hrs on different types of multi lane highways spreadover the country. The observed traffic volume on theseroad sections was analyzed and the ranges of trafficcomposition of various vehicle types are presented inTable 6.

Table 6 Observed Traffic Composition on Varying Types of Divided Carriageways

Vehicle Type Traffic Composition on Divided Carriageways (in per cent)

Four-Lane Six-Lane Eight-Lane

Two Wheelers 4 - 59 (24) 9 - 50 (28) 9 - 29 (18)

Autos 0 - 23 (6) 0 - 12 (4) 0 - 1 (0.5)

Small Cars 1 - 36 (14) 7 - 55 (33) 23 - 53 (37)

Big Cars 1 - 43(15) 14 - 33(23) 27 - 48 (37)

Buses 1 - 45 (10) 1 - 11(3) 1 - 9 (3)

LCVs 1 - 40 (7) 1 - 6 (2) 1 - 18 (4)

HCVs 1 - 39 (11) 1 - 6 (2) 1 - 9 (1)

MAVs 1 - 32 (7) 1 - 11 (4) 1 - 9 (1)

Cycles & other Slow 0 - 28 (5) 0 - 4 (1) 0 - 1 (0.5)Moving Vehicles (SMVs)

Note: Value in the parenthesis indicates the average share (in per cent) of the specific vehicle type in the trafficstream

From Table 6, it can be inferred that the two wheelerscontribute for the major proportion of traffic on four-laneand six-lane divided carriageways compared to eight-lanedivided carriageway, whereas, the share of autorickshaws is very insignificant on eight-lanecarriageways. Cars dominate the proportion of traffic inall types of multi lane carriageways and share of bothtwo wheelers and cars together constitutes more than 80per cent on six-lane and eight-lane carriageways. Incontrast, the heavy vehicles share in total volume is morein case of four-lane compared to six-lane and eight-lanecarriageways. This phenomenon of higher passengertraffic on six and eight lane divided carriageways and

relatively less share of goods traffic may be due to theselection of test sections comparatively nearer to theurban center (i.e. 10 - 40 km away from the city center).The share of cycles and SMVs including tractors, animalcarts etc constitute less than 5 per cent on four-lanewhereas, it is negligible on six-lane and eight-lanecarriageways. In order to develop speed- flow equationsand estimate roadway capacity, it is necessary to convertthese observed traffic volume into a common unit, whichis termed as Passenger Car Unit (PCU). In the presentstudy, the PCU factors as per Table 7 given in IRC:64(1990) has been used for converting the total volume intoPCUs.



The observed traffic volume of different vehicle types isconverted into PCUs based on the PCU factors given inTable 7. Using the analogy explained in Section 3.4, thefirst ever attempt was done in India in the present studyby segregating speed - flow data into uncongested andcongested conditions. Subsequently speed - flow

Table 7 PCU Factors adopted based on IRCSpecifications (IRC: 64-1990)

Vehicle Type PCUFactor

Motor Cycles (MC) 0.5

Scooters (SC) 0.5

Autos (A) 1

Cycle Rick. & Other Slow Vehicles (OT) 1.5

Small Cars (<1400 cc) (CS) 1.0

Big Cars (CB) 1.0

Cycles (CY) 0.5

Buses (B) 3.0

Mini Buses (MB) 3.0

Tractors and Tractor Trailers (TT) 3.0

Light Commercial Vehicles (LCV) 1.0

Two Axle Commercial Vehicles (HCV) 3.0

Multi Axle Commercial Vehicles (MAV) 3.0

relationships were developed for different vehicle typesusing both non-linear and linear formulations consideringuncongested and congested areas of speed - flow dataseparately. On critical examination of the statistical validityof each of the developed speed - flow equations, the BPRmodel and linear model were considered for developingthe speed-flow equations. For upper curve(Uncongested) BPR equations was considered whereasfor lower curve (Congested), linear formulations wasconsidered in the case of four-lane divided carriagewayconsidering statistical validity of the equations. In case ofsix-lane and eight-lane divided carriageways, linear modelswere considered for both upper and lower curve as theyare showing higher statistical validity compared to othermodels. A summary of developed speed-flow equationsfor different vehicle types are given in Table 8, 9 and 10

for four-lane, six-lane and eight-lane divided carriagewayrespectively. For the estimation of goodness-of-fit in termsof R2 and other statistical estimates in non-linear form ofthe BPR equation, the software called Statistical Packagesfor Social Studies (SPSS) has been used.

From Table 8, 9 and 10, it can be seen that the developedlinear and BPR speed - flow equations exhibit goodstatistical validity in terms of good R2 values. Hence, thedeveloped equations are considered appropriate forestimating speeds under varying traffic conditions andcan be explored for evolving roadway capacity.

5.1.2 Roadway Capacity from Traditional Model

Roadway capacity is the maximum number of vehicleswhich has a reasonable expectation of passing over agiven section of a lane or a roadway in one direction (orin both directions for a two-lane highway) during a givenperiod of time under prevailing roadway and trafficconditions. The capacity is usually expressed as an hourlyvolume. The theoretical speed - flow curve which is thefundamental diagram of traffic flow is parabolic in shape.The maximum speed is the free speed. The parabola startsfrom the free speed and as the volume increases, thespeed generally falls down. At a point, known as themaximum capacity the parabola takes an invert turn asalready shown in Fig. 1. In the present study, roadwaycapacity was estimated from the intersecting point ofupper curve and lower curves. The estimated roadwaycapacity of four-lane, six-lane and eight-lane dividedcarriageways through traditional model is presented inFig. 5. From the Fig. 5, it can be observed that theestimated roadway capacity i.e. intersecting point of upperand lower curve for four-lane, six-lane and eight-lanedivided carriageway is 6050, 6400 and 10500 PCUs/hour/direction.

5.1.3 Comparison of Observed Free Speeds andIntercept of Speed - Flow Equation

To demonstrate the suitability of developed speed - flowequations through traditional model, the intercept of theequations are compared with the free speeds (refer Table3, 4 and 5) and the comparison is presented in Table 11.



Table 8 Speed-Flow Equations Derived from Traditional Models for Different Vehicleson Four Lane Divided Carriageways

S. No. Vehicle Type Uncongested (Upper) Curve Congested (Lower) CurveNon-Linear (BPR) Equation Linear Equation

1 Auto y=66.731/(1+2.320*(x/6000.165)^¹) y = 0.011*x + 14.20R² = 0.863 R² = 0.952

2 TW y=99.49/(1+2.585*(x/7000.385)^¹) y = 0.008*x + 19.52R² = 0.880 R² = 0.814

3 Cars y=110.761/(1+1.564*(x/6999.968)^1) y = 0.004*x + 21.29R² = 0.887 R² = = 0.861

4 Bus y=94.080/(1+2.794*(x/6998.148)^1.544) y = 0.006*x + 22.23R² = 0.885 R² = 0.674

5 LCV y=87.345/(1+2.083*(x/6998.876)^1) y = 0.005*x + 20.83R² = 0.766 R² = 0.900

6 HCV y=84.230/(1+2.056*(x/6999.049^1.089) y = 0.002*x + 24.69R² = 0.713 R² = 0.713

7 MAV y=67.709/(1+3.466*(x/6995.476)^2.208) y = 0.002*x + 24.67R² = 0.690 R² = 0.701

Note: y = speed (kmph); x = Flow (PCU/hr/Dir)

Table 9 Speed-Flow Equations from Traditional Model for Different Vehicleson Six Lane Divided Carriageways


1 Auto y = -0.004x + 59.39 y = 0.009x + 19.64R² = 0.525 R² = 0.570

2 TW y = -0.009x + 77.50 y = 0.009x + 5.971R² = 0.638 R² = 0.525

3 Cars y = -0.011x + 112.3 y = 0.004x + 15.52R² = 0.493 R² = 0.646

4 Bus y = -0.007x + 92.47 y = 0.013x + 3.790R² = 0.663 R² = 0.528

5 LCV y = -0.005x + 97.66 y = 0.012x + 3.273R² = 0.362 R² = 0.777

6 HCV y = -0.011x +82.60 y = 0.011x + 2.227R² = 0.559 R² = 0.739

7 MAV y = -0.011x + 92.38 y = 0.005x + 12.04R² = 0.635 R² = 0.538




Table 10 Speed-Flow Equations from Traditional Model for Different Vehicles on Eight Lane DividedCarriageways


1 Auto y = -0.001x + 53.45 y = 0.001x + 31.20R² = 0.606 R² = 0.709

2 TW y = -0.004x + 93.49 y = 0.001x + 31.20R² = 0.823 R² = 0.603

3 Cars y = -0.002x + 86.28 y = -0.001x + 33.84R² = 0.726 R² = 0.495

4 Bus y = -0.002x + 72.69 y = -0.002x + 24.42R² = 0.830 R² = 0.704

5 LCV y = -0.002x + 70.31 y = 0.002x + 22.87R² = 0.786 R² =0.812

6 HCV y = -0.001x +66.55 y = 0.001x + 32.33R² = 0.589 R² = 0.553

7 MAV y = -0.003x + 74.39 y = -0.001x + 29.31R² = 0.481 R² = 0.643


Fig. 5 Roadway Capacity of Four-Lane, Six-Lane and Eight-Lane Divided Carriageways Evolved through Traditional Models



Table 11 Comparison of Observed Free Speed with Intercept on Speed-Flow Equation

Carriageway Vehicle Type Observed Mean Free Intercept of Speed- ErrorSpeed (kmph) Flow Equation (kmph) (per cent)

Auto 54.2 66.7 23

Two Wheeler 74.2 99.5 34

Big Car 93.0 110.8 19

Four-Lane Small Car 92.4

Bus 71.1 94.1 32

LCV 68.6 87.3 27

HCV 68.5 84.2 23

MAV 64.0 67.7 6

Auto 56.6 59.4 5


Big Car 95.5 112.3 19

Six-Lane Small Car 93.1

Bus 74.4 92.5 24

LCV 73.6 97.7 33

HCV 70.7 82.6 17

MAV 70.5 92.4 31

Auto 56.7 53.5 6


Big Car 98.0 86.3 13

Eight-Lane Small Car 101.4

Bus 75.7 72.7 4

LCV 74.1 70.3 5

HCV 73.2 66.6 9

MAV 72.0 74.4 3

From Table 11, it can be observed that the error betweenobserved free speed and intercept of speed flow equationsof different vehicle types on four-lane dividedcarriageway is ranging from 6 to 34 per cent, whereas inthe case of six-lane and eight-lane divided carriageway,the range is 3 to 31 per cent and 3 to 21 per centrespectively. From the above results, it can be inferredthat even though the developed speed - flow equationsare exhibiting good statistical validity, the intercept derivedfrom the traditional models has not appropriately

represented the field conditions as the error differencebetween the observed free speed and intercept of speed- flow equations of different vehicle types is high. Thisphenomenon can be attributed to the dataset consideredfor developing the speed - flow relationships encompassesaggregated traffic data (i.e. which includes traffic flowand average free speed over a specified time interval)and also not accounting of the typical random lane changebehaviour experienced on Indian highways in all thetraditional models derived in this study. To overcome this



limitation, the microscopic simulation approach has beenattempted in this study which is capable of representingindividual vehicles on road section and estimate the driverbehaviour more realistically. The microscopic simulationmodel has been developed to estimate speed and flowcharacteristics for varying carriageway widths and thedetails are furnished in the succeeding section.

5.2 Microscopic Simulation Model

5.2.1 Need for Microscopic Simulation

The traditional capacity estimation methods assumehomogeneous conditions and lane discipline, however, itis not applicable for Indian conditions. In suchcircumstances, roadway capacities could be eitherunderestimated or overestimated. Capacity estimation isprimarily depends on vehicular movements on the roadstretch and in this regard, lane change behaviour canseverely affect the movements. On Indian roads, vehiclesseldom observe lane discipline and make their own virtuallanes instead of the demarcated physical lanes. Theconventional methods ignore vehicle movements andinteractions and these behaviours cannot be explainedwhich has great impact on speed - flow relationships andcapacity estimation. In the absence of accounting for suchsituations, the output might be far from reality. Asdescribed earlier, microscopic simulation considers eachand every vehicle movement on a roadway and hencesuch a lane change behaviour and vehicle interactionscan be described. More realistic estimation ofspeed - flow relationships can be achieved throughmicroscopic simulation system, which can lead to theestimation of capacity with reasonable degree ofaccuracy. This is because tremendous advancements thathave been brought forth with by deploying microscopicsimulation techniques for modelling transportationsystems. Such microscopic simulations are able to modelindividual vehicles and pedestrians in a large area and itis possible to estimate realistic speed - flow characteristicsand capacity considering all possible lane changebehaviour even under heterogeneous traffic conditions.Further, these techniques are highly useful in estimatingthe traffic characteristics under different traffic flow anddriver behaviour conditions, which cannot be observed

on the field. However, it is to be borne in mind that thedata collection task needed to develop the microscopicsimulation can be a bit tedious and cumbersome. To arriveat speed - flow characteristics and establish capacitynorms through microscopic simulation, one has to modelthe flow of individual vehicles in a detailed manner forwhich established simulation packages can be used. Thedata collection and methodology followed for this phaseis explained in the succeeding sections.

5.2.2 Data Collection

In order to develop a microscopic simulation model, thetraffic data was again collected on Delhi - Mathurasection of NH-2 near Hodal which is a four-lane dividedcarriageway considered to develop speed - flow equationsthrough traditional method (S.No. 1 and 2 in Table 1).This section was specifically chosen to check thesuitability of these two models namely microscopicsimulation and traditional models. The reconnaissancesurvey was conducted on 23rd March 2010 and thevideography survey was eventually conducted from9.30 am to 2:00 pm on 25th March 2010 by capturing thetraffic plying during the morning and afternoon timeperiods on both directions of travel.

5.2.3 Development of Microscopic SimulationModel

The methodology followed for the microscopic simulationis shown in the form of flow chart in Fig. 6. From theFig. 6, it can be observed that the data collection is thefirst and foremost requirement for understandingspeed-flow characteristics on multi-lane highways. Tocapture lane change behavior on these multi-lane highspeed corridors, videography method was adopted fordata collection. The recorded film was replayed ontelevision screen in the laboratory of CRRI, New Delhiand the required data were decoded through manualmethod. The vehicles were divided into ten categoriesand the data extracted from video recording were Volume,Space Mean Speed (SMS) and number of lane changesby individual vehicles during every five minute timeinterval. The video data on classified traffic volume counts,SMS and lane change behavior were decoded in a



synchronized fashion. Using this data, a model isdeveloped in VISSIM 4.10, a microscopic simulationsoftware. Then the model is appropriately calibrated andvalidated using the observed data considering volume,speed and number lane changes. Using the validatedsimulation model, speed - flow relationships have beendeveloped under two scenarios namely 'with' random lanechange behavior (which is a common phenomenon onmulti-lane highways in India) and also by assuming'without' lane change conditions. The roadway capacityestimated under these two scenarios is used to assessthe impact of lane change as shown in Fig. 6.

compared. The base model development can besummarized in the following steps:

1. Developing base network.

2. Defining model parameters.

3. Calibrating the network.

4. Validating the model.

Development of a network that accurately determinesthe constraints of a road network is an important stage inthe modelling process. The basic key network buildingcomponents are: Links and Connectors. In the presentsimulation model, links are created spanning for 130 mrepresenting the test section near Hodal on NH-2 forboth directions. However, a buffer link is provided forbuffering process of the network which is taken 100 m.Both test section link and buffer links are appropriatelyconnected by connectors. Fig. 7 shows the links createdseparately for Delhi - Hodal and Hodal - Delhi directionsin VISSIM.

As mentioned earlier, the test section selected on NH-2is a four-lane divided carriageway with approximately2.0 m paved shoulder and 0.5 m earthen shoulders.Accordingly, the links are created in VISSIM with totalof four lanes on each link including two lanes of maincarriageway, one lane of paved shoulder and one lane ofearthen shoulder as shown in Fig. 7. During thereconnaissance survey at the site, it was observed thatthe majority of fast moving vehicles movements are usingthe main carriageway and major proportion of slow movingvehicles and some proportion of the two wheelers areusing the paved shoulders. By considering thisphenomenon, road links are created as shown inFig. 7.

Fig. 6 Methodology for Estimating Capacity ConsideringImpact of Lane Change Behaviour

Fig. 7 Created Links with Main Carriageway and Shoulders forFour-Lane Divided Carriageway in VISSIM

In microscopic simulation, a model which accuratelyrepresents the existing situation is known as the 'BaseModel'. The base model is constructed by representingthe network area that was defined in the model scopeand using actual, observed traffic flow data. The validatedbase model is used to develop a 'future year base model'against which scenarios and design options can be



5.2.3 Calibration of Microscopic Simulation Model

Calibration is a process of adjusting the model parameters,network and vehicle demand to reflect and representobserved data and/or observed site conditions to a

sufficient level to satisfy the model objectives. Thecalibration process is explained in the form of flow chartas shown in Fig. 8.

By giving the parameters listed in Fig. 8 as an input tosimulation model, simulation runs were carried out in orderto estimate the output. In this simulation model, the outputsobtained are volume, speed of vehicles and number oflane changes. Since the observed data on theseparameters were collected in the field for validation ofthe developed simulation model. The comparison ofestimated values with observed values is carried out anderror is estimated. This iterative process of simulationmodel calibration was carried out through the modificationof the various model parameters and simulation runs wereperformed till the error is within the satisfactory level.

5.2.4 Validation of Microscopic Simulation Model

Validation is the process of checking the developedsimulation model in terms of predicted traffic performancefor road system against field measurements of trafficperformance such as traffic volumes, travel times,average speeds, and lane changes. In the present study,the calibration and validation process was carried out bytrial and error method. After carrying out many trials, theprediction error in volume, speed and lane changes is

reduced to satisfactory level. The final validation resultsfor volume speed and lane change criteria are estimatedfor Delhi to Hodal and Hodal to Delhi directionsseparately. Fig. 9 and 10 shows the validation results oftraffic volume, speed and lane changes for Hodal to Delhiand Delhi to Hodal directions, respectively.

From the Fig. 9, it can be observed that the error inestimation of traffic volume is less than 10 per cent fordifferent vehicle types except in the case of buses andbicycles on Hodal to Delhi direction whereas the overallerror in the estimation of traffic volume is almost zerowhich represents the accuracy of the developedsimulation model. The comparison of observed andestimated data of different vehicle speeds shows thatthe error in vehicular speeds is ranging from 2 per centto 20 per cent for different vehicle types except in thecase of trucks (due to large variation in observed speedsof trucks) which represents the developed simulationmodel is reasonably accurate. The simulated lane changesduring each 5-minute time interval is also compared withthe observed lane change data and it can be observed

Fig. 8 Calibration Procedure Adopted in Development of Simulation Model



Fig. 9 Comparison of Observed and Estimated Traffic Volume,Speed and Lane Changes (Hodal to Delhi Direction)

Fig. 10 Comparison of Observed and Estimated Traffic Volume,Speed and Lane Changes (Delhi to Hodal Direction)

from Fig. 9 that the overall error in predicting number oflane changes made by different vehicle types is about 20per cent in Hodal to Delhi direction which can also beregarded to be reasonably accurate in reflecting the realworld conditions considering the traffic mix beingsimulated is heterogeneous in nature and resorting torandom lane changes.

From Fig. 10, it can be seen that the error in estimationof traffic volume is less than 10 per cent for differentvehicle types on Delhi to Hodal direction while the overallerror is about 1 per cent implying the effectiveness ofthe calibrated model in replicating the ground conditions.At the same time, it can be seen that the error in speedprediction on Hodal to Delhi direction of NH-2 is foundto be ranging between 1 - 19 per cent except in the caseof cars and two wheelers which is about 30 per cent.The high error in cars and two wheelers may be attributedto the high influence of local conditions (such as median



gap and roadside friction.) on Delhi to Hodal directionacting as a major impediment, which is causing significantreduction of observed speeds of cars and two wheelersas compared to other direction.

On the other hand, the predictive capability of the modelin terms of lane changes in the case of different vehicletypes like two wheeler, auto rickshaw, small car, big carand multi axle truck is less than 20 per cent error, whereasthe overall lane change error is only about 2 per cent onHodal to Delhi direction. From the above calibrated andvalidated results, it can be inferred that the developedsimulation models are able to predict the vehicularmovements (i.e. flow, speed and lane changes) withreasonable degree of accuracy under heterogeneoustraffic conditions for four-lane divided carriageways.Based on the developed simulation models, the evolutionof speed - flow relationships is attempted and using thesame, the roadway capacity can be estimated.

5.2.5 Development of Speed - Flow Equations andRoadway Capacity through Simulation

Using the developed simulation model, the speed data fordifferent vehicle is estimated for different traffic volumeconditions for four-lane divided carriageway. Thesimulation runs are carried for following scenarios oftraffic volumes for estimating capacity:

a) Observed Flow (ranging from 1000 to 1500Vehicles/hr)

b) Flow of 2000 Vehicles/hr

c) Flow of 4000 Vehicles/hr

d) Flow of 6000 Vehicles/hr

e) Flow of 8000 Vehicles/hr

In the same way, the developed simulation model isapplied to estimate speeds of the vehicle for differenttypes of carriageway namely six-lane and eight-lanedivided carriageways. However, traffic flow up to 10000vehicles/hr was considered for six-lane and eight-lanedivided carriageways. For this purpose, separate networkhas been created by introducing extra lanes so as toformulate six-lane and eight-lane divided carriagewayscenarios. The lane restrictions are also considered for

these carriageways same as that of four-lane dividedcarriageways. However, the driving behaviour is keptsame as four-lane divided carriageway for six-lane andeight-lane divided carriageway assuming that it will notdrastically change in spite of increase in the number oflanes. These aspects would be further investigated byobserving real data on these carriageways as the desiredspeed characteristics might be different on thesecarriageways compared to four-lane dividedcarriageways. This may be regarded as the limitation ofthe present model and it is worthwhile to study this aspectin future scope of the study. Considering the above flowconditions, the simulation runs are made to estimatespeeds of different vehicles on four-lane, six-lane andeight-lane divided carriageways. The estimated speeddata of the cars are plotted against given traffic flowwith linear equations. The developed linear speed - flowequations speed-flow equations are having high goodness-of-fit as the R2 values are more than 0.9 for all thecarriageways, however, the intercept of the speed-flowequation, which is also considered as free speed of thevehicle is 99.96 km/hr, 97.0 km/hr and 96.35 km/hr, whichis slightly decreasing as the number of lanes increasesfrom four-lane to eight-lane divided carriagewaysrespectively. Further, the capacity of these carriagewaysis calculated from these linear speed-flow equations byassuming the fact that capacity would be occurring athalf of the free speed. Accordingly, half of the free speedis substituted in the speed-flow equation to estimateroadway capacity. From this exercise, the capacity isestimated as 5,553 PCU/hour/Dir, 9,700 PCU/hour/Dirand 14,160 PCU/hour/Dir for four-lane, six-lane andeight-lane divided carriageways, respectively. Though thefit of the speed-flow equation is very good, the estimatedfree speed and capacities are not realistic especially inthe case of six-lane and eight-lane divided carriageways.Free speeds are under predicted and capacities are overpredicted as the slope of the equation line is very mildwhich shows insignificant impact of traffic volume onvehicle speeds. Since the linear method had producedunrealistic values of capacities and free speeds, non-linearmethod has been subsequently attempted. A non-linearequation has been formulated from the second order



polynomial equation and the final form of the equation

under non-linear form is as given below:

V = a1 + (a

12 + a

2 * F)0.5 ...(3)

where, V is speed in km/hr,

F is flow in PCU/hr/dir

a1, a

2 are parameters to be estimated

Using the estimated speed data of the cars and traffic

flows from the different simulation runs, the equation

shown in Eqn. (3) has been developed for four-lane,

six-lane and eight-lane divided carriageways. A

comparison of speed-flow equations evolved for cars

through traditional and microscopic simulation approaches

are given in Table 12. To demonstrate the validity of

developed speed - flow equations through microscopic

simulation model, the estimated free speed which is the

intercept of the equations (at flow almost equal to 0

Vehicles/hr) are compared with the observed free speeds

(refer Table 3, 4 and 5) and presented in Fig. 11. The

above fig. illustrated the error ranges obtained from

microscopic simulation model by comparing with the

traditional model:

a) Four-lane: 0.3 to 10 per cent (which is 6 to 34

per cent in the case of traditional model)

b) Six-lane: 0.1 to 16 per cent (which is 3 to 31

per cent in case of traditional model)

c) Eight-lane: 2 to 18 percent (which is 3 to 21

per cent in case of traditional model)

From the above results, it can be concluded that developed

speed - flow equations through simulation model has

significantly reduced the prediction error in free speeds

compared to traditional model except in the case of LCVs

and MAVs on eight-lane divided carriageway. The

occurrence of relative larger error for LCVs and MAVs

can be examined by conducting traffic studies and

calibrating the simulation model for eight-lane using

observed data on the field. From these results, it can be

noted that the developed microscopic simulation model is

able to predict the traffic phenomenon on multi-lane

highways more realistically compared to traditional model.

Thus the evolved roadway capacity through simulation

approach can be adjudged to be realistic for the

heterogeneous traffic conditions observed on multi-lane

divided carriageways. Using this evolved speed - flow

relationships, the roadway capacity is estimated as shown

in Fig. 12.

From the Fig. 12, it can be observed that the

speed-flow equations are having high goodness-of-fit as

the R2 values are about 0.77 for all the carriageways and

from this it can be said that that the developed

speed-flow equations can be used to predict the speed of

cars for given flow conditions with reasonable degree of

accuracy. Further, the capacities of the carriageways are

calculated based on the non-linear speed-flow equations

and from this exercise, the roadway capacity is estimated

as 5574 PCU/hour/dir, 7733 PCU/hour/dir and

9796 PCU/hour/dir for four-lane, six-lane and eight-lane

divided carriageways, respectively.

Based on the detailed analysis, it can be inferred that

both traditional method and microscopic simulation method

are estimating the comparable results in the case of

four-lane and eight-lane divided carriageway, however

traditional method is under predicting the capacity in case

of six-lane divided carriageway. This can be attributed to

the paucity of data used for model development in the

case of six-lane carriageways, whereas in the case of

microscopic simulation model, the speeds can be

estimated just by substituting for any flow conditions and

thus estimate the capacity thereafter. This is the biggest

advantage of the simulation model over the traditional

method. Hence, it can be concluded from this detailed

evaluation, that the microscopic simulation model is able

to predict the speeds and flow conditions and thereafter

roadway capacities were estimated with good degree of

statistical validity.



Table 12 Comparison of Speed-Flow Equations of Cars for different Multi-lane Carriageways

S. Carriageway Traditional Method Congested Microscopic SimulationNo. Uncongested Method (Non-linear)

1 Four-lane divided y = ( )1

110.761

1 1.564* 6999.968x+

y = 0.004x + 21.29 y = 47.633+(2268.931-

R² = 0.887 R² = 0.861 0.407x)0.5

Capacity=6,050 R² = 0.761PCU/hour/dir Capacity=5,574

PCU/hour/dir

2 Six-lane divided y = -0.011x + 112.3 y = 0.004x + 15.52 y = 47.651+(2270.637-R² = 0.493 R² = 0.646 0.294x)0.5


PCU/hour/dir

3 Eight-lane divided y = -0.003x + 88.81 y = 0.002x + 32.91 y = 47.676+(2273.011-R² = 0.851 R² = 0.532 0.232x)0.5


PCU/hour/dir

Fig. 11 Comparison of Error between Observed and EstimatedFree Speeds through Traditional and MicroscopicSimulation Models

Fig. 12 Roadway Capacity of Four-Lane, Six-Lane and Eight-LaneDivided Carriageways Evolved through MicroscopicSimulation Model



5.3 Design Service Volume

Design Service Volume (DSV) is defined as the maximumhourly volume at which vehicles can reasonably beexpected to traverse a point or uniform section of a laneor roadway during a given time period under the prevailingroadway, traffic and control conditions while maintaininga designated Level of Service (LOS). The flow conditionsat LOS - C, D and E involve significant vehicle interactionleading to lower level of comfort and convenience. Incontrast, LOS - B represents a stable flow zone whichaffords reasonable freedom to drivers in terms of speedselection and manoeuvres within the traffic stream. Undernormal circumstances, therefore, the use of LOS - B isconsidered desirable for the design of rural highways. Atthis level, volume of traffic will be around 0.5 times thecapacity and this is taken as the DSV for the purpose of

Table 13 DSV for Four-Lane, Six-Lane and Eight Lane Divided Carriageways

Peak Hour Ratio Design Service Volume (PCU/day/direction)

(per cent) Four-lane Six-lane Eight-lane

LOS - B LOS - C LOS - B LOS - C LOS - B LOS - C

7 31,851 47,777 44,189 66,283 55,977 83,966

8 27,870 41,805 38,665 57,998 48,980 73,470

9 24,774 37,160 34,369 51,554 43,538 65,307

10 22,296 33,444 30,932 46,398 39,184 58,776

determining the carriageway width. The DSV values areestimated from the capacity values (presented in Fig. 11)considering different Peak Hour Ratios of 7, 8, 9 and 10percent. The estimated DSV values expressed in PCUs/day/direction are presented in Table 13.

From a close look at Table 13, the following inferencescan be drawn:

a) The DSV under LOS - B and LOS - C offour-lane divided carriageway is ranging from22296 to 47777 PCUs/day/direction for differentpeak hour ratios of 7 to 10 percent. In case ofsix-lane divided carriageway it is ranging from30932 to 66283 PCUs/day/direction and for eightlane divided carriageway, it is ranging from 39184to 83966 PCUs/day/direction for different peakhour ratios of 7 to 10 percent.

b) On multi-lane highways, it will normally not be

desirable to adopt LOS - C. Therefore, the DSV

values corresponding to LOS - C can be taken

as the holding capacity of four-lane divided

carriageways for upgrading the facility to

six-lane divided carriageway. Similarly, the DSV

values obtained corresponding to LOS - C can

be taken as the holding capacity for upgrading

the six-lane divided carriageway to eight-lane

divided carriageway.

c) In this regard, the DSV values arrived in this study

can be regarded to be handy tool for the

estimation of capacity of multi lane highways and

useful in determining the decisions to upgrade

the highways in terms of increasing road width.

6 IMPACT OF LANE CHANGE

BEHAVIOUR ON ROADWAY CAPACITY

As described earlier, lane change behaviour significantly

influences the traffic movement on the road section. Due

to that there might be an impact on the roadway capacity.

In view of this, it is proposed to study the impact of lane

change behaviour on roadway capacity. To find out impact

of lane change behaviour, traffic is simulated by not



allowing any lane change in the selected section of the

road. To carry out such exercise in VISSIM 4.10, it is

necessary to develop a separate road network in order to

restrict lane changes. In the case of without lane change

situation, connector type of links is used as lane change

restriction zone. Before that 100 m buffer link is provided

for smooth entrance of the vehicles.

Using the developed simulation model, the speed data for

different vehicle is estimated for different traffic volume

conditions for four-lane, six-lane and eight-lane divided

carriageway under without lane change behaviour. The

simulation runs are carried for different scenarios of

traffic volumes for estimating capacity namely Observed

Flow (ranging from 1000 to 1500 Veh/hr), Flow of 2000

Veh/hr, 4000 Veh/hr, 6000 Veh/hr and 8000 Veh/hr.

Considering these different flow conditions, the simulation

runs are formulated to estimate speeds of different

vehicles on four-lane, six-lane and eight-lane divided

carriageways. Using the estimated speed data of the cars

and traffic flows from the different simulation runs, the

equation shown in Eqn. (3) has been calibrated for

four-lane, six-lane and eight-lane divided carriageways

under 'without' lane change behaviour conditions and they

are given in Table 14. For comparison purpose, the

equations with lane change behaviour situation are also

shown in Table 14. The estimated speed data of the cars

are plotted against given traffic flow with non-linear

equation under without lane change behaviour condition

as shown in Fig. 13 for four-lane, six-lane and

eight-lane divided carriageways.

From the Table 14 and Fig. 13, it can be observed that

the speed-flow equations exhibit moderate goodness-of-

fit as the R2 values are ranging from 0.56 to 0.65 for

varying carriageway widths and from this it can be said

that the developed speed - flow equations can be used to

predict the speed of cars for given flow conditions with

reasonable level of authenticity. However, the estimated

free speed of the vehicle from the equation (at flow = 0

Veh/hr) is around 97.0 km/hr, which is almost same for

all type of carriageways ranging from four-lane to

eight-lane. The free speed of cars under 'without' lane

change condition has slightly increased compared to

allowing of random lane change.

This aspect can be attributed to the random lane change

resorted by the slow moving vehicles and thereby

obstructing the flow of fast moving vehicles under the

lane change conditions which is completely absent under

'without' lane change conditions. Further, the capacities

of the carriageways have been evolved from these

non-linear speed-flow equations under without lane

change behaviour condition and from this exercise, the

capacity is estimated as 5504 PCU/hour/dir, 7508 PCU/

hour/dir and 8941 PCU/hour/dir for four-lane, six-lane

and eight-lane divided carriageways, respectively.

The comparison of predicted roadway capacities under

with and without lane change conditions are shown in

Fig. 14.

From Fig. 14, it can be observed that roadway capacity

of different carriageways under 'without' lane change

behaviour has reduced marginally (about 3 per cent) in

case of four-lane and six-lane divided carriageways

whereas, in the case of eight-lane divided carriageway,

the roadway capacity has reduced significantly about 9

per cent. From this analysis it can be inferred that the

lane change behaviour significantly influences the

vehicular movements on a road section and thus affecting

the roadway capacity. At the same time, it is to be noted

that though the imposition of restriction of lane change

behaviour reduces the roadway capacity ranging from 3

per cent to 9 per cent; it improves average free speeds

marginally coupled with enhancing the safety situation

as the interactions amongst vehicles during the lane

change would be drastically curtailed especially, on these

multi-lane highways.



Table 14 Summary of Speed-Flow Relationships of Cars for different Multi-lane Carriagewaysunder 'With' and 'Without' Lane Change Behaviour

S. No. Carriageway With Lane Change Behaviour Without Lane Change Behaviour

1 Four-lane divided y = 47.633+(2268.931-0.407x)0.5 y = 48.843+(2385.614-0.441x)0.5

R² = 0.761 R² = 0.645Free Speed = 95.3 Km/hr Free Speed = 97.7 Km/hr

Capacity=5,574 PCU/hour/dir Capacity=5,408 PCU/hour/dir

2 Six-lane divided y = 47.651+(2270.637-0.294x)0.5 y = 48.824+(2383.749-0.318x)0.5



3 Eight-lane divided y = 47.676+(2273.011-0.232x)0.5 y = 48.508+(2353.067-0.263x)0.5



(a) Four-Lane Divided Carriageway(b) Six-Lane Divided Carriageway

(c) Eight-Lane Divided Carriageway

Fig. 13 Non-Linear Speed- Flow Relationship of Cars and Roadway Capacity Under Without Lane Change Behaviour



7 CONCLUSIONS

In this study, free speed profiles and speed - flowequations for different vehicle types for varying types ofmulti-lane highways has been established for the first timein the country based on traditional and microscopicsimulation models and subsequently roadway capacityhas been estimated. Further, the lane change behaviourof different vehicle types has been extensively studiedand its impact on roadway capacity has been criticallyevaluated on multi-lane highways. Finally, the DSV forvarying types of divided carriageways has been evolvedfor multi-lane highways in India encompassing four-lane,six-lane and eight-lane with reasonable degree ofauthenticity for the prevailing heterogeneous trafficconditions. The conclusions drawn from the above studiesare summarized below:

a) The mean free speeds of different vehicle typeson eight-lane are higher when compared tofour-lane and six-lane divided carriageways. Freespeeds of two wheelers and cars marginallyincreased from four-lane to six-lane while theincrease is somewhat significant from six-laneto eight-lane. This can be attributed to theachievement of their desired speeds on four-lanedivided carriageway itself; hence there isinsignificant improvement of speeds on six-lanecarriageway. However, the addition of one morelane on eight-lane divided carriageway offering

Fig. 14 Impact of Lane Change Behaviour on Roadway Capacityof Four-Lane, Six-Lane and Eight-Lane DividedCarriageways

higher freedom for vehicular movements mighthave aided in attaining substantial increase indesired speeds and thus resulting in enhancedfree speeds.

b) Free speeds of heavy vehicles and autossignificantly increased from four-lane to six-lanewhile marginally increased from six-lane toeight-lane. This can be attributed to the abovevehicle types not able to attain their desired speedlevels on four-lane divided whereas on six-lanedivided carriageway the presence of additionallane is helping in achieving significant increasein free speed from four-lane to six-lane. However,increase in free speeds is negligible on eight-laneas compared to six lane carriageways. As autoand heavy vehicles have achieved their desiredspeed levels on six-lane itself, there is no impactof eight-lane divided carriageway though it offershigher LOS for vehicle movements compared tosix and four lane divided carriageways.

c) The first ever attempt of segregating speed - flowdata into uncongested and congested areas hasbeen successfully accomplished in the presentstudy and subsequently roadway capacities hasbeen estimated through traditional model.

d) Further, a critical evaluation of the speed-flowequations and roadway capacities throughtraditional model and microscopic simulationmodel has been undertaken. The study revealedthat the roadway capacities estimated throughmicroscopic simulation approach (2450 - 2790PCU/hr/Lane) in this study has replicated groundconditions more realistically for varying types ofmulti-lane carriageways compared to traditionalapproach.

e) The present study affirms that the adoption(through developing adjustment factors) of theroadway capacities determined for developedworld scenarios would not yield realistic results.At the same time, it can be inferred that theroadway capacity evolved in the present study



are comparable with the study results obtainedin a developing country like Indonesia i.e. 2300PCU/hr/Lane (Bang et. al., 1997). Further, thisstudy has reiterated reduction of roadway capacityin terms of PCU/hour/lane with increase in thenumber of lanes, which is consistent with the studyresults arrived for a developing country, like, China(Yang and Zhang, 2005).

f) The simulation study carried out in this paper hassubstantiated the fact that the lane changebehavior significantly influences the vehicularmovements on a road section and thus affectingthe roadway capacity. It can be observed fromthis study that the roadway capacity of differentcarriageways under 'without' lane changebehavior has reduced marginally (about 3 percent) in case of four-lane and six-lane dividedcarriageways whereas in the case of eight-lanedivided carriageway, the roadway capacity hasreduced significantly about 9 per cent. At thesame time, it is to be noted that though theimposition of restriction of lane change behaviorreduces the roadway capacity ranging from 3per cent to 9 per cent; it improves average freespeeds marginally coupled with enhancing thesafety situation due to less vehicular interactions.

g) On plain terrains, the DSV under LOS - B andLOS - C of four-lane divided carriageway isranging from 22296 to 47777 PCUs/day/direction for different peak hour ratios of 7 to10 percent. Similarly, in case of six-lane dividedcarriageway it is ranging from 30932 to 66283PCUs/day/direction, whereas on eight lanedivided carriageways, it is ranging from 39184 to83966 PCUs/day/direction for different peakhour ratios of 7 to 10 percent on plain terrains.

h) On multi-lane highways, it will normally not bedesirable to adopt LOS - C. Therefore, the DSVvalues corresponding to LOS - C can be takenas the holding capacity of four-lane dividedcarriageways for upgrading the facility to

six-lane divided carriageway. Similarly, the DSVvalues obtained corresponding to LOS - C canbe taken as the holding capacity for upgradingthe six-lane divided carriageway to eight-lanedivided carriageway. In a nutshell, the DSV valuesarrived in this study can be regarded as a handytool for the estimation of capacity of varying typesof multi-lane highways on plain terrain and alsouseful for setting the timeline for capacityaugmentation as well.

7.1 Limitations and Future Scope

It may be noted that the driver behaviour has beencalibrated for the parameters defined through the VISSIMsoftware taking into account the traffic flow and roadconditions prevalent on a typical four-lane dividedcarriageway on plain terrain considering theheterogeneous traffic conditions and the lane changebehaviour as observed in the field. Further, the drivingbehaviour observed on four-lane divided carriageways isassumed to exist on six-lane and eight-lane dividedcarriageway as well and therefore this model has beenapplied for these multi-lane divided carriageways. At thesame time, this aspect needs to be investigated byobserving actual traffic behaviour on six / eight dividedhighways as the observed free speed characteristics arehigher (refer Fig. 4) on such carriageways compared tofour-lane divided carriageways. As mentioned earlier, thenumber of six-lane divided carriageway road sectionsconsidered in this study is 3, whereas eight lane dividedcarriageway considered is only 1. Therefore, efforts arebeing made to include typical test sections for free speedand speed - flow data collection on the inter cityexpressways, like, the Mumbai - Pune expressway (whichwould enable to assess the roadway capacity on rollingand hilly terrain road sections for multi lane highways inIndia) and Ahmedabad - Vaododara expressway as partof the ongoing study of CRRI titled, "Development ofRoad User Cost Models for High Speed Corridors".After accomplishing studies on these expressways, thestudy results obtained for six-lane and eight-lane dividedcarriageways can be refined. Considering the above



inherent limitations in this study, a few of the related

avenues for further work are listed below:

a) The geographical transferability of the developed

simulation model developed in this study can be

tested through collection of traffic data on

four-lane divided carriageways on plain terrain

road sections spread over the country.

b) Similarly, the suitability of the model for application

on six-lane and eight-lane divided carriageways

can be further examined in detail through

collection of traffic data on such types of

carriageways on plain terrain spread across the

country.

c) As mentioned earlier, the lane change behavioral

aspects of the drivers has been studied only on

four-lane divided carriageways and the same has

been applied, while carrying out simulation runs

on six-lane and eight-lane divided carriageways.

Therefore, it is worthwhile to carry out research

studies aimed at assessing the phenomenon of

lane change behavior occurring on six-lane and

eight-lane divided carriageways. Based on the

enumerated number of lane changes on six-lane

and eight-lane land divided carriageways,

simulation model can be refined and thus estimate

the lane change behavior more accurately, which

can be further applied to assess its impact on

roadway capacity of these highways.

ACKNOWLEDGEMENTS

Authors are thankful to the technical services rendered

by Mr. Sher Singh, Mr. S. K. Biswas, Mr. Anand Kumar

Srivatsava and Mr. Fida Hussain during the field studies

of this study is gratefully acknowledged. The analysis

work carried out by Mr. Mayur Patel, Ms. N. Sabita and

Mr. Dhaval Barot and also the ongoing research studies

by Ms. Himani Patel, Mr. Mahesh Solanki and Ms. Deepa

as part of their dissertation works under the guidance of

couple of authors is highly acknowledged.

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CRITICAL EVALUATION OF ROADWAY CAPACITY OF MULTI-LANE HIGH SPEED CORRIDORS UNDER

HETEROGENEOUS TRAFFIC CONDITIONS THROUGH TRADITIONAL AND MICROSCOPIC SIMULATION MODELS

free speeds

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