Strip Based Approach For Simulation Computer Science Essay

Vehicles in the homogenous traffic follow lane-based motion and can be handily modeled utilizing car-following and lane-changing theoretical accounts. The former one trades with the longitudinal motion behaviour while the ulterior trades with the sidelong motion behaviour. However, typical heterogenous traffic is characterized by the presence of multiple vehicle types and non-lane-based motion. Because of the off-centered places of the vehicles, following driver is non needfully influenced by a individual leader. Additionally, the undermentioned behaviour of the capable vehicle depends on the type of the front vehicle. Unlike distinct lane alterations in the instance of lane-based traffic, heterogenous traffic watercourses require mold of uninterrupted sidelong motions. Hence, the bing driver behavioural theoretical accounts may non be able to stand for the heterogenous traffic behaviour accurately plenty. To turn to these critical issues of heterogenous traffic, a infinite discretization based simulation model is proposed. The lane is divided into strips and the vehicles are assumed to travel along the strips. A vehicle may busy multiple figure of strips governed by its breadth. A theoretical account for longitudinal motion is proposed to take attention of the multiple leader and vehicle type dependent following behaviour. The sidelong motion theoretical account allows tactical catching manoeuvre by a vehicle ( in expectancy of better traffic conditions ) which may necessitate multiple strip alterations. Therefore, uninterrupted sidelong motion can be modeled by specifying really little strip breadths. On the other manus, the proposed simulation model retains the construct of lane based motion when the strip breadth equals the lane breadth and can be used even for the traditional lane-based traffic watercourses. The same construct was extended to pattern the vehicular motions at and within the intersection. The proposed model viz. , SiMTraM, is implemented in a traditional lane-based simulator, SUMO, an unfastened beginning traffic simulator. The theoretical account was calibrated and validated with informations from Mumbai, India and the consequences indicate better representation of the assorted traffic motion.

Keywords: Mixed traffic ; Strip ; Car-following ; Lane-changing ; Traffic simulation, SiMTraM.

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Introduction

The ability of traffic simulation to emulate the clip variableness of traffic phenomena makes it a alone tool for capturing the complexness of traffic systems. Several micro-simulation theoretical accounts have been developed and the most well-known are likely AIMSUN ( Barcelo et al. , 2002 ) , VISSIM ( PTV, 2012 ) , Paramics ( Quadstone, 2004a, Quadstone, 2004b ) , MITSIMLab ( Toledo et al. , 2003 ) , and CORSIM ( FHWA, 1996 ) . All car-following and lane changing theoretical accounts developed in the aforesaid microscopic simulation tools are built on the implicit in constructs of lane-based traffic flow theory of homogenous traffic. However, typical heterogenous traffic is characterized by the presence of multiple vehicle types and non-lane-based motion. Because of the off-centered places of the vehicles, following driver is non needfully influenced by a individual leader. Additionally, the undermentioned behaviour of the capable vehicle depends on the type of the front vehicle. Because of which, the ‘car-following ‘ impression used in homogenous traffic flow theoretical accounts may non be applicable in the context of heterogenous traffic. Unlike distinct lane alterations in the instance of lane-based traffic, heterogenous traffic watercourses require mold of uninterrupted sidelong motions. Therefore, the sidelong motion discretized in stairss of whole lanes ( lane-changing ) as in the instance of homogenous traffic may non be suited for heterogenous traffic. These typical features of heterogenous traffic leave the pertinence of the bing simulation tools questionable to pattern them.

Lane-based behavioural theoretical accounts are thought to be unsuitable for assorted traffic conditions, non because driver behaviour is significantly different. It is because of the non-lane-based motion and assorted sizes of vehicles doing it possible for the drivers ‘ to utilize the route infinite more expeditiously ( using the sidelong spreads ( breadths ) between predating vehicles ) than homogeneous traffic. To turn to this issue of non-lane-based motion, Hossain et.al. ( 1996 ) developed MIXNETSIM based on co-ordinate referencing technique. Arasan et.al. ( 2005 ) developed a similar theoretical account for a mid-block subdivision in India. However, this attack did non see the frogman behaviour from the behavioural position point of views. Furthermore, this attack involves high computational clip doing it unsuitable for big webs. To turn to the computational clip issue of the uninterrupted infinite theoretical account, Cellular Automata ( CA ) theoretical account was used in some assorted traffic surveies with simple regulations ( Gundaliya et.al. 2006 ) . However, the car-following regulations of both the space-oriented and time-oriented CA-models deficiency intuitive entreaty and may non be able to stand for the realistic driver behaviour. Furthermore, they may non be able to depict the microscopic inside informations of traffic flow ( e.g. tactical passing and meeting ) sufficiently accurate from a individual driver ‘s position. To avoid these booby traps, a fresh thought is to develop a theoretical account which brings into understanding both the complex driver behavior and the computational issue. Sutomo ( 1998 ) and Hoque ( 1994 ) used the construct of spliting the route infinite into strips and using the lane analogy to it get the better ofing the aforesaid issues. Following this, an improved strip based theoretical account was developed by Oketch ( 2000 ) taking into history some alone behaviours such as ooze to foreparts of waiting lines by two wheeled vehicles and coincident usage of two lanes. However, the vehicle type dependent following behaviour was non addressed.

It is with this background, a simulation model, ‘SiMTraM ( Simulation of Mixed Traffic Mobility ) ‘ is proposed utilizing the construct of strips where a lane is divided into figure of strips and the vehicle may busy multiple figure of strips harmonizing to its breadth. The motion of the vehicle is along the strips alternatively of lanes. The proposed strip-based simulation model was implemented in an unfastened beginning simulator, SUMO ( Daniel, 2002 ) , for both mid-block and intersections by doing the necessary alterations.

The specific part of this survey is the development of a simulation model that can be used to stand for both lane based homogenous traffic every bit good as non-lane based assorted vehicle type heterogenous traffic. The influence of the type of vehicle and the presence of multiple leaders in the longitudinal motion is addressed by modifying the traditional car-following theoretical accounts. Further, the characteristic vehicle steering for catching of non-lane based traffic is modeled by sing the motion along the strips. Hence, the proposed model and the theoretical accounts is an of import part towards edifice robust and versatile traffic simulation theoretical accounts.

The remainder of this paper is organized as follows: the following subdivision describes how a non-lane based technique ( the construct of strips ) has been applied to the cardinal lane-based nucleus theoretical accounts. The execution inside informations of the theoretical account to account for the necessary alterations are described in subdivision 3. Testing of the built theoretical account and comparing with the bing 1s is presented in subdivision 4. Section 5 is devoted to the informations aggregation and proof facets. Finally, this paper closes with the reasoning comments and the findings from the present survey.

Proposed Strip-based Technique

While vehicles exhibit a broad scope for acceleration, slowing and velocities in the longitudinal way, the scope of values for sidelong motion is really limited. The sidelong velocity of vehicles depends on their manoeuvrability, turning radius and type of vehicle. Heavy vehicles have much slower sidelong motion than two Wheelers. Even in the instance of two-wheelers, the maximal sidelong velocity is less. Therefore, spliting the route infinite laterally into strips is more practical to capture the sidelong motions at a microscopic degree. This division makes verifying algorithms for motion easier and it is computationally more efficient than a full co-ordinate based theoretical account. Concepts of lanes are of course extended to suit strips.

Strip-based Representation

The route infinite is divided into strips, with each lane holding an built-in figure of strips ( Figure 1 ) . The strip breadth is configurable and is related to the lane breadth. Vehicle breadths are specified in footings of strips. Every vehicle occupies an built-in figure of strips. Therefore, while the longitudinal gesture of vehicles is uninterrupted, the sidelong gesture is distinct ( vehicles move laterally merely one strip at a clip ) . If powdered simulation is required, the strip breadth can be made smaller. In fact, it can even come close a coordinate-based theoretical account with a strip size of 0.1 metre or less.

& lt ; & lt ; Insert Figure 1 about here & gt ; & gt ;

Strip-based Driver Behavioral Models

The bing car-following theoretical account in SUMO is non suited for multiple-leader and vehicle-type dependent following behaviour of assorted traffic conditions. Furthermore, the lane-changing theoretical account does non account for the uninterrupted sidelong motion. To account for these alterations, required alterations are made to the bing theoretical accounts. The sidelong motions at and within the intersection were besides modelled based on the strip construct. These inside informations are presented in this subdivision.

Longitudinal Movement Model

To be in conformity with the proposed technique, alterations were made to the bing version of the time-discrete and space-continuous car-following theoretical account by Kraus ( 1998 ) . This theoretical account was found to be better in retroflexing the heterogenous traffic motion to a certain extent when compared to the other car-following theoretical accounts ( Ravi Shankar et.al. , 2011 ) . The theoretical account is based on a derivation of a safe spread which a topic vehicle needs to halt behind a taking vehicle without clashing with it.

( 1 )

where

is the safe speed for clip T ( in m/s )

is reaction clip ( in s )

is the maximal slowing ability ( in m/s2 )

is leader ‘s speed at clip T ( in m/s ) ;

is the spread ( between follower ‘s forepart and leader ‘s dorsum ) at clip T ( in m ) .

In lane-based motion, every vehicle has a individual vehicle in forepart of it ( its leader ) and a individual vehicle behind it ( its follower ) . The leader and follower are used in car-following theoretical accounts to cipher the speed in following clip measure ( whether a vehicle should speed up or slow ) . The vehicle modifies its speed to avoid hit with the vehicle in forepart. But in strip-based motion, a vehicle may busy more than one strip and so it may hold more than one vehicle in forepart of it ( or behind it ) . In such a instance, the designation of the leader is somewhat different. In the strips theoretical account, the leader is the vehicle among the list of possible leaders which is closest in distance to the vehicle. The algorithm is given below.

Algorithm: Identify Leader

1: for all Strips occupied by current vehicle do

2: Get iterator to current vehicle in strip

3: Increase iterator ( to acquire following vehicle in strip )

4: Shop vehicle in a list of possible leaders

5: terminal for

6: Find vehicle with minimal longitudinal place among possible leaders

7: Tax return this as leader for current vehicle

To take into history the vehicle type dependent following behaviour, alterations were made to the bing car-following theoretical account. The safe speed for a capable vehicle type with leader vehicle type is calculated as:

( 2 )

The spread in equation ( 1 ) is derived as per lane based undermentioned conditions i.e. , the spread is ever maintained changeless for all combinations of leader-follower vehicles. On the contrary, the spread maintained varies harmonizing to the leader-follower combination in assorted traffic conditions. Keeping this in position, a construct of tantamount car-car ( c-c ) spread is proposed ( equation 2 ) . The spread, a peculiar leader-follower combination should keep is converted into an tantamount spread of auto following a auto utilizing a simple arrested development theoretical account ( equation 3 ) and is so used in the computation of the safe speed given in equation ( 2 ) .

( 3 )

where

and are the graduated coefficients of the theoretical account

is the spread maintained between type follower and type leader.

For illustration, if a auto is following a truck at a certain velocity and the spread between them is say, so the tantamount spread would be. Presently 15 leader-follower combinations are considered. The algorithm for tantamount spread computation is given below.

Algorithms: Equivalent Gap Calculation

1: for all capable vehicles

2: Get spread, leader velocity, leader maximal slowing, leader vehicle type, capable vehicle type

3: Calculate tantamount spread depending on the topic and leader vehicle types

4: Tax return this as spread to cipher safe speed

5: terminal for

Lateral Movement Model

The bing lane-changing behaviour solves the navigational portion of lane-changing job by calculating a valid way through the web, i.e. , the chosen lanes can be used for go oning the path. Each lane of the route, the ‘follower ‘ is presently at and of the roads following along its path, up to a screening distance is examined. Besides the distance the ‘follower ‘ may go on utilizing the regarded lane without the demand to alter to another lane, the lanes ‘ tenancies are collected. Given these descriptions of lanes, it is decided for the ‘follower ‘ whether a lane alteration into the way of a valid lane is needed. This is the instance if the ‘follower ‘s ‘ distance left to the place from which the path can non be continued is lower than an false distance needed for the lane alteration. For the tactical portion of the lane changing ( the want to travel fast ) , an attack based on Ehmans ( 2001 ) is present in SUMO. During his thrust, a driver shops the benefits of altering the lane. The benefit to alter a lane is the difference between the safe velocities on the neighbour and on the current lane, computed utilizing the car-following theoretical account, and normalized by the maximal speed the vehicle could utilize under free-flow status.

( 4 )

where

is the benefit of a vehicle altering to lane

and are the vehicles current and adjacent lanes

is the safe speed in the adjacent lane

is the safe speed in the current lane

is the maximal speed possible in the current lane

Using the benefits for neighbour lanes, a driver-internal “ memory ” variable, which represents the fake driver ‘s wish to alter to a neighbour lane, is adapted. If the benefit to alter the lane is & gt ; 0, this benefit is added to this memory, signed by the way. If the benefit is & lt ; 0, i.e. , the current lane is faster than the neighbour lane, the memory value is divided by two, stamp downing the wish to alter into this way. A lane alteration is initiated if the absolute value of the memory variable is larger than a certain threshold. The mark of the memory variable represents the way of the lane alteration. Of class, a lane alteration is merely possible if the lane, a vehicle wants to alter to, has adequate infinite at its current place. Additionally, the ensuing spreads must let further collision-free continuance of drive.

A naif alteration of a lane altering theoretical account for strips would see left and right strips as campaigners for strip alteration at every clip measure. But this attack leads to myopic drive and does non imitate the assorted traffic conditions accurately as vehicles frequently change to the following strip with the purpose of altering multiple strips. Therefore, even if there is no local benefit of altering a strip, there might be a planetary benefit. The vehicles need to look farther than merely one strip because a driver ‘s operational strip altering determination may be due to a tactical passing manoeuvre that requires multiple strip alterations. For patterning this behaviour, alteration to basic lane-changing theoretical account was done to look at all strips on all lanes of a route. This is done by adding up benefits of all strips to the driver ‘s memory variable utilizing an exponential theoretical account. The benefit added for each strip is calculated utilizing

( 5 )

where

is the benefit of a vehicle altering to deprive

and are the vehicles current and adjacent strips

is the safe speed in the adjacent strip

is the safe speed in the current strip

is the maximal speed possible in the current strip

is the parametric quantity of the strips theoretical account

is the figure of strip alterations required for the vehicle to busy the coveted strip

Strip-based Vehicular Movement at and within Intersection

Drivers nearing an intersection are confronted with the determination of how to negociate their intended manoeuvre in the intersection. To do this determination, drivers have to measure vehicle places and velocities in other attacks to the intersection every bit good as their ain desired velocity within the intersection. Although many surveies have targeted homogenous and heterogenous traffic flow, there is usually small or no attending to driver reaction go throughing intersection. Understanding complex motion within an intersection is the critical undertaking particularly in assorted watercourses as the sidelong motions play a major function in this critical zone. The cellular-automata like representation of the intersection to find the ability of a vehicle to execute its motion within intersection country can non manage the complex interactions of assorted vehicle types. The defect of this attack is in the usage of simplistic tenancy regulations.

A theoretical account that explicitly addresses the above considerations has been implemented utilizing the strips construct. In the first measure of the theoretical account, the vehicle is tagged as nearing an intersection. This is done at a distance from the intersection related to the halting distance of the vehicle and the visibleness of the intersection. Once a vehicle is tagged, it starts seting its velocity to the intersection. A maximal coveted manoeuvre velocity is defined for each vehicle. This velocity is based on the particulars of the manoeuvre the vehicle is about to undergo in the intersection.

A labeled vehicle starts to see strip alterations harmonizing to its desired way when doing acceleration determinations before the stop-line. Due to the coincident usage of a individual lane by more than a vehicle in assorted traffic conditions, multiple critical leader vehicles are present at the stop line. The vehicles geting after the first vehicle follow the strip-based car-following and lane-changing regulations and consequently would fall in the waiting line. However, small-sized vehicles do non usually remain at the place at which they arrive at the waiting line, but instead, they normally creep at reduced velocities toward the forepart. This is facilitated in the theoretical account by leting rating of empty strip for the vehicles geting after the first vehicle. Once the critical leader vehicles are identified, each of them starts to see empty strips within the intersection country when doing the acceleration determinations. The consideration of the empty strips consists of four constituents:

Identifying conflicting vehicles: The driver identifies conflicting vehicles that need to be yielded to. The execution is based on the hierarchy of right-of-way in the intersection. The vehicle identifies the motions that have precedence over its ain, identifies the strips that these motions would be utilizing to near the intersection, and identifies the first vehicle in each of these strips.

Predicting empty strips: Once the conflicting vehicles are identified, the driver predicts the empty strips. The anticipation of these empty strips is based on the arrival times of the vehicle and the conflicting vehicles at the struggle zone. The vehicle ‘s ain reaching clip anticipation is conditional on the acceleration the driver would use. Since the driver can non cognize how other drivers will act, the anticipation of their arrival times is based merely on their current velocities.

Measuring empty strips and make up one’s minding acceleration: Having predicted the available empty strip, the driver evaluates it, decides whether to accept it or non, and applies the appropriate acceleration. The strip alteration determination is based on comparing of the bing strip breadth with its ain breadth.

Lateral Movement within critical zone: Once the vehicle is within the intersection country, the benefit of altering the strips is calculated and its way is updated consequently.

Figure 2 illustrates the motion of the vehicle at and within intersection which is updated harmonizing to the benefit map rating and the strip-based auto following regulations. The algorithm is given below.

Algorithm: Identify Critical Vehicle and Update Movement in Internal Lane

1: For each clip measure ( T )

2: For all borders ( E )

3: For all lanes in a border ( L )

4: For all strips in a lane ( S )

5: Identify the critical vehicle ( V ) ( nearing to intersection )

6: For all identified vehicles

7: Put the petition in MSLink ( Connector )

8: Apply associate entree algorithm

9: Let the vehicle behave harmonizing to response from nexus

10: Travel to step 1

& lt ; & lt ; Insert Figure 2 about here & gt ; & gt ;

Execution of Strips Model

The construction of the modified vehicular motion logic is presented in this subdivision. The overall category construction with the freshly induced categories ‘ are explained in item. The execution inside informations of the proposed theoretical accounts are besides presented. The model of SiMTraM is presented in Figure 3. The major portion of the proposed simulation theoretical account is the computation of vehicle places in the web harmonizing to deprive based car-following and lateral-movement regulations.

& lt ; & lt ; Insert Figure 3 about here & gt ; & gt ;

Initiation of Strip-class for a Mid-block

The lane-based theoretical account in SUMO has different categories taking attention of the assorted patterning constituents required for the simulation. To take into history the proposed strips theoretical account, a new category MSStrip has been added. The category diagram with the needed alterations for implementing the strips theoretical account is shown in Figure 4. At the web coevals degree, the proposed theoretical account along with its category takes two new parametric quantities. One is the figure of strips that a lane has, and the other is the breadth of a type of vehicle ( in footings of figure of strips ) . These input parametric quantities are read from the type of vehicle and the path files severally. In add-on, support for strips has been added in the end product, which now includes a new strip ticket. These facets are presented in item in this subdivision.

& lt ; & lt ; Inser Figure 4 about here & gt ; & gt ;

A new MSStrip category has been added which represents a strip on the route. A lane contains a figure of strips, so MSLane has a container ‘myStrips ‘ which is a list of strips ( in order from left to compensate ) . The new strip category takes up about all of the lane degree duties of the lane based theoretical account. The MSVehicle category besides has a list of strips ( myStrips ) . These are the strips which the vehicle occupies. In the vehicle category, the construct of a ‘main strip ‘ was besides introduced. This is the leftmost strip that a vehicle occupies. It is used in all processs where an operation to the vehicle merely one time is needed. In such a instance, the operation is done merely to the vehicle ‘s chief strip, and all other strips are ignored. Furthermore, the ‘main strip ‘ construct allows the vehicles to look into the sidelong spacing when the strip-change by the vehicle is desired. With the debut of the strip construct, the MSLane category now defers most actions to its strip objects. The MSLane: :moveCritical ( ) and setCritical ( ) maps which are used to travel a vehicle in a lane now call the several strip ‘s moveCritical ( ) and setCritical ( ) maps which do the existent moving. Similarly, partial business, where a vehicle is non entirely on a lane ( longitudinally- it occupies multiple lanes ) is now handled at the strip degree. Emission of a vehicle into the web is done by taking a group of strips which has vehicles which are farthest off from the start place. Alternatively of altering one lane in one time-step, the strips theoretical account allows a vehicle to alter one strip in one clip measure. This avoids the ‘jumping ‘ behaviour in the lane-based theoretical account. As opposed to the vehicles instantaneous motion from one lane to the following in one time-step, vehicles now bit by bit change lanes. The MSLaneChanger: :change ( ) is the map where the bulk of the lane alteration functionality is implemented. In add-on to the leader and follower in the current strip, the leader and follower in the mark strip ( which is one strip to the right or left ) are besides considered. The leader and follower in the mark strip are identified based on the place of the current vehicle by the undermentioned regulations:

Search the mark strip for the first vehicle which has place greater than the current vehicle ‘s place on the current strip.

The vehicle obtained in the old measure is the leader of the mark strip. Now decrement the iterator indicating to this leader to acquire the vehicle behind it in the same strip. This is the follower of the mark strip.

Initiation of Strip category within Intersection

A new MSStrip category has been added in the MSInternalLane category at an intersection ( Figure 5 ) . All lane degree duties at and within intersection are taken attention of by this category. First, all the strips in all lanes are scanned for the geting vehicle to make up one’s mind on whether it is the critical vehicle ( i.e. , the first vehicle to get at the stop line ) . If it is non, so the vehicular place updation will be taken attention of by the normal auto following regulations leting it to make the needed sidelong motion as per its directional restraint. The critical vehicle would look at all the strips of the MSInternalLane category to happen out an empty strip and gets pushed into the critical zone. The vehicles in waiting line follow the strip based longitudinal and sidelong motion regulations one time they enter into the critical zone of the intersection which enhances the theoretical account by leting the vehicles to derive the benefit of sidelong motion even within the intersection which closely replicates the existent universe scenario of the assorted traffic watercourses.

& lt ; & lt ; Insert Figure 5 about here & gt ; & gt ;

Evaluation of the Strips Model

For measuring the developed theoretical account, consequences from lane-based theoretical account of SUMO and modified theoretical account with strips were compared. All the simulation experiments were carried out on a 500m mid-block subdivision ( Figure 6 ) with changing input volumes. Besides, the consequence of changing strip breadth on the throughput was studied.

& lt ; & lt ; Insert Figure 6 about here & gt ; & gt ;

Model Calibration

The car-following theoretical account was calibrated with the proper values of the parametric quantities. The Krauss car-following theoretical account has two parametric quantities: -representing driver reaction clip and -representing the driver imperfectness. A sensitiveness analysis of these parametric quantities was done. The at-capacity status ( 1800 vph/lane ) for lane based status was obtained at 0.5 and 0.8 values of and severally.

The consequence of strip breadth on the throughput was studied explicitly ( Figure 7 ) . It can be observed that, lesser the strip breadth more is the throughput. This clearly indicates that the vehicles use the infinite on the route efficaciously when the strip breadth is scaled down which is an indicant of the more infinite oriented ( i.e. , maximal use of the infinite by the vehicles within and across the lanes ) motion of the assorted traffic. Besides, the throughput of strip breadth equal to the lane breadth is comparable with the lane based theoretical account clearly bespeaking the capableness of the theoretical account to imitate the traditional watercourse flows really expeditiously.

& lt ; & lt ; Insert Figure 7 about here & gt ; & gt ;

A general confirmation was besides undertaken to guarantee that the theoretical account yielded consequences that were plausible and consistent with the general traffic behaviour. Examination of cardinal relationships between traffic flow parametric quantities ( velocity, flow and denseness ) is of import in verifying that theoretical account consequences are in understanding with the traffic theory. The velocity, flow and denseness relationship for a two-lane route with lone autos is shown in Figure 8.

& lt ; & lt ; Insert Figure 8 about here & gt ; & gt ;

Simulation Experiments

The capacities and speed-density relationships were analyzed for an uninterrupted stretch of a two lane route were analyzed. Streams incorporating assorted proportions of non-standard vehicles in add-on to autos were considered in the analysis and are denoted by the per centum of the non-standard vehicles they contain. For illustration, streams denoted as 25 % two-wheelers ( 2W ) base for a watercourse composed of 75 % autos and 25 % two-wheelers. Mixed watercourse is composed of assorted non- criterion vehicles, normally in the same proportion, in add-on to autos.

Stream capacities and Fundamental Relationships

A sum-up of the consequences is provided in Table 1 and Fig. 10 for watercourses with 25 % of the assorted non-standard vehicles. Streams with private autos merely has a mid-block capacity of 1828 vph per lane and that with 25 % trucks has a capacity of 1575 vph per lane severally. On the contrary, the mid block capacity with 25 % bikes was obviously higher.

& lt ; & lt ; Insert Table 1 about here & gt ; & gt ;

& lt ; & lt ; Insert Figure 9 about here & gt ; & gt ;

Field Data Collection and Model Validation

For proof of the developed theoretical account, picture based information was collected on the Eastern Express Highway in Mumbai metropolis for an hr. A mid-block section of 300m metres ( 4 lanes of width 3.5 metres each, disregarding service lane ) ( Fig. 10 ) was used to pull out velocity, acceleration, breadths and the composing of different vehicle types. The inside informations are presented in Table 3. One minute interval mean velocities were compared with the fake 1s ( Fig. 11 ) . Different statistics are presented in Table 4. As the Theil ‘s U statistic is closer to 0, it can be inferred that the developed theoretical account closely replicates the watercourse features as observed in the field.

& lt ; & lt ; Insert Figure 10 about here & gt ; & gt ;

& lt ; & lt ; Insert Table 2 about here & gt ; & gt ;

& lt ; & lt ; Insert Figure 11 about here & gt ; & gt ;

& lt ; & lt ; Insert Table 3 about here & gt ; & gt ;

Decisions

Developing a model to imitate heterogenous traffic conditions characterized by the presence of multiple vehicle types and non-lane based motion is the focal point of this paper. A simulation model is proposed utilizing the construct of strips where a lane is divided into figure of strips and the vehicles can busy multiple figure of strips governed by the breadth of the vehicle. The motion of vehicles is governed by strips alternatively of lanes. The lane-based car-following theoretical account was modified to account for the type of the leader vehicle and the presence of multiple leader vehicles. Canonic lane-changing theoretical account was modified to account for the gradual sidelong motion of the vehicles in stead of instantaneous lane displacements in homogenous traffic. A simple benefit map was besides formulated for ciphering the benefit of doing a sidelong motion.

The proposed simulation model, SiMTraM, was implemented in an unfastened beginning simulator, SUMO for both mid-block and intersections. A new strip category is proposed which is derived from the lane category to manage the strip based motions. Several trials were conducted to measure the suitableness of the simulation theoretical account. First, the consequence of strip breadth was studied and it was found that throughput additions when the strip breadth is reduced, perchance due to better infinite use. Attempt was besides made to build the cardinal diagrams and to analyze the sensitiveness of traffic composing. The consequences show the suitableness of the underlying traffic theoretical accounts. Finally, informations collected from a existent mid-block subdivision is used to formalize the theoretical account and found to hold a closer understanding with the field values demoing the efficaciousness of the proposed behavioural theoretical accounts.

It may be noted that the proposed simulation model is non able to stand for the untypical two-wheeler driver behavioural facets like tailgating, sheering, oblique following etc. In order to depict the narrowness of bikes and their filtering behaviour, the thought of a practical lane – that is, a lane divided into several sub-lanes is applicable at macroscopic degree. However, such a construct suggests the motions of bikes as still being lane-based, i.e. , a bike is simply regarded as a little rider auto that travels in sub-lanes. This description does non cover the sidelong motions of bikes exactly. Developing a better theoretical account to take into history the behaviour of two-wheeled vehicles is a promising sweetening to the current research.