LiU-ITN-TEK-A--13/068-SE
An LTE implementation based on a
road traffic density model
Muhammad Attaullah
LiU-ITN-TEK-A--13/068-SE
An LTE implementation based on a
road traffic density model
Examensarbete utfört i Elektroteknik
vid Tekniska högskolan vid
Linköpings universitet
Muhammad Attaullah
Handledare Carl-Henrik Häll
Examinator Scott Fowler
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Analyzing
of LTE Implementation
Based
on a Road Traffic Density Model
Muhammad Asim Rashid Muhammad Attaullah 2013‐11‐13 Department of Science and Technology Linköpings universitet, SE‐581 83 Linköping, Sweden Norrköping 2013
Abstract
The increase in vehicular traffic has created new challenges in determining the behavior of performance of data and safety measures in traffic. Hence, traffic signals on intersection used as cost effective and time saving tools for traffic management in urban areas. But on the other hand the signalized intersections in congested urban areas are the key source of high traffic density and slow traffic. High traffic density causes the slow network traffic data rate between vehicle to vehicle and vehicle to infrastructure. To match up with the emerging technologies, LTE takes the lead with good packet delivery and versatile to changes in the network due to vehicular movements and density.
This thesis is about analyzing of LTE implementation based on a road traffic density model. This thesis work is aimed to use probability distribution function to calculate density values and develop a real traffic scenario in LTE network using density values.
In order to analyze the traffic behavior, Aimsun simulator software has been used to represent the real situation of traffic density on a model intersection. For a realistic traffic density model field measurement were used for collection of input data. After calibration and validation process, a close to realty results extracted and used a logistic curve of probability distribution function to find out the density situation on each part of intersection. Similar traffic scenarios were implemented on MATLAB based LTE system level simulator.
Results were concluded with the whole traffic scenario of 90 seconds and calculating the throughput at every traffic signal time and section. It is quite evident from the results that LTE system adopts the change of traffic behavior with dynamic nature and allocates more bandwidth where it is more needed.
Acknowledgments
We would like to express our gratefulness to our supervisor and examiner at Linkoping University, Prof. Scott Fowler and Prof. Carl Henrik Häll for providing the opportunity to work with them, with valuable suggestions and guidance throughout the work period. Our warm thanks go to all teachers of ITS and WNE department that have provided us valuable knowledge and assistance during the completion of this study work. We want to thanks the division of communication and transport system, Linkoping University for providing statistical data and structural maps of study area.
A special thanks to all of our friends for always being good discussions on study work, and providing their helpful assistance.
We would like to express our deep love and regards to our families for their precious support and encouragement from the beginning of our life.
Norrköping, August 2013
Muhammad Asim Rashid Muhammad Attaullah
Abbreviations
& Glossary of Terms
AMC Adaptive Modulation and Coding AWGN Additive White Gaussian Noise BLER Block Error Rate
BSC Base Station Controller eNodeB Evolved NodeB
FDD Frequency Division Duplexing FDMA Frequency Division Multiple Access FFR Fractional Frequency Reuse
FHWA Federal Highway Administration FTP File Transfer Protocol GBR Guaranteed Bit Rate GHz Gigahertz GSM Global System for Mobile Communications HCM Highway Capacity Manual HTTP Hyper Text Transfer Protocol IP Internet Protocol
ITS Intelligent Transportation Systems LOS Line of Sight
LTE Long Term Evolution
MCS Modulation and Coding Scheme MIMO Multiple Inputs Multi Outputs MME Mobility Management Entity MPI Message Passing interface NGBR Non Guaranteed Bit Rate O/D Origin to Destination
OFDM Orthogonal Frequency Division Multiplexing OFDMA Orthogonal Frequency Division Multiple Access PDF Probability Distribution Function
PEL Packet Error Loss PCU Passenger Car Unit
QAM Quadrature Amplitude Modulation QCI QoS Class Indicator
QoS Quality of Service
QPSK Quadrature Phase Shift Keying RB Resource Block
RI Resource Interface RNC Radio Network Controller SC‐FDMA Single Carrier FDMA S‐GW Serving Gateway
SINR Signal to Interference plus Noise Ratio SNR Signal to Noise Ratio TBs Transport Blocks TCP Transmission Control Protocol TDD Time Division Duplexing TSS Transport Simulation System TTI Transmission Time Interval UE User Equipment
UMTS Universal Mobile Telecommunications System V2I Vehicle to Infrastructure
V2V Vehicle to Vehicle
VANET Vehicular ad‐hoc Network VoIP Voice over IP
WAVE Wireless Access in Vehicular Environment
Table of Contents
List of Figures ... ix List of Tables ... x 1. Introduction ... 11 1.1. Aim and Purpose ... 12 1.2. Scope of work ... 12 1.3. Report Outline ... 12 1.4. Assumptions ... 13 2. ITS Literature Study ... 14 2.1. Road Traffic Density ... 14 2.2. Capacity ... 16 2.3. Headway ... 17 2.4. Shock Waves ... 18 2.5. Assessment of Calibration Procedure ... 20 2.6. Related work ... 22 3. LTE Literature Study ... 26 3.1. LTE Overview ... 26 3.2. OFDMA in LTE ... 27 3.3. LTE offers higher data rates ... 28 3.3.1. MIMO (Multiple Inputs Multiple Outputs) ... 28 3.3.2. Band Aggregation ... 29 3.3.3. OFDMA ... 29 3.3.4. Simplified Architecture ... 29 3.4. ITS System Model in LTE ... 29 3.4.1. Traffic priority ... 30 3.4.2. Users ... 30 3.4.3. Quality of Service (QoS) Parameters ... 30 3.4.4. Latency ... 30 3.4.5. Modulation and Channel bandwidths ... 30 3.5. Related work on LTE ... 313.5.1. Vehicle‐to‐Vehicle Communication ... 31 3.5.2. Vehicular‐to‐ Infrastructure Communication ... 32 3.5.3. Traffic Density Model ... 33 4. Methodology ... 35 4.1. Traffic Density Model ... 35 4.2. LTE ... 35 5. Evaluation of Traffic Density Model ... 36 5.1. Data Collection ... 36 5.1.1. Site Selection ... 36 5.1.2. Site Study Visit... 38 5.1.3. Traffic Signals ... 39 5.1.4. Traffic Flow & Turning Proportion ... 40 5.1.5. Travel Time ... 41 5.1.6. Density (Real Time) ... 42 5.2. Calibration and Validation ... 43 5.2.1. Simulation Software (Aimsun) ... 44 5.2.2. Intersection Geometry ... 44 5.2.3. Simulation Runs ... 46 5.2.4. Feasibility Test ... 48 6. Evaluation of LTE model ... 51 6.1. Simulator Overview ... 51 6.1.1. Link Measurement Model ... 52 6.1.2. Link Performance Model ... 52 6.2. Model Implementation ... 52 6.2.1. ITS User Traffic Data ... 53 6.2.2. Generating Users ... 53 6.2.3. System parameters and Setup Scenarios ... 54 7. Analysis of Results ... 56 7.1. Analysis of Traffic Density Model ... 56 7.2. Analysis of LTE Network ... 62 7.2.1. System throughput analysis with respect to number of users ... 62 7.2.2 Throughput response of primary sections ... 64
7.2.3. Throughput response of secondary sections ... 68 7.2.4. Average user delay with respect to number of users ... 74 8. Conclusion and Future Work: ... 76 8.1. Conclusion ... 76 8.2. Future work ... 76 References ... 77
List of Figures
Figure 2.1 Flow density relationship based on the description from 14
Figure 2.2 Different state of density on signalized intersection 15 Figure 2.3 Headway distance between two consecutive vehicle 17 Figure 2.4 Discharge headway in signalized intersection 18 Figure 2.5 Shockwaves at signalized intersection 19 Figure 3.1 LTE architecture 26 Figure 3.2 Resource block grid in frequency domain 27 Figure 3.3 Time domain frame structure 28 Figure 5.1 Location of intersection on a geographical map 37 Figure 5.2 Closed satellite view of study area 38 Figure 5.3 A detailed sketch of a study area 39 Figure 5.4 Signal timing with amber time and direction 40 Figure 5.5 Graphical observation of travel time calculation 41 Figure 5.6 Graphical framework of calibration and validation 43 Figure 5.7 Intersection geometry 44 Figure 5.8 Traffic signal phases 45 Figure 5.9 A bird eye view of traffic situation in each section 45 Figure 5.10 Flow, Average density during simulation period 47 Figure 6.1 Schematic block diagram of LTE system level simulation 51 Figure 6.2 Simulator Test bed 54 Figure 7.1 A logistic curve with different density behavior 56 Figure 7.2 Density situation on section 59 57 Figure 7.3 Density situation on section 41 59 Figure 7.4 Density situation on section 43 60 Figure 7.5 Density situation on section 10 61 Figure 7.6 An overview of traffic on model intersection 63 Figure 7.7 System Throughput Response 64 Figure 7.8 Throughput Vs Number of Users at Section 41 65 Figure 7.9 Throughput Vs Number of Users at Section 10 66 Figure 7.10 Throughput Vs Number of Users at Section 43 67 Figure 7.11 Throughput Vs Number of Users at Section 59 68 Figure 7.12 Throughput Vs Number of Users at Section 11 69 Figure 7.13 Throughput Vs Number of Users at Section 58 70 Figure 7.14 Throughput Vs Number of Users at Section 44 71 Figure 7.15 Throughput Vs Number of Users at Section 42 72 Figure 7.16 Throughput Vs Number of Users at Junction 73 Figure 7.17 Average user delay Vs Number of users 74
List of Tables
Table 5.1 Real time traffic flow, queue length with average signal cycle 40
Table 5.2 Travel time observation during peak hours 42 Table 5.3 Real time density estimation 43 Table 5.4 Minimum number of default replications 46 Table 5.5 First simulation density output with real measured density 46 Table 5.6 Simulation output of 10 replications 47 Table 5.7 Field measured travel time vs. Simulation travel time output 48 Table 5.8 t‐test result, section a‐c and section c‐a 49 Table 5.9 t‐test result, section b‐d and section d‐b 50 Table 6.1 Traffic detail worksheet at every section 53 Table 6.2 LTE simulation parameters 55
1. Introduction
With the support of wireless communication, Intelligent Transportation Systems (ITSs) will play an important role in improving the traffic conditions, where each vehicle will be connected to a unique node in vehicular communication network. Intelligent communication systems offer real‐time communication among the plenty of cars crossing cities, highways and opening new challenges to deal with security, management and communication on the road. In urban areas, signalized intersections are commonly used to control and manage the traffic streams. Being a part of road infrastructure, signalized intersections are generating more traffic congestion. Traffic congestion reduces the efficiency of traffic system and increases the travel time, and C02
emission. Improving the traffic density situation is an important issue where many countries have great emphasis on it (Shengwu Tu, 201O). Next generation of smart cars will have to adopt the vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) communication, in order to deal with traffic management and vehicle performance.
Communication technology has seen significant amount of success in past 2 decades. Wireless communication is the future, whether its telecommunications, multimedia or other networks. Due to simpler installations, mobility and low cost, wireless networks has advantage over wired networks that comes with high cost of laying, maintenance, and zero mobility. At the same time, Wireless networks come with its own drawbacks such as path loss, interference and fading; whereas these factors are quite minimal in wired networks.
As wireless networks cover wide range of areas with the help of base stations arranged in a compact way that there is no gaps in between the clusters. This chain of base stations forms a unique network that can work cooperative driver assistance functions and can be utilized in traffic safety. Among the technologies, vehicular communication systems have been deployed for assistance and management of road traffic; where vehicles and communication nodes can interact. This cooperative study would lead a paradigm shift into the world of Intelligent Transport System.
Cellular networks offers high‐speed and good amount of data to communicate and can be divided into small cells, and it can cover up the whole traffic signal junctions. In cellular networks, LTE has been favored by the most telecom operators, which offers increased bandwidths at lower latencies than other cellular systems. LTE offers considerably good range and coverage to deploy in high density traffic. LTE provides robust mechanism for mobility management in heavy load cells by high penetration rate and high speed terminal support. Vehicular communication requires high‐bandwidth and sensitive QoS; LTE fits the demand in supporting vehicular applications with information and entertainment. Organized with eNodeBs, LTE forms wide area coverage with high speed nodes, which resolves the network
fragmentation and connectivity. IEEE 802.11p supports ITS applications on ad‐hoc mode with low cost and easy deployment, but vulnerable to delays, QoS issues, short distance connectivity in V2I. LTE takes the advantage over 802.11 by coverage area, high data rate and high speed terminal support (Araniti G et al, May 2013). Designing signalized road traffic in LTE network would be an interesting analysis.
1.1. Aim and Purpose
The Aim of this thesis work is to develop a real traffic scenario using probability distribution function estimated values in LTE network and analysis of the throughput and delay with respect to time and number of users. The traffic scenario is completely based on real time calculations. The purpose of this thesis is to design an analytical framework for analyzing the effects of road traffic density model on LTE network in relation to network data. This study involves road segments linked to signalized junction as basic building block of urban traffic system. Further we use the dynamics of the reliability metrics and characterize the region on the road segment. It also investigates the performance evaluation of LTE in high road traffic. Following are the objectives to reach our aim: Design a traffic density model on the basis of real conditions. Use traffic simulator for validation of model. Implementation of traffic density results in LTE simulator. Analyzing the results. 1.2. Scope of work LTE part simulation is work done on MATLAB based LTE system‐level simulator developed by the institute of Communications and Radio Frequency Engineering, Vienna University of Technology. Downlink channel performance and different scheduling techniques can be simulated and the simulator helps in studying the behavior of LTE network by changing the traffic scenarios.
1.3. Report Outline
The thesis is divided into the following chapters:
Chapter 1: This chapter includes the introduction, aim and purpose, scope of work and the assumptions considered for thesis work.
Chapter 2: Theoretical background of traffic density on an intersection, different ways to measure traffic density, and the related work on estimation of density.
Chapter 3: Theoretical background of LTE and its preference over the other existing networks and the related work on other networks.
Chapter 4: Methodology and implementation of the traffic density model and LTE scenarios. Chapter 5: Evaluation of traffic density model covers procedure of selection of study area, real traffic data collection, selection of simulation software and multiple simulations runs to get the valid model.
Chapter 6: Evaluation of LTE model, a detail overview of LTE simulator, and traffic density model implementation in LTE.
Chapter 7: Analysis of traffic density model results and the impact of density on LTE network.
Chapter 8: Conclusion and future work.
References: This part covers the sources of helping materials.
1.4. Assumptions
On road traffic density model part, we have the following assumptions:
We are considering Vehicle‐to‐Vehicle (V2V) and Vehicle‐to‐Infrastructure (V2I) communication only, not cellular communication using by people living around the model area, pedestrians, public transport passengers etc. During simulation of traffic model we used real data for current scenario but studies of more dense traffic situations; we need to make some assumptions to check the possible effects of LTE on road traffic density. On LTE part, we have the following assumptions: Due to complexity in LTE simulator, we consider the road traffic in one cell. Therefore, using 1 eNodeB and 1 sector with no handovers. Considering the whole road traffic in one sector. Number of users is fixed at the start of simulator, so we cannot use dynamic users. No fading scenarios. Just considering the road traffic, we have ignored the buildings,
hills, tunnels or other objects which results in different kinds of fading.
2. ITS Literature Study
Main focus of this research work is to develop a high density traffic model as a base model to implementing the LTE technology and determining the behavior and performance of LTE data traffic. The research work is divided into two sections, traffic density model and implementation of LTE technology on model. Literature study related to traffic density is known as the traffic theory. 2.1. Road Traffic Density Traffic density (K) is the average number of vehicles (N) per space unit over stretch of roadway (L). Vehicles per kilometer is known unit of density. Analysis of two important kinds of densities, jamming density and critical density “optimal or maximum free flow density” are helpful to perform operational analysis of intersections. Density achieved under high congestion is known as maximum density under congestion, this is also known as jamming density. The critical or optimal density (Kc) is achieved under free flow. Mostly critical density builds on highways
during maximum but free flow. An overview of density flow relationship is shown in Figure 2.1 below:
Another form of density is increasing density that is achieved under the circumstances of state change of traffic flow. Free flow traffic that reaches into a jam density state is the main cause of making increasing density. An overview of different states of density into a signalized intersection is shown in Figure 2.2 below:
Figure 2.2: Different states of density on a signalized intersection
Intersections are the main spots where density builds because other than traffic signals there are many other conflict points affected on density such as turning movements, number of lanes, geographical area of an intersection, width of lanes, pedestrian and cyclist flow, capacity and so forth. The Capacity of a link is an important factor to generate density, because when a lane reaches to jam density, it is considered that the lane is full so no more vehicles can enter the lane until the first vehicle in the lane leaves it. So the high capacity of a link takes more time to build jamming density state as compared to less capacity link (Aimsun 6.1, 2010). Road traffic density has been estimated using a number of techniques. Traditionally estimation of density is based on observed data from a road segment. Most common techniques used for density estimations are roadside loop detectors, wireless vehicle sensors, speed guns and surveillance cameras but for using these techniques it is necessary the detection devices to be pre‐installed. Density for a specific road segment can be calculated after observations using the traditional equation:
For efficient results there are many simulators available to verify the observed data with different scenarios and conditions. Aimsun is one of the transport modeling software that can
dynamically simulate adaptive traffic control systems. It is using two different methods to calculate the density, i.e. lane density and section density. The meaning of lane density is to calculate the density of every lane separately on desired road segment. The lane density of a system is calculated as follows: Where: L = total length of all lanes of all sections of the network (meters). NVeh(t) = Number of vehicles in the system at time t. I = interval of statistics (seconds). T = instants when the number of vehicles in the system changes. To calculate the density of a section following equation is used (Aimsun 6.1, 2010): Where: Ll = length of lane l (meters). NVeh l,t = number of vehicles in the lane l at time t. I = interval of statistics (seconds). T = instants when the number of vehicles in lane l changes. 2.2. Capacity
The capacity of road is defined as the maximum hourly rate at which vehicles can travel a section of a roadway under normal traffic conditions. The capacity of an intersection is define as the maximum hourly rate at which vehicles can be expected to pass through a uniform section during a given time period under popular traffic signals control conditions (HCM 2000, p2‐2)
Capacity analysis of signalized intersection is affected by different parameters include turning proportions, number of lanes, width of lanes, geographic area type of intersection and so forth. Capacity analysis is helpful to specify cycle time size of intersection, number of lanes and
phasing of each approach. Capacity of an intersection lane or lane group can be computed as (HCM 2000): ∗ Where: Ci = capacity of lane or lane group i, (veh/h) Si = saturation flow rate for lane or lane group i, (veh/h) gi = effective green time for lane or lane group I, (sec) C = signal cycle length, (sec) 2.3. Headway The measured distance between two consecutive vehicles is called the headway (Figure 2.3). Time and space headways are the two main categories of headway. The space between two successive vehicles as they pass a point on the roadway, measured from the same common features of both vehicles e.g. the front axle or the front bumper is known as space headway (HCM, 2000). Figure 2.3: Headway distance between two consecutive vehicles The difference between time when the front of a vehicle arrives at a point on the highway and the time the front of the next vehicle arrives at the same point is known as time headway. ̅ ∗ Where: = average time headway in seconds ̅ = average travel time per unit distance = average space headway The difference in position between the front of a vehicle and the front of upcoming vehicle is known as space headway. ∗
Where: = average space headway = space mean speed = average time headway
In a signalized intersection the interval of time, of a vehicle stop and leaving from a queue is the fundamental component. Because during red phase of signal cycle a jam density situation is built behind the stop line, starts defusing during green phase. As the queue moves, headway measurements are taken as follows: The time between the beginning of the green signal and the time the front wheels of first vehicle cross the stop line is the first discharge headway. The time between the first vehicle’s front wheels during green signal and the time that second vehicle’s front wheels cross the stop line. Headway’s of the rest of the vehicles can be measured similarly. During the green signal the relation between numbers of vehicles pass through the intersection and headway (sec) is shown in Figure (HCM, 2000). Figure 2.4: Discharge headway in signalize intersection based on (Luis Francisco, 2006) 2.4. Shock Waves A shockwave is started whenever a stream of traffic flowing under certain stream conditions “speed = Ua, density = Ka, and flow = Qa” meets another stream under different conditions
“speed = Ub, density = Kb and flow Qb” (Chakroborty Partha, 2005). Sudden change in capacity
The sudden reduction could be due to signal turning red to green or green to red, serious incident on highway and a small road with maximum capacity creating a situation on road segment where the capacity changes to less capacity with a corresponding change in critical density from lower to high. An overview of forward, backward moving shockwaves corresponding with jamming density is shown in Figure below: Figure 2.5: Shock waves at signalized intersection based on (Nicholas J. Garber, 4th edition) When flow and density on a road segment are relatively large, the speed of the vehicles will have to be reduced while passing the bottleneck. Bottleneck is known as sudden reduction of capacity on a road segment. When capacity is reduced below the demand flow rate resulting in the formation of a queue upstream of the bottleneck the backward forming shockwaves are produced. As shown in Figure 2.5 these shockwaves normally occurs at signalized intersections when signal is red. During the forming of shockwaves, two different flows along with two different densities connected to each other. The velocity of the shockwaves can be calculated using this equation (Nicholas J. Garber, 4th edition):
This gives the velocity of the shockwave created by sudden change of density from ka to kb on a
traffic stream. When signal is red, flow suddenly decreases to zero that’s make the density behind the signal is jamming density which is calculated using this equation:
Where: r = Red phase of signal cycle (t) qa = Flow (Veh/h) L = Length of queue (meter). 2.5. Assessment of Calibration Procedure
The process of modifying and adjusting of default simulation model parameters by using ground measured data to reflect local traffic condition is known as model calibration (Park et al, 2005). From the past few decades microscopic traffic simulation models have been widely used in the evaluation of transportation engineering. Attractive animation, easier to work, risk free, less costly and faster than field testing and implementation is the main reasons for popularity of simulation models. Simulation models include various types of parameters that can be used to describe traffic flow, density, and traffic control systems. Many of input parameters used in simulation such as number of vehicles, geometry and traffic signal settings are easy to measure but some parameters such as queue discharge rate, headway distance and density calculation are difficult to obtain in the field (Schneebeger et al, 2003). The calibration has some required procedures; following procedures are used in this thesis: Input data requirements: The quality of the model is highly dependent on the availability and accuracy of input data. In order to build a good model the user must beware of required data use in the calibration procedure such as travel time, flow, capacity turning proportion and so forth (Aimsun 6.1, 2010).
Network layout: A network model is composed of a set of section connected to each other
through nodes. Sections are usually referred to one way links and the nodes referred to as intersections. Following input data is required to build a network model; a digitized map of the area, number of lanes for every section and the turning movements and speed limits for every section (Aimsun 6.1, 2010).
Traffic demand data: Traffic flows and O/D matrix are the two different ways to define traffic demand data. Depending on the type of model selected the following input data must be provided. For traffic flow: Vehicle type and their attributes, flows at the input sections for each vehicle type and turnings proportions. For O/D matrix: traffic sources and skins, number of trips going from every origin to desired destination (Aimsun 6.1, 2010).
Traffic control: Traffic signals are the most common way to use control of traffic on intersections. Locations of signals, sequence of phases, and the signal groups are the main input requirements to define traffic controls. Error checking: Initial step of calibration process is the error checking and it is used to ensure that the simulation model input data has been entered properly. Error checking process can be used to improve the efficiency and effectiveness of the model (Dowling, 2002). Review input and demand data are the main kind of error checking procedure.
Calibration parameters: Minimum headway distance, lane change distance and waiting time
are some major parameters that have impact on calibration. Parameters are only selected on the bases of model description with an acceptable range of values. Moreover avoid the use of unrealistic parameters that can effect on final results. First evaluation: Comparison between default simulation model and the field data is the first evaluation of a successfully created simulation model. It can be used for succeeding analysis if a close match is found between the default model and the field data. If match is not so close then proceed to further calibration steps.
Multiple simulation runs: The most common problem in using micro simulation tools is the
output results from one replication are not useful. This is the reason to run multiple simulations until get the close result to real situation. Micro simulation results will differ from run to run and can be affected by random seed number used in each run so it is important to estimate the variation and level of confidence interval in the results as to estimate accurate average value of that result (Center for microcomputers in Transportation, 1997). To calculate the minimum number of multiple runs federal highway administration proposed a methodology. According to that methodology following information is needed (FHWA, 2004): Estimation of sample standard deviation Selection of desired confidence level Computation of desired confidence interval Desired range Feasibility test: A statistical method “t‐test” can be used to compare the simulated model and the field data values. A t‐test compares two samples of test data that can help to determine whether the mean values are the same. Statistical hypothesis testing is used to determine whether the calibrated results are equal to the field data or not. Hypothesis testing procedure is defined as following (FHWA, 2004):
Where: nx = sample size of alternative x ny = sample size of alternative y Sx = alternative x standard deviation results Sy = alternative y standard deviation results Sp = pooled standard deviation T‐test statistics value can be calculating as following: Where: x = the mean of alternative x y = the mean of alternative y Critical t value according to degree of freedom and the given alpha can be calculated as: | |
And if T > tnx + ny –2 (α/2) , then reject the null hypothesis.
2.6. Related work
Road traffic density at signalized intersection is a common problem in urban areas that has been widely studied and many models have been proposed to observe the behavior of density. According to a literature study (Libman Lavy et al, 2011) existing local density model does not describe the general behavior of traffic throughout the road segment as compared to a global traffic density model that can explain the traffic behavior at any position throughout the road segment. Finding of this study is that the density divided into three main types, a jamming traffic density caused by vehicle building up a queue, a growing traffic density caused by vehicles decelerating and density during free flow. A generalized logistic curve can describe the three density regions formed during the red phase and partly during the green phase. So the traffic density can be expressed by a simplified logistic curve (Libman Lavy et al, 2011): , Where: Kj = jamming density (upper bound) A = lower bound traffic density
B = growth rate of density from lower to upper bound
K(x, t) = density at position x at time t during traffic light cycle M = the time of the maximum growth
This logistic curve also known as the Richards curve widely use for the growth modeling. The shape and dynamics of the different density regions formed during a red and green phase on signalized intersection are analogous to the shape and dynamic of logistic curve (Sharon E Kingsland). Headway distance refers to the breaking performance. In a stable free flow state, a following vehicle must keep the safe headway distance from leading vehicle to avoid the collision. The safe headway distance is equal to the distance that the following vehicle with velocity V and average deceleration R should maintain in order to be able to make a full stop when leading vehicle suddenly (Libman Lavy et al, 2011). According to (Anderson J. Edward, 2009), one vehicle of length L is cruising at a speed V and is followed by another vehicle traveling at the same speed with nose to tail separation H. During the red phase, the lead vehicle decelerates to stop instantaneously. The vehicle behind senses the failure and after a small reaction time tr begins to decelerate to stop, the minimum safe
time headway is:
Where: h(min) = minimum safe headway
V = speed of the vehicle tr = reaction time db = breaking distance Due to the signalized intersection it is necessary to calculate the headway in meters that means tip to tip headway. The tip to tip headway is simply the tip to tail headway plus the length of the vehicle, expressed as (Anderson J. Edward, 2009): Where: L = length of the vehicle Before the growing density the vehicles are in free flow state. The lower bound of logistic curve directly linked to free flow condition of traffic. Flow is the number of vehicle passing through a specific point per unit of time. Flow rate cannot be estimated from a single snapshot of a length of road. Flow rate, q can be counted as number of vehicle N, divided by per unit time T (Fred L Hall):
The inverse of flow is the headway that means the time between two consecutive vehicles passing through a specific point. So the flow can be expressed as:
Where L = specific lane of a road
Ht = time headway
The interconnection of different links, such as urban road segments, on‐and off‐ramps and freeway sections are the main part of a road network. During peak time, a link has certain spatial occupancy or vehicle count, which is the number of vehicles in the link. The number of vehicles that will move from an upstream link depends on its vehicle count during the existing speed. At the same time number of vehicles that a downstream link can accept is limited by the downstream link vehicle count. Thus, the state of the road network at any time consists of the vehicle count and travel time in every link (Kwong et al, 2010). The (Tan Evan et al, 2013) proposed a novel approach of combining an unsupervised clustering scheme called auto class with Hidden Markov Models to determine the traffic density state in the selected region of a road. This approach has three main parts, firstly low level features are extracted from the select area then an unsupervised clustering algorithm applied to obtain a set of clusters for each predefined traffic density state and finally four Hidden Markov Models are constructed for each traffic state respectively and finally the resultant density determined from these traffic states. According to (Anand R. Asha, 2011) the estimation of density using travel time was based on the following equations: ,
Where q is the flow in PCU/hour, k is the density in PCU/km, V is the space mean speed in km/h, with x being the distance and t being the time. The density k at the time t can be represented as:
∆ ∗ ,∆ ,
Where qentry(t‐1, t) and qexit (t‐1, t) are respectively the flow in PCU/h at the entry and exit
points during the time interval (t‐1) to t. ∆ is the data aggregation interval. PCU is the passenger car units.
The vehicle detector sensors systems are used in today’s roadways provide a direct measurement of traffic flow, average speed and the roadway occupancy. As commonly known the traffic density does not measure directly, these sensors also measure different parameters that can be use for measuring density such as traffic flow and speed. The (Meng Cao, 2011) developed the systematic techniques to measure traffic conditions by utilizing both on‐and off‐ board computer vision systems. The system used a unique development technique with a combined computer program and global positioning system equipped with mobile traffic surveillance system to measure localized traffic density. The localized density measurement from mobile system compares with the flow estimates from an embedded vehicle detector sensor system using space time diagram give the reliability of the system. Finally the combined analysis of temporal spatial variable density and the embedded loop sensor data will provide a better and more reliable method for traffic condition estimation and prediction.
3. LTE Literature Study
3.1. LTE Overview
LTE is a mobile broadband access technology designed to combine high data rates and low‐ latency, in order to meet today’s fastest network requirement. LTE is designed to support Packet‐Switched services, by providing Internet Protocol (IP) connectivity between the users and network (Stefania S et al, 2011). Evolved NodeB (eNodeB) provides link between User Equipment (UE) and core network. eNodeB interconnected each other by X2 interface; whereas eNodeB connected to core network (MME/S‐GW) by S1 interface and UE are connected by Uu interface. In the core network, MME (Mobility Management Entity) processes the signaling between UE and Core network, Whereas, Serving Gateway (S‐GW) routes the user data packets and handles User’s inter‐eNodeBs handover. Figure 3.1: LTE architecture (Stefania S, 2011) The Radio interface for Downlink uses Orthogonal Frequency Division Multiple Access (OFDMA) and for Uplink, LTE uses Single Carrier FDMA (SC‐FDMA) with two modes of transmission: FDD and TDD. LTE supports multiple‐antenna techniques such as Multiple Inputs ‐Multiple Outputs
(MIMO). OFDMA and SC‐FDMA are belongs to multiple‐access versions of OFDM with Single‐ Carrier Frequency‐Domain Equalization (SC‐FDE) modulation scheme. OFDM sends data symbol in parallel with multicarrier modulation scheme and SC‐FDMA transmits data symbol in series with single carrier.
SC‐FDMA has smaller peak‐to‐average ratio (PAR) as compared to OFDMA, which allows the reduction in power consumption. LTE uses SC‐FDMA in Uplink for its low power consumption and OFDMA in Downlink for resistance to multipath fading. 3.2. OFDMA in LTE OFDMA offers multi user diversity with greater flexibility in allocating the radio resources. Each OFDM subcarriers covers spacing of 15 KHz and can be modulated by one of the modulation schemes like QPSK, 16‐QAM or 64‐QAM. In order to use the available resources efficiently, each subcarrier requires channel gain. Radio resources in LTE are divided into frequency domain and time domain. Each LTE downlink frame has 10ms duration and further divided into sub‐frames (10 sub frames) each of which has 1ms duration, known as Transmission Time Interval (TTI) and each TTI consists of two time slots of 0.5ms duration.
The total number of subcarriers depends on the system bandwidth. Each Resource Block (RB) contains one time slot in time domain and 12 adjacent sub‐carriers in frequency domain. The resource allocation in frequency domain takes with 180 kHz bandwidth RB in Uplink and Downlink, with each subcarrier is 15 kHz. Whereas in time domain, each RB gets one time slot of 0.5 ms and contains 7 OFDM symbols as shown in figure 3.3. These instantaneous channel reports the eNodeB by UE and decisions are made. At every single TTI, UE reports that includes Signal to Noise Ratio (SNR). From this report, we perceive parameters like throughput, delay etc. Figure 3.3 Time domain frame structure (Zyren J, 2007) 3.3. LTE offers higher data rates There are number of techniques which make LTE to provide high data rates, 3.3.1. MIMO (Multiple Inputs Multiple Outputs) MIMO technology is widely used in LTE, by introducing multiple antennas at eNodeBs and UEs, in order to use the reflected signals. In order to maximize the gain in throughput, each mode is designed to take advantage of different network conditions and eNodeB antenna configuration. When UE or eNodeB is unable to support MIMO operations, then SISO operation can be used. Normally a signal is subjected to interference within the cell, which impacts in Signal‐to‐ Interference plus Noise Ratio (SINR) and results in error rate.
By the availability of multiple antennas at the eNodeBs and UEs, MIMO systems use multiple transmissions send on the same frequency to the receiver, even after the multipath fading and interference within the cell, a high throughput is achieved. MIMO combines the multipath signals and selects the best signal, this results out in high SNR. MIMO uses two formats Spatial Diversity Spatial Multiplexing Spatial Diversity: redundant data is transmitted from the different paths to get more robust in transmission, and increases the throughput, but no increase in data rates. Spatial Multiplexing: data is divided into separate data streams and transmitted on separate antennas and this result the data in parallel and increases the data rate. 3.3.2. Band Aggregation LTE works on different bandwidth ranging from 1.4 to 20 MHz. In order to achieve higher data rates, transmission bandwidth must be increased and for this reason, carriers are aggregated into one channel and the terminal considers it as enlarged channel and the band gaps are emitted. This results in aggregated channel bandwidth of 100 MHz of aggregating five 20 MHz carriers. The 20 MHz bandwidth can provide up to 150 Mbps peak data rate at Downlink and 75 Mbps at Uplink with 2 × 2 MIMO. 3.3.3. OFDMA LTE uses OFDMA in order to carry high data rates by splitting the available frequency band into small orthogonal subcarriers. In order to prevent Intra‐cell interference, Sub‐carriers are spaced by 15 kHz apart to maintain orthogonality, as sub‐carriers are for single subscriber in time slot. 3.3.4. Simplified Architecture LTE works on IP based packets which gives advantage over the other mobile technologies, by removing Radio Network Controller (RNC in UMTS) / Base Station Controller (BSC in GSM). This move transfers the functions to eNodeB directly. 3.4. ITS System Model in LTE While designing a Traffic model in LTE environment, we need to specify the real interests for the users, and parameters to evaluate the results. In order to see the traffic behavior in LTE, LTE features that are transformed in traffic model (users) and create a link between the different traffic.
3.4.1. Traffic priority
Traffic is prioritized on the high, medium and low levels. In the case of Emergency, the scenario would be different by broadcasting alert signals to the users and allowing limited services like phone calls with low data rate. In normal conditions, the Traffic model is analyzed and adopted to routine schedule and services are given in medium priority with monitoring the available bandwidth. In low traffic, majority of the bandwidth is being unused, additional services adds up in the chart.
3.4.2. Users
The vehicles in the coverage area of network are considered to be the UEs. UEs are classified on the traffic behavior, such as signaling (ITS). UEs play an important role in the analysis. The shift in the distance towards/away from the eNodeB will be the defining characteristics of this analysis. 3.4.3. Quality of Service (QoS) Parameters As QoS are considered on the two different categories based on their traffic priorities: Guaranteed Bit Rate (GBR) Non‐Guaranteed Bit Rate (NGBR) GBR allows multimedia services such as VoIP, Video, and gaming: delay cannot be neglected. NGBR doesn’t require guarantee bit rate to serve best effort services like FTP, HTTP. In ITS traffic, UEs changes dynamically, so dynamic service level adjustment needed in order to enhance the QoS. QoS Class Indicator (QCI) provides the information about the treatment of the packets in network by Packet Delay and Packet Error Loss (PEL). 3.4.4. Latency In any cellular network, throughput and latency defines performance and speed. Throughput is the quantity of data sent in specific time, whereas latency is the time taken for the data transaction. Latency is very sensitive in broadband communication based on TCP (Nokia Siemens, 2009)
End‐to‐End delay in ITS traffic would be greater with respect to the speed and position of the UEs. Measurements are made to latency components in order to minimize the delay requirements. Latency can be improved at the access layer by providing the server closer to the user.
3.4.5. Modulation and Channel bandwidths
LTE has three modulation settings: QPSK, 16QAM and 64QAM and the best results are achieved at the cell centre with 64QAM. Reduced peak rate is available elsewhere in the cell area with
QPSK at the edges. LTE uses channel bandwidth of 1.4 MHz, 3MHz, 5MHz, 10MHz, 15MHz, and 20MHz.
3.5. Related work on LTE
The main focus of this research is to implement Vehicular to Infrastructure communication in LTE network. Vehicular communication aims to make the passenger experience more comfortable, increase traffic efficiency, to reduce fuel use and most importantly it address to the road safety. Some applications use multiple hops to transfer information termed as vehicle to vehicle (V2V) communication. In some application local transceiver are implied at the roadside which mostly used to provide information like road accidents, road hurdles, work zones and weather information and this type of applications come in the class of vehicle to infrastructure (V2I) which is the topic of this research too.
Passenger safety is another issue in a traffic system, and widely usage of vehicle to vehicle and vehicle to infrastructure communication can minimize the risk of accidents. The communication society intensified its focus on vehicular communication architectures because this includes scenarios such as route optimizing, safety application, post‐crash emergency notifications and the traffic congestions systems. Mainly WLAN and UMTS are the main players, use for the vehicle to vehicle and vehicle to infrastructure communications (Yunpeng Z et al, 2009) but as traffic congestion increase with the increasing of population it is necessary to think about new protocols that manage the traffic between vehicles and infrastructure. Compared to WLAN, UMTS based traffic information system with the LTE networks, the weaknesses of UMTS networks are quite solvable using LTE networks (Christoph Sommer et al, 2010).
Related work is divided into three sections. First section focuses on Vehicle‐to‐Vehicle communication, second section focuses on work related to Vehicle‐to‐Infrastructure and third section is on Traffic Density Model.
3.5.1. Vehicle‐to‐Vehicle Communication
In V2V communication information travel from one vehicle to the other therefore this kind of communication are mostly paved roads and it can be implemented in rural, suburban and urban areas. V2V communication can be useful in the areas where cellular infrastructure is not available. As it is direct communication from one vehicle to other; therefore information travel without delay which results in speedy and on time travel of information specially related to the location of nearby vehicles.
There are few disadvantages of V2V communication that it required minimum threshold distance between two vehicles to transfer the information successfully. Also in V2V communication, transmit and receive elevation antennas are at low heights which cause obstruction in the line of sight path which result in scattering and reflecting the signal.
Moreover in V2V communication transmit and receive objects are mostly mobile which produce time varying Doppler spectra. These two effects might cause fading in the information signal which might be more severe than cellular network (Matolak DW et al, 2011). LTE performance is discussed in (Matolak DW et al, 2011) using different MIMO schemes in V2V channel. The author simulated at 5 GHz and turned high data rates to V2V end user, the throughput is doubled when doubling the system bandwidth.
V2V communication based on VANET (Vehicular ad‐hoc Network) has been done by using vehicles as nodes, using different line of sight (LOS) which resulted in multiple scattering and fading. In V2V, based on 802.11p, wireless access in vehicular environments (WAVE) is carried out; the communication is vulnerable to data loss, connectivity disruptions and high delay due to its decentralized ad‐hoc nature, as a result, vehicles cannot always maintain its connectivity (Maria A Vegni et al, 2011). To encounter this problem, we need a longer‐range connection by integrating the wireless technologies with the vehicular network for efficient Vehicle‐to‐ Infrastructure (V2I) communication. V2I advances V2V in the terms of transmissions and provides greater coverage range (Alessandro Bazzi et al, 2011).
3.5.2. Vehicular‐to‐ Infrastructure Communication
Wireless networks broke out into incredible amount of advancement to the transportation system by establishing communication between wireless network and vehicle and vice versa. Different technologies were used to gather up the different parameters that ensure the vehicles safety measures in order to avoid any collision.
Wireless link can be developed by different technologies between the vehicles and network. Similar work is done in (Belanovic P et al, 2009), where analysis of coverage and capacity are taken in three different wireless links such as DVB‐H, UMTS, and DSRC. The results show that capacity of users is limited in digital broadcasting system, whereas cellular link is limited to capacity and DSRC system limited to coverage. Therefore V2I needs a better link in the terms of coverage and capacity. Especially in Urban areas, user’s capacity change frequently due to its traffic nature.
In (Mangel T et al, 2010), to support the argument, a comparative vehicular safety communication is analyzed in UMTS and LTE on uplink and downlink. Performance analysis shows that LTE provide higher transmission rate and latency than UMTS, whereas 802.11p is subjected to Non LOS issue due to short range. So, LTE takes the lead in V2I communication as it provides higher transmission rate as well as long range.
Vehicles can be used as hops to use LTE relay capabilities in V2I communication. In (Luca Reggiani et al, 2013), small base stations deployed and interconnected each other, and scheduling and allocation of resources are designed by prioritizing different classes of data
traffic. In order to decrease co‐channel interference, all base stations are operated in same frequency.
While dealing with Vehicular traffic, scheduling is an important process in which physical resources are dynamically allocated to the UEs and algorithms are developed depending on its traffic. In (Kihl M et al, 2012), traffic safety applications been addressed on the basis of scheduling strategies on LTE downlink. The authors devolved collusion warnings in LTE network by evaluating the performance of LTE in vehicular communication. LTE can broadcasts emergency messages along with multimedia services. V2I communication in LTE is very good for rural setup where the distance between the vehicles are spaced far, but at high density areas, due to small distance, it causes delay by resource scheduling. An interesting idea would minimize the LTE delay by introducing V2V communication at short range, and V2I communication at long range through setting a threshold distance and switching the emergency messages.
3.5.3. Traffic Density Model
Radio overlapping for urban traffic model is analyzed in (Bastani S et al, 2011). Traffic density model is based on a signalized junction and road segments. The experimental results are taken in Vehicular Ad hoc Networks (VANETs), determining the performance aspects of data and safety message communication in the developed density model. As a result, unified data rate or power cannot be assigned to all vehicles, due to heterogeneous traffic. Channel load is greater on two/way road segments than one‐way scenario. The paper concludes with the design of robust mechanism to adjust power and data rates on the radio overlapping results.
In (Landfeldt B et al, 2011), safety messages are generated at unexpected critical events in VANETs. The work is extension to its previous work in (Bastani S et al, 2011) using the same traffic density model with hidden terminal effect in the reliability of transmitting the safety messages. Urban scenario is created at different densities of traffic with hidden node. Markov model is used for determining the probability of event warning messages. Our work differs from the above works in different ways: (i) Adopting a simulation approach with real time urban traffic data and analyzing the density parameters; (ii) simulation is done in LTE network with urban setting: not considering fading; (iii) Implementing road segments linked to signalized junction would give a better approach in studying the Urban traffic in LTE network; (iv) considering the LTE traffic in Downlink, i.e. all vehicles are idle; (v) To reduce the complexity in calculations, buildings/obstacles/fading excluded.
We adopt an appropriate urban traffic behavior in LTE network by vehicular movements between the intersections with signalized junction and a road segment as mentioned in (Bastani S et al, 2011). The Urban traffic data is taken from real calculation on ITS part with
accuracy. Since this is the 1st time signalized traffic being analyzed in LTE network, it creates great amount of interest to analyze the results.
A system level LTE simulator is use to cover all the aspects of LTE networks. Our goal is to analyze the average throughput and delay of the whole network with respect to the urban traffic density model.
4. Methodology
This research work is a combination of two different fields. To make traffic density model has the need of a different methodology then the implementation of the LTE technology. Different methodology sections are needed to explain the research work in detail. 4.1. Traffic Density Model The initial step was to gather essential data for an appropriate traffic density model. Selection of study area with congested signalized intersection was the first step to make a density model. A look on the previous studies regarding signalized intersections and density models provide the additional information that can be used to form a theoretical background. The main part of the method relied on concepts and techniques for data collection. As simulation software requirements four parameters were needed to measure which included travel time, queue length traffic flow and the turning proportion for different type of vehicles. Different methods were used to collect the data regarding input parameters. Geometry data of the chosen intersection and traffic signal data were collected from the division of communication and transport systems of the university and also compared to the study area. One important task was the selection of simulation software that will be used for develop density model according to project description and all the inputs needed were stored and analyzed to clarify if the available data was sufficient.A quantitative method was applied to calibrate and validate the simulation traffic method in the software. It is very important to find suitable measure that is reasonable to compare when evaluating the scenarios because suitable measure is very important during analysis of the traffic density. After validating, the traffic density model data will be used for the base model for implementation in LTE technology. 4.2. LTE The purpose of this research is to implement signalized junction in downlink (all users in idle state) LTE network and analyze its traffic density results at the end. We acquired complete data regarding the road traffic in the form of Probability Distribution Function (PDF). In order to use the PDF, we divided the traffic density model data into unit time frames, and use it inside the LTE simulator. Once we get the exact number of traffic moving at every node, we can code it in the LTE simulator by generating data sheet of traffic data per unit time. As the simulator does not support dynamic user entry therefore to create the moving users effect in the simulation we took several results for the same user at different positions. And we added up the whole results in the form of system throughput, and delay plotted against time and users.
