Framework for fundamental characteristics of traffic flow.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Traffic flow theories seek to describe in a precise mathematical way the interactions between the vehicles and their operators and the infrastructure. As such, these theories are an indispensable construct for all models and tools that are being used in the design and operation of roads. The scientific study of traffic flow had its beginnings in the 1930’s with the study of models relating volume and speed Greenshields, 1935, application of probability theory to the description of road traffic Adams 1936 and the investigation of performance of traffic at intersections Greenshields et al., 1947. After World War II, with the tremendous increase in use of automobiles and the expansion of the highway system, there was also a surge in the study of traffic characteristics and the development of traffic flow theories. The field of traffic flow theory and transportation while better understood and more easily characterised through advanced computation technology, are just as important today as they were in the early days. The fundamentals of traffic flow and their characteristics have become important and form the foundation for all the theories, techniques and procedures that are being applied in the design, operation, and development of road transportation systems. Traffic Flow is characterised by the movement of individual drivers and vehicles between two points and the interactions they make with one another. Unfortunately, studying traffic flow characteristics is difficult because driver behavior is something that cannot be predicted with one-hundred percent certainty. Fortunately, however, drivers tend to behave within a reasonably consistent range and, thus, traffic streams tend to have some reasonable consistency and can be roughly represented mathematically. The fundamental characteristics of traffic flow are flow, speed and concentration. These characteristics can be observed and studied at the microscopic and macroscopic levels. Table 1 provides a framework for distinguishing these characteristics.
\n\t\t\tTraffic Characteristics | \n\t\t\t\t\t\tMicroscopic | \n\t\t\t\t\t\tMacroscopic | \n\t\t\t\t\t
Flow | \n\t\t\t\t\t\tTime Headways | \n\t\t\t\t\t\tFlow Rates | \n\t\t\t\t\t
Speed | \n\t\t\t\t\t\tIndividual Speeds | \n\t\t\t\t\t\tAverage Speeds | \n\t\t\t\t\t
Concentration | \n\t\t\t\t\t\tDistance Headways | \n\t\t\t\t\t\tDensity | \n\t\t\t\t\t
Framework for fundamental characteristics of traffic flow.
This chapter is concerned with the macroscopic traffic flow characteristics and the associated analysis.
\n\t\tFlow, the macroscopic traffic flow characteristic, is quantified directly through point measurements, and by definition, requires measurement over time. Thus, flow (q) also termed as volume, is defined as number of vehicles passing a point on a highway during stated period of time, which is given by,
\n\t\t\t\twhere, N = Number of vehicles passing a point on the roadway in T;
\n\t\t\t\t\n\t\t\t\t\t T = Total observation period.
\n\t\t\t\tFlow rates are usually expressed in terms of vehicles per hour, although the actual measurement interval can be much less.
\n\t\t\tMeasurement of the speed requires observation over both time and space. The distinction can be made between different ways of calculating the average speed of a set of vehicles. The average traffic stream speed can be computed in two different ways: a time-mean speed and a space-mean speed. The difference in speed computations is attributed to the fact that the space-mean speed reflects the average speed over a spatial section of roadway, while the time-mean speed reflects the average speed of the traffic stream passing a specific stationary point over a specified period of time.
\n\t\t\t\tThe average speed of a traffic stream computed as the length of roadway segment (L) divided by the total time required to travel the segment is the space mean speed. To calculate space mean speed, speeds of individual vehicles are first converted to individual travel time rates, then, an average travel time rate is calculated. Finally, using the value of average travel time rate, an average speed is calculated which is termed as space mean speed. It is given by,
\n\t\t\t\t\tWhere ti\n\t\t\t\t\t\t is the time for the vehicle i to cross the distance L, which is given by ti = L/ui\n\t\t\t\t\t\t, Hence,
\n\t\t\t\t\twhere, ui\n\t\t\t\t\t\t = speed of an individual vehicle i;
\n\t\t\t\t\t\n\t\t\t\t\t\tN = the number of vehicles passing a point during the selected time period.
\n\t\t\t\tThe average speed obtained by taking the arithmetic mean of the speed observations is termed the time-mean speed. Since individual speeds will be recorded for vehicles passing a particular point over a selected time period, time mean speed can be expressed as
\n\t\t\t\twhere, ui\n\t\t\t\t\t = speed of an individual vehicle i;
\n\t\t\t\t\n\t\t\t\t\tN = the number of vehicles passing a point during the selected time period.
\n\t\t\t\tAll authors agree that for computations involving mean speeds to be theoretically correct, it is necessary to ensure that one has measured space mean speed, rather than time mean speed, since time-mean speeds do not provide reasonable travel time estimates unless the speed of the point sampled is representative of the speed of all other points along a roadway segment. Under congested conditions, it is important to distinguish between these two mean speeds. For freely flowing traffic, however, there will not be any significant difference between the two. When there is great variability of speeds, there will be considerable difference between the two.
\n\t\t\tTraffic density, the macroscopic characteristic of traffic concentration, is defined as the number of vehicles occupying unit length of roadway at any instant of time and is given by,
\n\t\t\twhere, L = length of the roadway;
\n\t\t\t\n\t\t\t\tN = the number of vehicles present over L at an instant of time.
\n\t\tTraffic engineers represent the location of a specific vehicle at a certain time with a time-space diagram. Figure 1 shows the trajectories of a set of five vehicles (numbered from 1 to 5), through time, as they move in one direction on a road. Certain characteristics, such as headway, speed, flow, density, etc. can be depicted using the diagram Figure 1.
\n\t\t\tIn the figure, the slope of time- space plot for each vehicle, dx/dt equals speed, u. The time difference between pairs of consecutive vehicles along the horizontal line is the headway between those vehicles. For example, ht23\n\t\t\t\t is the time headway between 2nd and 3rd vehicles in the traffic stream (t3\n\t\t\t\t minus t2\n\t\t\t\t). The difference in position between consecutive vehicles along vertical line is the spacing (space headway) between vehicles. For example, hs23\n\t\t\t\t is the spacing between 2nd and 3rd vehicles in the traffic stream (x\n\t\t\t\t\t2\n\t\t\t\t minus x\n\t\t\t\t\t3\n\t\t\t\t). The number of vehicles that the observer would be able to count at a point A on the road over a period of observation T, namely flow, is equal to the number of vehicle trajectories the horizontal line AA intersects during the time period. The number of time-space plot that are intersected by the vertical line BB corresponds to the number of vehicles occupying the section of roadway at that instant of time, which is nothing but density (k).\n\t\t\t
\n\t\tTime-space diagram showing trajectories of vehicles over time and space.
The relationship between the basic variables of traffic flow, namely speed (us), flow (q), and density (k) is called the fundamental relations of traffic flow characteristics and mathematically, it is expressed as,
\n\t\t\tThe relationships between the three basic characteristics, taken two at a time, are illustrated from Figure2 through 4. It may be noted that the nature of the relationships depicted in the figures are based on the assumption that the speed and density are linearly related (Greenshields model).
\n\t\t\tFlow-Density Curve.
Speed-Density Curve Fig. 4Speed-Flow Curve.
The flow and density vary with time and location. The graphical representation of the relation between density and the corresponding flow, on a given stretch of road, is referred to as the fundamental diagram of traffic flow Figure 2. Some characteristics of an ideal flow-density relationship are listed below:
\n\t\t\t\t1.When density is zero, flow will also be zero, since there are no vehicles on the road.
2.When the number of vehicles gradually increases, the density as well as flow increases.
3.Increases in density beyond the point of maximum flow (qmax) results in reduction of flow.
4.When more and more vehicles are added, it reaches a saturation level where, vehicles can\'t move. This is referred to as the jam density or the maximum density (kjam). At jam density, flow will be zero because the vehicles are not moving.
5.The relationship is normally represented by a parabolic curve
The simplest assumption is that the variation of speed with density is linear Figure 3. Corresponding to the zero density, vehicles will be flowing with their desire speed, or free-flow speed (uf). When the density is jam density, the speed of the vehicles becomes zero. It is also possible to have non-linear relationships between speed and density as shown by the dotted lines.
\n\t\t\tThe relationship between the speed and flow Figure 4 can be postulated as follows. The flow is zero either because there are no vehicles or there are too many vehicles so that they cannot move. At maximum flow, the speed will be in between zero and free flow speed.
\n\t\t\t\tSo far, the fundamentals of traffic flow and the necessity to study them in detail was briefly explained. Also, the characteristics of traffic flow such as flow, density and speed and the relationship between them were explained. Generally, motorists perceive lowering of the quality of service when the traffic concentration on the road increases. In other words, for a given roadway, the quality of flow, changes with the traffic concentration on the road. Thus, the measure ‘concentration’ provides a clear indication of both the level of service being provided to the users and the productive level of facility use. Hence, there is a need for in-depth understanding of traffic flow characteristics with specific reference to concentration. Accordingly, the following sections are focused on the in-depth study of traffic concentration.
\n\t\t\tConcentration is a traffic measure which explains the extent of usage of road space by vehicles. It is a broader term encompassing both density and occupancy. The first is a measure of concentration over space; the second measures concentration over time of the same vehicle stream.
\n\t\t\tTraffic density, as mentioned earlier is the number of vehicles occupying unit length of roadway at any instant of time. Hence, traffic density can be measured only along a stretch of roadway. The length and width of roadway, usually considered for measurement of density are 1 km and one traffic lane, respectively. The existing Techniques include photographic and input-output counts. In photographic technique, the number of vehicles occupying a section of roadway is counted using the photograph taken aerially on the road section. In input-output counts technique the number of vehicles, crossing two reference points chosen on a section of roadway, are counted. The number of vehicles occupying a section of roadway between these two reference points is obtained by finding the difference between the numbers of vehicles counted at two reference points at any instant of time.
\n\t\t\tDue to difficulty in the field measurement, density needs to be calculated from speed and flow. Density can be calculated from field measured values of traffic volume and speed as
\n\t\t\twhere, k = density in vehicles per lane per km;
\n\t\t\t\n\t\t\t\t q = flow rate in vehicles per hour;
\n\t\t\t\n\t\t\t\tus\n\t\t\t\t = space mean speed in km per hour.
\n\t\t\tDensity, expressed as number of vehicles per unit length of roadway, is valid only under highly homogeneous traffic conditions, wherein the difference in individual vehicle speeds and vehicle dimensions are negligible. In practice, however, even under homogeneous traffic conditions, there are significant differences in the said two characteristics (speed and dimension) of vehicles. The measure, density, hence, becomes inapplicable for conditions with variations in the speed and dimensions of vehicles in the traffic stream. Hence, there is a need for development of an appropriate alternative measure to represent traffic concentration with potential for application to traffic conditions where there is significant difference in speed and dimension of vehicles.
\n\t\t\tRealizing the need for development of an appropriate measure to represent traffic concentration, several research attempts have been made on the subject matter in the past. First, an attempt was made to compute concentration based on the number and speeds of vehicles measured at a point Greenshields, 1960 and this computed factor named, ‘occupancy’ was used as a surrogate for density. Then, as refinement of the concept, occupancy was defined as a non-dimensional variable which can be measured directly by the proportion of time during which a single point on a roadway is covered by all vehicles Athol, 1965. It is calculated as,
\n\t\t\t\n\t\t\t\tOccupancy (ρ)\n\t\t\t\t\n\t\t\t\t
where, ti = time during which a single point on a roadway is covered by vehicle i;
\n\t\t\tT = the total observation period.
\n\t\t\tAs can be seen from equation (7), occupancy is a non-dimensional variable. Since occupancy is a function of speed and length of a vehicle, it could consider the effect of varying vehicle lengths and speeds. Hence, occupancy can be used as a surrogate of density for measuring concentration of road traffic where there is significant difference in speed and dimension of vehicles.
\n\t\t\tMost traffic control systems that use a macroscopic density characteristic as the control variable can use the measured occupancy directly without converting to density May, 1990. Hence occupancy can be considered as a logical substitute of density. It has been used as an indicator of establishing level of service criteria for freeways by developing a relationship between speed flow and occupancy Lin et al., 1996.
\n\t\t\tResearchers also derived relationships between density and occupancy for homogeneous traffic conditions. Initially, a linear relationship was postulated assuming that all the sub-streams had constant vehicle length and constant speed Athol, 1965, which is expressed as,
\n\t\t\tOccupancy, ρ \n\t\t\t\t
where, k = density of traffic stream;
\n\t\t\td = length of the detection zone;
\n\t\t\tl = length of the vehicle.
\n\t\t\tThe difficulty in using equation (8) to estimate density is that the equation is valid only under ideal traffic condition wherein vehicles are of same length and maintain same speed.
\n\t\tBased on the definition of occupancy (equation 7), it can be inferred that vehicles are to be detected while passing a point or line across the roadway to get the required data for estimating occupancy. In practice, however, one is no longer dealing strictly with a point measurement, but with measurement along a short section of road using detectors. In other words, occupancy, based on practical consideration, is defined as the percentage of time the detection zone is occupied by the vehicles May, 1990 and is given by,
\n\t\t\twhere, (ti)O = time during which the detection zone on a roadway is covered by vehicle i and the subscript, o stands for occupancy;
\n\t\t\tT = the total observation period.
\n\t\t\tSince, the time during which a detection zone is covered by vehicles, is automatically recorded by detectors, occupancy can be easily measured in the field using detectors.
\n\t\t\tOccupancy measured using detectors depends on the length of the detection zone, each detector type has a differing zone of influence (detector length) and the length of the zone of influence is effectively added to the vehicle length to calculate occupancy. Hence, the measured occupancy may be different for different detection zones (detectors) even for the same roadway and traffic conditions, depending on the size and nature of the detectors. This implies that it is necessary to consider length of the detection zone (on which the presence of vehicle is detected) also in the formulation, in order to standardize the measurement of occupancy.
\n\t\tThe afore mentioned concept of occupancy is specific to lane based traffic flow. The road traffic in countries like India is highly heterogeneous comprising vehicles of wide ranging static and dynamic characteristics as shown in Figure 5. The different types of vehicles present in the traffic on major urban roads in India can be broadly grouped into eight categories as follows: 1. Motorized two-wheelers, which include motor cycles, scooters and mopeds, 2. Motorized three-wheelers, which include Auto-rickshaws – three wheeled motorized transit vehicles to carry passengers and tempos – three wheeled motorized vehicles to carry small quantities of goods, 3.Cars including jeeps and small vans, 4. Light commercial vehicles comprising large passenger vans and small four wheeled goods vehicles, 5. Buses, 6. Trucks, 7. Bicycles and 8.Tricycles, which include Cycle-rickshaws- three wheeled pedal type transit vehicles to carry a maximum of two passengers and three wheeled pedal type vehicles to carry small quantity of goods over short distances. By virtue of their wide ranging characteristics, the vehicles do not follow traffic lane and occupy any lateral position over the width of roadway depending on the availability of road space at a given instant of time. Hence, it is nearly impossible to impose lane discipline under such conditions. To analyze such characteristics of the heterogeneous traffic, it becomes necessary to consider the whole of the width of road as single unit. Hence, when the occupancy concept is applied to heterogeneous traffic, it is necessary to consider the area (length and width) of the detection zone and the area of vehicles as the bases.
\n\t\tThe heterogeneous traffic on an Indian road.
Considering all the said issues related to occupancy, a modified concept of occupancy, named, ‘Area-Occupancy’, appropriate for heterogeneous traffic condition, is proposed here, which considers the horizontal projected area of the vehicle, without any restriction on the length of detection zone and width of road (treating the whole of the width of road as single unit, without consideration of traffic lanes), as the basis for measurement Arasan & Dhivya, 2008. Accordingly, considering a stretch of road, area-occupancy is expressed as the proportion of time the set of observed vehicles occupy the detection zone on the chosen stretch of a roadway. Thus, area-occupancy can be expressed mathematically as follows;
\n\t\t\twhere, (ti)AO = time during which the detection zone is occupied by vehicle i in s and the subscript, AO stands for area-occupancy;
\n\t\t\tai = area of vehicle i falling on the detection zone in m2;
\n\t\t\tA = area of the whole of the road stretch (detection zone) in m2;
\n\t\t\tT = total observation period in s.
\n\t\t\tThe area-occupancy is not affected by the length of the detection zone since it considers the length of the detection zone in its formulation. Also, the effect of heterogeneity and lane less nature of the traffic is incorporated in the concept of area-occupancy by consider the area (length and width) of the vehicle in its formulation. Hence, the concept of area-occupancy is valid to measure, accurately, the extent of usage of road space by vehicles. Thus, area-occupancy, rather than occupancy, can be used as indicator of road traffic concentration at any flow level because of its ability to accurately replicate the extent of usage of the road. It may be noted that the area-occupancy concept can be applied to any traffic condition, from highly homogeneous to highly heterogeneous, and to any length of the detection zone.
\n\t\t\tIn the case of measurement of area-occupancy using vehicle-detection loops, according to the definition of time (ti)AO in equation (10), it is the time interval from the instant the front of a vehicle enters the detection zone to the instant the rear of the vehicle leaves the detection zone.
\n\t\t\t\tAs the area-occupancy concept is applicable for any length of detection zone, two cases are possible in the measurement of area-occupancy. One is the measurement of area-occupancy on the detection zone whose length is less than the vehicle length Figure 6 a and the other is the measurement of area-occupancy on a detection zone of length more than the vehicle length Figure 6 b. The time (ti)AO of a vehicle can be split into three different time components, namely, t1, t2 and t3, where t1 is the time taken for the projected area of vehicle to fully cover the detection zone (when the length of the detection zone is less than the vehicle length) / fully fall on the detection zone (when the length of the detection zone is more than the vehicle length), t2 is the time interval, after t1, during which the horizontal projected area of the vehicle falling on the detection zone remains unchanged and t3 is the time taken by the vehicle, after t2, to clear off the detection zone. Thus, the distance traveled by a vehicle during the time t1+t2+t3 is (l+d), where, l is the length of the vehicle and d is the length of the detection zone.
\n\t\t\t\tPrinciple involved in the measurement of area-occupancy.
There are some issues involved in the measurement of time component (ti)AO of area-occupancy and these are explained using the trajectories of vehicles depicted in Figure 7a) and 7b. It can be seen that during the time t2, the whole length of the detection zone will be occupied by the vehicle in the case where the length of the detection zone (d) is less than the vehicle length (l) (B in Figure 7 a) or the whole length of the vehicle will be occupying the detection zone where the length of the detection zone is more than the vehicle length (B in Fig 7 b). Hence, during the time t2, the area of the detection zone occupied by the vehicle, at any instant of time, will be constant, which is equal to ai = length of the detection zone multiplied by the width of the vehicle (when (d < l)) or length of the vehicle multiplied by the width of the vehicle (when (d > l)). On the other hand, during the times t1 and t3, the detection zone is occupied by the vehicle only partially. Hence, in the time durations t1 and t3, the area of the detection zone occupied by the vehicle progressively varies and it is always less than ai. Due to this, during the times t1 and t3, in the formulation of area-occupancy, the component, ai(ti)AO (with ti being considered to be t1+t2+t3) will overestimate the occupancy of the vehicle. That is, the occupancy will be estimated as (A+B+C+D+E) while the actual occupancy value is only (A+B+C).
\n\t\t\t\tAssuming the speed maintained by a vehicle to be constant on the detection zone, the contribution of this vehicle, to occupancy, during the time interval t1 (A in Figure 7 a and 7b) will be equal to the occupancy-contribution during the time interval t3 (C in Figure 7a and 7b). Hence, the problem of over estimation of occupancy value can be solved if the time interval (ti)AO is considered as either (t1+t2) or (t2+t3)) as (A+B+C) is equal to either B+(A+D) or B+(C+E). Also, the area ai in equation (10) is equal to the length of the detection zone multiplied by the width of the vehicle (when (d < l)) or equal to the horizontal projected area of the vehicle (when (d > l)).
\n\t\t\t\tPrinciple of measurement of time component of area-occupancy.
The applicability of the area-occupancy concept to real world condition can be verified by estimating the area-occupancy value for a wide range of roadway and traffic conditions and checking for the logical validity of the results. For estimation of area-occupancy, it is necessary to measure accurately the characteristics of heterogeneous traffic flow and the other relevant factors on vehicular movement over a stretch of roadway. Measurement of these complex characteristics in the field is difficult and time consuming. Also, it may be difficult to carry out such experiments in the field covering a wide range of roadway and traffic conditions. Hence, it is necessary to model road–traffic flow for in depth understanding of the related aspects. The study of these complex characteristics, which may not be sufficiently simplified by analytical solution, can be done using appropriate modeling technique like computer simulation. Simulation is already proven to be a popular traffic-flow modeling tool for applications related to road-traffic-flow studies. Hence, simulation technique has been used here to validate the concept of area-occupancy.
\n\t\t\tSimulation models may be classified as being static or dynamic, deterministic or stochastic, and discrete or continuous. A simulation model, which does not require any random values as input, is generally called deterministic, whereas a stochastic simulation model has one or more random variables as inputs. Random inputs lead to random outputs and these can only be considered as estimates of the true characteristics of the system being modeled. Discrete and continuous models are defined in an analogous manner. The choice of whether to use a discrete or continuous simulation model is a function of the characteristics of the system and the objectives of the study Banks et al., 2004. For this study, a dynamic stochastic type discrete event simulation is adopted in which the aspects of interest are analysed numerically with the aid of a computer program.
\n\t\t\tAs this study pertains to the heterogeneous traffic conditions prevailing in India, the available traffic-simulation program packages such as CORSIM, MITSIM, and VISSIM etc. cannot be directly used to study the characteristics of heterogeneous traffic flow as these are based on homogeneous traffic-flow conditions. Also, the research attempts made earlier Katti & Raghavachari, 1986, Khan & Maini, 2000, Kumar & Rao, 1996, Marwah & Singh, 2000, Ramanayya, 1988 to simulate heterogeneous traffic flow on Indian roads were limited in scope as they were location and traffic-condition specific. Moreover, these studies did not truly represent the absence of lane and queue discipline in heterogeneous traffic. Hence, an appropriate traffic simulation model, named HETEROSIM has been developed at Indian Institute of Technology Madras, India Arasan & Koshy, 2005 to replicate heterogeneous traffic flow conditions accurately.
\n\t\t\tThe modelling framework is explained briefly here to provide the background for the study. For the purpose of simulation, the entire road space is considered as single unit and the vehicles are represented as rectangular blocks on the road space, the length and breadth of the blocks representing respectively, the overall length and the overall breadth of the vehicles Figure 8. The entire road space is considered to be a surface made of small imaginary squares (cells of convenient size - 100 mm square in this case); thus, transforming the entire space into a matrix. The vehicles will occupy a specified number of cells whose co-ordinates would be defined before hand. The front left corner of the rectangular block is taken as the reference point, and the position of vehicles on the road space is identified based on the coordinates of the reference point with respect to an origin chosen at a convenient location on the space. This technique will facilitate identification of the type and location of vehicles on the road stretch at any instant of time during the simulation process.
\n\t\t\tReference axes for representing vehicle positions on the roadway.
The simulation model uses the interval scanning technique with fixed increment of time. For the purpose of simulation, the length of road stretch as well as the road width can be varied as per user specification. Due to possible unsteady flow condition at the start of the simulation stretch, a 200m long road stretch, from the start of the simulation stretch is used as warm up zone. Similarly, to avoid the possible unsteady flow at the end of the simulation stretch due to free exit of vehicles, a 200m long road stretch at the end of the simulation stretch is treated as tail end zone. Thus, the data of the simulated traffic flow characteristics are collected covering the middle portion between the warm up and tail end zones. The said details are shown in Figure 9.
\n\t\t\tRoad stretch considered for simulation of traffic flow.
Also, to eliminate the initial transient nature of traffic flow, the simulation clock was set to start only after the first 50 vehicles reached the exit end of the road stretch. The model measures the speed maintained by each vehicle when it traverses a given reference length of roadway. The output also includes the number of each category of vehicles generated, the values of all the associated headways generated, number of vehicles present over a given length of road (concentration), number of overtaking maneuvers made by each vehicle, and speed profile of vehicles. The logic formulated for the model also permit admission of vehicles in parallel across the road width, since it is common for smaller vehicles such as Motorised two-wheelers to move in parallel in the traffic stream without lane discipline. The model was implemented in C++ programming language with modular software design. The flow diagram illustrating the basic logical aspects involved in the program is shown as Figure 10.
\n\t\t\tMajor logical steps involved in the simulation model.
The model is also capable of displaying the animation of simulated traffic movements through mid block sections. The animation module of the simulation model displays the model’s operational behavior graphically during the simulation runs. The snapshot of animation of heterogeneous traffic flow, obtained using the animation module of HETEROSIM, is shown in Figure 11. The model has been applied for a wide range of traffic conditions (free flow to congested flow conditions) through an earlier study Arasan & Koshy, 2005 and has been found to replicate the field observed traffic flow to a satisfactory extent.
\n\t\tSnapshot of the animation of simulated heterogeneous traffic flow.
The concept of area-occupancy can be said to be applicable for any traffic stream under both heterogeneous and homogeneous traffic conditions. To check for the validity of the concept of area-occupancy, as the first step, the occupancy and area-occupancy of a homogeneous traffic stream are related independently to the density of a stream under homogeneous (cars-only) traffic condition for different lengths of detection zone. For this purpose, in respect of occupancy, the relationship between density and occupancy developed by Athol, 1965, given in equation (8) is used. In respect of area-occupancy, a relationship between density and area-occupancy under homogeneous traffic condition is newly developed, the details of which are given in the following sub-section.
\n\t\t\tThe relationship between density and area-occupancy is derived as follows:
\n\t\t\t\tDuring the time interval (ti)AO, let the distance traveled by a vehicle with speed ui be L. Then, substituting (ti)AO = L/ui in equation (10), we get,
\n\t\t\t\tArea-Occupancy, ρA\n\t\t\t\t\t\n\t\t\t\t\t
Under highly homogeneous traffic condition, ai can be considered to be the same, say a, for all vehicles and hence, equation (11) can be written as,
\n\t\t\t\tMultiplying and dividing the right hand side of equation 12 by the total number of vehicles, denoted by N, the expression will become,
\n\t\t\t\tThe space mean speed of a traffic stream, as per equation (2), is,
\n\t\t\t\tAlso, the flow of a traffic stream, as per equation (1), is,
\n\t\t\t\tThen, using equations 14 and 15, equation 13 can be written as,
\n\t\t\t\tAs per Equation 6, (q/us) is equal to k, and hence, the relationship between density and area-occupancy, under homogeneous traffic condition, from equation (16), can be obtained as,
\n\t\t\t\tWhen the length of the detection zone (d) is less than the vehicle length (l), then, the distance travelled by a vehicle in time, (ti)AO is l and the value of a is d multiplied by w and hence, the relationship between area-occupancy and density, from equation 17, in this case, can be given as,
\n\t\t\t\tThat is,
\n\t\t\t\twhere, w = width of the vehicle;
\n\t\t\t\tW = width of the detection zone;
\n\t\t\t\tav = area of the vehicle.
\n\t\t\t\tWhen the length of the detection zone is more than the vehicle length (d > l), then, the distance traveled by a vehicle in time, (ti)AO is d and the value of a is l multiplied by w and hence, the relationship between area-occupancy and density, from equation (17), in this case, can be given as,
\n\t\t\t\tThat is,
\n\t\t\t\tThus, it is clear from equations 18 and 19 that the value of area-occupancy for a given roadway and traffic conditions is unaffected by change in the length of detection zone. This mathematical result can also be cross checked by performing traffic simulation experiments and the simulation experiments are explained in the following section.
\n\t\t\tSince the scope of the experiment is to prove a fundamental relationship, uniform traffic flow on a single traffic lane was considered. Accordingly, the HETEROSIM model was used for simulating the cars-only traffic (100% passenger cars of assumed length 4 m and width 1.6 m) on a 3.5m wide road space - single traffic lane (with no passing). The traffic flow was simulated for one hour (T = 1h) over a stretch of one km. The simulation runs were made with three random number seeds and the averages of the three values were taken as the final model output. The simulation was also run with various volume levels for various lengths (1m, 2m, 3m and 4m) of detection zone. Using the features of the simulation model, the times (ti)O and (ti)AO were recorded for each of the simulated vehicles, (assuming the speed maintained by the vehicles on the detection zone is constant) and the values are given in columns 3 and 4 of Table 2. It may be noted that the distance traveled by the vehicle during time (ti)AO is l, which is constant for all the vehicles under homogeneous traffic condition and hence, the time (ti)AO remains unchanged for the different lengths of detection zone for a given traffic volume. The occupancy and area-occupancy were then calculated using equations 9) and 10 respectively and the values are shown in columns 5 and 6 of Table 2. Density was then estimated using equation 8 as well as equation 18. The density values, thus calculated, are given in columns 7 and column 8 of Table 2.
\n\t\t\t\tIt can be seen that the density values estimated from the relationship with area-occupancy (equation 18) is the same as those estimated from the relationship with occupancy (equation 8). This implies that area-occupancy can be a substitute for occupancy.
\n\t\t\t\tFlow (veh./h) | \n\t\t\t\t\t\t\tMean Speed (km/h) | \n\t\t\t\t\t\t\t(ti)O (s) | \n\t\t\t\t\t\t\t(ti)AO (s) | \n\t\t\t\t\t\t\tOccupancya (%) | \n\t\t\t\t\t\t\tArea-Occupancyx (%) | \n\t\t\t\t\t\t\tDensity (veh./km) obtained as \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\tDensity (veh./km) obtained as \n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
(1) | \n\t\t\t\t\t\t\t(2) | \n\t\t\t\t\t\t\t(3) | \n\t\t\t\t\t\t\t(4) | \n\t\t\t\t\t\t\t(5) | \n\t\t\t\t\t\t\t(6) | \n\t\t\t\t\t\t\t(7) | \n\t\t\t\t\t\t\t(8) | \n\t\t\t\t\t\t
Length of detection zone: 1m | \n\t\t\t\t\t\t|||||||
494 | \n\t\t\t\t\t\t\t73.68 | \n\t\t\t\t\t\t\t122.16 | \n\t\t\t\t\t\t\t97.72 | \n\t\t\t\t\t\t\t3.39 | \n\t\t\t\t\t\t\t1.24 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t
989 | \n\t\t\t\t\t\t\t72.59 | \n\t\t\t\t\t\t\t247.96 | \n\t\t\t\t\t\t\t198.37 | \n\t\t\t\t\t\t\t6.89 | \n\t\t\t\t\t\t\t2.52 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t
1487 | \n\t\t\t\t\t\t\t71.46 | \n\t\t\t\t\t\t\t377.84 | \n\t\t\t\t\t\t\t302.27 | \n\t\t\t\t\t\t\t10.50 | \n\t\t\t\t\t\t\t3.84 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t
1988 | \n\t\t\t\t\t\t\t69.99 | \n\t\t\t\t\t\t\t514.33 | \n\t\t\t\t\t\t\t411.47 | \n\t\t\t\t\t\t\t14.29 | \n\t\t\t\t\t\t\t5.22 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t
2495 | \n\t\t\t\t\t\t\t67.19 | \n\t\t\t\t\t\t\t669.74 | \n\t\t\t\t\t\t\t535.79 | \n\t\t\t\t\t\t\t18.60 | \n\t\t\t\t\t\t\t6.80 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t
2930 | \n\t\t\t\t\t\t\t58.03 | \n\t\t\t\t\t\t\t930.59 | \n\t\t\t\t\t\t\t744.47 | \n\t\t\t\t\t\t\t25.85 | \n\t\t\t\t\t\t\t9.45 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t
Length of detection zone: 2m | \n\t\t\t\t\t\t|||||||
494 | \n\t\t\t\t\t\t\t73.68 | \n\t\t\t\t\t\t\t146.59 | \n\t\t\t\t\t\t\t97.72 | \n\t\t\t\t\t\t\t4.07 | \n\t\t\t\t\t\t\t1.24 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t
989 | \n\t\t\t\t\t\t\t72.59 | \n\t\t\t\t\t\t\t297.56 | \n\t\t\t\t\t\t\t198.37 | \n\t\t\t\t\t\t\t8.27 | \n\t\t\t\t\t\t\t2.52 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t
1487 | \n\t\t\t\t\t\t\t71.46 | \n\t\t\t\t\t\t\t453.41 | \n\t\t\t\t\t\t\t302.27 | \n\t\t\t\t\t\t\t12.59 | \n\t\t\t\t\t\t\t3.84 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t
1988 | \n\t\t\t\t\t\t\t69.99 | \n\t\t\t\t\t\t\t617.20 | \n\t\t\t\t\t\t\t411.47 | \n\t\t\t\t\t\t\t17.14 | \n\t\t\t\t\t\t\t5.22 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t
2495 | \n\t\t\t\t\t\t\t67.19 | \n\t\t\t\t\t\t\t803.68 | \n\t\t\t\t\t\t\t535.79 | \n\t\t\t\t\t\t\t22.32 | \n\t\t\t\t\t\t\t6.80 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t
2930 | \n\t\t\t\t\t\t\t58.03 | \n\t\t\t\t\t\t\t1116.71 | \n\t\t\t\t\t\t\t744.47 | \n\t\t\t\t\t\t\t31.02 | \n\t\t\t\t\t\t\t9.45 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t
Length of detection zone: 3m | \n\t\t\t\t\t\t|||||||
494 | \n\t\t\t\t\t\t\t73.68 | \n\t\t\t\t\t\t\t171.02 | \n\t\t\t\t\t\t\t97.72 | \n\t\t\t\t\t\t\t4.75 | \n\t\t\t\t\t\t\t1.24 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t
989 | \n\t\t\t\t\t\t\t72.59 | \n\t\t\t\t\t\t\t347.15 | \n\t\t\t\t\t\t\t198.37 | \n\t\t\t\t\t\t\t9.64 | \n\t\t\t\t\t\t\t2.52 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t
1487 | \n\t\t\t\t\t\t\t71.46 | \n\t\t\t\t\t\t\t528.98 | \n\t\t\t\t\t\t\t302.27 | \n\t\t\t\t\t\t\t14.69 | \n\t\t\t\t\t\t\t3.84 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t
1988 | \n\t\t\t\t\t\t\t69.99 | \n\t\t\t\t\t\t\t720.07 | \n\t\t\t\t\t\t\t411.47 | \n\t\t\t\t\t\t\t20.00 | \n\t\t\t\t\t\t\t5.22 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t
2495 | \n\t\t\t\t\t\t\t67.19 | \n\t\t\t\t\t\t\t937.63 | \n\t\t\t\t\t\t\t535.79 | \n\t\t\t\t\t\t\t26.05 | \n\t\t\t\t\t\t\t6.80 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t
2930 | \n\t\t\t\t\t\t\t58.03 | \n\t\t\t\t\t\t\t1302.83 | \n\t\t\t\t\t\t\t744.47 | \n\t\t\t\t\t\t\t36.19 | \n\t\t\t\t\t\t\t9.45 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t
Length of detection zone: 4m | \n\t\t\t\t\t\t|||||||
494 | \n\t\t\t\t\t\t\t73.68 | \n\t\t\t\t\t\t\t195.45 | \n\t\t\t\t\t\t\t97.72 | \n\t\t\t\t\t\t\t5.43 | \n\t\t\t\t\t\t\t1.24 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t\t7 | \n\t\t\t\t\t\t
989 | \n\t\t\t\t\t\t\t72.59 | \n\t\t\t\t\t\t\t396.74 | \n\t\t\t\t\t\t\t198.37 | \n\t\t\t\t\t\t\t11.02 | \n\t\t\t\t\t\t\t2.52 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t\t14 | \n\t\t\t\t\t\t
1487 | \n\t\t\t\t\t\t\t71.46 | \n\t\t\t\t\t\t\t604.54 | \n\t\t\t\t\t\t\t302.27 | \n\t\t\t\t\t\t\t16.79 | \n\t\t\t\t\t\t\t3.84 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t\t21 | \n\t\t\t\t\t\t
1988 | \n\t\t\t\t\t\t\t69.99 | \n\t\t\t\t\t\t\t822.93 | \n\t\t\t\t\t\t\t411.47 | \n\t\t\t\t\t\t\t22.86 | \n\t\t\t\t\t\t\t5.22 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t\t29 | \n\t\t\t\t\t\t
2495 | \n\t\t\t\t\t\t\t67.19 | \n\t\t\t\t\t\t\t1071.58 | \n\t\t\t\t\t\t\t535.79 | \n\t\t\t\t\t\t\t29.77 | \n\t\t\t\t\t\t\t6.80 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t\t37 | \n\t\t\t\t\t\t
2930 | \n\t\t\t\t\t\t\t58.03 | \n\t\t\t\t\t\t\t1488.94 | \n\t\t\t\t\t\t\t744.47 | \n\t\t\t\t\t\t\t41.36 | \n\t\t\t\t\t\t\t9.45 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t\t52 | \n\t\t\t\t\t\t
It can also be seen that the occupancy values (column (5) in Table 2) estimated for the detection zone lengths of 1, 2, 3, and 4 m are significantly different from one another for a given traffic volume level. Thus, it is clear that even a small change (1m) in detection-zone length results in a considerable change in the value of occupancy corroborating the fact that occupancy is specific to the length of the detection zone. On the other hand, the area-occupancy (column (6) in Table 2) remains unchanged for the different lengths of detection zone for a given volume of traffic. This implies that the concept of area-occupancy is valid for any length of detection zone. Hence, area-occupancy can be applied to measure accurately the extent of usage of road space by vehicles without any restriction on the length of detection zone.
\n\t\t\t\tAlso, to depict the validity of area-occupancy as replacement for density (concentration), the results of the simulation experiment (Table 2) were used to make plots relating (i) area-occupancy with speed and flow and (ii) density with speed and flow as shown in Figure 12. It can be seen that area-occupancy and density exhibit similar trends of relationships with speed and flow.
\n\t\t\tRelationship between traffic flow characteristics.
The concept of area-occupancy can be applied as indicated earlier, for heterogeneous traffic condition also and relationships can be developed between flow, area-occupancy and traffic stream speed. The relationship is derived as follows:
\n\t\t\tThe formula for area-occupancy of heterogeneous traffic, from equation 10, can be written as,
\n\t\t\twhere, j = vehicle category;
\n\t\t\ti = subject vehicle within category j;
\n\t\t\taj = horizontal projected area of the vehicle of category j falling on the detection zone;
\n\t\t\tT = observation period;
\n\t\t\tA = area of the detection zone;
\n\t\t\tn = total number of vehicle categories in the heterogeneous traffic.
\n\t\t\tTherefore, for each substream j,
\n\t\t\tAs the area of vehicle category j is constant for all the vehicles with in that category, equation 21 can be written as,
\n\t\t\tDuring the time interval (ti)AO, let the distance traveled by a vehicle of category j with speed ui be L. Then, substituting (ti)AO= L/ui in equation 22, and multiplying and dividing the right hand side of the equation by the total number of vehicles of category j, Nj, the expression will become,
\n\t\t\tThat is,
\n\t\t\tThe space mean speed of sub stream j, (uj) can be expressed as
\n\t\t\tAlso the flow of sub stream j (qj) can be written as
\n\t\t\tHence, using equations 24 and 25, equation 23 can be written as,
\n\t\t\tWhen the length of the detection zone (d) is less than the vehicle length (l) then, the distance traveled by a vehicle (L) is l, when (d < l) and d when (d > l). The value of ai is (d. w) when (d < l) and (l. w) when (d > l) and then equation (26) can be simplified as,
\n\t\t\twhere, W = width of the detection zone;
\n\t\t\tavj = area of the vehicle category j.
\n\t\t\tfor both the cases, namely, (d < l) and (d > l) as for the steps followed to derive equations (18) and (19).
\n\t\t\tThen, the area occupancy of whole of the traffic stream is
\n\t\t\tHence, it is clear from equation 27 that by knowing the flow and speed of the different categories of vehicles in a heterogeneous traffic stream, the area-occupancy of different susbstreams can be calculated. Also, the area-occupancy of the whole of the traffic stream can be calculated using the relationship given in equation 28.
\n\t\t\tUnder heterogeneous traffic condition, there may be several substreams, each with different vehicle categories in the whole of the traffic stream. Hence, to determine the area-occupancy of the whole of the traffic stream and subsequently to formulate relationship between area-occupancy, flow and speed for the traffic stream as a whole, it is necessary to express the area-occupancy value of each of the substreams in terms of area-occupancy of an equivalent stream consisting of a standard vehicle. The same can be written, based on equation (27), mathematically, as,
\n\t\t\twhere, (qsv)j = flow of standard vehicle, equivalent to substream j, expressed in No. of vehicles/h;
\n\t\t\t(usv)j = space mean speed of the standard vehicles with flow (qsv)j ;
\n\t\t\tasv = area of the standard vehicle;
\n\t\t\tEquation 29 can be simplified as,
\n\t\t\tMultiplying and dividing the right hand side of equation 27 by asv, we get,
\n\t\t\tFrom equation 30, equation 31 can be written as,
\n\t\t\tFrom equation 27, the area of occupancy of traffic stream considered in terms of standard vehicles with flow qsv can be written as
\n\t\t\tAlso,
\n\t\t\tWhere, ρA = area-occupancy of the whole of the traffic stream considered in terms of standard vehicle;
\n\t\t\tqsv = flow of the whole of the traffic stream considered in terms of standard vehicles;
\n\t\t\tusv = space mean speed of the whole of the stream considered in terms of standard vehicle.
\n\t\t\tFrom equation (28), equation (33) can be written as
\n\t\t\tFrom equation 32, equation 35 can be written as,
\n\t\t\tThat is,
\n\t\t\tFrom equation 34, the stream speed of traffic considered in terms of standard vehicles (equation 36) can be written as,
\n\t\t\tIf heterogeneous traffic is converted into equivalent standard vehicles based on the concept of area-occupancy (by satisfying the condition given in equation 29, then, equation 33 is the fundamental equation of heterogeneous traffic flow in which flow is expressed in terms of standard vehicles.
\n\t\tAs the concept of area-occupancy takes into account the variations in traffic composition, it remains unaffected by change in traffic composition. To validate this statement, heterogeneous traffic with three different compositions, as shown in Figure 13 were considered.
\n\t\t\tLCV- Light Commercial Vehicle, M.Th.W – Motorised Three-Wheelers, M.T.W - Motorised Two-Wheelers
The traffic flow was simulated on a six lane divided road with 10.5m wide main carriageway and 1.5m of paved shoulder for various volume levels, for each of the three cases. The traffic flow was simulated on one km long road stretch for one hour and three simulation runs, with different random number seeds, were made and the mean values of the three runs were taken as the final values of the relevant parameters obtained through the simulation process. Using the features of the simulation model, the time (ti)AO was recorded for each of the simulated vehicles, considering a detection-zone of length 3m. The area-occupancy was estimated using the equation 20 (It can also be estimated using the equations 27 and 28). In this case, the area of the detection zone is equal to (3 X 12 = 36 m2). The value of area-occupancy of traffic for the three different traffic compositions were plotted, on a single set of axes, against V/C ratio, and the same is depicted in Figure 14. It can be seen that the values of area-occupancy of the three heterogeneous traffic streams are nearly the same for any given flow (V/C) level and it can be concluded that the area-occupancy can be used as a measure of traffic concentration for any traffic and roadway condition.
\n\t\tRelationship between area-occupancy and V/C ratio.
To reinforce the fact that the concept of area-occupancy can be applied to study the various characteristics of heterogeneous traffic flow, the area-occupancy of heterogeneous traffic was estimated by simulating one-way flow of heterogeneous traffic on a 7.5 m wide road (equivalent to one half of four-lane divided road) for various volume levels with the representative traffic composition prevailing on urban roads in India (depicted in Figure 15). The traffic flow was simulated on one km long road stretch for one hour and three simulation runs, with different random number seeds, were made and the mean values of the three runs were taken as the final values of the relevant parameters obtained through the simulation process. Using the features of the simulation model, the time (ti)AO was recorded for each of the simulated vehicles, considering a detection zone of length 3m.
\n\t\t\tLCV- Light Commercial Vehicle, M.Th.W – Motorised Three-Wheelers, M.T.W - Motorised Two-WheelersRepresentative composition of the traffic prevailing on urban roads in India.
The area-occupancy was estimated using the equation 20. In this case, the area of the detection zone is equal to (3 X 7.5 = 22.5 m2). The average stream speeds and exit volume of the heterogeneous traffic, for the various volume levels were then obtained using the simulation output. Then, plots relating the area-occupancy, speed and flow were made as shown in Figure 16. It may be noted that there is decrease in speed with increase in area-occupancy Figure 16 a and increase in traffic flow with increase in area-occupancy Figure 16 b, which are logical indicating the appropriateness of the area-occupancy concept for heterogeneous traffic. Hence, it is inferred that the concept of area-occupancy is valid and can be applied to measure accurately the traffic concentration.
\n\t\t\t(a) Speed Vs area-occupancy (b) Flow Vs area-occupancy
Concentration is a traffic measure which explains the extent of usage of road space by vehicles. It is a broader term encompassing both density and occupancy. The first is a measure of concentration over space; the second measures concentration over time of the same vehicle stream. The review of the literature on traffic flow characteristics indicates that the traffic density, expressed as number of vehicles per unit length of roadway, can not be appropriate for accurate measurement of traffic concentration, due to variation in the dimensions and speed of vehicles, even on a given traffic lane. Hence, there is a need for development of an appropriate alternative measure to represent traffic concentration with potential for application to traffic conditions where there is significant difference in speed and dimensions of vehicles.
\n\t\t\tOccupancy, defined as the proportion of time during which the detection zone on a highway is covered by all vehicles, can help to remove the deficiencies of the concept of density to a significant extent. Occupancy, thus, takes into account the traffic composition (variation in the dimensions of vehicles) and speed variations, in its measurement, simultaneously and gives a more reliable indicator of the extent of road being used by vehicles. Hence, occupancy is more meaningful than density. Since, occupancy depends on the size of the detection zone, the measured occupancy, however, may be different for different detection-zone lengths, even for the same roadway and traffic conditions. Also, the concept of occupancy can not be directly applied under highly heterogeneous traffic conditions such as those prevailing on Indian roads, as the traffic has no lane discipline (vehicles occupy any lateral position on the road depending on the availability of road space at a given instant of time) and hence, it is necessary to consider the whole of the road as single unit to analyze the traffic flow.
\n\t\t\tHence, a new measure, named, ‘area-occupancy’ is proposed, which considers the horizontal projected area of the vehicle, without any restriction on the length of the detection zone and width of road (treating the whole of the width of road as single unit without consideration of traffic lanes) as the bases. Thus, on a stretch of road, area-occupancy is expressed as the proportion of the area of the detection zone covered by all the vehicles, traversing the zone during the observation time. The area-occupancy is not affected by the length of the detection zone since it considers the length of the detection zone in its formulation. Also, the effect of heterogeneity and lane less nature of the traffic is incorporated in the concept of area-occupancy by consider the horizontal projected area (length and width) of the vehicle in its formulation. Hence, the concept of area-occupancy is valid to measure, accurately, the extent of usage of road space by vehicles. It may be noted that the area-occupancy concept can be applied to any traffic condition, from highly homogeneous to highly heterogeneous, and to any length of the detection zone.
\n\t\t\tTo check for the validity of the concept, the occupancy and area-occupancy of a homogeneous traffic stream were estimated for different lengths of detection zone through simulation experiments and the values were related to the density of the stream. It was found from the results of the simulation experiment, that area-occupancy can be a substitute for occupancy. Also, the estimated area-occupancy is found to remain unchanged with respect to change in the length of the detection zone. Thus, it has been proved that area-occupancy, rather than occupancy, can be used as indicator of road traffic concentration at any flow level because of its ability to accurately replicate the extent of usage of the road space. Also, to depict the validity of area-occupancy as replacement for density (concentration), plots relating (i) area-occupancy with speed and flow and (ii) density with speed and flow were made and it was found that area-occupancy and density exhibit similar trends of relationships with speed and flow.
\n\t\t\tIt was found through simulation experiments that the area-occupancy remains stable with respect to change in traffic composition under heterogeneous traffic conditions. Also, relationships between flow, speed and area-occupancy of heterogeneous traffic for a most common roadway and traffic conditions prevailing in India have been developed and found to be logical. This further reinforces the fact that area-occupancy is an appropriate measure of traffic concentration for any roadway and traffic conditions.
\n\t\tProstate cancer remains the most commonly diagnosed cancer in men in the United States with over 200,000 new cases detected annually [1]. Gleason grade of prostate cancer, developed by Dr. Donald Gleason in the 1960s, remains the most prognostic indicator of prostate cancer to date. Gleason grade ranges from 1 (normal) to 5 (most abnormal) and is assigned based on the histology of prostate tissues from biopsies. The Gleason score ranges from 2 to 10 and is the sum of the two most common Gleason grades. However, assessing Gleason grade requires invasive tissue biopsies. Less than one-third of men tested for prostate cancer through biopsy are diagnosed with cancer by histological analysis. Meanwhile a negative biopsy does little to reassure patients and clinicians of negative cancer status. This leads to a large number of patients undergoing painful initial biopsy procedures that may ultimately be repeated due to uncertainty of diagnosis [2]. Prostate cancer biopsies are a painful and invasive procedure, with the chance of complications including bleeding and infection [3, 4, 5].
\nOf those patients with positive diagnoses, roughly 10% will progress to metastatic prostate cancer, resulting in about 30,000 deaths annually in the United States. It is obvious that these patients should receive aggressive treatment at the earliest sign of disease. However there is concern as to over-diagnosis and over-treatment of indolent prostate cancer [6], resulting in some cases of high risk prostate cancer being treated conservatively with active surveillance, or first step intervention with radiation or radical prostatectomy. It is imperative to the respective disparate patient populations to receive the most accurate, timely, prognostic diagnosis.
\nThe national cancer institute (NCI) dictionary of cancer terms defines biomarker as a “biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or a condition or disease.” Advancements in the field of biomarker discovery have shaped the way medicine is performed and patients are diagnosed [7]. Biomarkers are used throughout the scope of clinical progression from early detection and diagnosis through clinical endpoint determinations.
\nIdeal biomarkers should have high sensitivity and specificity. That is, the power to correctly identify a high proportion of true cases, or those that will experience an event - in this case, developing prostate cancer. A biomarker should also have high specificity, correctly identifying patients who are truly negative for harboring prostate cancer. Typically, a balance is met between specificity and sensitivity. These results are presented as receiver operating characteristic (ROC) curves which are a visual means to describe the statistical ability of a model to correctly classify cases from non-cases [8]. Complete random distribution creates an “area under the curve” or AUC value of 0.50, graphed as a straight slope line, while the value from a perfect prediction model would be 1.0. A reliable prediction model therefore should have an AUC value nearing 1.0.
\nHere we will discuss the scope of biomarkers currently used for prostate cancer diagnosis, as well as prognosis to aid in disease monitoring and treatment oriented decision-making. Prostate cancer biomarkers are currently among three categories: blood, tissue or urine based biomarkers.
\nAn ideal biomarker screen is non-invasive. Blood collection is considered a minimally invasive technique with quick turnaround time that is also indicative of real-time alterations and disease states in the body, while robust or stable enough for findings to be reproducible across clinics. For prostate cancer as with many other diseases, the use of blood to monitor disease progression was the first and hallmark analysis performed to detect the disease.
\nIn recent years, the phrase “liquid biopsy” has been coined, expanding a simple blood draw into an extensive cancer screen, testing for circulating tumor cells or circulating tumor free DNA in the blood. These tests have shown great promise in detecting cancer at early stages across a wide array of malignancies including prostate cancer [9].
\nToday, we continue to use blood based screens as means to detect prostate cancer, inform patients and clinicians on necessity of treatment, as well as to plan and monitor treatment response.
\nThe discovery of prostate and prostatic fluid associated antigens, most notably prostate-specific antigen (PSA) occurred in 1970 by Richard Ablin [10]. PSA is a glycoprotein produced by human kallikrein-3 (hK3), a member of a class of highly homologous serine proteases, the tissue kallikreins. PSA is normally produced by the epithelial cells of the prostate gland and secreted into the lumen to aid in liquefaction of semen ejaculate. However, other pathological conditions in the prostate, such as benign prostatic hyperplasia (BPH) or prostatitis can elevate the serum PSA levels, resulting in a “false positive” PSA test. PSA concentration in blood has been heavily explored for detection of prostate cancer, as well as treatment response and progression free survival monitoring thereof.
\nThe number of diagnosed prostate cancer cases surged with the implementation of PSA screening tests, peaking at around the time of its approval by the United Stated Food and Drug Administration (FDA) in 1994 for prostate cancer detection. While PSA is prostate specific, it is not, however, specific to cancer, being additionally increased in the aforementioned benign prostate conditions. Since its discovery, PSA has been extensively studied in randomized clinical trials as a screening test for prostate cancer. Despite this discovery, prostate cancer remains the most commonly diagnosed non-cutaneous cancer in men in the United States, with solid tumor-associated deaths only second to lung cancer [1]. In addition to poor cancer specificity, PSA also has low sensitivity. Reported in the prostate cancer prevention trial (PCPT) 15% of men with PSA 0–4 ng/ml have prostate cancer, 15% of those are high Gleason score [11, 12].
\nUltimately, implementation of PSA as a screening tool led to an over-diagnosis and subsequent over-treatment of low-risk disease. Perspective studies indicated up to 10% of patients who received curative therapy by either radical prostatectomy or radiation were over-treated [13]. In 2012 The United States Preventative Services Task Force (USPSTF) recommended against PSA based screening for prostate cancer [14]. This recommendation has recently been amended to suggest PSA may be used specifically in men 55–69 on a case-by-case basis with informed patient consent regarding potential harms of screening. In all, the usefulness of PSA will continue to persist especially in disease monitoring, but recent advances lose faith in PSA alone as a diagnostic tool. In-depth analysis of PSA has revealed several molecular variations and functions of PSA which may prove to be more specific to cancerous tissues.
\nPSA is typically observed complexed to protease inhibitors such as alpha 1-antichymotrypsin (ACT) or alpha 2-macroglobulin, known as bound PSA [15, 16]. Discovered in the 1990s, a higher ratio of free PSA, that is, not bound to protease inhibitors and considered inactive, is associated with increased likelihood of BPH rather than cancer [15, 17, 18, 19]. Specific assays have also been developed to measure bound PSA (complexed-PSA), which is usually PSA-ACT and is elevated in cancer [20]. A percentage free PSA (free PSA/total PSA) can be calculated, and is typically lower in men with prostate cancer [21], where early studies linked <25% free PSA to detection of prostate cancer with a sensitivity of 95% [22]. However, follow-up analyses have had less promising results likely due to the relative instability of free or uncomplexed PSA compared to bound, making this an unreliable clinical parameter for patient diagnosis [23, 24].
\nFree PSA can be found in three different forms; proenzyme PSA (proPSA), benign PSA (BPSA) and intact PSA. proPSA is found increased in patients with prostate cancer [25, 26].Several isoforms exist of proPSA based on varying truncations including [−2] and [−4] proPSA. [−2] proPSA or p2PSA has shown promise as a prostate cancer biomarker as it is not detected in BPH, and in trials increased the AUC from 0.52 for PSA or 0.53 for percentage free PSA to 0.73 [27]. [−2] proPSA has been used preferentially to total or free PSA for prostate cancer detection or biopsy [28, 29, 30].
\nThe PSAD test attempts to add specificity to PSA testing in prostate cancer by determining the amount of PSA produced in relation to size of the gland, as size has been highly correlated with prostate cancer prognosis [31, 32]. Prostate size can be measured with magnetic resonance imaging or transrectal ultrasound by a physician. High density indicates that a small volume prostate is responsible for making a large amount of PSA, and reflective of prostate cancer. In contrast, low density reflects an enlarged prostate, most likely due to BPH that is responsible for the PSA elevation.
\nAnother factor suggested to provide more accuracy to PSA in ability to predict prostate cancer lies in the rate at which increase is observed, referred to as PSA velocity. PSA testing is performed at routine intervals in men on active surveillance, and elderly men at risk of developing prostate cancer. Elevated PSA is considered in the range of 4.0–10.0 ng/ml, though prostate cancer may still be found in men below this range. PSA velocity factors the rate of PSA increase over time, such that an increase greater than 0.5 ng/ml per year may be indicative of prostate cancer [33].
\nThe Prostate Health Index (PHI) is an intuitive formula based upon utilization of several well characterized PSA forms—total PSA, free PSA, and [−2] proPSA or p2PSA, such that:
\nThe PHI’s multifactorial approach has compounded the precision of each of the PSA measurements providing one patient score, shown to drastically increase the specificity for prostate cancer [28, 34]. PHI has been approved by the FDA for men with PSA in the 4.0–10.0 ng/ml range.
\nSeveral clinical trials have retrospectively performed direct comparison to PHI against other early detection biomarkers across blood or urine analysis. In a European cohort of men undergoing either an initial or repeat biopsy, comparing PHI to PSA or free PSA, PHI increased the AUC values to 0.70 compared to 0.65 or 0.53 respectively [35]. In one prospectively performed trial it was determined that 30.1% of patients who underwent a biopsy could have been spared the painful procedure based on PHI score [36]. PHI has additionally been compared against urine biomarkers (to be discussed further in this chapter), with PHI increasing AUC over PCA3 or TMPRSS2:ERG [35, 37]. While results were similar, PHI was the only one correlated with Gleason grade greater than 7 [38].
\nThe four-kallikrein panel, subsequently referred to as the 4Kscore test is a reflex, or follow-up blood test for men who have an abnormal PSA or digital rectal exam (DRE) result and are being considered for an initial or repeat prostate biopsy after a prior negative biopsy result. True to its name, the test is based upon inclusion of four–kallikreins, total PSA, intact PSA, free PSA in addition to human kallikrein-2 (hK2). The test is generated by OPKO Labs (Nashville, TN) and has been marketed as accurately identifying the risk of aggressive prostate cancer (Gleason >7) in a subsequent biopsy or radical prostatectomy, aiding in patient action plan–based decision making.
\nThe first clinical report of the four-kallikrein panel was among 740 previously unscreened men who underwent biopsy for a PSA above 3.0 ng/ml in the European Randomized Study of Screening for Prostate Cancer [39]. Subsequent studies have been performed for at least 10 cohorts totaling over 15,000 subjects (reviewed in [40]), each of which observed an AUC between 0.80 and 0.90 for the four-kallikrein testing. Results from these studies consistently demonstrate the four-kallikrein panel effectively identified high-grade disease while reducing the number of unnecessary biopsies 49–57% among men being screened for the first time. The 4 k panel is the only test, aside from PSA that has been linked to long-term end-points including prostate cancer metastasis [17, 41]. Studies were initially performed in Europe, and limitations include only retrospective analysis, in primarily white populations with an alternative Gleason scale used. In translation to the United States to incorporate FDA guidelines, modifications of the test were implemented with positive results.
\nThe Stockholm 3 model (S3M) is a combination of blood biomarkers initially including PSA, free PSA, intact PSA, hK2, MSMB, MIC1, genetic polymorphisms (SNPs) and other variabilities such as age, family history, previous prostate biopsies or exam [42, 43]. Later algorithm modification replaced intact PSA with HOXB13 [44]. The goal of the study was to increase the accuracy of high-risk prostate cancer diagnosis. S3M was tested in over 100,000 men, 50–69 years of age with no diagnosis of prostate cancer in Stockholm, Sweden [42, 43]. The performance of S3M was compared to PSA alone. The use of S3M was found to decrease the number of biopsies by more than 50%, avoid negative biopsies and significantly improve the detection of high-risk prostate cancer [42, 43].
\nProstate-specific membrane antigen (PSMA) is another glycoprotein with enzymatic function uniquely expressed in the prostate. As its name suggests, what makes it unique from PSA is that it is not a secreted protein, rather it is an integral membrane protein. Yet, like PSA, PSMA is also not specific to prostate cancer.
\nWhile PSMA has had little success as a serum based diagnostic marker, it is now being used as the target of an FDA approved radiographic scan (ProstaScint) in which an antibody against PSMA (7E11) is linked to a radiographic agent 111indium. ProstaScint increased predictive value for metastatic prostate cancer, identifying positive lymph node metastases [45]. Several new PSMA specific tracers have been developed for use in PET and PET/CT scanning with performance characteristics that exceed those of ProstaScint and are likely to be approved soon for clinical use.
\nProstatic acid phosphatase (PAP), or prostatic specific acid phosphatase (PSAP) is a glycoprotein enzyme secreted by prostate cells like PSA. Discovered in the 1930s as a diagnostic biomarker [46], it was replaced with the discovery of PSA in the 1970s. However, PAP reemerged following the discovery that PAP was highly expressed in correlation with tumor staging, and is the target of the first prostate cancer immunotherapy, Sipuleucel-T, approved by the FDA in April, 2010 [47, 48], and which increased overall survival of men with metastatic prostate cancer in its first IMPACT trial [49].
\nAndrogen receptor splice variant 7 (AR-V7) is a splice variant of androgen receptor (AR) that lacks the ligand binding domain leading to its constitutive transcriptional activity independent of androgens. Due to its androgen independent function, AR-V7 has been implicated in the resistance to second-generation anti-androgen therapies. AR-V7 can be detected in circulating tumor cells (CTCs) and its presence is correlated with resistance to second generation anti-androgens including enzalutamide and abiraterone [50, 51]. These results suggest the use of AR-V7 as a treatment selection biomarker.
\nTissue based prognosticators are among the most diverse in functionality. Tissue based assays can be performed from as little tissue as a single core of a biopsy up to radical prostatectomy. Yet, these assays are the most invasive due to the nature of tissue extraction through surgical resection or biopsy. Patients may undergo one or multiple sets of biopsies in the course of disease detection and active surveillance. Biomarker screening may also be performed on patients post radical prostatectomy to predict treatment response, recurrence free survival and likelihood of disease progression. Many of these tests are commercial panels available to analyze multiple mRNA signatures in the prostate, but recent advancements in protein and cancer epigenetics are expanding the possibilities of prostate cancer prognosis. Assays are monitored by The National Comprehensive Cancer Network (NCCN), an alliance of U.S. cancer centers directing clinical practice guidelines, as well as the Food and Drug Administration (FDA).
\nHere we will discuss several of the most commonly used tests:
\nDeoxyribonucleic acid (DNA) is the genetic material of all living things. In comparison to other cancers, prostate cancer has little in the way of genetic mutations. Researchers have used the several well described genomic alterations to their advantage as prognosticators of disease.
\nCurrent biopsy strategies sample areas of the prostate in a gridded fashion in attempt to have the most representative assessment of the prostate. Even with this strategy in place, less than 1% of the prostate is sampled. As less than one-third of biopsies return positive results for cancer, there is large concern over inconclusive biopsy results.
\nThe field cancerization effect was first observed in the 1950s when it was noticed that tissues surrounding cancerous lesions contained markers associated with tumor development of oral squamous-cell carcinoma [52]. This phenomenon has since been observed in most solid tumors. Further understanding of the concept is explained in [53]. Today, field effect can translate to modifications in cellular morphology, epigenetics, genomic or mitochondrial DNA alterations, and changes in gene expression or protein levels (reviewed in [54]).
\nOne such assay, ConfirmMDx, tests the epigenetic field effect by observing the molecular changes in methylations occurring in prostate cancer. DNA methylation is among the most common measures of epigenetic abnormality, and easiest to test. These alterations are not detectable in histological analyses, but visible with methylation specific PCR (MSP). Biologically these methylations may be responsible for silencing of key tumor-suppressive genes critical to preventing cancer development, and because of the cancer field effect, this test dramatically amplifies the tested area of the prostate. ConfirmMDx is recommended for men having undergone an initial negative biopsy.
\nProstate cancer-associated epigenetic biomarkers used in this assay include glutathione S-transferase-Pi (GSTP1), APC and RASSF1. Methylation of GSTP1 is among the most common somatic alterations observed in prostate cancer with high specificity and sensitivity, and which correlates strongly with Gleason score, age, PSA and DRE [55, 56, 57].
\nPhosphatase and tensin homolog (PTEN) is a tumor suppressor commonly lost in many cancers. Loss of PTEN is one of few genomic alterations occurring in prostate cancer. PTEN deletion associates with poor outcome and is an established prognostic biomarker for prostate cancer. Analysis of prostatic tissue by Immunohistochemistry (IHC) or Fluorescence in situ hybridization (FISH) demonstrated that PTEN loss is associated with prostate cancer biochemical recurrence, disease progression and metastasis [58, 59, 60, 61, 62, 63].
\nTMPRSS2:ERG fusion is found in ~50% of prostate cancer [64, 65]. TMPRSS2:ERG is a result of gene rearrangement and fusion between androgen regulated transmembrane protease, serine 2 (TMPRSS2) and ERG transcriptional factor genes [64, 65]. This leads to significant overexpression of ERG reported to promote prostate cancer oncogenesis [66, 67, 68, 69, 70]. TMPRSS2-ERG rearrangements are accompanied by PTEN loss, which cooperates to promote prostate cancer progression [69, 70]. Moreover, loss of PTEN and presence of TMPRSS2:ERG fusion together predict prostate cancer biochemical recurrence [71] and Metamark further provides screening for loss of PTEN and ERG rearrangement in their PTEN/ERG screen.
\nMessenger RNA or ribonucleic acid (mRNA) is genetic material carrying information between DNA and protein.
\nOncotype DX offers a diverse array of genomic health testing including breast, colon and prostate cancer. The Genomic Prostate Score (GPS) is a prostate specific array which aids in decision-making between initiating immediate treatment or active surveillance. The test measures expression from 12 genes in four prostate cancer associated biological pathways: androgen signaling (AZGP1, FAM13C, KLK2, SRD5A2), cellular organization (FLNC, GSN, GSTM2, TPM2), stromal response (BGN, COL1A1, SFRP4) and cellular proliferation (TPX2), as well as 5 reference genes (ARF1, ATF5E, CLTC, GPS1, PGK1) [72]. This assay has been validated prospectively as an independent predictor of tumor aggressiveness based on adverse pathology, and death associated with prostate cancer and metastasis [73, 74]. GPS is advised for patients with low-risk clinical prostate cancer (very low, low or intermediate NCCN risk). AZGP1 was further validated as a potential biomarker for significant disease [75]. Loss of AZGP1 assessed by RNA in situ hybridization and immunohistochemical analysis is associated with worse outcome and overall survival [75].
\nProlaris is a prognostic genetic test developed by Myria Genetic Laboratories based on a 46-gene expression signature strongly tied to cell cycle progression genes. Uniquely paired with cellular proliferation and Gleason grading, The Prolaris Score is generated as a metric of an individual’s prostate cancer aggressiveness. This score provides a relative risk among patients of the same risk group defined by the American Urological Association (AUA), and a 10-year prostate cancer specific mortality risk in men with localized disease [76].
\nThe Decipher Biopsy was generated from GenomeDX, based on whole genome technology. In men with localized prostate cancer undergoing biopsy or radical prostatectomy, this test divides patients into Low Risk or High Risk, aiding clinicians and patients in decision making toward active surveillance or intensification of treatment with multi-modal therapies. Decipher Biopsy measures 22 RNA biomarkers to correlate the probability of clinical metastasis within 5 years following radical prostatectomy [77] and is predictive of lymph node metastasis [78]. This test can be performed on either biopsy or prostatectomy samples reproducibly [79].
\nProMark is the first protein based prognostic test for prostate cancer from Metamark Genetics Inc. Based on the understanding that mRNA levels may not be completely reflective of a diseased state, ProMark assays protein levels in intact, formalin fixed biopsy samples to infer prognostic information about the patients’ condition at the time of biopsy. Based on a quantitative multiplex immunofluorescence (QMPI) platform in which tissues are fixed in formalin, samples are stained for eight protein markers for cancer and normal regions and quantified in situ [80]. Markers include SMAD4, PDSS2, HSPA9, FIS, pS6, and YBOX1 to designate regions of prostate cancer, as well as proteins found in tumor and benign tissues, DERL1 and CUL2. Selected by computational modeling, these combinations of protein markers reflect the morphology from tumor epithelium for reliable prognostication [80, 81, 82]. Cost to perform ProMark protein screening is additionally quite low compared to usual guideline-based care [83]. ProMark has been utilized in clinical studies to predict lethal outcome. The test is currently recommended for men with Gleason grade 3 + 3 or 3 + 4 prostate cancer as part of the NCCN Clinical Care Guidelines.
\np63 has been identified as a marker of basal cells in multiple epithelial tissues including normal prostate [84]. Significant downregulation or loss p63 is commonly observed in prostate cancer [84, 85]. Alpha-methylacyl coenzyme A racemase (AMACR) is commonly found overexpressed in prostate cancer and exhibits little to no expression in the normal prostate tissues [86, 87, 88, 89]. A combination of high-molecular weight cytokeratins, AMACR and loss of p63 can be used to define normal prostate tissues, prostate intraepithelial neoplasia and prostate adenocarcinoma [90, 91].
\nUrine analysis is a non-invasive screening technique for prostate cancer.
\nProstate cancer antigen 3 (PCA3) (also known as DD3) is a non-coding mRNA specifically expressed in human prostate tissues, and highly overexpressed in prostate cancer [92].
\nThe Progensa PCA3 assay is an FDA approved urine based molecular test to aid in repeat biopsy decisions from Hologic [93]. Following DRE, a simple “first-catch” urine test captures prostate epithelial cells released into the urine. PCA3 mRNA levels are quantified in proportion to PSA. Also included in the NCCN’s Clinical Practice guidelines for prostate cancer early detection, this test’s specificity lies in PCA3 which is highly upregulated in prostate cancer cells and not affected by instances of benign prostatic hyperplasia (BPH), prostatitis or other conditions as is the case for PSA. PCA3 testing is currently FDA approved for men previously having a negative biopsy with a persistently elevated PSA to help identify men who need a repeat biopsy. PCA3 is calibrated to identify men at low risk for a positive biopsy such that PCA3 < 25 indicates that it is safe to forgo the biopsy. Increase in score was directly correlated with likelihood of positive repeat biopsies, and predictive of 4-year biopsy outcome [94, 95, 96]. PCA3 has subsequently been explored and proven to positively predict detection of prostate cancer in initial biopsies with high specificity and may aid in initial biopsy decision making [97, 98].
\nUtilizing first catch post-DRE urine, SelectMDx tests for mRNA levels of genes DLX1 and HOXC6. Analysis for this test incorporates multifactorial data from PSA density and prior biopsy data to increase significance of this liquid biopsy. This test has shown promise over PCA3 in two prospective clinical trials in identification of patients with high-grade prostate cancer (AUC of 0.90 in first cohort and 0.86 in validation cohort) [99]. SelectMDx was recently added to the European Association of Urology’s list for added decision making before a repeat biopsy.
\nAs ERG rearrangements occur at the genomic level, prostate cancer associated gene fusions such as TMPRSS2:ERG rearrangements are also detectable in patient urine [71, 86, 87, 88, 89]. Urine TMPRSS2:ERG was found to associate with Gleason score and tumor size in a large multicenter study with 1312 men [100]. This strategy utilizes transcription mediated amplification (TMA) assay to quantify TMPRSS2-ERG mRNA normalized to PSA mRNA. Additionally, it was demonstrated that the combination of urine TMPRSS2:ERG with urine PCA3, improves the performance of serum PSA for predicting prostate cancer risk [100, 101, 102].
\nThe Michigan Prostate Score (MiPS) combines serum PSA levels with urine analysis for TMPRSS2:ERG and PCA3 mRNA as a predictive model for a positive prostate cancer biopsy. This compounded analysis of three independent prostate markers are closely correlated with presence of prostate cancer in an initial or repeat biopsy and provides a more accurate predictive model of biopsy detected prostate cancer [101].
\nExosomes are small extracellular vesicles secreted from cells ranging in size from 30 to 120 nm. A portion of the parent cell cytoplasm is contained inside each exosome for the biological function of cell-to-cell communication. For the purpose of clinical diagnostics, this mechanism can be manipulated to measure exosomal genetic material released into blood, urine or other biological fluids. RNA expression from tumor cells is promising as they are highly representative of cell of origin. As exosomes are secreted freely into the urine, exosomal based testing does not require biopsies to detect oncogenic signatures [103, 104]. For instance, PCA3 and ERG mRNAs can be detected in exosomes and be predictive for high grade prostate cancer [105].
\nExosomal analysis of PCA3 and ERG RNA copy number from prostate cancer patient urine was determined to positively predict presence of high-grade prostate cancer [105, 106]. This test, now marketed as ExoDX Prostate(IntelliScore) is considered a liquid biopsy, combining urine with PSA screening from blood sample. Clinically, Prostate(IntelliScore) correctly predicts the occurrence of Gleason scores above 7, and has been recommended for men over 50 with PSA levels in the 2–10 ng/ml range. Further evaluation of this biomarker assay is currently underway.
\nNew technologies are expanding to increase the capture and analysis for extracellular vesicles as experimental material. Exosome Diagnostics, who market the Prostate(IntelliScore) additionally provide isolation kids for exosomal RNA. Alternatively, devices such as the Exosome Total Isolation Chip (ExoTIC), have been generated specific for the high-yield isolation of extracellular vesicles from biofluids (blood, urine, and lavage), even allowing for separation among vesicles based on size. This work has initially been applied to protein and microRNA analysis, increasing the scope of assayable markers for prostate cancer [107].
\nAssays and technologies have vastly improved prediction strategies for recurrent prostate cancer and metastatic disease. Collectively, the biomarkers and assays presented within this chapter represent great advances in the diagnosis and prognostic assessment of patients with prostate cancer and aid in decision-making for subsequent treatment strategies (reviewed in Table 1). However, even with this extensive armamentarium there is still improvement to be made in risk-stratification to accurately identify patients with cancer, and among them, those at risk of developing high grade disease. As biomarkers become available it is increasingly important to understand how these tests are helpful to know when and on which patients these tests should be utilized. Guidelines for care are consistently monitored by urological associations globally. While recommendations vary based on country and among individual institutions and providers, more than ever, patient led decision making is at the forefront of screening. This was evidenced by USPSTF’s recent removal of PSA screening for healthy men as routine procedure, instead recommending individualized decision-making by physician counseling of patients as to the potential risks of inaccurate diagnosis leading to over-treatment.
\nScreening assays for prostate cancer, classified by specimen and genetic material tested, invasiveness of the assay, clinical uses, biomarkers tested, and status of FDA approval. Certain tests have been proven exempt from FDA regulations, and these are also specified. The Clinical Laboratory Improvement Amendments (CLIA) certifies clinical laboratory developed tests to perform additional testing.
Human nature understandably dictates a need for testing to be as minimally invasive as possible to eliminate painful procedures, and increase patient compliance and willingness to participate in early disease screening. The development of non-invasive screening methods such as blood and urine assays to limit prostate biopsy aids in reducing painful, and in the case of prostate cancer, often unnecessary procedures. This is even further amplified when taken into account the number of men who have to undergo the biopsy procedure repeatedly in the course of diagnosis and disease progression. The future of cancer screening, and hopefully diagnosis will come from less invasive procedures. One such advance may be the implementation of imaging strategies such as multi-parametric magnetic resonance imaging (mpMRI) to locate and diagnose prostate cancer. Offered prior to biopsy, patients with negative results are spared the biopsy, while those with cancerous lesions can undergo a targeted biopsy aided by mpMRI, minimizing complications, and obtaining accurate biopsies with ample cancer tissue to aid in treatment plan determination. This technique has tested more accurate than standard of care transrectal ultrasound (TRUS) biopsy in predicting presence of aggressive prostate cancer [108]. The role of imaging in prostate cancer diagnosis is still evolving and these technologies stand to introduce new avenues to the field of prostate cancer diagnosis and even treatment which may lead to better patient risk-stratification with increased survival rates of aggressive prostate cancer.
\nThis chapter is written in dedication to Gerald Robert Howley.
\nThe authors thank James D. Brooks for helpful comments and review.
\nT.S. is supported by the National Institute of Health/National Cancer Institute and Stanford McCormick-Gabilan Faculty Award.
\nThe authors declare no conflicts.
IntechOpen's Authorship Policy is based on ICMJE criteria for authorship. An Author, one must:
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